Data Mining - Retrieving Information From Data

Data Mining - Retrieving Information From Data

Data mining definition is the process of retrieving information from data. It has become very important now days because data that is processed is usually kept for future reference and mainly for security purposes in a company. Data transforms is processed into information and it is mostly used in different ways depending on what information one is extracting and from where the person is extracting the information.

It is commonly used in marketing, scientific information and research work, fraud detection and surveillance and many more and most of this work is done using a computer. This definition can come in different terms data snooping, data fishing and data dredging all this refer to data mining but it depends in which department one is. One must know data mining definition so that he can be in a position to make data.

The method of data mining has been there for so many centuries and it is used up to date. There were early methods which were used to identify data mining there are mainly two: regression analysis and bayes theorem. These methods are never used now days because a lot of people have advanced and technology has really changed the entire system.

With the coming up or with the introduction of computers and technology, it becomes very fast and easy to save information. Computers have made work easier and one can be able to expand more knowledge about data crawling and learn on how data is stored and processed through computer science.

Computer science is a course that sharpens one skill and expands more about data crawling and the definition of what data mining means. By studying computer science one can be in a position to know: clustering, support vector machines and decision trees there are some of the units that are found on computer science.

It's all about all this and this knowledge must be applied here. Government institutions, small scale business and supermarkets use data.

The main reason most companies use data mining is because data assist in the collection of information and observations that a company goes through in their daily activity. Such information is very vital in any companies profile and needs to be checked and updated for future reference just in case something happens.

Businesses which use data crawling focus mainly on return of investments, and they are able to know whether they are making a profit or a loss within a very short period. If the company or the business is making a profit they can be in a position to give customers an offer on the product in which they are selling so that the business can be a position to make more profit in an organization, this is very vital in human resource departments it helps in identifying the character traits of a person in terms of job performance.

Most people who use this method believe that is ethically neutral. The way it is being used nowadays raises a lot of questions about security and privacy of its members. Data mining needs good data preparation which can be in a position to uncover different types of information especially those that require privacy.

A very common way in this occurs is through data aggregation.

Data aggregation is when information is retrieved from different sources and is usually put together so that one can be in a position to be analyze one by one and this helps information to be very secure. So if one is collecting data it is vital for one to know the following:

    How will one use the data that he is collecting?
    Who will mine the data and use the data.
    Is the data very secure when am out can someone come and access it.
    How can one update the data when information is needed
    If the computer crashes do I have any backup somewhere.

It is important for one to be very careful with documents which deal with company's personal information so that information cannot easily be manipulated.

source : http://ezinearticles.com/?Data-Mining---Retrieving-Information-From-Data&id=5054887

One of the Main Differences Between Statistical Analysis and Data Mining

One of the Main Differences Between Statistical Analysis and Data Mining

Two methods of analyzing data that are common in both academic and commercial fields are statistical analysis and data mining. While statistical analysis has a long scientific history, data mining is a more recent method of data analysis that has arisen from Computer Science. In this article I want to give an introduction to these methods and outline what I believe is one of the main differences between the two fields of analysis.

Statistical analysis commonly involves an analyst formulating a hypothesis and then testing the validity of this hypothesis by running statistical tests on data that may have been collected for the purpose. For example, if an analyst was studying the relationship between income level and the ability to get a loan, the analyst may hypothesis that there will be a correlation between income level and the amount of credit someone may qualify for.

The analyst could then test this hypothesis with the use of a data set that contains a number of people along with their income levels and the credit available to them. A test could be run that indicates for example that there may be a high degree of confidence that there is indeed a correlation between income and available credit. The main point here is that the analyst has formulated a hypothesis and then used a statistical test along with a data set to provide evidence in support or against that hypothesis.

Data mining is another area of data analysis that has arisen more recently from computer science that has a number of differences to traditional statistical analysis. Firstly, many data mining techniques are designed to be applied to very large data sets, while statistical analysis techniques are often designed to form evidence in support or against a hypothesis from a more limited set of data.

Probably the mist significant difference here, however, is that data mining techniques are not used so much to form confidence in a hypothesis, but rather extract unknown relationships may be present in the data set. This is probably best illustrated with an example. Rather than in the above case where a statistician may form a hypothesis between income levels and an applicants ability to get a loan, in data mining, there is not typically an initial hypothesis. A data mining analyst may have a large data set on loans that have been given to people along with demographic information of these people such as their income level, their age, any existing debts they have and if they have ever defaulted on a loan before.

A data mining technique may then search through this large data set and extract a previously unknown relationship between income levels, peoples existing debt and their ability to get a loan.

While there are quite a few differences between statistical analysis and data mining, I believe this difference is at the heart of the issue. A lot of statistical analysis is about analyzing data to either form confidence for or against a stated hypothesis while data mining is often more about applying an algorithm to a data set to extract previously unforeseen relationships.

Source:http://ezinearticles.com/?One-of-the-Main-Differences-Between-Statistical-Analysis-and-Data-Mining&id=4578250

Web Data Extraction Services

Web Data Extraction Services

Web Data Extraction from Dynamic Pages includes some of the services that may be acquired through outsourcing. It is possible to siphon information from proven websites through the use of Data Scrapping software. The information is applicable in many areas in business. It is possible to get such solutions as data collection, screen scrapping, email extractor and Web Data Mining services among others from companies providing websites such as Scrappingexpert.com.

Data mining is common as far as outsourcing business is concerned. Many companies are outsource data mining services and companies dealing with these services can earn a lot of money, especially in the growing business regarding outsourcing and general internet business. With web data extraction, you will pull data in a structured organized format. The source of the information will even be from an unstructured or semi-structured source.

In addition, it is possible to pull data which has originally been presented in a variety of formats including PDF, HTML, and test among others. The web data extraction service therefore, provides a diversity regarding the source of information. Large scale organizations have used data extraction services where they get large amounts of data on a daily basis. It is possible for you to get high accuracy of information in an efficient manner and it is also affordable.

Web data extraction services are important when it comes to collection of data and web-based information on the internet. Data collection services are very important as far as consumer research is concerned. Research is turning out to be a very vital thing among companies today. There is need for companies to adopt various strategies that will lead to fast means of data extraction, efficient extraction of data, as well as use of organized formats and flexibility.

In addition, people will prefer software that provides flexibility as far as application is concerned. In addition, there is software that can be customized according to the needs of customers, and these will play an important role in fulfilling diverse customer needs. Companies selling the particular software therefore, need to provide such features that provide excellent customer experience.

It is possible for companies to extract emails and other communications from certain sources as far as they are valid email messages. This will be done without incurring any duplicates. You will extract emails and messages from a variety of formats for the web pages, including HTML files, text files and other formats. It is possible to carry these services in a fast reliable and in an optimal output and hence, the software providing such capability is in high demand. It can help businesses and companies quickly search contacts for the people to be sent email messages.

It is also possible to use software to sort large amount of data and extract information, in an activity termed as data mining. This way, the company will realize reduced costs and saving of time and increasing return on investment. In this practice, the company will carry out Meta data extraction, scanning data, and others as well.

Source: http://ezinearticles.com/?Web-Data-Extraction-Services&id=4733722

Data Mining vs Screen-Scraping

Data Mining vs Screen-Scraping

Data mining isn't screen-scraping. I know that some people in the room may disagree with that statement, but they're actually two almost completely different concepts.

In a nutshell, you might state it this way: screen-scraping allows you to get information, where data mining allows you to analyze information. That's a pretty big simplification, so I'll elaborate a bit.

The term "screen-scraping" comes from the old mainframe terminal days where people worked on computers with green and black screens containing only text. Screen-scraping was used to extract characters from the screens so that they could be analyzed. Fast-forwarding to the web world of today, screen-scraping now most commonly refers to extracting information from web sites. That is, computer programs can "crawl" or "spider" through web sites, pulling out data. People often do this to build things like comparison shopping engines, archive web pages, or simply download text to a spreadsheet so that it can be filtered and analyzed.

Data mining, on the other hand, is defined by Wikipedia as the "practice of automatically searching large stores of data for patterns." In other words, you already have the data, and you're now analyzing it to learn useful things about it. Data mining often involves lots of complex algorithms based on statistical methods. It has nothing to do with how you got the data in the first place. In data mining you only care about analyzing what's already there.

The difficulty is that people who don't know the term "screen-scraping" will try Googling for anything that resembles it. We include a number of these terms on our web site to help such folks; for example, we created pages entitled Text Data Mining, Automated Data Collection, Web Site Data Extraction, and even Web Site Ripper (I suppose "scraping" is sort of like "ripping"). So it presents a bit of a problem-we don't necessarily want to perpetuate a misconception (i.e., screen-scraping = data mining), but we also have to use terminology that people will actually use.

Source: http://ezinearticles.com/?Data-Mining-vs-Screen-Scraping&id=146813

Three Common Methods For Web Data Extraction

Three Common Methods For Web Data Extraction

Probably the most common technique used traditionally to extract data from web pages this is to cook up some regular expressions that match the pieces you want (e.g., URL's and link titles). Our screen-scraper software actually started out as an application written in Perl for this very reason. In addition to regular expressions, you might also use some code written in something like Java or Active Server Pages to parse out larger chunks of text. Using raw regular expressions to pull out the data can be a little intimidating to the uninitiated, and can get a bit messy when a script contains a lot of them. At the same time, if you're already familiar with regular expressions, and your scraping project is relatively small, they can be a great solution.

Other techniques for getting the data out can get very sophisticated as algorithms that make use of artificial intelligence and such are applied to the page. Some programs will actually analyze the semantic content of an HTML page, then intelligently pull out the pieces that are of interest. Still other approaches deal with developing "ontologies", or hierarchical vocabularies intended to represent the content domain.

There are a number of companies (including our own) that offer commercial applications specifically intended to do screen-scraping. The applications vary quite a bit, but for medium to large-sized projects they're often a good solution. Each one will have its own learning curve, so you should plan on taking time to learn the ins and outs of a new application. Especially if you plan on doing a fair amount of screen-scraping it's probably a good idea to at least shop around for a screen-scraping application, as it will likely save you time and money in the long run.

So what's the best approach to data extraction? It really depends on what your needs are, and what resources you have at your disposal. Here are some of the pros and cons of the various approaches, as well as suggestions on when you might use each one:

Raw regular expressions and code

Advantages:

- If you're already familiar with regular expressions and at least one programming language, this can be a quick solution.
- Regular expressions allow for a fair amount of "fuzziness" in the matching such that minor changes to the content won't break them.
- You likely don't need to learn any new languages or tools (again, assuming you're already familiar with regular expressions and a programming language).
- Regular expressions are supported in almost all modern programming languages. Heck, even VBScript has a regular expression engine. It's also nice because the various regular expression implementations don't vary too significantly in their syntax.

Disadvantages:

- They can be complex for those that don't have a lot of experience with them. Learning regular expressions isn't like going from Perl to Java. It's more like going from Perl to XSLT, where you have to wrap your mind around a completely different way of viewing the problem.
- They're often confusing to analyze. Take a look through some of the regular expressions people have created to match something as simple as an email address and you'll see what I mean.
- If the content you're trying to match changes (e.g., they change the web page by adding a new "font" tag) you'll likely need to update your regular expressions to account for the change.
- The data discovery portion of the process (traversing various web pages to get to the page containing the data you want) will still need to be handled, and can get fairly complex if you need to deal with cookies and such.

When to use this approach: You'll most likely use straight regular expressions in screen-scraping when you have a small job you want to get done quickly. Especially if you already know regular expressions, there's no sense in getting into other tools if all you need to do is pull some news headlines off of a site.

Ontologies and artificial intelligence

Advantages:

- You create it once and it can more or less extract the data from any page within the content domain you're targeting.
- The data model is generally built in. For example, if you're extracting data about cars from web sites the extraction engine already knows what the make, model, and price are, so it can easily map them to existing data structures (e.g., insert the data into the correct locations in your database).
- There is relatively little long-term maintenance required. As web sites change you likely will need to do very little to your extraction engine in order to account for the changes.

Disadvantages:

- It's relatively complex to create and work with such an engine. The level of expertise required to even understand an extraction engine that uses artificial intelligence and ontologies is much higher than what is required to deal with regular expressions.
- These types of engines are expensive to build. There are commercial offerings that will give you the basis for doing this type of data extraction, but you still need to configure them to work with the specific content domain you're targeting.
- You still have to deal with the data discovery portion of the process, which may not fit as well with this approach (meaning you may have to create an entirely separate engine to handle data discovery). Data discovery is the process of crawling web sites such that you arrive at the pages where you want to extract data.

When to use this approach: Typically you'll only get into ontologies and artificial intelligence when you're planning on extracting information from a very large number of sources. It also makes sense to do this when the data you're trying to extract is in a very unstructured format (e.g., newspaper classified ads). In cases where the data is very structured (meaning there are clear labels identifying the various data fields), it may make more sense to go with regular expressions or a screen-scraping application.

Screen-scraping software

Advantages:

- Abstracts most of the complicated stuff away. You can do some pretty sophisticated things in most screen-scraping applications without knowing anything about regular expressions, HTTP, or cookies.
- Dramatically reduces the amount of time required to set up a site to be scraped. Once you learn a particular screen-scraping application the amount of time it requires to scrape sites vs. other methods is significantly lowered.
- Support from a commercial company. If you run into trouble while using a commercial screen-scraping application, chances are there are support forums and help lines where you can get assistance.

Disadvantages:

- The learning curve. Each screen-scraping application has its own way of going about things. This may imply learning a new scripting language in addition to familiarizing yourself with how the core application works.
- A potential cost. Most ready-to-go screen-scraping applications are commercial, so you'll likely be paying in dollars as well as time for this solution.
- A proprietary approach. Any time you use a proprietary application to solve a computing problem (and proprietary is obviously a matter of degree) you're locking yourself into using that approach. This may or may not be a big deal, but you should at least consider how well the application you're using will integrate with other software applications you currently have. For example, once the screen-scraping application has extracted the data how easy is it for you to get to that data from your own code?

When to use this approach: Screen-scraping applications vary widely in their ease-of-use, price, and suitability to tackle a broad range of scenarios. Chances are, though, that if you don't mind paying a bit, you can save yourself a significant amount of time by using one. If you're doing a quick scrape of a single page you can use just about any language with regular expressions. If you want to extract data from hundreds of web sites that are all formatted differently you're probably better off investing in a complex system that uses ontologies and/or artificial intelligence. For just about everything else, though, you may want to consider investing in an application specifically designed for screen-scraping.

As an aside, I thought I should also mention a recent project we've been involved with that has actually required a hybrid approach of two of the aforementioned methods. We're currently working on a project that deals with extracting newspaper classified ads. The data in classifieds is about as unstructured as you can get. For example, in a real estate ad the term "number of bedrooms" can be written about 25 different ways. The data extraction portion of the process is one that lends itself well to an ontologies-based approach, which is what we've done. However, we still had to handle the data discovery portion. We decided to use screen-scraper for that, and it's handling it just great. The basic process is that screen-scraper traverses the various pages of the site, pulling out raw chunks of data that constitute the classified ads. These ads then get passed to code we've written that uses ontologies in order to extract out the individual pieces we're after. Once the data has been extracted we then insert it into a database.

source: http://ezinearticles.com/?Three-Common-Methods-For-Web-Data-Extraction&id=165416

Assuring Scraping Success with Proxy Data Scraping

Assuring Scraping Success with Proxy Data Scraping


Have you ever heard of "Data Scraping?" Data Scraping is the process of collecting useful data that has been placed in

the public domain of the internet (private areas too if conditions are met) and storing it in databases or spreadsheets

for later use in various applications. Data Scraping technology is not new and many a successful businessman has

made his fortune by taking advantage of data scraping technology.

Sometimes website owners may not derive much pleasure from automated harvesting of their data. Webmasters

have learned to disallow web scrapers access to their websites by using tools or methods that block certain ip

addresses from retrieving website content. Data scrapers are left with the choice to either target a different website,

or to move the harvesting script from computer to computer using a different IP address each time and extract as

much data as possible until all of the scraper's computers are eventually blocked.

Thankfully there is a modern solution to this problem. Proxy Data Scraping technology solves the problem by using

proxy IP addresses. Every time your data scraping program executes an extraction from a website, the website thinks

it is coming from a different IP address. To the website owner, proxy data scraping simply looks like a short period of

increased traffic from all around the world. They have very limited and tedious ways of blocking such a script but

more importantly -- most of the time, they simply won't know they are being scraped.

You may now be asking yourself, "Where can I get Proxy Data Scraping Technology for my project?" The "do-it-

yourself" solution is, rather unfortunately, not simple at all. Setting up a proxy data scraping network takes a lot of

time and requires that you either own a bunch of IP addresses and suitable servers to be used as proxies, not to

mention the IT guru you need to get everything configured properly. You could consider renting proxy servers from

select hosting providers, but that option tends to be quite pricey but arguably better than the alternative: dangerous

and unreliable (but free) public proxy servers.

There are literally thousands of free proxy servers located around the globe that are simple enough to use. The trick

however is finding them. Many sites list hundreds of servers, but locating one that is working, open, and supports the

type of protocols you need can be a lesson in persistence, trial, and error. However if you do succeed in discovering a

pool of working public proxies, there are still inherent dangers of using them. First off, you don't know who the server

belongs to or what activities are going on elsewhere on the server. Sending sensitive requests or data through a public

proxy is a bad idea. It is fairly easy for a proxy server to capture any information you send through it or that it sends

back to you. If you choose the public proxy method, make sure you never send any transaction through that might

compromise you or anyone else in case disreputable people are made aware of the data.

A less risky scenario for proxy data scraping is to rent a rotating proxy connection that cycles through a large number

of private IP addresses. There are several of these companies available that claim to delete all web traffic logs which

allows you to anonymously harvest the web with minimal threat of reprisal. Companies such as offer large scale

anonymous proxy solutions, but often carry a fairly hefty setup fee to get you going.

Source:http://ezinearticles.com/?Assuring-Scraping-Success-with-Proxy-Data-Scraping&id=248993

How Xpath Plays Vital Role In Web Scraping Part 2

How Xpath Plays Vital Role In Web Scraping Part 2

Here is a piece of content on  Xpaths which is the follow up of How Xpath Plays Vital Role In Web Scraping

Let’s dive into a real-world example of scraping amazon website for getting information about deals of the day. Deals of the day in amazon can be found at this URL. So navigate to the amazon (deals of the day) in Firefox and find the XPath selectors. Right click on the deal you like and select “Inspect Element with Firebug”:

If you observe the image below keenly, there you can find the source of the image(deal) and the name of the deal in src, alt attribute’s respectively.

So now let’s write a generic XPath which gathers the name and image source of the product(deal).

  //img[@role=”img”]/@src  ## for image source
  //img[@role=”img”]/@alt   ## for product name

In this post, I’ll show you some tips we found valuable when using XPath in the trenches.

If you have an interest in Python and web scraping, you may have already played with the nice requests library to get the content of pages from the Web. Maybe you have toyed around using Scrapy selector or lxml to make the content extraction easier. Well, now I’m going to show you some tips I found valuable when using XPath in the trenches and we are going to use both lxml and Scrapy selector for HTML parsing.

Avoid using expressions which contains(.//text(), ‘search text’) in your XPath conditions. Use contains(., ‘search text’) instead.

Here is why: the expression .//text() yields a collection of text elements — a node-set(collection of nodes).and when a node-set is converted to a string, which happens when it is passed as argument to a string function like contains() or starts-with(), results in the text for the first element only.

from scrapy import Selector
html_code = “””<a href=”#”>Click here to go to the <strong>Next Page</strong></a>”””
sel = Selector(text=html_code)
xp = lambda x: sel.xpath(x).extract()           # Let’s type this only once
print xp(‘//a//text()’)                                       # Take a peek at the node-set
[u’Click here to go to the ‘, u’Next Page’]   # output of above command
print xp(‘string(//a//text())’)                           # convert it to a string
  [u’Click here to go to the ‘]                           # output of the above command

Let’s do the above one by using lxml then you can implement XPath by both lxml or Scrapy selector as XPath expression is same for both methods.

lxml code:

from lxml import html
html_code = “””<a href=”#”>Click here to go to the <strong>Next Page</strong></a>””” # Parse the text into a tree
parsed_body = html.fromstring(html_code)  # Perform xpaths on the tree
print parsed_body(‘//a//text()’)                      # take a peek at the node-set
[u’Click here to go to the ‘, u’Next Page’]   # output
print parsed_body(‘string(//a//text())’)              # convert it to a string
[u’Click here to go to the ‘]                    # output

A node converted to a string, however, puts together the text of itself plus of all its descendants:

>>> xp(‘//a[1]’)  # selects the first a node
[u'<a href=”#”>Click here to go to the <strong>Next Page</strong></a>’]

>>> xp(‘string(//a[1])’)  # converts it to string
[u’Click here to go to the Next Page’]

Beware of the difference between //node[1] and (//node)[1]//node[1] selects all the nodes occurring first under their respective parents and (//node)[1] selects all the nodes in the document, and then gets only the first of them.

from scrapy import Selector

html_code = “””<ul class=”list”>
<li>1</li>
<li>2</li>
<li>3</li>
</ul>

<ul class=”list”>
<li>4</li>
<li>5</li>
<li>6</li>
</ul>”””

sel = Selector(text=html_code)
xp = lambda x: sel.xpath(x).extract()

xp(“//li[1]”) # get all first LI elements under whatever it is its parent

[u'<li>1</li>’, u'<li>4</li>’]

xp(“(//li)[1]”) # get the first LI element in the whole document

[u'<li>1</li>’]

xp(“//ul/li[1]”)  # get all first LI elements under an UL parent

[u'<li>1</li>’, u'<li>4</li>’]

xp(“(//ul/li)[1]”) # get the first LI element under an UL parent in the document

[u'<li>1</li>’]

Also,

//a[starts-with(@href, ‘#’)][1] gets a collection of the local anchors that occur first under their respective parents and (//a[starts-with(@href, ‘#’)])[1] gets the first local anchor in the document.

When selecting by class, be as specific as necessary.

If you want to select elements by a CSS class, the XPath way to do the same job is the rather verbose:

*[contains(concat(‘ ‘, normalize-space(@class), ‘ ‘), ‘ someclass ‘)]

Let’s cook up some examples:

>>> sel = Selector(text='<p class=”content-author”>Someone</p><p class=”content text-wrap”>Some content</p>’)

>>> xp = lambda x: sel.xpath(x).extract()

BAD: because there are multiple classes in the attribute

>>> xp(“//*[@class=’content’]”)

[]

BAD: gets more content than we need

 >>> xp(“//*[contains(@class,’content’)]”)

     [u'<p class=”content-author”>Someone</p>’,
     u'<p class=”content text-wrap”>Some content</p>’]

GOOD:

>>> xp(“//*[contains(concat(‘ ‘, normalize-space(@class), ‘ ‘), ‘ content ‘)]”)
[u'<p class=”content text-wrap”>Some content</p>’]

And many times, you can just use a CSS selector instead, and even combine the two of them if needed:

ALSO GOOD:

>>> sel.css(“.content”).extract()
[u'<p class=”content text-wrap”>Some content</p>’]

>>> sel.css(‘.content’).xpath(‘@class’).extract()
[u’content text-wrap’]

Learn to use all the different axes.

It is handy to know how to use the axes, you can follow through these examples.

In particular, you should note that following and following-sibling are not the same thing, this is a common source of confusion. The same goes for preceding and preceding-sibling, and also ancestor and parent.

Useful trick to get text content

Here is another XPath trick that you may use to get the interesting text contents: 

//*[not(self::script or self::style)]/text()[normalize-space(.)]

This excludes the content from the script and style tags and also skip whitespace-only text nodes.

Tools & Libraries Used:

Firefox
Firefox inspect element with firebug
Scrapy : 1.1.1
Python : 2.7.12
Requests : 2.11.0

 Have questions? Comment below. Please share if you found this helpful.

Source: http://blog.datahut.co/how-xpath-plays-vital-role-in-web-scraping-part-2/

Why Outsourcing Data Mining Services?

Why Outsourcing Data Mining Services?

Are huge volumes of raw data waiting to be converted into information that you can use? Your organization's hunt for valuable information ends with valuable data mining, which can help to bring more accuracy and clarity in decision making process.

Nowadays world is information hungry and with Internet offering flexible communication, there is remarkable flow of data. It is significant to make the data available in a readily workable format where it can be of great help to your business. Then filtered data is of considerable use to the organization and efficient this services to increase profits, smooth work flow and ameliorating overall risks.

Data mining is a process that engages sorting through vast amounts of data and seeking out the pertinent information. Most of the instance data mining is conducted by professional, business organizations and financial analysts, although there are many growing fields that are finding the benefits of using in their business.

Data mining is helpful in every decision to make it quick and feasible. The information obtained by it is used for several applications for decision-making relating to direct marketing, e-commerce, customer relationship management, healthcare, scientific tests, telecommunications, financial services and utilities.

Data mining services include:

•     Congregation data from websites into excel database
•     Searching & collecting contact information from websites
•     Using software to extract data from websites
•     Extracting and summarizing stories from news sources
•     Gathering information about competitors business

In this globalization era, handling your important data is becoming a headache for many business verticals. Then outsourcing is profitable option for your business. Since all projects are customized to suit the exact needs of the customer, huge savings in terms of time, money and infrastructure can be realized.

Advantages of Outsourcing Data Mining Services:

•     Skilled and qualified technical staff who are proficient in English
•     Improved technology scalability
•     Advanced infrastructure resources
•     Quick turnaround time
•     Cost-effective prices
•     Secure Network systems to ensure data safety
•     Increased market coverage

Outsourcing will help you to focus on your core business operations and thus improve overall productivity. So data mining outsourcing is become wise choice for business. Outsourcing of this services helps businesses to manage their data effectively, which in turn enable them to achieve higher profits.

Source: http://ezinearticles.com/?Why-Outsourcing-Data-Mining-Services?&id=3066061

Scraping Yelp Data and How to use?

Scraping Yelp Data and How to use?

We get a lot of requests to scrape data from Yelp. These requests come in on a daily basis, sometimes several times a day. At the same time we have not seen a good business case for a commercial project with scraping Yelp.

We have decided to release a simple example Yelp robot which anyone can run on Chrome inside your computer, tune to your own requirements and collect some data. With this robot you can save business contact information like address, postal code, telephone numbers, website addresses etc.  Robot is placed in our Demo space on Web Robots portal for anyone to use, just sign up, find the robot and use it.

How to use it:

    Sign in to our portal here.
    Download our scraping extension from here.
    Find robot named Yelp_us_demo in the dropdown.
    Modify start URL to the first page of your search results. For example: http://www.yelp.com/search?find_desc=Restaurants&find_loc=Arlington,+VA,+USA
    Click Run.
    Let robot finish it’s job and download data from portal.

Some things to consider:

This robot is placed in our Demo space – therefore it is accessible to anyone. Anyone will be able to modify and run it, anyone will be able to download collected data. Robot’s code may be edited by someone else, but you can always restore it from sample code below. Yelp limits number of search results, so do not expect to scrape more results than you would normally see by search.

In case you want to create your own version of such robot, here it’s full code:

// starting URL above must be the first page of search results.
// Example: http://www.yelp.com/search?find_desc=Restaurants&find_loc=Arlington,+VA,+USA

steps.start = function () {

   var rows = [];

   $(".biz-listing-large").each (function (i,v) {
     if ($("h3 a", v).length > 0)
       {
        var row = {};
        row.company = $(".biz-name", v).text().trim();
        row.reviews =$(".review-count", v).text().trim();
        row.companyLink = $(".biz-name", v)[0].href;
        row.location = $(".secondary-attributes address", v).text().trim();
        row.phone = $(".biz-phone", v).text().trim();
        rows.push (row);
      }
   });

   emit ("yelp", rows);
   if ($(".next").length === 1) {
     next ($(".next")[0].href, "start");
   }
 done();
};

Source: https://webrobots.io/scraping-yelp-data/

How to do data scraping from PDF files using PHP?

How to do data scraping from PDF files using PHP?

Situations arise when you want to scrap data from PDF or want to search PDF files for matching text. Suppose you have website where users uploads PDF files and you want to give search functionality to user which searches all uploaded PDF file content for matching text and show all PDFs that contains matching search keywords.

Or you might have all London real estate properties details in PDF report file and you want to quickly grab scrape data from PDF reports then you might need PDF scraping library.

To integrate such functionality to web application is not similar to normal search functionality that we do with database search.

Here is the straight solution for this problem. This involves PDF Data Scraping to plain text and match search terms. I have written this post for the people who want to do PDF data scraping or want to make their PDF files to be Searchable.

We are going to use class named class.pdf2text.php which converts PDF text to into ASCII text, so the class is known for PDF extraction. This PHP class ignores anything in PDF that is not a text.

Let’s see very basic example (Taken from author’s file):

<?php

include "class.pdf2text.php";

$a = new PDF2Text();
$a->setFilename('web-scraping-service.pdf'); //grab the pdf file reside in folder where PHP files resides.

$a->decodePDF();//converts PDF content to text
echo $a->output();

?>

“Web Scraping is a technique using which programmer can automate the copy paste manual work and save the time. This is PDF w eb scraping using PHP. We at Web Data Scraping offer Web Scraping and Data Scraping Service. Vist our website www.webdata-scraping.com”

For more complex extraction you can apply regular expression on the text you get and can parse text that you want from PDF. But keep in mind this has limitation and do not work with all types of PDF extraction.

But the wonderful use of this class is to make utility that allow user to search inside PDF when they search on web search bar. Last but not least, You can also find many PDF scraping software available in market that can do complex scraping from PDF files.

Source: http://webdata-scraping.com/data-scraping-pdf-files-using-php/

Run Code Template – New Feature Added to Fminer Web Scraping Tool

Run Code Template – New Feature Added to Fminer Web Scraping Tool

Fminer is one of the powerful web scraping software, I already given brief of all the Fminer features in previous post. In this post I am going to introduce one of the interesting feature of fminer which is Run Code Template that is recently added to Fminer, this feature is similar to “Fminer Run Code” action but it’s different in a way you can use it. The Run Code Action you can use inside the data scraping flow and python code get executed when scraper start running.

While Run Code Templates are the saved python code snippets that you can run on the data tables after scraping completes. Assume if you get white space in scraped data then you can easily trim this left and right spaces by just executing “strip_column” template, see the code of that template below.

'''Strip all data of a column in data table
Remove the blank of data in the head and the tail.
'''

tabName = '[%table1|data table%]'
colName = '[%table1.column1|table column for strip%]'

tab = tables[tabName]
for i, row in enumerate(tab):
    row[colName] = row[colName].strip()   
    tab.edit_row(i, row)

This template comes with Fminer and few other template like “merge_tables_with_same_columns”.  Below are the steps how you can execute template python code on scraped data.

Step 1: Click on second icon from right that says “Run Code” under the Data section

Step 2: One popup will appear, you need to click on “Templates” icon and choose the template you want to execute and then click on Ok.

Step 3: Now the window will appear for configuration that will ask you to choose the table and column under that table on which you want to execute the code. Now click on Ok again.

Step 4: Now you can see the code of that template, now you can click on execute icon and script will start running, based on number of records it will take time to finish execution.

In many web scraping projects I found this template code very handy for cleaning data and making life easy. Templates are stored at following path so you can create your own template with customized code.

C:\Program Files (x86)\FMiner\templates

I have created one template which I use to remove HTML code that comes while scraping badly organized HTML pages. Below is the code of template for stripping html:

'''Strip HTML will remove all html tags of a column in data table.
'''
import re
tabName = '[%table1|data table%]'
colName = '[%table1.column1|table column for substring%]'
colNew = '[%table1.column1|table column to add new data%]'
tab = tables[tabName]
for i, row in enumerate(tab):
    cleanr =re.compile('<.*?>')
    cleantext = re.sub(cleanr,'', row[colName])
    row[colNew] = cleantext 
    tab.edit_row(i, row)

Stay connected as I am going to post more code templates that will make your web scraping life easy and manipulate data on fly.

Source: http://webdata-scraping.com/run-code-template-new-feature-added-fminer-web-scraping-tool/

How Web Scraping for Brand Monitoring is used in Retail Sector

How Web Scraping for Brand Monitoring is used in Retail Sector

Structured or unstructured, business data always plays an instrumental part in driving growth, development, and innovation for your dream venture. Irrespective of industrial sectors or verticals, big data, seems to be of paramount significance for every business or enterprise.

The unsurpassed popularity and increasing importance of big data gave birth to the concept of web scraping, thus enhancing growth opportunities for startups. Large or small, every business establishment will now achieve successful website monitoring and tracking.
How web scraping serves your branding need?

Web scraping helps in extracting unorganized data and ordering it into organized and manageable formats. So if your brand is being talked about in multiple ways (on social media, on expert forums, in comments etc.), you can set the scraping tool algorithm to fetch only data that contains reference about the brand. As an outcome, marketers and business owners around the brand can gauge brand sentiment and tweak their launch marketing campaign to enhance visibility.

Look around and you will discover numerous web scraping solutions ranging from manual to fully automated systems. From Reputation Tracking to Website monitoring, your web scraper can help create amazing insights from seemingly random bits of data (both in structured as well as unstructured format).
Using web scraping

The concept of web scraping revolutionizes the use of big data for business. With its availability across sectors, retailers are on cloud nine. Here’s how the retail market is utilizing the power of Web Scraping for brand monitoring.

Determining pricing strategy

The retail market is filled with competition. Whether it is products or pricing strategies, every retailer competes hard to stay ahead of the growth curve. Web scraping techniques will help you crawl price comparison sites’ pricing data, product descriptions, as well as images to receive data for comparison, affiliation, or analytics.

As a result, retailers will have the opportunity to trade their products at competitive prices, thus increasing profit margins by a whopping 10%.

Tracking online presence

Current trends in ecommerce herald the need for a strong online presence. Web scraping takes cue from this particular aspect, thus scraping reviews and profiles on websites. By providing you a crystal clear picture of product performance, customer behavior, and interactions, web scraping will help you achieve Online Brand Intelligence and monitoring.
Detection of fraudulent reviews

Present-day purchasers have this unique habit of referring to reviews, before finalizing their purchase decisions. Web scraping helps in the identification of opinion-spamming, thus figuring out fake reviews. It will further extend support in detecting, reviewing, streamlining, or blocking reviews, according to your business needs.
Online reputation management

Web data scraping helps in figuring out avenues to take your ORM objectives forward. With the help of the scraped data, you learn about both the impactful as well as vulnerable areas for online reputation management. You will have the web crawler identifying demographic opinions such as age group, gender, sentiments, and GEO location.

Social media analytics

Since social media happens to be one of the most crucial factors for retailers, it will be imperative to Scrape Social Media websites and extract data from Twitter. The web scraping technology will help you watch your brand in Social Media along with fetching Data for social media analytics. With social media channels such as Twitter monitoring services, you will strengthen your firm’s’ branding even more than before.
Advantages of BM

As a business, you might want to monitor your brand in social media to gain deep insights about your brand’s popularity and the current consumer behavior. Brand monitoring companies will watch your brand in social media and come up with crucial data for social media analytics. This process has immense benefits for your business, these are summarized over here –

Locate Infringers

Leading brands often face the challenge thrown by infringers. When brand monitoring companies keep a close look at products available in the market, there is less probability of a copyright infringement. The biggest infringement happens in the packaging, naming and presentation of products. With constant monitoring and legal support provided by the Trademark Law, businesses could remain protected from unethical competitors and illicit business practices.

Manage Consumer Reaction and Competitor’s Challenges

A good business keeps a check on the current consumer sentiment in the targeted demographic and positively manages the same in the interest of their brand. The feedback from your consumers could be affirmative or negative but if you have a hold on the social media channels, web platforms and forums, you, as a brand will be able to propagate trust at all times.

When competitor brands indulge in backbiting or false publicity about your brand, you can easily tame their negative comments by throwing in a positive image in front of your target audience. So, brand monitoring and its active implementation do help in positive image building and management for businesses.
Why Web scraping for BM?

Web scraping for brand monitoring gives you a second pair of eyes to look at your brand as a general consumer. Considering the flowing consumer sentiment in the market during a specific business season, you could correct or simply innovate better ways to mold the target audience in your brand’s favor. Through a systematic approach towards online brand intelligence and monitoring, future business strategies and possible brand responses could be designed, keeping your business actively prepared for both types of scenarios.

For effective web scraping, businesses extract data from Twitter that helps them understand ‘what’s trending’ in their business domain. They also come closer to reality in terms of brand perception, user interaction and brand visibility in the notions of their clientele. Web scraping professionals or companies scrape social media websites to gather relevant data related to your brand or your competitor’s that has the potential to affect your growth as a business. Management and organization of this data is done to extract out significant and reference building facts. Future strategy for your brand is designed by brand monitoring professionals keeping in mind the facts accumulated through web scraping. The data obtained through web scraping helps in –

Knowing the actual brand potential,
Expanding brand coverage,
Devising brand penetration,
Analyzing scope and possibilities for a brand and
Design thoughtful and insightful brand strategies.

In simple words, web scraping provides a business enough base of information that could be used to devise future plans and to make suggestive changes in the current business strategy.

Advantages of Web scraping for BM

Web scraping has made things seamless for businesses involved in managing their brands and active brand monitoring. There is no doubt, that web scraping for brand monitoring comes with immense benefits, some of these are –

Improved customer insight

When you have in hand and factual knowledge about your consumer base through social media channels, you are in a strong position to portray your positive image as a brand. With more realistic data on your hands, you could develop strategies more effectively and make realistic goals for your brand’s improvement. Social media insights also allows marketers to create highly targeted and custom marketing messages – thus leading to better likelihood of sales conversion.

Monitoring your Competition

Web scraping helps you realize where your brand stands in the market among the competition. The actual penetration of your brand in the targeted segment helps in getting a clear picture of your present business scenario. Through careful removal of competition in your concerned business category, you could strengthen your brand image.

Staying Informed

When your brand monitoring team is keeping track of all social media channels, it becomes easier for you to stay informed about latest comments about your business on sites like Facebook, Twitter and social forums etc. You could have deep knowledge about the consumer behavior related to your brand and your competitors on these web destinations.

Improved Consumer Satisfaction and Sales

Reputation tracking done through web scraping helps in generating planned response at times of crisis. It also mends the communication gap between consumer and the brand, hence improving the consumer satisfaction. This automatically translates into trust building and brand loyalty improving your brand’s sales.

To sign off

By granting opportunities to monitor your social media data, web scraping is undoubtedly helping retail businesses take a significant step towards perfect branding. If you are one of the key players in this sector, there’s reason for celebration ahead!

Source: https://www.promptcloud.com/blog/How-Web-Scraping-for-Brand-Monitoring-is-used-in-Retail-Sector

How to use Social Media Scraping to be your Competitors’ Nightmare

How to use Social Media Scraping to be your Competitors’ Nightmare

Big data and competitive intelligence have been in the limelight for quite some time now. The almost magical power of big data to help a company make just the right decisions have been talked about a lot. When it comes to big data, the kind of benefits that a business can get totally depends upon the sources they acquire it from. Social media is one of the best sources from where you can get data that helps your business in a multitude of ways. Now that every business is deep rooted on the internet, social media data becomes all the more relevant and crucial. Here is how you can use data scraped from social media sites to get an edge in the competition.

Keeping watch on your competitors

Social media is the best place to watch your competitors’ activity and take counter initiatives to keep up or take over them. If you want to know what your competitors are up to, a social media scraping setup for scraping the posts that mention your competitors’ brand/product names can do the trick. This can also be used to learn a thing or two from their activities on social media so that you can take respective measures to stay ahead of them. For example, you could know if your competitor is running a special promotional offer at the moment and come up with something better than theirs to keep up. This can do wonders if you are in a highly competitive industry like Ecommerce where the competition is intense. If you are not using some help from web scraping technology to keep a close watch on your competitors, you could easily get left over in this fast-paced business scene.

Solving customer issues at the earliest

Customers are vocal about their experience with different products and services on social media sites these days. If you have a customer whose issue was left unsolved, there is a good chance that he/she will take it to the social media to vent the frustration. Watching out for such instances and giving them prompt support should be something you should do if you want to retain these customers and stop them from ruining your brand’s image. By scraping social media sites for posts that mention your product/service, you can easily find out if there are such grievances from customers. This can make sure to an extent that you don’t let unhappy customers stay that way, which eventually hurts your business in the long run. Customers can make or break your company, so using social media scraping to serve the customers better can help you succeed eventually.

Sentiment analysis

Social media data can play a good job at helping you understand user sentiments. With the help of social media scraping, a business can get the big picture about general perception of their brand by their users. This can go a long way since this level of feedback can help you fix unnoticed issues with your company and service quickly. By rectifying them, you can make your brand more appealing to the customers. Sentiment analysis will provide you with the opportunity to transform your business into how customers want it to be. Social media scraping is the one and only way to have access to this user sentiment data which can help you optimize your business for the customers.

Web crawling for social media data

When social media data possess so much value to businesses, it makes sense to look for efficient ways to gather and use this data. Manually scrolling through millions of tweets doesn’t make sense, this is why you should use social media scraping to aggregate the relevant data for your business. Besides, web scraping technologies make it possible to handle huge amounts of data with ease. Since the size of data is huge when it comes to business related requirements, web scraping is the only scalable solution worth considering. To make things even simpler, there are reliable web scraping solutions that offer social media scraping services for brand monitoring.

Bottom line

Since social media has become an integral part of online businesses, the data available on these sites possess immense value to companies in every industry. Social media scraping can be used for brand monitoring and gaining competitive intelligence that can be used to optimize your business model for maximum effectiveness. This will in turn make your company stand out from the competition and the added advantage of insights gained from social media data will help you to take over your competitors.

Source: https://www.promptcloud.com/blog/social-media-scraping-for-competitive-intelligence

ERP Data Conversions - Best Practices and Steps

ERP Data Conversions - Best Practices and Steps

Every company who has gone through an ERP project has gone through the painful process of getting the data ready for the new system. The process of executing this typically goes through the following steps:

(1) Extract or define

(2) Clean and transform

(3) Load

(4) Validate and verify

This process is typically executed multiple times (2 - 5+ times depending on complexity) through an ERP project to ensure that the good data ends up in the new system. If the data is either incorrect, not well enough cleaned or adjusted or loaded incorrectly in to the new system it can cause serious problems as the new system is launched.

(1) Extract or define

This involves extracting the data from legacy systems, which are to be decommissioned. In some cases the data may not exist in a legacy system, as the old process may be spreadsheet-based and has to be created from scratch. Typically this involves creating some extraction programs or leveraging existing reports to get the data in to a format which can be put in to a spreadsheet or a data management application.

(2) Data cleansing

Once extracted it normally reviewed is for accuracy by the business, supported by the IT team, and/or adjusted if incorrect or in a structure which the new ERP system does not understand. Depending on the level of change and data quality this can represent a significant effort involving many business stakeholders and required to go through multiple cycles.

(3) Load data to new system

As the data gets structured to a format which the receiving ERP system can handle the load programs may also be build to handle certain changes as part of the process of getting the data converted in to the new system. Data is loaded in to interface tables and loaded in to the new system's core master data and transactions tables.

When loading the data in to the new system the inter-dependency of the different data elements is key to consider and validate the cross dependencies. Exceptions are dealt with and go in to lessons learned and to modify extracts, data cleansing or load process in to the next cycle.

(4) Validate and verify

The final phase of the data conversion process is to verify the converted data through extracts, reports or manually to ensure that all the data went in correctly. This may also include both internal and external audit groups and all the key data owners. Part of the testing will also include attempting to transact using the converted data successfully.

The topmost success factors or best practices to execute a successful conversion I would prioritize as follows:

(1) Start the data conversion early enough by assessing the quality of the data. Starting too late can result in either costly project delays or decisions to load garbage and "deal with it later" resulting in an increase in problems as the new system is launched.

(2) Identify and assign data owners and customers (often forgotten) for the different elements. Ensure that not only the data owners sign-off on the data conversions but that also the key users of the data are involved in reviewing the selection criteria's, data cleansing process and load verification.

(3) Run sufficient enough rounds of testing of the data, including not only validating the loads but also transacting with the converted data.

(4) Depending on the complexity, evaluate possible tools beyond spreadsheets and custom programming to help with the data conversion process for cleansing, transformation and load process.

(5) Don't under-estimate the effort in cleansing and validating the converted data.

(6) Define processes and consider other tools to help how the accuracy of the data will be maintained after the system goes live.

Source: http://ezinearticles.com/?ERP-Data-Conversions---Best-Practices-and-Steps&id=7263314

How to Scrape a Website into Excel without programming

How to Scrape a Website into Excel without programming

This web scraping tutorial will teach you visually step by step how to scrape or extract or pull data from websites using import.io(Free Tool) without programming skills into Excel.

Personally, I use web scraping for analysing my competitors’ best-performing blog posts or content such as what blog posts or content received most comments or social media shares.

In this tutorial,We will scrape the following data from a blog:

    All blog posts URLs.
    Authors names for each post.
    Blog posts titles.
    The number of social media shares each post received.

Then we will use the extracted data to determine what are the popular blog posts and their authors,which posts received much engagement from users through social media shares and on page comments.

Let’s get started.

Step 1:Install import.io app

The first step is to install import.io app.A free web scraping tool and one of the best web scraping software.It is available for Windows,Mac and Linux platforms.Import.io offers advanced data extraction features without coding by allowing you to create custom APIs or crawl entire websites.

After installation, you will need to sign up for an account.It is completely free so don’t worry.I will not cover the installation process.Once everything is set correctly you will see something similar to the window below after your first login.

Step 2:Choose how to scrape data using import.io extractor

With import.io you can do data extraction by creating custom APIs or crawling the entire websites.It comes equipped with different tools for data extraction such as magic,extractor,crawler and connector.

In this tutorial,I will use a tool called “extractor” to create a custom API for our data extraction process.

To get started click the “new” red button on the right top of the page and then click “Start Extractor” button on the pop-up window.

After clicking  “Start Extractor” the Import.io app internal browser window will open as shown below.

Step 3:Data scraping process

Now after the import.io browser is open navigate to the blog URL you want to scrape data from. Then once you already navigated to the target blog URL turn on extraction.In this tutorial,I will use this blog URL bongo5.com  for data extraction.

You can see from the window below I already navigated to www.bongo5.com but extraction switch is still off.

Turn extraction switch “ON” as shown in the window below and move to the next step.

Step 4:Training the “columns” or specifying the data we want to scrape

In this step,I will specify exactly what kind of data I want to scrape from the blog.On import.io app specifying the data you want to scrape is referred to as “training the columns”.Columns represent the data set I want to scrape(post titles,authors’ names and posts URLs).

In order to understand this step, you need to know the difference between a blog page and a blog post.A page might have a single post or multiple posts depending on the blog configuration.

A blog might have several blog posts,even hundreds or thousands of posts.But I will take only one session to train the “extractor” about the data I want to extract.I will do so by using an import.io visual highlighter.Once the data extraction is turned on the-the highlighter will appear by default.

I will do the training session for a single post in a single blog page with multiple posts then the extractor will extract data automatically for the remaining posts on the “same” blog page.
Step 4a:Creating “post_title” column

I will start by renaming “my_column” into the name of the data I want to scrape.Our goal in this tutorial is to scrape the blog posts titles,posts URLs,authors names and get social statistics later so I will create columns for posts titles,posts URLs,authors names.Later on, I will teach you how to get social statistics for the post URLs.

After editing “my_column” into “post_title” then point the mouse cursor over to any of the Posts title on the same blog page and the visual highlighter will automatically appear.Using the highlighter I can select the data I want to extract.

You can see below I selected one of the blog post titles on the page.The rectangular box with orange border is the visual highlighter.

The app will ask you how is the data arranged on the page.Since I have more than one post in a single page then you have rows of repeating data.This blog is having 25 posts per page.So you will select “many rows”.Sometimes you might have a single post on a page for that case you need to select “Just one row”.

Source: http://nocodewebscraping.com/web-scraping-for-dummies-tutorial-with-import-io-without-coding/

Data Discovery vs. Data Extraction

Data Discovery vs. Data Extraction

Looking at screen-scraping at a simplified level, there are two primary stages involved: data discovery and data extraction. Data discovery deals with navigating a web site to arrive at the pages containing the data you want, and data extraction deals with actually pulling that data off of those pages. Generally when people think of screen-scraping they focus on the data extraction portion of the process, but my experience has been that data discovery is often the more difficult of the two.

The data discovery step in screen-scraping might be as simple as requesting a single URL. For example, you might just need to go to the home page of a site and extract out the latest news headlines. On the other side of the spectrum, data discovery may involve logging in to a web site, traversing a series of pages in order to get needed cookies, submitting a POST request on a search form, traversing through search results pages, and finally following all of the "details" links within the search results pages to get to the data you're actually after. In cases of the former a simple Perl script would often work just fine. For anything much more complex than that, though, a commercial screen-scraping tool can be an incredible time-saver. Especially for sites that require logging in, writing code to handle screen-scraping can be a nightmare when it comes to dealing with cookies and such.

In the data extraction phase you've already arrived at the page containing the data you're interested in, and you now need to pull it out of the HTML. Traditionally this has typically involved creating a series of regular expressions that match the pieces of the page you want (e.g., URL's and link titles). Regular expressions can be a bit complex to deal with, so most screen-scraping applications will hide these details from you, even though they may use regular expressions behind the scenes.

As an addendum, I should probably mention a third phase that is often ignored, and that is, what do you do with the data once you've extracted it? Common examples include writing the data to a CSV or XML file, or saving it to a database. In the case of a live web site you might even scrape the information and display it in the user's web browser in real-time. When shopping around for a screen-scraping tool you should make sure that it gives you the flexibility you need to work with the data once it's been extracted.

Source: http://ezinearticles.com/?Data-Discovery-vs.-Data-Extraction&id=165396

Scraping data from LinkedIn

Scraping data from LinkedIn

How to scrape data from LinkedIn public profile for marketing purposes?

You can scrape data from a LinkedIn public profile using data scraper software. LinkedIn data extraction is most beneficial for marketers and most medium size companies rely on LinkedIn for their marketing purpose.

I would recommend you to use "LinkedIn Lead Extractor" software, which helps to quickly scrape public profiles from LinkedIn. With this tool your can scrape profile link, First Name, Last Name, Email, Phone Address, Twitter id, Yahoo messenger id, Skype Id, Google Talk ID, Job Role, Company Name, Address, Country, Connections. This company has built this tool specially for LinkedIn marketers who are not satisfied with their drop ship supplier's digital data.

LinkedIn advance search provides you the targeted customers profiles list with your requirements like country, country, city, company, job title, and much more.

In few weeks you can developed new ways to set-up differently the sales teams and create a much more technologic environment in the strategy department. An internal platform that generated targeted leads can be of a very big help. You can easily execute go to market to any area or city in so much little time compared with some years ago.

Source: http://www.ahmadsoftware.com/blogs/4/scraping-data-from-linkedin.html

Web Scraping Best Practices

Extracting data from the World Wide Web has several challenges as more webmasters are working day and night to lower cases of scraping and crawling of their data in order to survive in the competitive world. There are various other problems you may face when web scraping and most of them can be avoided by adapting and implementing certain web scraping best practices as discussed in this article.

Have knowledge of the scraping tools

Acquiring adequate knowledge of hurdles that may be encountered during web scraping, you will be able to have a smooth web scraping experience and be on the safe side of the law. Conduct a thorough research on the types of tools you will use for scraping and crawling. Firsthand knowledge on these tools will help you find the data you need without being blocked.

Proper proxy software that acts as the middle party works well when you know how to work around HTTP and HTML protocols. Use tools that can change crawling patterns, URLs and data retrieved even when you are crawling on one domain. This will help you abide to the rules and regulations that come with web scraping activities and escaping any legal issues.

Conduct your scraping activities during off-peak hours

You may opt to extract data during times that less people have access for instance over the weekends, during late night hours, public holidays among others. Visiting a website on several instances to retrieve the same type of data is a waste of bandwidth. It is always advisable to download the entire site content to your computer and thereafter you can access it whenever need arises.

Hide your scrapping activities

There is a thin line between ethical and unethical crawling hence you should completely evade being on the top user list of a particular website. Cover up your track as best as you can by making use of proxy IPs to avoid any legal problems. You may also use multiple IP addresses or VPN services to conceal your scrapping activities and lower chances of landing on a website’s blacklist.

Website owners today are very protective of their data and any other information existing under their unique url. Be keen when going through the terms and conditions indicated by websites as they may consider crawling as an infringement of their privacy. Simple etiquette goes a long way. Your web scraping efforts will be fruitful if the site owner supports the idea of sharing data.

Keep record of your activities

Web scraping involves large amount of data.Due to this you may not always remember each and every piece of information you have acquired, gathering statistics will help you monitor your activities.

Load data in phases

Web scraping demands a lot of patience from you when using the crawlers to get needed information. Take the process in a slow manner by loading data one piece at a time. Several parallel request to the same domain can crush the entire site or retrace the scrapping attempts back to your local machine.

Loading data small bits will save you the hustle of scrapping afresh in case that your activity has been interrupted because you will have already stored part of the data required. You can reduce the loading data on an individual domain through various techniques such as caching pages that you have scrapped to escape redundancy occurrences. Use auto throttling mechanisms to increase the amount of traffic to the website and pause for breaks between requests to prevent getting banned.

Conclusion

Through these few mentioned web scraping best practices you will be able to work around website and gather the data required as per clients’ request without major hurdles along the way. The ultimate goal of every web scraper is to be able to access vital information and at the same time remain on the good side of the law.

Source URl : http://nocodewebscraping.com/web-scraping-best-practices/

How to Avoid the Most Common Traps in Web Scraping?

A lot of industries are successfully using web scraping for creating massive data banks of applicable and actionable data which can be used on every day basis for further business interests as well as offer superior services to the customers. However, web scraping does have its own roadblocks and problems.

Using automated scraping, you could face many common problems. The web scraping spiders or programs present a definite picture to their targeted websites. Then, they use this behavior for making out between the human users as well as web scraping spiders. According to those details, a website can employ a certain web scraping traps for stopping your efforts. Here are some of the most common traps:

How Can You Avoid These Traps?

Some measures, which you can use to make sure that you avoid general web scraping traps include:

• Begin with caching pages, which you already have crawled and make sure that you are not required to load them again.
• Find out if any particular website, which you try to scratch has any particular dislikes towards the web scraping tools.
• Handle scraping in moderate phases as well as take the content required.
• Take things slower and do not overflow the website through many parallel requests, which put strain on the resources.
• Try to minimize the weight on every sole website, which you visit to scrape.
• Use a superior web scraping tool that can save and test data, patterns and URLs.
• Use several IP addresses to scrape efforts or taking benefits of VPN services and proxy servers. It will assist to decrease the dangers of having trapped as well as blacklisted through a website.

Source URL :http://www.3idatascraping.com/category/web-data-scraping

Scraping the Royal Society membership list

To a data scientist any data is fair game, from my interest in the history of science I came across the membership records of the Royal Society from 1660 to 2007 which are available as a single PDF file. I’ve scraped the membership list before: the first time around I wrote a C# application which parsed a plain text file which I had made from the original PDF using an online converting service, looking back at the code it is fiendishly complicated and cluttered by boilerplate code required to build a GUI. ScraperWiki includes a pdftoxml function so I thought I’d see if this would make the process of parsing easier, and compare the ScraperWiki experience more widely with my earlier scraper.

The membership list is laid out quite simply, as shown in the image below, each member (or Fellow) record spans two lines with the member name in the left most column on the first line and information on their birth date and the day they died, the class of their Fellowship and their election date on the second line.

Later in the document we find that information on the Presidents of the Royal Society is found on the same line as the Fellow name and that Royal Patrons are formatted a little differently. There are also alias records where the second line points to the primary record for the name on the first line.

pdftoxml converts a PDF into an xml file, wherein each piece of text is located on the page using spatial coordinates, an individual line looks like this:

<text top="243" left="135" width="221" height="14" font="2">Abbot, Charles, 1st Baron Colchester </text>

This makes parsing columnar data straightforward you simply need to select elements with particular values of the “left” attribute. It turns out that the columns are not in exactly the same positions throughout the whole document, which appears to have been constructed by tacking together the membership list A-J with that of K-Z, but this can easily be resolved by accepting a small range of positions for each column.

Attempting to automatically parse all 395 pages of the document reveals some transcription errors: one Fellow was apparently elected on 16th March 197 – a bit of Googling reveals that the real date is 16th March 1978. Another fellow is classed as a “Felllow”, and whilst most of the dates of birth and death are separated by a dash some are separated by an en dash which as far as the code is concerned is something completely different and so on. In my earlier iteration I missed some of these quirks or fixed them by editing the converted text file. These variations suggest that the source document was typed manually rather than being output from a pre-existing database. Since I couldn’t edit the source document I was obliged to code around these quirks.

ScraperWiki helpfully makes putting data into a SQLite database the simplest option for a scraper. My handling of dates in this version of the scraper is a little unsatisfactory: presidential terms are described in terms of a start and end year but are rendered 1st January of those years in the database. Furthermore, in historical documents dates may not be known accurately so someone may have a birth date described as “circa 1782? or “c 1782?, even more vaguely they may be described as having “flourished 1663-1778? or “fl. 1663-1778?. Python’s default datetime module does not capture this subtlety and if it did the database used to store dates would need to support it too to be useful – I’ve addressed this by storing the original life span data as text so that it can be analysed should the need arise. Storing dates as proper dates in the database, rather than text strings means we can query the database using date based queries.

ScraperWiki provides an API to my dataset so that I can query it using SQL, and since it is public anyone else can do this too. So, for example, it’s easy to write queries that tell you the the database contains 8019 Fellows, 56 Presidents, 387 born before 1700, 3657 with no birth date, 2360 with no death date, 204 “flourished”, 450 have birth dates “circa” some year.

I can count the number of classes of fellows:

Select distinct class,count(*) from `RoyalSocietyFellows` group by class

Make a table of all of the Presidents of the Royal Society

select * from `RoyalSocietyFellows` where StartPresident not null order by StartPresident desc

…and so on. These illustrations just use the ScraperWiki htmltable export option to display the data as a table but equally I could use similar queries to pull data into a visualisation.

Comparing this to my earlier experience, the benefits of using ScraperWiki are:

•    Nice traceable code to provide a provenance for the dataset;

•    Access to the pdftoxml library;

•    Strong encouragement to “do the right thing” and put the data into a database;

•    Publication of the data;

•    A simple API giving access to the data for reuse by all.

My next target for ScraperWiki may well be the membership lists for the French Academie des Sciences, a task which proved too complex for a simple plain text scraper…

Sources URL :                             http://yellowpagesdatascraping.blogspot.in/2015/06/scraping-royal-society-membership-list.html

Web Data Extraction Services and Data Collection Form Website Pages

For any business market research and surveys plays crucial role in strategic decision making. Web scrapping and data extraction techniques help you find relevant information and data for your business or personal use. Most of the time professionals manually copy-paste data from web pages or download a whole website resulting in waste of time and efforts.

Instead, consider using web scraping techniques that crawls through thousands of website pages to extract specific information and simultaneously save this information into a database, CSV file, XML file or any other custom format for future reference.

Examples of web data extraction process include:
• Spider a government portal, extracting names of citizens for a survey
• Crawl competitor websites for product pricing and feature data
• Use web scraping to download images from a stock photography site for website design

Automated Data Collection
Web scraping also allows you to monitor website data changes over stipulated period and collect these data on a scheduled basis automatically. Automated data collection helps you discover market trends, determine user behavior and predict how data will change in near future.

Examples of automated data collection include:
• Monitor price information for select stocks on hourly basis
• Collect mortgage rates from various financial firms on daily basis
• Check whether reports on constant basis as and when required

Using web data extraction services you can mine any data related to your business objective, download them into a spreadsheet so that they can be analyzed and compared with ease.

In this way you get accurate and quicker results saving hundreds of man-hours and money!

With web data extraction services you can easily fetch product pricing information, sales leads, mailing database, competitors data, profile data and many more on a consistent basis.

Source URL :    http://ezinearticles.com/?Web-Data-Extraction-Services-and-Data-Collection-Form-Website-Pages&id=4860417

Web scraping in under 60 seconds: the magic of import.io

This post was written by Rubén Moya, School of Data fellow in Mexico, and originally posted on Escuela de Datos.

Import.io is a very powerful and easy-to-use tool for data extraction that has the aim of getting data from any website in a structured way.
It is meant for non-programmers that need data (and for programmers who don’t want to overcomplicate their lives).

I almost forgot!! Apart from everything, it is also a free tool (o_O)

The purpose of this post is to teach you how to scrape a website and make a dataset and/or API in under 60 seconds. Are you ready?

It’s very simple. You just have to go to http://magic.import.io; post the URL of the site you want to scrape, and push the “GET DATA” button.
Yes! It is that simple! No plugins, downloads, previous knowledge or registration are necessary. You can do this from any browser; it even
works on tablets and smartphones.

For example: if we want to have a table with the information on all items related to Chewbacca on MercadoLibre (a Latin American version
of eBay), we just need to go to that site and make a search – then copy and paste the link (http://listado.mercadolibre.com.mx/chewbacca)
on Import.io, and push the “GET DATA” button.

You’ll notice that now you have all the information on a table, and all you need to do is remove the columns you don’t need. To do this, just
place the mouse pointer on top of the column you want to delete, and an “X” will appear.

Finally, it’s enough for you to click on “download” to get it in a csv file.
In our example, we have 373 pages with 48 articles each. So this option will be very useful for us.

Good news for those of us who are a bit more technically-oriented! There is a button that says “GET API” and this one is good to, well,
generate an API that will update the data on each request. For this you need to create an account (which is also free of cost).

As you saw, we can scrape any website in under 60 seconds, even if it includes tons of results pages. This truly is magic, no? For more
complex things that require logins, entering subwebs, automatized searches, et cetera, there is downloadable import.io software… But I’ll
explain that in a different post.

Source : http://schoolofdata.org/2014/12/09/web-scraping-in-under-60-seconds-the-magic-of-import-io/

Exploring Web Data Extraction And Its Different Techniques

Web scraping or web data extraction is a distinctive process based on computer software to extract information from different websites. Mostly business organizations are dependent on the web resources for collecting crucial information relating to decision making. With the analysis of such data, they can identify the existing trends of market, details, prices, and product specification. Looking at the time consuming process of manual data extraction, the prominence of data extraction techniques increases.

Different data scraping techniques

Several data extraction techniques are available for the businesses to extract useful information for successful operations. Some of them may include:

    Logical extraction: It comprises logical data extraction of complete source system as well as incremental.
    Physical extraction: This technique involves two different mechanisms for web scrapping that include both online as well as offline.
    HTTP programming: You can also extract data from both dynamic and static websites by implying the technique of socket programming. It allows you to post HTTP requests on the remote web servers.
    Web scraping software: Several software tools are available in the market that serves your individual needs of extracting data with ease. It automatically attempts to recognize the structure of data for a page and extracts the content for further analysis.
    Web scrapping tools: Besides the availability of reliable software, numerous user-friendly web scrapping tools are also helpful in simplifying the entire web scraping process.

Hire a website scrapper

Hiring a suitable website scraper that offers website data extraction services for all your business requirements is an ideal way amongst all other techniques. It provides you filtered and reliable data according to your need for analysis. Some of the major advantages of using website scrapping services may include:

    Automation of data.
    It can retrieve web pages of both static as well as dynamic websites.
    It is also capable of transforming the content into useful information.
    Provides reliable and accurate data.
    It also recognizes several semantic annotations.

Scraping service versus tools

Web scraping services gain more privilege than other tools and software. The basic reason behind this preference is that the service providers are comparatively cheaper than the tools. In fact, they maintain better accuracy and reliability of data.

Summary: It is advisable to look out for suitable web data extraction services instead of any tools or software. This helps in acquiring customized and structured data for your business in legal manner.


 Source : http://www.web-parsing.com/blog/exploring-web-data-extraction-and-its-different-techniques/

Extensive Benefits of Data Mining Services to Marketing – Retail and Outreach Sectors…!!!

There is a vast ocean out there – An ocean of information on internet which is massive, brimming with a lot of data; in fact, it is constantly getting updated, increase the volume with each passing day. In fact, it is believed that around 90% of total information generated in the last two years, is now available on the internet.

Picking right set of information from this heap of data is like searching a needle in the haystack. It is almost next to impossible to search it manually – You need a powerful magnet in form of data mining service provider…!!!

Data mining services work like a magnet – It helps you in finding the right kind of information from huge databases available in the digital world. And with databases getting mammoth every minute, the importance of partnering with a professional and reliable data mining company cannot be overlooked.Though, loaded with a lot of negative connotations; data mining still reigns like a king! In fact, in order to truly appreciate the concept behind data mining, one needs to know it in its entirety.

Every coin has two sides – If there is a brighter side; there tends to be a dark side as well. Though, advantages of web extraction, outweighs disadvantages the fact is it is always the dark underbelly that is highlighted and shown to the world. However, as wise men say, focus on positive sides – Lets see what amazing advantages it can offer to your business and how well you can gain from hiring a professional data mining services.

Upside or Advantage of Data Extraction Services:

While data mining is used primarily in business, it is interesting to know that benefits of data mining goes beyond and across boundaries; it helps various industries as well.

Marketing/Retailing

Data mining can prove to be extremely helpful to the marketers and retailers who are looking out for potential clients as well as aspires to maintain consumer satisfaction. This is one of the methods that allows the businesses to know their potential clients better by acquiring their personal information and preferences.

Not just data extraction helps in determining the trends in goods and services by presenting an overview of online data. With adequate information, you can improve your goods and services, along with changing or choosing the ones which are more in demand. Consequently, success in business has been made quicker and easier these days because of data mining.

Streamline Outreach

Outreach forms an integral part of any business – And to effectively carry out outreach activities; one needs to have a huge cache of database, that can help the marketers to learn how to approach a particular set of customers. Information like that includes relevant e-mail addresses, mailing addresses or social media pages needs to be streamlined any mailers to get the best results.

Data extraction makes this easier; since it gets all the updated information; and in process saves your time and money.

And as it is “the lotus flower grows in mud, but makes our world fragrant” – data mining services is marred by criticism and controversy; however, its extensive advantages outweighs these negativity to a great extent.

Source : http://www.habiledata.com/blog/extensive-benefits-of-data-mining-services-to-marketing-retail-and-outreach-sectors/