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

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/

Are You Screen Scraping or Data Mining?

Are You Screen Scraping or Data Mining?

Many of us seem to use these terms interchangeably but let’s make sure we are clear about the differences that make each of these approaches different from the other.

Basically, screen scraping is a process where you use a computer program or software to extract information from a website.  This is different than crawling, searching or mining a site because you are not indexing everything on the page – a screen scraper simply extracts precise information selected by the user.  Screen scraping is a useful application when you want to do real-time, price and product comparisons, archive web pages, or acquire data sets that you want to evaluate or filter.

When you perform screen scraping, you are able to scrape data more directly and, you can automate the process if you are using the right solution. Different types of screen scraping services and solutions offer different ways of obtaining information. Some look directly at the html code of the webpage to grab the data while others use more advanced, visual abstraction techniques that can often avoid “breakage” errors when the web source experiences a programming or code change.

On the other hand, data mining is basically the process of automatically searching large amounts of information and data for patterns. This means that you already have the information and what you really need to do is analyze the contents to find the useful things you need. This is very different from screen scraping as screen scraping requires you to look for the data, collect it and then you can analyze it.

Data mining also involves a lot of complicated algorithms often based on various statistical methods. This process has nothing to do with how you obtain the data. All it cares about is analyzing what is available for evaluation.

Screen scraping is often mistaken for data mining when, in fact, these are two different things. Today, there are online services that offer screen scraping. Depending on what you need, you can have it custom tailored to meet your specific needs and perform precisely the tasks you want. But screen scraping does not guarantee any kind of analysis of the data.

Source: http://www.connotate.com/are-you-screen-scraping-or-data-mining/

Scraping LinkedIn Public Profiles for Fun and Profit

Scraping LinkedIn Public Profiles for Fun and Profit

Reconnaissance and Information Gathering is a part of almost every penetration testing engagement. Often, the tester will only perform network reconnaissance in an attempt to disclose and learn the company's network infrastructure (i.e. IP addresses, domain names, and etc), but there are other types of reconnaissance to conduct, and no, I'm not talking about dumpster diving. Thanks to social networks like LinkedIn, OSINT/WEBINT is now yielding more information. This information can then be used to help the tester test anything from social engineering to weak passwords.

In this blog post I will show you how to use Pythonect to easily generate potential passwords from LinkedIn public profiles. If you haven't heard about Pythonect yet, it is a new, experimental, general-purpose dataflow programming language based on the Python programming language. Pythonect is most suitable for creating applications that are themselves focused on the "flow" of the data. An application that generates passwords from the employees public LinkedIn profiles of a given company - have a coherence and clear dataflow:

(1) Find all the employees public LinkedIn profiles → (2) Scrap all the employees public LinkedIn profiles → (3) Crunch all the data into potential passwords

Now that we have the general concept and high-level overview out of the way, let's dive in to the details.

Finding all the employees public LinkedIn profiles will be done via Google Custom Search Engine, a free service by Google that allows anyone to create their own search engine by themselves. The idea is to create a search engine that when searching for a given company name - will return all the employees public LinkedIn profiles. How? When creating a Google Custom Search Engine it's possible to refine the search results to a specific site (i.e. 'Sites to search'), and we're going to limit ours to: linkedin.com. It's also possible to fine-tune the search results even further, e.g. uk.linkedin.com to find only employees from United Kingdom.

The access to the newly created Google Custom Search Engine will be made using a free API key obtained from Google API Console. Why go through the Google API? because it allows automation (No CAPTCHA's), and it also means that the search-result pages will be returned as JSON (as oppose to HTML). The only catch with using the free API key is that it's limited to 100 queries per day, but it's possible to buy an API key that will not be limited.

Scraping the profiles is a matter of iterating all over the hCards in all the search-result pages, and extracting the employee name from each hCard. Whats is a hCard? hCard is a micro format for publishing the contact details of people, companies, organizations, and places. hCard is also supported by social networks such as Facebook, Google+, LinkedIn and etc. for exporting public profiles. Google (when indexing) parses hCard, and when relevant, uses them in search-result pages. In other words, when search-result pages include LinkedIn public profiles, it will appear as hCards, and could be easily parsed.

Let's see the implementation of the above:

#!/usr/bin/python
#
# Copyright (C) 2012 Itzik Kotler
#
# scraper.py is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# scraper.py is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with scraper.py.  If not, see <http://www.gnu.org/licenses/>.

"""Simple LinkedIn public profiles scraper that uses Google Custom Search"""

import urllib
import simplejson


BASE_URL = "https://www.googleapis.com/customsearch/v1?key=<YOUR GOOGLE API KEY>&cx=<YOUR GOOGLE SEARCH ENGINE CX>"


def __get_all_hcards_from_query(query, index=0, hcards={}):

    url = query

    if index != 0:

        url = url + '&start=%d' % (index)

    json = simplejson.loads(urllib.urlopen(url).read())

    if json.has_key('error'):

        print "Stopping at %s due to Error!" % (url)

        print json

    else:

        for item in json['items']:

            try:

                hcards[item['pagemap']['hcard'][0]['fn']] = item['pagemap']['hcard'][0]['title']

            except KeyError as e:

                pass

        if json['queries'].has_key('nextPage'):

            return __get_all_hcards_from_query(query, json['queries']['nextPage'][0]['startIndex'], hcards)

    return hcards


def get_all_employees_by_company_via_linkedin(company):

    queries = ['"at %s" inurl:"in"', '"at %s" inurl:"pub"']

    result = {}

    for query in queries:

        _query = query % company

        result.update(__get_all_hcards_from_query(BASE_URL + '&q=' + _query))

    return list(result)

Replace <YOUR GOOGLE API KEY> and <YOUR GOOGLE SEARCH ENGINE CX> in the code above with your Google API Key and Google Search Engine CX respectively, save it to a file called scraper.py, and you're ready!

To kick-start, here is a simple program in Pythonect (that utilizes the scraper module) that searchs and prints all the Pythonect company employees full names:

"Pythonect" -> scraper.get_all_employees_by_company_via_linkedin -> print

The output should be:

Itzik Kotler

In my LinkedIn Profile, I have listed Pythonect as a company that I work for, and since no one else is working there, when searching for all the employees of Pythonect company - only my LinkedIn profile comes up.
For demonstration purposes I will keep using this example (i.e. "Pythonect" company, and "Itzik Kotler" employee), but go ahead and replace Pythonect with other, more popular, companies names and see the results.

Now that we have a working skeleton, let's take its output and start crunching it. Keep in mind that every "password generation forumla" is merely a guess. The examples below are only a sampling of what can be done. There are, obviously many more possibilities and you are encouraged to experiment. But first, let's normalize the output - this way it's going to be consistent before operations are performed on it:

"Pythonect" -> scraper.get_all_employees_by_company_via_linkedin -> string.lower(''.join(_.split()))

The normalization procedure is short and simple: convert the string to lowercase and remove any spaces, and so the output should be now:

itzikkotler

As for data manipulation, out of the box (Thanks to The Python Standard Library) we've got itertools and it's combinatoric generators. Let's start by applying itertools.product:

"Pythonect" -> scraper.get_all_employees_by_company_via_linkedin -> string.lower(''.join(_.split())) -> itertools.product(_, repeat=4) -> print

The code above will generate and print every 4 characters password from the letters: i, t, z, k, o, t, l , e, r. However, it won't cover passwords with uppercase letters in it. And so, here's a simple and straightforward implementation of a cycle_uppercase function that cycles the input letters yields a copy of the input with letter in uppercase:

def cycle_uppercase(i):
    s = ''.join(i)
    for idx in xrange(0, len(s)):
        yield s[:idx] + s[idx].upper() + s[idx+1:]

To use it, save it to a file called itertools2.py, and then simply add it to the Pythonect program after the itertools.product(_, repeat=4) block, as follows:

"Pythonect" -> scraper.get_all_employees_by_company_via_linkedin \
    -> string.lower(''.join(_.split())) \
        -> itertools.product(_, repeat=4) \
            -> itertools2.cycle_uppercase \
                -> print

Now, the program will also cover passwords that include a single uppercase letter in it. Moving on with the data manipulation, sometimes the password might contain symbols that are not found within the scrapped data. In this case, it is necessary to build a generator that will take the input and add symbols to it. Here is a short and simple generator implemented as a Generator Expression:

[_ + postfix for postfix in ['123','!','$']]

To use it, simply add it to the Pythonect program after the itertools2.cycle_uppercase block, as follows:

"Pythonect" -> scraper.get_all_employees_by_company_via_linkedin \
    -> string.lower(''.join(_.split())) \
        -> itertools.product(_, repeat=4) \
            -> itertools2.cycle_uppercase \
                -> [_ + postfix for postfix in ['123','!','$']] \
                    -> print

The result is that now the program adds the strings: '123', '!', and '$' to every generated password, which increases the chances of guessing the user's right password, or not, depends on the password :)

To summarize, it's possible to take OSINT/WEBINT data on a given person or company and use it to generate potential passwords, and it's easy to do with Pythonect. There are, of course, many different ways to manipulate the data into passwords and many programs and filters that can be used. In this aspect, Pythonect being a flow-oriented language makes it easy to experiment and research with different modules and programs in a "plug and play" manner.

Source:http://blog.ikotler.org/2012/12/scraping-linkedin-public-profiles-for.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/

Data Scraping – Will Definitely Benefit a Business Startup

With increasingly data shared using internet, the data collected as well as the usage cases are increasing with an unbelievable pace. We’ve entered into the “Big Data” age and data scraping is among the resources to supply big data engines, the latest data for analytical analytics, contest monitoring, or just to steal the data.

From the technology viewpoint, competent data scraping is fairly complicated. It has many open-source projects that allow anybody to run a web data scraper through him. Nevertheless it’s the entire different story while it needs to be an interior of the business as well as that you require not only maintaining your scrapers but also scaling them as well as extract the data smartly as you need.

That is the reason why different services are selling the “data scraping” as service. Their work is taking care about all the technical characteristics so that you can have the data required without any industrial knowledge. Fundamentally all these startups pay attention for collecting the data and then extract its value for selling it to the customers.

Let’s take some examples:

• Sales Intelligence – The scrapers monitor competitors, marketplaces, online directories, and data from the public markets to discover leads. For instance, some tool’s track websites that drop or add JavaScript tags from the competitors therefore you can call them as eligible leads.

• Price Intelligence – A very ordinary use is the price monitoring. If this is in with e-commerce, travel, or property industry monitoring competitors’ prices as well as adjusting yours consequently is generally the key. All these services monitor the prices and using the analytical algorithms they may provide you advice about where the puck can be.

• Marketing – Data scraping may also be used for monitoring how the competitors are doing. From the reviews they have on the marketplaces to get coverage as well as financially published data one can find out a lot. Concerned about marketing, there is a development hacking class which teaches how to use scraping for the marketing objectives.

Finance intelligence, economic intelligence, etc have more and more financial, political, and economical data accessible online with the newer type of services that collect and add up of that, are increasing.

Let’s go through some points concerned with the market:

• It’s tough to evaluate how huge the data scraping market is as this is with the intersection of many big industries like sales, IT security, finance and marketing intelligence. This method is certainly a small part of all these industries however is expected to increase in the coming years.

• It’s a secured bet to indicate that increasingly SaaS will get pioneering applications for the web data scraping as well as progressively startups will use data scraping services from the safety viewpoint.

• As all the startups are generally entering huge markets using niche products / approaches (web data scraping isn’t a solution of everything, it’s more like a feature) they are expected to be obtained by superior players (within the safety, sales, or marketing tools industries). The technological barriers are also there.

Source URL : http://www.3idatascraping.com/data-scraping-will-definitely-benefit-a-business-startup.php

Data Scraping - What Are Hand-Scraped Hardwood Floors and What Are the Benefits?

If you love the look of hardwood flooring with lots of character, then you may want to check out hand-scraped hardwood flooring. Hand-scraped wood provides a warm vintage look, providing the floor instant character. These types of scraped hardwoods are suitable for living rooms, dining rooms, hallways and bedrooms. But what exactly is hand-scraped hardwood flooring?

Well, it is literally what you think it is. Hand-scraped hardwood flooring is created by hand using specialized wood working tools to make each board unique and giving an overall "old worn" appearance.

At Innovation Builders we offer solid wood floors finished on site with an actual hand-scraping technique followed by stain and sealer. Solid wood floors are installed by an expert team of technicians who work each board with skilled craftsman-like attention to detail. Following the scraping procedure the floor is stained by hand with a customer selected stain color, and then protected with multiple coats of sealing and finishing polyurethane. This finishing process of staining, sealing and coating the wood floors contributes to providing the look and durability of an old reclaimed wood floor, but with today's tough, urethane finishes.

There are many, many benefits to hand-scraped wood flooring. Overall, these floors are extremely durable and hard wearing, providing years of trouble-free use. These wood floors remain looking newer for longer because the texture that the process provides hides the typical dents, dings and scratches that other floors can't hide so easily. That's great news for households with kids, dogs, and cats.

These types of wood flooring have another unique advantage as well. When you do scratch these floors during their lifetime, the scratches are easily repaired. As long as the scratch isn't too deep you can make them practically disappear without ever having to hire a professional. It's simple to hide the scratch by using a color-matched stain marker or repair kit that is readily available through local flooring distributors. These features make hand-scraped hardwood flooring a lot more durable and hassle-free to maintain than other types of wood flooring.

The expert processes utilized in the creation of these floors provides a custom look of worn wood with deep color and subtle highlights. When the light hits the wood at different times during the day, it provides an understated but powerful effect of depth and beauty. They instantly offer your rooms a rustic look full of character, allowing your home to become a warm and inviting environment. The rustic look of this wood provides a texture, style and rustic appeal that cannot be matched by any other type of flooring.

Hand-Scraped Hardwood Flooring is a floor that says welcome and adds a touch of elegance to any home. If you are looking to buy a new home and you haven't had the opportunity to see or feel hand scraped hardwoods, stop in any of the model homes at Innovation Builders in Keller, North Richland Hills or Grand Prairie, Texas and check it out!

Source URL :   http://yellowpagesdatascraping.blogspot.in/2015/06/data-scraping-what-are-hand-scraped.html

Increasing Accessibility by Scraping Information From PDF

You may have heard about data scraping which is a method that is being used by computer programs in extracting data from an output that comes from another program. To put it simply, this is a process which involves the automatic sorting of information that can be found on different resources including the internet which is inside an html file, PDF or any other documents. In addition to that, there is the collection of pertinent information. These pieces of information will be contained into the databases or spreadsheets so that the users can retrieve them later.

Most of the websites today have text that can be accessed and written easily in the source code. However, there are now other businesses nowadays that choose to make use of Adobe PDF files or Portable Document Format. This is a type of file that can be viewed by simply using the free software known as the Adobe Acrobat. Almost any operating system supports the said software. There are many advantages when you choose to utilize PDF files. Among them is that the document that you have looks exactly the same even if you put it in another computer so that you can view it. Therefore, this makes it ideal for business documents or even specification sheets. Of course there are disadvantages as well. One of which is that the text that is contained in the file is converted into an image. In this case, it is often that you may have problems with this when it comes to the copying and pasting.

This is why there are some that start scraping information from PDF. This is often called PDF scraping in which this is the process that is just like data scraping only that you will be getting information that is contained in your PDF files. In order for you to begin scraping information from PDF, you must choose and exploit a tool that is specifically designed for this process. However, you will find that it is not easy to locate the right tool that will enable you to perform PDF scraping effectively. This is because most of the tools today have problems in obtaining exactly the same data that you want without personalizing them.

Nevertheless, if you search well enough, you will be able to encounter the program that you are looking for. There is no need for you to have programming language knowledge in order for you to use them. You can easily specify your own preferences and the software will do the rest of the work for you. There are also companies out there that you can contact and they will perform the task since they have the right tools that they can use. If you choose to do things manually, you will find that this is indeed tedious and complicated whereas if you compare this to having professionals do the job for you, they will be able to finish it in no time at all. Scraping information from PDF is a process where you collect the information that can be found on the internet and this does not infringe copyright laws.

 Source  URL : http://ezinearticles.com/?Increasing-Accessibility-by-Scraping-Information-From-PDF&id=4593863

What are the ethics of web scraping?

Someone recently asked: "Is web scraping an ethical concept?" I believe that web scraping is absolutely an ethical concept. Web scraping (or screen scraping) is a mechanism to have a computer read a website. There is absolutely no technical difference between an automated computer viewing a website and a human-driven computer viewing a website. Furthermore, if done correctly, scraping can provide many benefits to all involved.

There are a bunch of great uses for web scraping. First, services like Instapaper, which allow saving content for reading on the go, use screen scraping to save a copy of the website to your phone. Second, services like Mint.com, an app which tells you where and how you are spending your money, uses screen scraping to access your bank's website (all with your permission). This is useful because banks do not provide many ways for programmers to access your financial data, even if you want them to. By getting access to your data, programmers can provide really interesting visualizations and insight into your spending habits, which can help you save money.

That said, web scraping can veer into unethical territory. This can take the form of reading websites much quicker than a human could, which can cause difficulty for the servers to handle it. This can cause degraded performance in the website. Malicious hackers use this tactic in what’s known as a "Denial of Service" attack.

Another aspect of unethical web scraping comes in what you do with that data. Some people will scrape the contents of a website and post it as their own, in effect stealing this content. This is a big no-no for the same reasons that taking someone else's book and putting your name on it is a bad idea. Intellectual property, copyright and trademark laws still apply on the internet and your legal recourse is much the same. People engaging in web scraping should make every effort to comply with the stated terms of service for a website. Even when in compliance with those terms, you should take special care in ensuring your activity doesn't affect other users of a website.

One of the downsides to screen scraping is it can be a brittle process. Minor changes to the backing website can often leave a scraper completely broken. Herein lies the mechanism for prevention: making changes to the structure of the code of your website can wreak havoc on a screen scraper's ability to extract information. Periodically making changes that are invisible to the user but affect the content of the code being returned is the most effective mechanism to thwart screen scrapers. That said, this is only a set-back. Authors of screen scrapers can always update them and, as there is no technical difference between a computer-backed browser and a human-backed browser, there's no way to 100% prevent access.

Going forward, I expect screen scraping to increase. One of the main reasons for screen scraping is that the underlying website doesn't have a way for programmers to get access to the data they want. As the number of programmers (and the need for programmers) increases over time, so too will the need for data sources. It is unreasonable to expect every company to dedicate the resources to build a programmer-friendly access point. Screen scraping puts the onus of data extraction on the programmer, not the company with the data, which can work out well for all involved.

Source : https://quickleft.com/blog/is-web-scraping-ethical.

A Content Marketer's Guide to Data Scraping

As digital marketers, big data should be what we use to inform a lot of the decisions we make. Using intelligence to understand what works within your industry is absolutely crucial within content campaigns, but it blows my mind to know that so many businesses aren't focusing on it.

One reason I often hear from businesses is that they don't have the budget to invest in complex and expensive tools that can feed in reams of data to them. That said, you don't always need to invest in expensive tools to gather valuable intelligence — this is where data scraping comes in.

Just so you understand, here's a very brief overview of what data scraping is from Wikipedia:

    "Data scraping is a technique in which a computer program extracts data from human-readable output coming from another program."

Essentially, it involves crawling through a web page and gathering nuggets of information that you can use for your analysis. For example, you could search through a site like Search Engine Land and scrape the author names of each of the posts that have been published, and then you could correlate this to social share data to find who the top performing authors are on that website.

Hopefully, you can start to see how this data can be valuable. What's more, it doesn't require any coding knowledge — if you're able to follow my simple instructions, you can start gathering information that will inform your content campaigns. I've recently used this research to help me get a post published on the front page of BuzzFeed, getting viewed over 100,000 times and channeling a huge amount of traffic through to my blog.

Disclaimer: One thing that I really need to stress before you read on is the fact that scraping a website may breach its terms of service. You should ensure that this isn't the case before carrying out any scraping activities. For example, Twitter completely prohibits the scraping of information on their site. This is from their Terms of Service:

    "crawling the Services is permissible if done in accordance with the provisions of the robots.txt file, however, scraping the Services without the prior consent of Twitter is expressly prohibited"

Google similarly forbids the scraping of content from their web properties:

Google's Terms of Service do not allow the sending of automated queries of any sort to our system without express permission in advance from Google.

So be careful, kids.

Content analysis

Mastering the basics of data scraping will open up a whole new world of possibilities for content analysis. I'd advise any content marketer (or at least a member of their team) to get clued up on this.

Before I get started on the specific examples, you'll need to ensure that you have Microsoft Excel on your computer (everyone should have Excel!) and also the SEO Tools plugin for Excel (free download here). I put together a full tutorial on using the SEO tools plugin that you may also be interested in.

Alongside this, you'll want a web crawling tool like Screaming Frog's SEO Spider or Xenu Link Sleuth (both have free options). Once you've got these set up, you'll be able to do everything that I outline below.

So here are some ways in which you can use scraping to analyse content and how this can be applied into your content marketing campaigns:

1. Finding the different authors of a blog

Analysing big publications and blogs to find who the influential authors are can give you some really valuable data. Once you have a list of all the authors on a blog, you can find out which of those have created content that has performed well on social media, had a lot of engagement within the comments and also gather extra stats around their social following, etc.

I use this information on a daily basis to build relationships with influential writers and get my content placed on top tier websites. Here's how you can do it:

Step 1: Gather a list of the URLs from the domain you're analysing using Screaming Frog's SEO Spider. Simply add the root domain into Screaming Frog's interface and hit start (if you haven't used this tool before, you can check out my tutorial here).

Once the tool has finished gathering all the URLs (this can take a little while for big websites), simply export them all to an Excel spreadsheet.

Step 2: Open up Google Chrome and navigate to one of the article pages of the domain you're analysing and find where they mention the author's name (this is usually within an author bio section or underneath the post title). Once you've found this, right-click their name and select inspect element (this will bring up the Chrome developer console).

Within the developer console, the line of code associated to the author's name that you selected will be highlighted (see the below image). All you need to do now is right-click on the highlighted line of code and press Copy XPath.

For the Search Engine Land website, the following code would be copied:

//*[@id="leftCol"]/div[2]/p/span/a

This may not make any sense to you at this stage, but bear with me and you'll see how it works.

Step 3: Go back to your spreadsheet of URLs and get rid of all the extra information that Screaming Frog gives you, leaving just the list of raw URLs – add these to the first column (column A) of your worksheet.
 Step 4: In cell B2, add the following formula:

=XPathOnUrl(A2,"//*[@id='leftCol']/div[2]/p/span/a")

Just to break this formula down for you, the function XPathOnUrl allows you to use the XPath code directly within (this is with the SEO Tools plugin installed; it won't work without this). The first element of the function specifies which URL we are going to scrape. In this instance I've selected cell A2, which contains a URL from the crawl I did within Screaming Frog (alternatively, you could just type the URL, making sure that you wrap it within quotation marks).

Finally, the last part of the function is our XPath code that we gathered. One thing to note is that you have to remove the quotation marks from the code and replace them with apostrophes. In this example, I'm referring to the "leftCol" section, which I've changed to ‘leftCol' — if you don't do this, Excel won't read the formula correctly.

Once you press enter, there may be a couple of seconds delay whilst the SEO Tools plugin crawls the page, then it will return a result. It's worth mentioning that within the example I've given above, we're looking for author names on article pages, so if I try to run this on a URL that isn't an article (e.g. the homepage) I will get an error.

 For those interested, the XPath code itself works by starting at the top of the code of the URL specified and following the instructions outlined to find on-page elements and return results. So, for the following code:

//*[@id='leftCol']/div[2]/p/span/a

We're telling it to look for any element (//*) that has an id of leftCol (@id='leftCol') and then go down to the second div tag after this (div[2]), followed by a p tag, a span tag and finally, an a tag (/p/span/a). The result returned should be the text within this a tag.

Don't worry if you don't understand this, but if you do, it will help you to create your own XPath. For example, if you wanted to grab the output of an a tag that has rel=author attached to it (another great way of finding page authors), then you could use some XPath that looked a little something like this:

//a[@rel='author']

As a full formula within Excel it would look something like this:

=XPathOnUrl(A2,"//a[@rel='author']")

Once you've created the formula, you can drag it down and apply it to a large number of URLs all at once. This is a huge time-saver as you'd have to manually go through each website and copy/paste each author to get the same results without scraping – I don't need to explain how long this would take.

Now that I've explained the basics, I'll show you some other ways in which scraping can be used…

2. Finding extra details around page authors

So, we've found a list of author names, which is great, but to really get some more insight into the authors we will need more data. Again, this can often be scraped from the website you're analysing.

Most blogs/publications that list the names of the article author will actually have individual author pages. Again, using Search Engine Land as an example, if you click my name at the top of this post you will be taken to a page that has more details on me, including my Twitter profile, Google+ profile and LinkedIn profile. This is the kind of data that I'd want to gather because it gives me a point of contact for the author I'm looking to get in touch with.

Here's how you can do it.

Step 1: First we need to get the author profile URLs so that we can scrape the extra details off of them. To do this, you can use the same approach to find the author's name, with just a little addition to the formula:

=XPathOnUrl(A2,"//a[@rel='author']", <strong>"href"</strong>)

The addition of the "href" part of the formula will extract the output of the href attribute of the atag. In Lehman terms, it will find the hyperlink attached to the author name and return that URL as a result.

 Step 2: Now that we have the author profile page URLs, you can go on and gather the social media profiles. Instead of scraping the article URLs, we'll be using the profile URLs.

So, like last time, we need to find the XPath code to gather the Twitter, Google+ and LinkedIn links. To do this, open up Google Chrome and navigate to one of the author profile pages, right-click on the Twitter link and select Inspect Element.

Once you've done this, hover over the highlighted line of code within Chrome's developer tools, right-click and select Copy XPath.

 Step 3: Finally, open up your Excel spreadsheet and add in the following formula (using the XPath that you've copied over):

=XPathOnUrl(C2,"//*[@id='leftCol']/div[2]/p/a[2]", "href")

Remember that this is the code for scraping Search Engine Land, so if you're doing this on a different website, it will almost certainly be different. One important thing to highlight here is that I've selected cell C2 here, which contains the URL of the author profile page and not just the article page. As well as this, you'll notice that I've included "href" at the end because we want the actual Twitter profile URL and not just the words ‘Twitter'.

You can now repeat this same process to get the Google+ and LinkedIn profile URLs and add it to your spreadsheet. Hopefully you're starting to see the value in this, and how it can be used to gather a lot of intelligence that can be used for all kinds of online activity, not least your SEO and social media campaigns.

3. Gathering the follower counts across social networks

Now that we have the author's social media accounts, it makes sense to get their follower counts so that they can be ranked based on influence within the spreadsheet.

Here are the final XPath formulae that you can plug straight into Excel for each network to get their follower counts. All you'll need to do is replace the text INSERT SOCIAL PROFILE URL with the cell reference to the Google+/LinkedIn URL:

Google+:

=XPathOnUrl(<strong>INSERTGOOGLEPROFILEURL</strong>,"//span[@class='BOfSxb']")

LinkedIn:

=XPathOnUrl(<strong>INSERTLINKEDINURL</strong>,"//dd[@class='overview-connections']/p/strong")

4. Scraping page titles

Once you've got a list of URLs, you're going to want to get an idea of what the content is actually about. Using this quick bit of XPath against any URL will display the title of the page:

=XPathOnUrl(A2,"//title")

To be fair, if you're using the SEO Tools plugin for Excel then you can just use the built-in feature to scrape page titles, but it's always handy to know how to do it manually!

A nice extra touch for analysis is to look at the number of words used within the page titles. To do this, use the following formula:

=CountWords(A2)

From this you can get an understanding of what the optimum title length of a post within a website is. This is really handy if you're pitching an article to a specific publication. If you make the post the best possible fit for the site and back up your decisions with historical data, you stand a much better chance of success.

Taking this a step further, you can gather the social shares for each URL using the following functions:

Twitter:

=TwitterCount(<strong>INSERTURLHERE</strong>)

Facebook:

=FacebookLikes(<strong>INSERTURLHERE</strong>)

Google+:

=GooglePlusCount(<strong>INSERTURLHERE</strong>)

Note: You can also use a tool like URL Profiler to pull in this data, which is much better for large data sets. The tool also helps you to gather large chunks of data from other social networks, link data sources like Ahrefs, Majestic SEO and Moz, which is awesome.

If you want to get even more social stats then you can use the SharedCount API, and this is how you go about doing it…

Firstly, create a new column in your Excel spreadsheet and add the following formula (where A2 is the URL of the webpage you want to gather social stats for):

=CONCATENATE("http://api.sharedcount.com/?url=",A2)

You should now have a cell that contains your webpage URL prefixed with the SharedCount API URL. This is what we will use to gather social stats. Now here's the Excel formula to use for each network (where B2 is the cell that contaiins the formula above):

StumbleUpon:

=JsonPathOnUrl(B2,"StumbleUpon")
  Reddit:
  =JsonPathOnUrl(B2,"Reddit")
  Delicious:
 =JsonPathOnUrl(B2,"Delicious")
 Digg:
 =JsonPathOnUrl(B2,"Diggs")
  Pinterest:
 =JsonPathOnUrl(B2,"Pinterest")

LinkedIn:

=JsonPathOnUrl(B2,"Linkedin")

Facebook Shares:

=JsonPathOnUrl(B2,"Facebook.share_count")

Facebook Comments:

=JsonPathOnUrl(B2,"Facebook.comment_count")

Once you have this data, you can start looking much deeper into the elements of a successful post. Here's an example of a chart that I created around a large sample of articles that I analysed within Upworthy.com.

 The chart looks at the average number of social shares that an article on Upworthy receives vs the number of words within its title. This is invaluable data that can be used across a whole host of different on-page elements to get the perfect article template for the site you're pitching to.

See, big data is useful!

5. Date/time the post was published

Along with analysing the details of headlines that are working within a site, you may want to look at the optimal posting times for best results. This is something that I regularly do within my blogs to ensure that I'm getting the best possible return from the time I spend writing.

Every site is different, which makes it very difficult for an automated, one-size-fits-all tool to gather this information. Some sites will have this data within the <head> section of their webpages, but others will display it directly under the article headline. Again, Search Engine Land is a perfect example of a website doing this…

 So here's how you can scrape this information from the articles on Search Engine Land:

=XPathOnUrl(<strong>INSERTARTICLEURL</strong>,"//*[@class='dateline']/text()")

Now you've got the date and time of the post. You may want to trim this down and reformat it for your data analysis, but you've got it all in Excel so that should be pretty easy.

Source : https://moz.com/blog/a-content-marketers-guide-to-data-scraping

Customized Web Data Scraping Services

To understand your customers’ behaviour it is crucial to organize the scattered data into a single repository. There are experts today who can scrape websites to extract data and develop analytics. Data extraction is a major requisite for any small or large company that deals with a massive volume of information that is stored in a complex structure. Premium data mining services help in extracting and structuring data from structured as well as semi-structured documents found on the internet or in other data warehouses.

Companies dealing with a large amount of data on a regular basis may need to convert these set of data into useful information. In that case, web scraping services will come to help. The experts offering such services will ensure that none of the data is missed. Customized data extraction is carried out mostly on the customer databases in order to analyse their behaviour and demographic characteristics. Personalized services offer a whole lot of benefits, which are;

Ensure Data Quality

The experts use a custom data extractor in order to ensure that the data extracted are of high quality. More than forty percent of the websites change their structure every month. Thus, it can be difficult for you to monitor the websites. A customized data extraction service will allow you to concentrate on your business’ larger goals, instead of wasting your time in trying DIY web data extraction.

Availability of Custom Scraper Tool

A reputed web scraping service provider is expected to have custom scraper tool with which they can extract information from the data efficiently without missing on anyone. By using the tools they can even scrape the most complex data and can provide it in any format.

Avoid Possible Human Errors

While extracting so many data sometimes even the professional service providers can also miss out on data. However, with customized services there will be no possibility of human error. Besides, a lot of time and cost can be saved too.

Great Speed

The custom web scraping service provider with their efficient tools can work really fast to convert the large amount of data into analytics. Also, they are able to extract data from multiple resources. The extracted data will be further preserved into customized structured formats such as, Microsoft Database, Text, script, HTML, SQL script etc.

Update Website

Additionally, you will have the leverage to update the website with the latest price and filter search by skipping the data, which do not match the keyword.

Tailor-made services even allow the professionals to extract data from emails and some other communication channels efficiently. With these data you will be able to spot the essentials required to implement in your business to convert the visitors into your customers. Also, you can make your business marketing plans accordingly.

Custom website data scraping service assist companies to have access to various on-demand data that are scraped from web, depending on the individual needs. The experts offering end-to-end data extraction services can also help in preparing the analytics for your business.

 Source : http://www.web-parsing.com/blog/customized-web-data-scrapping-services

Data Extraction: Tips to Get Exemplary Results

Data extraction is a skill, the more you master it – more are the chances of having a lucid picture of the volatile market and getting better perceptive of constantly changing trends. Escalating volatility in the market and intensifying competition has been the most contributing factors that have led to the rise of data extraction and data mining.

Data extraction is primarily used by companies (large and small, alike) to collect data from a specific industry, or data related to targeted customers or about their competition in the market. In fact, it has become a primary tool for marketers to plan their moves for branding and promoting particular products or services. It helps a wide plethora of industrial sectors to find and learn about specific data, based on their requirements.

And now with the rise of internet, web scraping has emerged as an important aspect that contributes to your success – the success of your venture or organization. It processes the HTML of a Web page to obtain data and convert it into to another format (i.e. HTML to XML).

Various extraction tools form an integral part of data extraction and data scrapping. Following offers a brief outline of some of these tools:

Email Extraction – An email extractor tool is used to acquire the email ids from any dependable sources automatically

Screen Scrapping – Screen scraping is a practice of reading text information from a screen and collecting visual data, rather than analyzing data as done in web scraping.

Data Mining as name suggests is a process of gathering patterns from information. It basically transforms the information into formats like CSV, MS excels, HTML and so and so forth, depending to your requirements

Web Spider – A Web spider is a computer program which browses internet in a systematic, automated manner. It is used by many search engines in order to provide up-to-date data

It is often seen that while extracting data; many get lost into the labyrinth of confusion, data overabundance, along with a lot of weird and not-so-familiar terms. Proper handling of these may sound easy, however; when not executed with appropriate procedure and processes; it may bring in disastrous results.

This no way means that data mining is a rocket science which only a few gifted and skilled people can take up. All it requires is undivided attention, keen preparation, and training, so brace up yourself for an overview of some practical tips that can help in successful data extraction and give a boost to your business.

Identify your Business Goals!:

Get a clear perspective in mind as to what are your business goals.

Data extraction can be bifurcated into various branches; and one needs to choose it wisely, depending on the business goals. E.g. your primary requirement is to get email ids of potential clients to conduct an email campaign; and for that you certainly need an email extractor. Use of this tool assists in extracting the email ids from trustworthy sources automatically. It essentially collects business contacts from various web pages, text files, HTML files, or any other format without duplicating the email ids. So, if you are not sure what you want; even applying the best tools will be of no use!

A crystal clear mindset helps in better understanding of market scenario and thus helps in formulation of powerful and effective strategies to get desired outcomes. E.g., people dealing in real estate business, should have a vision for it and which area they want to target specifically. With a clear vision they can clearly spell out what you want and where it should be.

Set Realistic Expectations:

Upon identifying your business goals, make sure to check out that they are realistic and attainable! Unrealistic and unachievable targets are the real cause for the obstacles and frustrations in the future.

Since, there are various tools that are and can be employed to extract data; vague or unclear goals make it difficult to determine which tool can be applied.

This crystal clear mindset; will help you give that insight about the direction your business is headed to.

Moreover, you can determine which method can be used to get excellent results. You can get a lucid picture of the past and present of your competitors and therefore helps in setting targets based on the others’ experiences. It is usually a wise move to set expectations that you have not achieved before.

Appoint Skilled Data Miner:

Skilled data miner with excellent data mining skills will reduce the painstaking and tiresome process of planning, devising and preparation.

For fresh start-ups, you can go ahead with the standard procedure however; if you have ample professionals at your disposal, pick up the right one who is not only knowledgeable but also reliable and sincere towards the task.

Prevent Data Deposits:

Being dead-sure of what you really want will help you avoid unnecessary data deposition.

Data mining just like real mining is a skill to know where the real treasure lies and being able to get it in the most efficient and effective way.

Being able to spot on authenticated & reliable resources, well researched information is what gives a short cut to locate the right and exact data.

If you are aimlessly opening every website; the results are bound to be ambiguous and would ultimately be a waste of time and effort.


Source:  http://www.habiledata.com/blog/data-extraction-is-not-a-rocket-science-follow-these-4-tips-to-get-exemplary-results