Web Data Scraping Services At Lowest Rate For Business Directory

We are the world's most trusted provider directory, your business data scrape, and scrape email scraping and sending the data needed. We scour the entire directory database or doctors, lawyers, brokers, financial advisers, etc. As the scraping of a particular industry category wise database scraping or data that can be adapted.

We are pioneers in the worldwide web scraping and data services. We must understand the value of our customer database, we email id with the greatest effort to collect data. We are lawyers, doctors, brokers, realtors, schools, students, universities, IT managers, pubs, bars, nightclubs, dance clubs, financial advisers, liquor stores, Face book, Twitter, pharmaceutical companies, mortgage broker scraped data, accounting firms, car dealers , artists, shop health and job portals.

Our business database development services to try and get real quality at the lowest possible industry. Example worked. We have a quick turnaround time can be a business mailing database. Our business database development services to try and get real quality at the lowest possible industry. Example worked. We have a quick turnaround time can be a business mailing database.

We are the world's most trusted provider directory, your business data scrape, and scrape email scraping and sending the data needed. We scour the entire directory database or doctors, lawyers, brokers, financial advisers, etc., as the scraping of a particular industry category wise database scraping or data that can be adapted.

We are pioneers in the worldwide web scraping and data services. We must understand the value of our customer database, we email id with the greatest effort to collect data. We are lawyers, doctors, brokers, realtors, schools, students, universities, IT managers, pubs, bars, nightclubs, dance clubs, financial advisers, liquor stores, Face book, Twitter, pharmaceutical companies, mortgage broker scraped data, accounting firms, car dealers , artists, shop health and job portals.

What a great resource for specific information or content with little success to gather and have tried to organize themselves in a folder? You no longer need to worry, and data processing services through our website search are the best solution for your problem.

We currently have an "information explosion" phase of the walk, where there is so much information and content information for an event or a small group of channels.

Order without the benefit of you and your customers a little truth to that information. You use information and material is easy to organize in a way that is needed. Something other than a small business guide, simply create a separate folder in less than an hour.

Our technology-specific Web database for you to a similar configuration and database development to use. In addition, we finished our services can help you through the data to identify the sources of information for web pages to follow. This is a cost effective way to create a database.

We offer directory database, company name, address, the state, country, phone, email and website URL to take. In recent projects we have completed. We have a quick turnaround time can be a business mailing database. Our business database development services to try and get real quality at the lowest possible industry.

Source:http://www.articlesbase.com/outsourcing-articles/web-data-scraping-services-at-lowest-rate-for-business-directory-5757029.html

What Kind of Legal Problems Can Web Scraping Cause

Web scraping software is readily available and has been used by many for legitimate purposes. It has also been used for illegal purposes. A website that engages in this practice should know the legal dangers of the activity.

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Black Hat SEO Popular Techniques

General Knowledge- VII

The idea of web scraping is not new. Search engines have used this type of software to determine which results appear when someone conducts a search. They use special software software to extract data from a website and this data is then used to calculate the rankings of the website. Websites work very hard to improve their ranking and their chance of being found by anyone making a search. This use of this practice is understood and is considered to be a legitimate use for the software. However, there are services that provide web scraping and screen scraping prevention services and help the webmaster to remain safe from the attack of bad bots.

The problem with duplicacy is that it is often used for less than legitimate reasons. Since the software responsible can collect all sorts of data from websites and store the information that is collected, it represents a danger to anyone who might be affected by it. The information that can be collected can be used for many practices that are not so legitimate and may even be illegal. Anyone who is involved in this practice of content duplicacy should be aware of the legal issues implicated with this practice. It may be wise for anyone who has a website to find ways to prevent a site from being scraped or to use professional services to block site scraping.

Legal problems

The first thing to worry about, if you have a website or are using web scraping software, is when you might run into legal problems. Some of the issues that web scraping can cause include:

•    Access. If the software is used to access sites it does not have the right to access and takes information that it is not entitled to, the owner of the web scarping software may find themselves in legal trouble.

•    Re-use. The software can collect and reuse information. If that information is copyrighted, that might be a legal problem. Any information that is reused without permission may create legal issues for anyone who uses it.

•    Robots. Some states have enacted laws that are designed to keep people from using scraping robots. These automatically search out information on websites and using them may be illegal in some states. It is up to the user of the web scraping software to comply with any laws in the state in which they are operating.

Who is Responsible

The laws and regulations surrounding this practice are not always clear. There are many grey areas that allow this practice to occur. The question is, who is responsible for determining whether the use of web scraping software is legal?

Websites collect the information, but they may not be the entity using the web scraping software. If they are using this type of software, it is not always enough to inform the website's visitors that this practice is occurring. Putting this information into the user agreement may or may not protect the website from legal problems.

It is also partly the responsibility of a site owner to prevent a site from being scraped. There is software that can be used that will do this for a website and will keep any information that is collected safe and secure. A website may or may not be held legally responsible for any web scraper that is able to collect information they have. It will depend on why the data was collected, how it was used, who collected it, and whether precautions were taken.

What to expect

The issue of content copying and the legal issues surrounding it will continue to evolve. As more courts take on this issue, the lines between legal and illegal web scraping will become clearer. Many of the cases that have been brought to court have occurred in civil court, although there are some that have been taken up in a criminal court. There will be times when such practice may actually be a felony.

Before you use spying software, you need to realize that the laws surrounding its use are not clear. If you operate a website, you need to know the legal issues that you may face if scraping software is used on your website. The best step is to use the software available to protect your website and stop web scraping and be honest on your site if web scraping is used.

Source: http://www.articlesbase.com/technology-articles/what-kind-of-legal-problems-can-web-scraping-cause-6780486.html

Central Qld Coal: Mining for Needed Investments

The Central Qld Coal Project is situated in the Galilee Coal Basin, Central Queensland with the purpose of establishing a mine to service international export markets for thermal coal. An estimated cost to such a project would be around $ 7.5 billion - the amount proves that the mining industry is one serious business to begin with.

In addition to the mine, the Central Qld Coal Project also proposes to construct a railway, potentially in excess of 400km depending on the final option: Either to transport processed coal to an expanded facility at Abbot Point or new export terminal to be established at Dudgeon Point. However, this would require new major water and power supply infrastructure to service the mine and port - hence, the extremely high cost. Because mining areas usually involve desolate areas where there is no direct risk to developed regions where the populace thrives, setting up new major water and power supplies would simply demand costs as high as the estimated cost - but this is not the only major percent of the whole budget of the Central Qld Coal Project.

The location for the Central Qld Coal Project is situated 40km northwest of Alpha, approximately 450 km west of Rockhampton and contains an amount of more than three billion tons. The proposed open-cut mine of the Central Qld Coal Project is expected to be developed in stages. It shall have an initial export capacity of 30 million tons per annum with a mine life expectancy of 30 years.

In terms of employment regarding Central Qld Coal Project, there will be around a total of 2,500 people to be employed during the construction and 1,600 permanent positions shall be employed in the operation stage of the Central Qld Coal Project.

Australia is a major coal exporter - the largest exporter of coal and fourth largest producer of coal. Australia is also the second largest producer of gold, second only to China. As for Opal, Australia is responsible for 95% of its production, thereby making her the largest producer worldwide. Australia would not also lose in terms of commercially viable diamond deposits - being third next after Russia and Botswana. This pretty much explains the significance of the mining industry to Australia. It is like the backbone of its economy; an industry focused on claiming the blessings the earth has giver her lands. The Central Qld Coal Project was made to further the exports and improve the trade. However, the Central Qld Coal Project requires quite a large sum for its project. It is only through the financial support of investments, both local and international, can it achieve its goals and begin reaping the fruits of the land.

Source: http://ezinearticles.com/?Central-Qld-Coal:-Mining-for-Needed-Investments&id=6314576

Scraping table from html web with CloudStat

You need to use the data from internet, but don’t type, you can just extract or scrape them if you know the web URL.

Thanks to XML package from R. It provides amazing readHTMLtable() function.

For a study case,

I want to scrape data:

    US Airline Customer Score.
    World Top Chess Players (Men).

A. Scraping US Airline Customer Score table from

http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines

Code:

airline = ‘http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines’

airline.table = readHTMLTable(airline, header=T, which=1,stringsAsFactors=F)

Result:

B. Scraping World Top Chess players (Men) table from http://ratings.fide.com/top.phtml?list=men

Code:

chess = ‘http://ratings.fide.com/top.phtml?list=men’

chess.table = readHTMLTable(chess, header=T, which=5,stringsAsFactors=F)

Result:

Done. You had successfully scraping data from any web page with CloudStat.

You can get the full version of this study case (code and result) at Scraping table from html web.

Then, you can analyze as usual! Great! No more retype the data. Enjoy!

Source:http://www.r-bloggers.com/scraping-table-from-html-web-with-cloudstat/

Data Extraction - A Guideline to Use Scrapping Tools Effectively

So many people around the world do not have much knowledge about these scrapping tools. In their views, mining means extracting resources from the earth. In these internet technology days, the new mined resource is data. There are so many data mining software tools are available in the internet to extract specific data from the web. Every company in the world has been dealing with tons of data, managing and converting this data into a useful form is a real hectic work for them. If this right information is not available at the right time a company will lose valuable time to making strategic decisions on this accurate information.

This type of situation will break opportunities in the present competitive market. However, in these situations, the data extraction and data mining tools will help you to take the strategic decisions in right time to reach your goals in this competitive business. There are so many advantages with these tools that you can store customer information in a sequential manner, you can know the operations of your competitors, and also you can figure out your company performance. And it is a critical job to every company to have this information at fingertips when they need this information.

To survive in this competitive business world, this data extraction and data mining are critical in operations of the company. There is a powerful tool called Website scraper used in online digital mining. With this toll, you can filter the data in internet and retrieves the information for specific needs. This scrapping tool is used in various fields and types are numerous. Research, surveillance, and the harvesting of direct marketing leads is just a few ways the website scraper assists professionals in the workplace.

Screen scrapping tool is another tool which useful to extract the data from the web. This is much helpful when you work on the internet to mine data to your local hard disks. It provides a graphical interface allowing you to designate Universal Resource Locator, data elements to be extracted, and scripting logic to traverse pages and work with mined data. You can use this tool as periodical intervals. By using this tool, you can download the database in internet to you spread sheets. The important one in scrapping tools is Data mining software, it will extract the large amount of information from the web, and it will compare that date into a useful format. This tool is used in various sectors of business, especially, for those who are creating leads, budget establishing seeing the competitors charges and analysis the trends in online. With this tool, the information is gathered and immediately uses for your business needs.

Another best scrapping tool is e mailing scrapping tool, this tool crawls the public email addresses from various web sites. You can easily from a large mailing list with this tool. You can use these mailing lists to promote your product through online and proposals sending an offer for related business and many more to do. With this toll, you can find the targeted customers towards your product or potential business parents. This will allows you to expand your business in the online market.

There are so many well established and esteemed organizations are providing these features free of cost as the trial offer to customers. If you want permanent services, you need to pay nominal fees. You can download these services from their valuable web sites also.

Source: http://ezinearticles.com/?Data-Extraction---A-Guideline-to-Use-Scrapping-Tools-Effectively&id=3600918

Online Data Entry and Data Mining Services

Data entry job involves transcribing a particular type of data into some other form. It can be either online or offline. The input data may include printed documents like Application forms, survey forms, registration forms, handwritten documents etc.

Data entry process is an inevitable part of the job to any organization. One way or other each organization demands data entry. Data entry skills vary depends upon the nature of the job requirement, in some cases data to be entered from a hard copy formats and in some other cases data to be entered directly into a web portal. Online data entry job generally requires the data to be entered in to any online data base.

For a super market, data associate might be required to enter the goods which have sold in a particular day and the new goods received in a particular day to maintain the stock well in order. Also, by doing this the concerned authorities will get an idea about the sale particulars of each commodity as they requires. In another example, an office the account executive might be required to input the day to day expenses in to the online accounting database in order to keep the account well in order.

The aim of the data mining process is to collect the information from reliable online sources as per the requirement of the customer and convert it to a structured format for the further use. The major source of data mining is any of the internet search engine like Google, Yahoo, Bing, AOL, MSN etc. Many search engines such as Google and Bing provide customized results based on the user's activity history. Based on our keyword search, the search engine lists the details of the websites from where we can gather the details as per our requirement.

Collect the data from the online sources such as Company Name, Contact Person, Profile of the Company, Contact Phone Number of Email ID Etc. are doing for the marketing activities. Once the data is gathered from the online sources into a structured format, the marketing authorities will start their marketing promotions by calling or emailing the concerned persons, which may result to create a new customer. So basically data mining is playing a vital role in today's business expansions. By outsourcing the data entry and its related works, you can save the cost that would be incurred in setting up the necessary infrastructure and employee cost.

Source:http://ezinearticles.com/?Online-Data-Entry-and-Data-Mining-Services&id=7713395

Git workflow for Scrapy projects

Our customers often ask us what’s the best workflow for working with Scrapy projects. A popular approach we have seen and used in the past is to split the spiders folder (typically project/spiders) into two folders: project/spiders_prod and project/spiders_dev, and use the SPIDER_MODULES setting to control which spiders are loaded on each environment. This works reasonably well, until you have to make changes to common code used by many spiders (ie. code outside the spiders folder), for example common base spiders.

Nowadays, DVCS (in particular, git) have become more popular and people are quite used to branching, so we recommend using a simple git workflow (similar to GitHub flow) where you branch for every change you make. You keep all changes in a branch while they’re being tested and finally merge to master when they’re finished. This means that master branch is always stable and contains only “production-ready” spiders.

If you are using our Scrapy Cloud platform, you can have 2 projects (myproject-dev, myproject-prod) and use myproject-dev to test the changes in your branch.  scrapy deploy in Scrapy 0.17 now adds the branch name to the version name (when using version=GIT or version=HG), so you can see which branch you are going to run directly on the panel. This is particularly useful with large teams working on a single Scrapy project, to avoid stepping into each other when making changes to common code.

Here is a concrete example to illustrate how this workflow works:y

•    the developer decides to work on issue 123 (could be a new spider or fixes to an existing spider)
•    the developer creates a new branch to work on the issue
•    git checkout -b issue123
•    the developer finishes working on the code and deploys to the panel (this assumes scrapy.cfg is configured with a deploy target, and using version=GIT – see here for more information)
•    scrapy deploy dev
•    the developer goes into the panel and runs the spider, where he’ll see the branch name (issue123) that will be run
•    the developer checks the scraped data looks fine through the item browser in the panel
•    whenever issues are found, the developer makes more fixes (always working on the same branch) and deploys new versions
•    once all issues are fixed, the developer merges the branch and deploys to production project
•    git checkout master
•    git merge issue123
•    git pull # make sure to pull latest code before deploying
•    scrapy deploy prod

We recommend you keep your common spiders well-tested and use Spider Contracts extensively to test your final spiders. Otherwise experience tell us that base spiders end up being copied (instead of reused) out of fear of breaking old spiders that depend on them, thus turning their maintenance into a nightmare.

Source:http://blog.scrapinghub.com/2013/03/06/git-workflow-scrapy-projects/

Handling exceptions in scrapers

When requesting and parsing data from a source with unknown properties and random behavior (in other words, scraping), I expect all kinds of bizarrities to occur. Managing exceptions is particularly helpful in such cases.

Here is some ways that an exception might be raised.
[][0] #The list has no zeroth element, so this raises an IndexError
{}['foo'] #The dictionary has no foo element, so this raises a KeyError

Catching the exception is sometimes cleaner than preventing it from happening in the first place. Here are some examples handling bizarre exceptions in scrapers.

Example 1: Inconsistant date formats

Let’s say we’re parsing dates.
import datetime
This doesn’t raise an error.
datetime.datetime.strptime('2012-04-19', '%Y-%m-%d')
But this does.
datetime.datetime.strptime('April 19, 2012', '%Y-%m-%d')

It raises a ValueError because the date formats don’t match. So what do we do if we’re scraping a data source with multiple date formats?

Ignoring unexpected date formats

A simple thing is to ignore the date formats that we didn’t expect.

import lxml.html
import datetime
def parse_date1(source):
    rawdate = lxml.html.fromstring(source).get_element_by_id('date').text
    try:
         cleandate = datetime.datetime.strptime(rawdate, '%Y-%m-%d')
    except ValueError:
         cleandate = None
    return cleandate

print parse_date1('<div id="date">2012-04-19</div>')

If we make a clean date column in a database and put this in there, we’ll have some rows with dates and some rows with nulls. If there are only a few nulls, we might just parse those by hand.

Trying multiple date formats

Maybe we have determined that this particular data source uses three different date formats. We can try all three.

import lxml.html
import datetime

def parse_date2(source):

    rawdate = lxml.html.fromstring(source).get_element_by_id('date').text

    for date_format in ['%Y-%m-%d', '%B %d, %Y', '%d %B, %Y']:

        try:
             cleandate = datetime.datetime.strptime(rawdate, date_format)
             return cleandate
        except ValueError:
             pass
    return None

print parse_date2('<div id="date">19 April, 2012</div>')

This loops through three different date formats and returns the first one that doesn’t raise the error.

Example 2: Unreliable HTTP connection

If you’re scraping an unreliable website or you are behind an unreliable internet connection, you may sometimes get HTTPErrors or URLErrors for valid URLs. Trying again later might help.

import urllib2
def load(url):
    retries = 3
    for i in range(retries):
        try:
            handle = urllib2.urlopen(url)
            return handle.read()
        except urllib2.URLError:
            if i + 1 == retries:
                raise
            else:
                time.sleep(42)
    # never get here

print load('http://thomaslevine.com')

This function tries to download the page thee times. On the first two fails, it waits 42 seconds and tries again. On the third failure, it raises the error. On a success, it returs the content of the page.

Example 3: Logging errors rather than raising them

For more complicated parses, you might find loads of errors popping up in weird places, so you might want to go through all of the documents before deciding which to fix first or whether to do some of them manually.

import scraperwiki
for document_name in document_names:
    try:
        parse_document(document_name)
    except Exception as e:
        scraperwiki.sqlite.save([], {
            'documentName': document_name,
            'exceptionType': str(type(e)),
            'exceptionMessage': str(e)
        }, 'errors')

This catches any exception raised by a particular document, stores it in the database and then continues with the next document. Looking at the database afterwards, you might notice some trends in the errors that you can easily fix and some others where you might hard-code the correct parse.

Example 4: Exiting gracefully

When I’m scraping over 9000 pages and my script fails on page 8765, I like to be able to resume where I left off. I can often figure out where I left off based on the previous row that I saved to a database or file, but sometimes I can’t, particularly when I don’t have a unique index.


for bar in bars:
    try:
        foo(bar)
    except:
        print('Failure at bar = "%s"' % bar)
        raise

This will tell me which bar I left off on. It’s fancier if I save the information to the database, so here is how I might do that with ScraperWiki.

import scraperwiki
resume_index = scraperwiki.sqlite.get_var('resume_index', 0)
for i, bar in enumerate(bars[resume_index:]):
    try:
        foo(bar)
    except:
        scraperwiki.sqlite.save_var('resume_index', i)
        raise
scraperwiki.sqlite.save_var('resume_index', 0)

ScraperWiki has a limit on CPU time, so an error that often concerns me is the scraperwiki.CPUTimeExceededError. This error is raised after the script has used 80 seconds of CPU time; if you catch the exception, you have two CPU seconds to clean up. You might want to handle this error differently from other errors.

import scraperwiki
resume_index = scraperwiki.sqlite.get_var('resume_index', 0)
for i, bar in enumerate(bars[resume_index:]):
    try:
        foo(bar)
    except scraperwiki.CPUTimeExceededError:
        scraperwiki.sqlite.save_var('resume_index', i)
    except Exception as e:
        scraperwiki.sqlite.save_var('resume_index', i)
        scraperwiki.sqlite.save([], {
            'bar': bar,
            'exceptionType': str(type(e)),
            'exceptionMessage': str(e)
        }, 'errors')
scraperwiki.sqlite.save_var('resume_index', 0)

tl;dr

Expect exceptions to occur when you are scraping a randomly unreliable website with randomly inconsistent content, and consider handling them in ways that allow the script to keep running when one document of interest is bizarrely formatted or not available.

Source: https://blog.scraperwiki.com/2012/05/handling-exceptions-in-scrapers/

Scraping and Analyzing Angel List Syndicates: Kimono Labs + Silk

Because we use Silk to tell stories and visualize data, we are always looking for interesting ways to pull data into a Silk. Right now that means getting data into the CSV format. Fortunately, a wave of new and powerful visual webscraping tools and services have emerged. These make it very simple for anyone (no technical skills required) to scrape data from a website and export that data into a CSV which we can quickly upload into a Silk.

Cool New Scraping Tools

One of the tools we love in this new space is Kimono Labs. Backed by Y Combinator, Kimono combines a visual scraping editor with the ability to do very granular code-inspector level editing to scraping paths. Saved scrapes can be turned into APIs and exported as JSON. Kimono also lets you save time-series versioning of scrapes.

Data from angel-list-syndicates.silk.co

Like many startups, we watch the goings on at AngelList very closely. Syndicates are of particular interest. Basically, these are DIY venture capital pools that allow a qualified investor to serve as a syndicate leader and aggregate small investments from other qualified investors who are members of AngelList. The idea of the syndicates is to democratize the VC process and make it easier and less risky for individuals to participate.

We used Kimono to scrape information on the Top 25 Syndicates ranked by dollars backing each round. Kimono makes it very easy to visually designate which parts of a page to scrape and how many rows there are on a page. (Here you can see me highlighting the minimum dollar investment). We downloaded the information as a CSV and did a quick scrub to get it ready for upload to Silk. The process took no more than 15 minutes.

We could tell by eyeballing the numbers beforehand that a serious Power Law was in effect. And the actual data analysis on Silk bore this out. We chose to use a pie chart to show distribution. Three syndicates control nearly two-thirds of all the committed capital by Angel.co members in the syndicate program. One of the top three - Tim Ferriss - has no background as a venture capitalist or building technology companies but is rapidly becoming a force in startup investing. The person with the largest committed syndicate pool, Gil Penachina, is someone who is a quiet mover and shaker in Silicon Valley but he clearly packs a huge punch.

The largest syndicate in terms of likely commitments of deals per year is Foundry Group Angels, a group led by Brad Feld (@bfeld). While they put in less per deal, they are planning to back over 50 deals per year - a huge number. Trailing far behind those three was media impresario and Launch conference mogul Jason Calacanis, who is one of the most visible people in the startup space.

Source: http://blog.silk.co/post/83501793279/scraping-and-analyzing-angel-list-syndicates

Web Scraping’s 2013 Review – part 1

Here we are, almost having ended another year and having the chance to analyze the aspects of the Web scraping market over the last twelve months. First of all i want to underline all the buzzwords on the tech field as published in the Yahoo’s year in review article . According to Yahoo, the most searched items wore

•     iPhone (including 4, 5, 5s, 5c, and 6)
•     Samsung (including Galaxy, S4, S3, Note)
•     Siri
•     iPad Cases
•     Snapchat
•     Google Glass
•     Apple iPad
•     BlackBerry Z10
•     Cloud Computing

It’s easy to see that none of this terms regards in any way with the field of data mining, and they rather focus on the gadgets and apps industry, which is just one of the ways technology can evolve to. Regarding actual data mining industry there were a lot of talks about it in this year’s MIT’s Engaging Data 2013 Conference. One of the speakers Noam Chomsky gave an acid speech relating data extraction and its connection to the Big Data phenomena that is also on everyone’s lips this year. He defined a good way to see if Big Data works by following a series of few simple factors: 1. It’s the analysis, not the raw data, that counts. 2. A picture is worth a thousand words 3. Make a big data portal (Not sure if Facebook is planning on dominating in cloud services some day) 4. Use a hybrid organizational model (We’re asleep already, soon)  let’s move 5. Train employees Other interesting declaration  was given by EETimes saying, “Data science will do more for medicine in the next 10 years than biological science.” which says a lot about the volume of required extracted data.

Because we want to cover as many as possible events about data mining this article will be a two parter, so don’t forget to check our blog tomorrow when the second part of this article will come up!

Source:http://thewebminer.com/blog/2013/12/