Data Collection Techniques for a Successful Thesis

Irrespective of the grade of the topic and the subject of research you have chosen, basic requirement and process of all remains same i.e. "research". Re-search in itself means searching on a searched content and this involves some proven fact along with some practical figures reflecting the authenticity and reliability of the study. These facts and figures which are required to prove the fundamentals of study are known as "data's".

These data's are collected according to the demand of research topic and its study undertaken. Also their collection techniques vary along with the topic in detail for example if the topic is like "Changing era of HR policies", the demanded data would be subjective and its technique thus depends on the same. Whereas if the topic is like "Causes of performance appraisal", then the demanded data would be objective and in the terms of figures which shows different parameters, reasons and factors affecting performance appraisal of different number of employees. So, let's have a broader look on the different data collection techniques which gives a reliable ground to your research -

• Primary Technique - Here, the data is collected by the first hand source directly are known as primary data's. Self-analysis is a sub classification of primary data collection - As understood; here you get self-response for a set of questions or a study. For example - personal in-depth interviews and questionnaires are self-analyzed data collection techniques, but its limitation lies in the fact that self-response can be sometimes biased or even confused. On the other, hand the advantage is in the court of most updated data as it is directly collected from the source.

• Secondary Technique - In this technique the data is collected from the pre-collected resources they are called as secondary data's. Data's are collected from articles, bulletins, annual reports, journals, published papers, government and non-government documents and case studies. Limitation of these is that they may not be the updated one or may be manipulated as it is not collected by the researcher itself.

Secondary data is easy to collect as they are pre-collected and are preferred when there is lack of time whereas primary data's are tough to amass. Thus, if researcher wants to bring up to date, reliable and factual data's they should prefer primary source of collection. But, these data collection techniques vary according to problem generated in the thesis. Hence, go through the demands of your thesis first before indulging yourself into data collection.

Source: http://ezinearticles.com/?Data-Collection-Techniques-for-a-Successful-Thesis&id=9178754

How Web Crawling Can Help Venture Capital Firms

How Web Crawling Can Help Venture Capital Firms

Venture capital firms are constantly on the lookout of innovative start-ups for investment. Whether you provide financial capital to early-stage start-ups in IT, software products, biotechnology or other booming industries, you will need the right information as soon as possible. In general, analysing media data to discover and validate insights is one of key areas in which analysts work. Hence, constantly monitoring popular media outlets is one of the ways VCs can deploy to spot trends. Read on to understand how web crawling can not only speed up this whole process but also improve the workflow and accuracy of insights.

What is web crawling

Web crawling simply refers to the use of automated computer programs to visit websites and extract specific bits of information. This is the same technology used by search engines to find, index and serve search results for user queries. Web crawling, as you’d have guessed is a technical and niche process. It takes skilled programmers to write programs that can navigate through the web to find and extract the needed data.

There are DIY tools, vertical specific data providers and DaaS (Data as a service) solutions that VC firms can deploy for crawling.  Although there is the option of setting up an in-house crawling setup, this isn’t recommended for Venture Capital firms. The high tech-barrier and complexity of web crawling process can lead to loss of focus for the VC firms. DaaS can be the ideal option as it’s suitable for recurring and large-scale requirements which only a hosted solution can offer.

How web crawling can help Venture Capital firms

Crawling start-up and entrepreneurship blogs using a web crawling service can help VC firms avail the much-needed data that they can use to discover new trends and validate their research. This can complement the existing research process and make it much more efficient.

1. Spot trends

Spotting new trends in the market is extremely important for venture capital firms. This helps identify the niches that have high probability of bringing in profit. Since investing in companies that have higher chances of succeeding is what Venture capital firms do, the ability to spot trends becomes an invaluable tool.

Web crawling can harvest enough data to identify trends in the market. Websites like Techcrunch and Venturebeat are great sources of start-up related news and information. Media sites like these talk about trending topics constantly. To spot trends in the market, you could use a web crawling solution to extract the article title, date and URL for the current time period and run this data through an analytics solution to identify the most used words in the article titles and URLs. Venture capital firms can then use these insights to target newer companies in the trending niches. Technology blogs, forums and communities can be great places to find relevant start-ups.

2. Validate findings

The manual research by the analysts needs to be validated before the firm can go ahead with further proceedings. Validation can be done by comparing the results of the manual work with the relevant data extracted using web crawling. This not only makes validation much easier but also helps in the weeding out process, thus reducing the possibilities of making mistakes. This can be partially automated by using intelligent data processing/visualisation tools on top the data.

3. Save time

Machines are much faster than humans. Employing web crawling to assist in the research processes in a venture capital firm can save the analysts a lot of time and effort. This time can be further invested in more productive activities like analytics, deep research and evaluation.

Source:-https://www.promptcloud.com/blog/web-crawling-for-venture-capital-firms

Why Customization is the Key Aspect of a Web Scraping Solution

Why Customization is the Key Aspect of a Web Scraping Solution

Every web data extraction requirement is unique when it comes to the technical complexity and setup process. This is one of the reasons why tools aren’t a viable solution for enterprise-grade data extraction from the web. When it comes to web scraping, there simply isn’t a solution that works perfectly out of the box. A lot of customization and tweaking goes into achieving a stable setup that can extract data from a target site on a continuous basis.

Customization web scraping service

This is why freedom of customization is one of the primary USPs of our web crawling solution. At PromptCloud, we go the extra mile to make data acquisition from the web a smooth and seamless experience for our client base that spans across industries and geographies. Customization options are important for any web data extraction project; Find out how we handle it.

The QA process

The QA process consists of multiple manual and automated layers to ensure only high-quality data is passed on to our clients. Once the crawlers are programmed by the technical team, the crawler code is peer reviewed to make sure that the optimal approach is used for extraction and to ensure there are no inherent issues with the code. If the crawler setup is deemed to be stable, it’s deployed on our dedicated servers.

The next part of manual QA is done once the data starts flowing in. The extracted data is inspected by our quality inspection team to make sure that it’s as expected. If issues are found, the crawler setup is tweaked to weed out the detected issues. Once the issues are fixed, the crawler setup is finalized. This manual layer of QA is followed by automated mechanisms that will monitor the crawls throughout the recurring extraction, hereafter.

Customization of the crawler

As we previously mentioned, customization options are extremely important for building high quality data feeds via web scraping. This is also one of the key differences between a dedicated web scraping service and a DIY tool. While DIY tools generally don’t have the mechanism to accurately handle dynamic and complex websites, a dedicated data extraction service can provide high level customization options. Here are some example scenarios where only a customizable solution can help you.

File download

Sometimes, the web scraping requirement would demand downloading of PDF files or images from the target sites. Downloading files would require a bit more than a regular web scraping setup. To handle this, we add an extra layer of setup along with the crawler which will download the required files to a local or cloud storage by fetching the file URLs from the target webpage. The speed and efficiency of the whole setup should be top notch for file downloads to work smoothly.

Resize images

If you want to extract product images from an Ecommerce portal, the file download customization on top of a regular web scraping setup should work. However, high resolution images can easily hog your storage space. In such cases, we can resize all the images being extracted programmatically in order to save you the cost of data storage. This scenario requires a very flexible crawling setup, which is something that can only be provided by a dedicated service provider.

Extracting key information from text

Sometimes, the data you need from a website might be mixed with other text. For example, let’s say you need only the ZIP codes extracted from a website where the ZIP code itself doesn’t have a dedicated field but is a part of the address text. This wouldn’t be normally possible unless you write a program to be introduced into the web scraping pipeline that can intelligently identify and separate the required data from the rest.
Extracting data points from site flow even if it’s missing in the final page

Sometimes, not all the data points that you need might be available on the same page. This is handled by extracting the data from multiple pages and merging the records together. This again requires a customizable framework to deliver data accurately.

Automating the QA process for frequently updated websites

Some websites get updated more of than others. This is nothing new; however, if the sites in your target list get updated at a very high frequency, the QA process could get time-consuming at your end. To cater to such a requirement, the scraping setup should run crawls at a very high frequency. Apart from this, once new records are added, the data should be run through a deduplication system to weed out the possibility of duplicate entries in the data. We can completely automate this process of quality inspection for frequently updated websites.

Source:https://www.promptcloud.com/blog/customization-is-the-key-aspect-of-web-scraping-solution

What is the future of Data Scraping and the Structured Web?

Big Data has become a hot topic over the past year. What do you think the reason for this is?

I think this is obvious. It’s difficult to imagine today’s world without data. When I got involved in IT, a 10 MB hard drive seemed gigantic, and today, hard drives capable of storing terabytes of data are a standard! Besides, the largest “drive” today is the Internet that contains an immeasurable amount of data and expands at a mind-blowing speed. We just need to learn to separate seeds from the chaff, and that’s what big data technologies are all about.


Do you have any tips & tricks for people who want to turn unstructured data  structured data from the Web?

The thing is, this is still a fairly complex task. Products vary from “low-level”, where you need to be familiar with things like regex, xpath, css, http and such, to “high-level”, where all you need to do is to make clicks on the data you want to extract. The first type is usually more universal, but requires some technical skills. The second one works even for inexperienced users, but is often not efficient enough for solving more complex tasks. That’s why I truly appreciate the efforts made by import.io and similar services to find the golden mean.

What do you think the future is for the Structured Web, and web data.
There is no doubt that connections between data on the Internet will grow (remember, it once started with the good old hypertext), and the speed of this process depends on how commercially profitable it will be. However, I don’t think that the problem of data scraping will ever go away. Even if all websites eventually become structurally interconnected, there will always be a need to untangle this huge knot 🙂

Source url :-https://www.import.io/post/what-is-the-future-scraping-and-the-structured-web/

Web Scraping using Chrome Scraper Extension

Do you want to get data from a web page or website to CSV or Excel Spreadsheet? The answer is web scraping. There are number of web scraping software and services available in the market like Visual Web Ripper, Mozenda, Kimono Labs, Outwit Hub, ScraperWiki and Automation Anywhere etc. for web data extraction. These all tools and services are paid and not easy to use for non-technical persons. Now I am going to discuss another method of doing web scraping that is easy to use and free.  There are various Google Chrome browser extensions available at Google Web Store (https://chrome.google.com/webstore/category/apps) using that we can do screen scraping/web scraping.

1. Web scraper
Official Website: http://www.webscraper.io

Install it by visiting following link:

https://chrome.google.com/webstore/detail/scraper/mbigbapnjcgaffohmbkdlecaccepngjd

Web Scraper is a chrome extension for scraping data out of web pages to Excel Spreadsheet or database. It allows you to create a plan/sitemap. According to that plan/sitemap a website is traversed and the data is extracted. The extracted data can be exported to CSV or stored in CouchDB. It also supports scraping from multiple pages with pagination. You can use Web Scraper for scraping multiple types of data like text, tables, images, links and more. It also supports web data extraction from dynamic web pages built up with modern web technologies like JavaScript and AJAX.

2. Data Miner
Install DataMiner by visiting following link:

https://chrome.google.com/webstore/detail/dataminer/nndknepjnldbdbepjfgmncbggmopgden

DataMiner is a standalone chrome browser plugin for extracting data from the websites. Later on extracted data can be exported to Microsoft Excel spreadsheets or Google Sheets.

Using DataMiner extension, you can scrape data from tables and lists on the websites and easily export them into CSV file or Microsoft Excel. It also supports XPath selectors. You can use it for scraping emails, Google search results, HTML tables etc.



3. Screen Scraper:
Install it by visiting following link:

https://chrome.google.com/webstore/detail/screenscraper/pfegffhjcgkneoemnlniggnhkfioidjg

Screen Scraper is another chrome scraper as it name suggest is a Chrome browser extension/plugin for screen scraping. Screen scraping is the process of automatically scraping/extracting information from websites. Later on, Scraped information can be downloaded as a CSV file or JSON file. It supports Element Selectors and Xpath Selectors method.

4. iMacro
Official Website of iMacro: http://imacros.net/

Install iMacro it by visiting following link:

https://chrome.google.com/webstore/detail/imacros-for-chrome/cplklnmnlbnpmjogncfgfijoopmnlemp?hl=en

iMacro is a macro recorder for your Google Chrome browser. Macro recorder is a piece of tool that records user actions. It allows users to record repetitious tasks on the web and replay it at later time. It is useful tool for web automation, data extraction and web testing. Using iMacros you can remember passwords, fill out web forms, download files and possibilities are endless. iMacros is useful to Web developers for web regression testing, performance testing and web transaction monitoring. To use iMacros you just need to record the task once and save it in your machine next time when you need to perform the same task you need not repeat the same task again and again. iMacro plugin comes for Chrome, Firefox and Internet Explorer too.

Source url :-http://webdata-scraping.com/web-scraping-using-chrome-scraper-extension/

Data Scraping Doesn’t Have to Be Hard

All You Need Is the Right Data Scraping Partner

Odds are your business needs web data scraping. Data scraping is the act of using software to harvest desired data from target websites. So, instead of you spending every second scouring the internet and copying and pasting from the screen, the software (called “spiders”) does it for you, saving you precious time and resources.

Departments across an organization will profit from data scraping practices.

Data scraping will save countless hours and headaches by doing the following:

- Monitoring competitors’ prices, locations and service offerings
- Harvesting directory and list data from the web, significantly improving your lead generation
- Acquiring customer and product marketing insight from forums, blogs and review sites
- Extracting website data for research and competitive analysis
- Social media scraping for trend and customer analysis
- Collecting regular or even real time updates of exchange rates, insurance rates, interest rates, -mortgage rates, real estate, stock prices and travel prices

It is a no-brainer, really. Businesses of all sizes are integrating data scraping into their business initiatives. Make sure you stay ahead of the competition by effectively data scraping.

Now for the hard part

The “why should you data scrape?” is the easy part. The “how” gets a bit more difficult. Are you savvy in Python and HTML? What about JavaScript and AJAX? Do you know how to utilize a proxy server? As your data collection grows, do you have the cloud-based infrastructure in place to handle the load? If you or someone at your organization can answer yes to these questions, do they have the time to take on all the web data scraping tasks? More importantly, is it a cost-effective use of your valuable staffing resources for them to do this? With constantly changing websites, resulting in broken code and websites automatically blacklisting your attempts, it could be more of a resource drain than anticipated.

Instead of focusing on all the issues above, business users should be concerned with essential questions such as:

- What data do I need to grow my business?
- Can I get the data I need, when I want it and in a format I can use?
- Can the data be easily stored for future analysis?
- Can I maximize my staffing resources and get this data without any programming knowledge or IT assistance?
- Can I start now?
- Can I cost-effectively collect the data needed to grow my business?

A web data scraping partner is standing by to help you!

This is where purchasing innovative web scraping services can be a game changer. The right partner can harness the value of the web for you. They will go into the weeds so you can spend your precious time growing your business.

Hold on a second! Before you run off to purchase data scraping services, you need to make sure you are looking for the solution that best fits your organisational needs. Don’t get overwhelmed. We know that relinquishing control of a critical business asset can be a little nerve-wracking. To help, we have come up with our steps and best practices for choosing the right data scraping company for your organisation.

1) Know Your Priorities

We have brought this up before, but when going through a purchasing decision process we like to turn to Project Management 101: The Project Management Triangle. For this example, we think a Euler diagram version of the triangle fits best.
Data Scraping and the Project Management Triangle

In this example, the constraints show up as Fast (time), Good (quality) and Cheap (cost). This diagram displays the interconnection of all three elements of the project. When using this diagram, you are only able to pick two priorities. Only two elements may change at the expense of the third:

- We can do the project quickly with high quality, but it will be costly
- We can do the project quickly at a reduced cost, but quality will suffer
- We can do a high-quality project at a reduced cost, but it will take much longer
Using this framework can help you shape your priorities and budget. This really, in turn, helps you search for and negotiate with a data scraping company.

2) Know your budget/resources.

This one is so important it is on here twice. Knowing your budget and staffing resources before reaching out to data scraping companies is key. This will make your search much more efficient and help you manage the entire process.

3) Have a plan going in.

Once again, you should know your priorities, budget, business objectives and have a high-level data scraping plan before choosing a data scraping company. Here are a few plan guidelines to get you started:

- Know what data points to collect: contact information, demographics, prices, dates, etc.
- Determine where the data points can most likely be found on the internet: your social media and review sites, your competitors’ sites, chambers of commerce and government sites, e-commerce sites your products/competitors’ products are sold, etc.
- What frequency do you need this data and what is the best way to receive it? Make sure you can get the data you need and in the correct format. Determine whether you can perform a full upload each time or just the changes from the previous dataset. Think about whether you want the data delivered via email, direct download or automatically to your Amazon S3 account.
- Who should have access to the data and how will it be stored once it is harvested?
- Finally, the plan should include what you are going to do with all this newly acquired data and who is receiving the final analysis.

4) Be willing to change your plan.

This one may seem counterintuitive after so much focus on having a game plan. However, remember to be flexible. The whole point of hiring experts is that they are the experts. A plan will make discussions much more productive, but the experts will probably offer insight you hadn’t thought of. Be willing to integrate their advice into your plan.

5) Have a list of questions ready for the company.

Having a list of questions ready for the data scraping company will help keep you in charge of the discussions and negotiations. Here are some points that you should know before choosing a data scraping partner:
- Can they start helping you immediately? Make sure they have the infrastructure and staff to get - you off the ground in a matter of weeks, not months.
- Make sure you can access them via email and phone. Also make sure you have access to those -actually performing the data scraping, not just a call center.
- Can they tailor their processes to fit with your requirements and organisational systems?
- Can they scrape more than plain text? Make sure they can harvest complex and dynamic sites -with JavaScript and AJAX. If a website’s content can be viewed on a browser, they should be-- able to get it for you.
- Make sure they have monitoring systems in place that can detect changes, breakdowns, and -quality issues. This will ensure you have access to a persistent and reliable flow of data, even - when the targeted websites change formats.
- As your data grows, can they easily keep up? Make sure they have scalable solutions that could - handle all that unstructured web data.
- Will they protect your company? Make sure they know discretion is important and that they will not advertise you as a client unless you give permission. Also, check to see how they disguise their scrapers so that the data harvesting cannot be traced back to your business.

6) Check their reviews.

Do a bit of your own manual data scraping to see what others business are saying about the companies you are researching.

7) Make sure the plan the company offers is cost-effective.

Here are a few questions to ask to make sure you get a full view of the costs and fees in the estimate:
- Is there a setup fee?
- What are the fixed costs associated with this project?
- What are the variable costs and how are they calculated?
- Are there any other taxes, fees or things that I could be charged for that are not listed on this -quote?
- What are the payment terms?

Source Url :-http://www.data-scraping.com.au/data-scraping-doesnt-have-to-be-hard/

How Artificial Intelligence Can be Applied to Web Data Extraction

How Artificial Intelligence Can be Applied to Web Data Extraction

Artificial intelligence is not a new topic at all. A lot has been written about it and it has been a popular theme of sci-fi movies from a decade ago. However, it was only recently that we started seeing AI in action. Thanks to the ever-increasing computing power, our machines are much faster and powerful now which also gives a huge boost to AI. It goes without saying that artificial intelligence requires more computing power to be truly intelligent and mimic the human brain.

artificial intelligence web data extraction

AI is finding its way into many everyday objects that we use. The voice assistant apps on your smartphone are a great example for this. Facebook’s face recognition algorithm is another example for intelligent pattern recognition technology in action. We believe that the extraction of data from web is something that humans shouldn’t be burdened with. Artificial intelligence could be the right solution to aggregating huge data sets from the web with minimal manual interference.

Artificial Intelligence VS Machine Learning

There is a stark difference between machine learning and artificial intelligence. In machine learning, you teach the machine to do something within narrowly defined rules along with some training examples. This training and rules are necessary for the machine learning system to achieve some level of success in the process it’s being taught. Whereas, in artificial intelligence, it does the teaching itself with minimal number of rules and loose training.  It can then go on to make rules for itself from the exposure that it gets, which contributes to the continued learning process. This is made possible by using artificial neural networks. Artificial neural networks and deep learning are used in artificial intelligence for speech and object recognition, image segmentation, modeling language and human motion.

Artificial intelligence in web data extraction

The web is a giant repository where data is vast and abundant. The possibilities that come with this amount of data can be ground breaking. The challenge is to navigate through this unstructured pile of information out there on the web and extract it. It takes a lot of time and effort to scrape data from the web, even with the advanced web scraping technologies. But things are about to change. Researchers from the Massachusetts Institute of Technology recently released a paper on an artificial intelligence system that can extract information from sources on the web and learn how to do it on its own.

The research paper introduces an information extraction system that can extract structured data from unstructured documents automatically. To put it simply, the system can think like humans while looking at a document. When humans cannot find a particular piece of information in a document, we find alternative sources to fill the gap. This adds to our knowledge on the topic in question. The AI system works just like this.
The AI system works on rewards and penalties

The working of this AI based data extraction system involves classifying the data with a ‘Confidence score’. This confidence score determines the probability of the classification being statistically correct and is derived from the patterns in the training data. If the confidence score doesn’t meet the set threshold, the system will automatically search the web for more relevant data. Once the adequate confidence score is achieved by extracting new data from the web and integrating it with the current document, it will deem the task successful. If the confidence score is not met, the process continues until the most relevant data has been pulled out.

This type of learning mechanism is called ‘Reinforcement learning’ and works by the notion of learning by reward. It’s very similar to how humans learn. Since there can be a lot of uncertainty associated with the data being merged together, especially where contrasting information is involved, the rewards are given based on the accuracy of the information. With the training provided, the AI learns how to optimally merge different pieces of data together so that the answers we get from the system is as accurate as possible.
AI in action

To test how well the artificial intelligence system can extract data from the web, researchers gave it a test task. The system was to analyse various data sources on mass shootings in the USA and extract the name of the shooter, number of injured, fatalities and the location. The performance was in fact mind blowing as it could pull up the accurate data the way it was needed while beating conventionally taught data extraction mechanisms by more than 10 percent.

The future of data extraction

With ever increasing need for data and the challenges associated with acquiring it, AI could be what’s missing in the equation. The research is promising and hints at a future where intelligent bots with human sight can read and crawl web documents to tell us the bits we need to know.

The AI system could be a game changer in research tasks that require a lot of manual work from humans now. A system like this will not only save time but also enables us to make use of the abundance of information out there on the web. Looking at the bigger picture, this new research is only a step towards creating the truly intelligent web spider that can master a variety of tasks just like humans rather than being focused at just one process.

Source:https://www.promptcloud.com/blog/artificial-intelligence-web-data-extraction