Basics of Online Web Research, Web Mining & Data Extraction Services

The evolution of the World Wide Web and Search engines has brought the abundant and ever growing pile of data and information on our finger tips. It has now become a popular and important resource for doing information research and analysis.

Today, Web research services are becoming more and more complicated. It involves various factors such as business intelligence and web interaction to deliver desired results.

Web Researchers can retrieve web data using search engines (keyword queries) or browsing specific web resources. However, these methods are not effective. Keyword search gives a large chunk of irrelevant data. Since each webpage contains several outbound links it is difficult to extract data by browsing too.

Web mining is classified into web content mining, web usage mining and web structure mining. Content mining focuses on the search and retrieval of information from web. Usage mining extract and analyzes user behavior. Structure mining deals with the structure of hyperlinks.

Web mining services can be divided into three subtasks:

Information Retrieval (IR): The purpose of this subtask is to automatically find all relevant information and filter out irrelevant ones. It uses various Search engines such as Google, Yahoo, MSN, etc and other resources to find the required information.

Generalization: The goal of this subtask is to explore users' interest using data extraction methods such as clustering and association rules. Since web data are dynamic and inaccurate, it is difficult to apply traditional data mining techniques directly on the raw data.

Data Validation (DV): It tries to uncover knowledge from the data provided by former tasks. Researcher can test various models, simulate them and finally validate given web information for consistency.

Should you have any queries regarding Web research or Data mining applications, please feel free to contact us. We would be pleased to answer each of your queries in detail.

Source:http://ezinearticles.com/?Basics-of-Online-Web-Research,-Web-Mining-and-Data-Extraction-Services&id=4511101

Web Data Extraction Services

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

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

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

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

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

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

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

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

Data Mining vs Screen-Scraping

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

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

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

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

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

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

There is No Need to Disrupt the Schedule to Keep the Kitchen Canopy and Extraction System Clean

After taking over a large and beautiful stately hotel its new owner quickly realised that the kitchen extract system would not be straightforward to maintain because the duct work for the extract system was somewhat ancient and therefore would be difficult to clean.

A prestige hotel needs to maintain a high level of hygiene as well as to minimise the risk of a kitchen fire.

So, if replacing the entire system is not an option what can the new owner do to find a solution that would meet exacting standards of cleanliness and ensure that the risk of a fire starting in the system is minimised while ensuring that the cleaning does disrupt the operation of the hotel and restaurant as a business?

Using an experienced specialist commercial cleaning service to asses the establishment, the types of food cooked, how and at what level of intensity is the first step.

It is difficult without this information to advice on how maintenance should be carried out.

The frequency of the cleaning cycle for a canopy and its components depends not only on the regularity and duration of cooking below but also on the type of cooking and the ingredients being used.

Where  the kitchen use is light canopies and extract systems may only need a 12-month cycle for maintenance and cleaning. However, in a busy hotel, kitchen activity is most likely to be heavy and the cleaning company may advise a three or four-month cycle.

Grease filters and canopies over the cookers should ideally be designed, sized and constructed to be robust enough for regular washing in a commercial dishwasher, which is the most thorough and efficient method of cleaning them yourself.

It's important to make sure when re-installing filters that they are fitted the right way around with any framework drain holes at the lowest, front edge. Of course, grease filters are covered with a coating of grease and can therefore be slippery and difficult to handle. Appropriate protyective gloves should be used when handling them.

The canopies and their component parts should be designed to be easy to clean, but if they are not, provided the cleaning intervals are fairly frequent, regular washing with soap or mild detergent and warm water, followed by a clean water rinse might be adequate. If too long a period is left between cleans, grease will become baked-on and require special attention.

No grease filtration is 100% efficient and therefore a certain amount of grease passes through the filters to be deposited on the internal surfaces of the filter housings and ductwork.

Left unattended, this layer of grease on the non-visible surfaces of the canopy creates both hygiene and fire risks.

Deciding on when cleaning should take place, and how often, is something an experienced specialist cleaning company can help with. The simplest guide is that if a surface or component looks dirty, then it needs cleaning.

Most important, however, is regular inspection of all surfaces and especially non-visible ones. The maintenance schedule for any kitchen installation should include inspections.

Copyright (c) 2010 Alison Withers

A regular maintenance and cleaning schedule is not impossible even in the kitchen of a hotel with an antiquated canopy and duct system with the help of a specialist commercial cleaning company to advise on how to do it without disrupting the work flow, as writer Ali Withers discovers.

Source: http://ezinearticles.com/?There-is-No-Need-to-Disrupt-the-Schedule-to-Keep-the-Kitchen-Canopy-and-Extraction-System-Clean&id=4877266

The Trouble With Bots, Spiders and Scrapers

With the Q4 State of the Internet - Security Report due out later this month, we continue to preview sections of it.

Earlier this week we told you about a DDoS attack from a group claiming to be Lizard Squad. Today we look at how
third-party content bots and scrapers are becoming more prevalent as developers seek to gather, store, sort and present
a wealth of information available from other websites.

These meta searches typically use APIs to access data, but many now use screen-scraping to collect information.

As the use of bots and scrapers continues to surge, there's an increased burden on webservers. While bot behavior is
mainly harmless, poorly-coded bots can hurt site performance and resemble DDoS attacks. Or, they may be part of a rival's competitive intelligence program.

Understanding the different categories of third-party content bots, how they affect a website, and how to mitigate their impact is an important part of building a secure web presence.

Specifically, Akamai has seen bots and scrapers used for such purposes as:

•    Setting up fraudulent sites
•    Reuse of consumer price indices
•    Analysis of corporate financial statements
•    Metasearch engines
•    Search engines
•    Data mashups
•    Analysis of stock portfolios
•    Competitive intelligence
•    Location tracking

During 2014 Akamai observed a substantial increase in the number of bots and scrapers hitting the travel, hotel and hospitality sectors. The growth in scrapers targeting these sectors is likely driven by the rise of rapidly developed mobile apps that use scrapers as the fastest and easiest way to collect information from disparate websites.

Scrapers target room rate pages for hotels, pricing and schedules for airlines. In many cases that Akamai investigated, scrapers and bots made several thousand requests per second, far in excess of what can be expected by a human using a web browser.

An interesting development in the use of headless browsers is the advent of companies that offer scraping as a service, such as PhantomJs Cloud. These sites make it easy for users to scrape content and have it delivered, lowering the bar to entry and making it easier for unskilled individuals to scrape content while hiding behind a service.

For each type of bot, there is a corresponding mitigation strategy.

The key to mitigating aggressive, undesirable bots is to reduce their efficiency. In most cases, highly aggressive bots are only helpful to their controllers if they can scrape a lot of content very quickly. By reducing the efficiency of the bot through rate controls, tar pits or spider traps, bot-herders can be driven elsewhere for the data they need.

Aggressive but desirable bots are a slightly different problem. These bots adversely impact operations, but they bring a benefit to the organization. Therefore, it is impractical to block them fully. Rate controls with a high threshold, or a user-prioritization application (UPA) product, are a good way to minimize the impact of a bot. This permits the bot access to the site until the number of requests reaches a set threshold, at which point the bot is blocked or sent to a waiting room. In the meantime, legitimate users are able to access the site normally.

Source: https://blogs.akamai.com/2015/01/performance-mitigation-bots-spiders-and-scrapers.html

Application of Web Data Mining in CRM

The process of improvising the customer relations and interactions and making them more amicable may be termed as Customer relationship management (CRM). Since web data mining is used in the utilization of the various modeling and data analysis methods in detecting given patterns and relationships in the data, it can be used as an effective tool in CRM. By the effectively using web data mining you are able to understand what your customers what.

It is important to note that web data mining can be used effectively in searching for the right and potential customers to be offered the right products at the right time. The result of this in any business is the increase in the revenue generated. This is made possible as you are able to respond to each customer in an effective and efficient way. The method further utilizes very few resources and can be therefore termed as an economical method.

In the next paragraphs we discuss the basic process of customer relationship management and its integration with web data mining service. The following are the basic process that should be used in understanding what your customers need, sending them the right offers and products, and reducing the resources used in managing your customers.

Defining the business objective. Web data mining can be used to define and inform your customers your business objective. By doing research you can be able to determine whether your business objective is communicated well to your customers and clients. Does your business objective take interest in the customers? Your business goal must be clearly outlined in your business CRM. By having a more precise and defined goal is the possible way of ensuring success in the customer relationship management.

Source: http://www.loginworks.com/blogs/web-scraping-blogs/application-web-data-mining-crm/