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Data Services

Web Data Extraction

Web Data Extraction

Data extraction is the act or process of retrieving data out of (usually unstructured or poorly structured) data sources for further data processing or data storage (data migration). The import into the intermediate extracting system is thus usually followed by data transformation and possibly the addition of metadata prior to export to another stage in the data workflow. Usually, the term data extraction is applied when (experimental) data is first imported into a computer from primary sources, like measuring or recording devices. Today’s electronic devices will usually present an electrical connector (e.g. USB) through which ‘raw data’ can be streamed into a personal computer.

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Competition Tracking

Competition Tracking

Competitor analysis in marketing and strategic management is an assessment of the strengths and weaknesses of current and potential competitors. This analysis provides both an offensive and defensive strategic context to identify opportunities and threats. Profiling combines all of the relevant sources of competitor analysis into one framework in the support of efficient and effective strategy formulation, implementation, monitoring and adjustment. Competitor analysis is an essential component of corporate strategy. It is argued that most firms do not conduct this type of analysis systematically enough. Instead, many enterprises operate on what is called “informal impressions, conjectures, and intuition gained through the tidbits of information about competitors every manager continually receives.” As a result, traditional environmental scanning places many firms at risk of dangerous competitive blindspots due to a lack of robust competitor analysis.

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Yellow Pages Scraping

Yellow Pages Scraping

The yellow pages are any telephone directory of businesses, organized by category rather than alphabetically by business name, and in which advertising is sold. The directories were originally printed on yellow paper, as opposed to white pages for non-commercial listings. The traditional term “yellow pages” is now also applied to online directories of businesses. In many countries, including Canada, the United Kingdom, Australia, and elsewhere, “Yellow Pages” (or any applicable local translations), as well as the “Walking Fingers” logo first introduced in the 1970s by the Bell System-era AT&T, are registered trademarks, though the owner varies from country to country, usually being held by the main national telephone company (or a subsidiary or spinoff thereof). However, in the United States, neither the name nor the logo was registered as trademarks by AT&T, and are freely used by several publishers.

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Price Comparison Data

Price Comparison Data

A comparison shopping website, sometimes called a price comparison website, Price Analysis tool, comparison shopping agent, shopbot or comparison shopping engine, is a vertical search engine that shoppers use to filter and compare products based on price, features, reviews and other criteria. Most comparison shopping sites aggregate product listings from many different retailers but do not directly sell products themselves, instead earning money from affiliate marketing agreements. In the United Kingdom, these services made between £780m and £950m in revenue in 2005 [needs update]. Hence, E-commerce accounted for an 18.2 percent share of total business turnover in the United Kingdom in 2012. Online sales already account for 13% of the total UK economy, and its expected to increase to 15% by 2017. There is a huge contribution of comparison shopping websites in the expansion of current E-commerce industry.

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Data Analysis

Data Analysis

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing on business information.[1] In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis.

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Big Data

Big Data

Big data is data sets that are so voluminous and complex that traditional data-processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. There are five concepts associated with big data: volume, variety, velocity and, the recently added, veracity and value[according to whom?. Lately, the term “big data” tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. “There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.” Analysis of data sets can find new correlations to “spot business trends, prevent diseases, combat crime and so on.” Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology and environmental research.

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Data Mining

Data Mining

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.It is an essential process where intelligent methods are applied to extract data patterns. It is an interdisciplinary subfield of computer science. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the “knowledge discovery in databases” process, or KDD. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate.

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Web Scraping

Web Scraping

Web scraping, web harvesting, or web data extraction is data scraping used for extracting data from websites.[1] Web scraping software may access the World Wide Web directly using the Hypertext Transfer Protocol, or through a web browser. While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a bot or web crawler. It is a form of copying, in which specific data is gathered and copied from the web, typically into a central local database or spreadsheet, for later retrieval or analysis. Web scraping a web page involves fetching it and extracting from it.[1][2] Fetching is the downloading of a page (which a browser does when you view the page). Therefore, web crawling is a main component of web scraping, to fetch pages for later processing. Once fetched, then extraction can take place. The content of a page may be parsed, searched, reformatted, its data copied into a spreadsheet, and so on. Web scrapers typically take something out of a page, to make use of it for another purpose somewhere else. An example would be to find and copy names and phone numbers, or companies and their URLs, to a list (contact scraping).

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