Web Mining Opportunities and Privacy Challenges

Web is a collection of billions of documents. The web is very enormous, diverse, flexible, and dynamic. The World Wide Web continues to grow both in the huge volume of traffic and the size and complexity of Web sites. It is difficult to identify the relevant information present in the web. Most of the contents in the web are unstructured in nature, but very little work deals with unstructured and heterogeneous information on the Web. The emerging field of web mining aims at finding and extracting relevant information that is hidden in Web related data, in particular in text documents published on the Web. Data Mining involves the concept of extraction meaningful and valuable information from large volume of data. Web mining is an important area in data mining where we extract the interesting patterns from the contents. Based on these kinds of information the Web Mining consists of 3 processes namely Web Content Mining, Web structure Mining and Web Usage Mining as shown in fig1. Web content mining deals with the raw data that is available on the web. The web structure mining mainly deals with the structure of the web sites (Chidansh & Kankanhalli, 2010). Web Usage mining involves mining the usage characteristics of the users of Web applications. It is in a semi structured format so that it needs lots of pre-processing and parsing before the actual extraction of the required information. This paper gives the survey of web mining techniques. Data mining process consist of several stages namely (Wenguo, 2008) Domain Understanding, Data selection, Data pre-processing and cleaning, Pattern discovery, Interpretation and Reporting.

automatically retrieves extracts and analyzes the information from web. Yang et al, (2012) [3] discuss about the various issues to be addressed in data mining. The major issues include Automated Data Cleaning, Over Fitting, Under Fitting and Oversampling of data, Scaling up for high dimensional data, Mining sequence and time series data. A poll was conducted and given by k d nuggets and many of the researchers suggested the important work for research as Scaling up Data Mining algorithms for huge data, mining text and automated data cleansing as the major issues discussed with highest priorities (Vellingiri & Pandian, 2011). Other issues include dealing with unbalanced data, mining data streams, link and networks. Security in mining and distributed data mining also caught the significance but not to as greater extent. A hotly debated technical issue is whether it is better to set up a relational database structure or a multidimensional one. Finally, there is the issue of price.

Big information Challenges: While the ascent of enormous information yields colossal open doors for people, associations and the general public everywhere, it additionally raises vital protection and moral issues (Oard, et. al., 2008). These issues are elements that may prompt circumstances in which the basic scientific models and frameworks are liable to effect protection adversely from both a lawful and a moral point of view, and consequently speak to conceivable hindrances for the huge information's capability to be completely figured it out. Web mining is Reshaping Medicine and Health Care: As all parts of social insurance (counting general wellbeing checking, human services conveyance and examination) turn out to be increasingly reliant on data innovation, partners in the medicinal services industry and wellbeing financial aspects are progressively ready to gather, process and share different sorts of information efficiently, insights. In actuality, gathering, preparing, and sharing this sort of information is just about as old as clinical drug. What makes this a more prominent theme of enthusiasm for the huge information age, in any case, is that social insurance investigators and experts can now: (i) Consolidate conventional wellbeing information with outside information - demographic, behavioral and therapeutic/wellness related sensor information - so as to gather bits of knowledge into human exercises and connections, and afterward (ii) Influence these experiences to enhance restorative research, find and screen generally imperceptible wellbeing pat-terns in a vast part of the populace, or to give new inventive customized medicinal items and administrations. Web mining and Financial Services: Money related foundations are progressively benefiting from late advancements in the field of IT and enormous information related apparatuses. They utilize these advances to gather and dissect gigantic measures of individual, monetary, and money related information, some of which are ongoing streams (e.g. those from stock and money related markets), keeping in mind the end goal to better comprehend and control the perplexing consistence challenges and budgetary dangers connected with conceivable new speculations (Schafer, et. al., 2001). As of late, credit offices and safety net providers have gotten to be energetic to gain by re-penny advancements in the field of IT and the simple access to person to person communication information to break down years of value-based information reflectively as they look to distinguish exceptionally complex pat-terns, which they can use for extortion location Brokerage firms' developing capacity to evaluate vast quantities of conceivable business sector situations, examine new sorts and wellsprings of information (e.g. breaking news and climate data, continuous sub-prime business sector information, online networking) may permit them to coax out possibly significant examples that would some way or another remain shrouded lair. They can then utilize those bits of knowledge to foresee securities exchange exhibitions and enhance exchanging choices. One illustrative case of this computational way to deal with securities exchange is high-recurrence stock exchanging: a developing type of exchanging that depends completely on fast PCs and smart calculations to settle on exact exchanging choices at rates measured in the request of milliseconds. Additionally, whole business sections are progressively depend loaning choices or overseeing dangers connected with client installments on the web (Michael & Motilal, 2008).

constant aggregations of cross-space information keeping in mind the end goal to pick up knowledge into customer conduct, recognize conceivably suspicious clients exercises, and subsequently settle on exact loaning choices with an exactness to a great extent thought inconceivable only a couple of years prior.. Summing up, enormous information driven monetary administrations can possibly add to more prominent money related incorporation which thusly is imperative for documenting comprehensive financial development.

In this paper we have discussed about the research issues and the drawbacks of the existing techniques. More research work need to be done on the web mining domain as it will rule the web in the near future. Web mining along with semantic web known as semantic web mining is to be concentrated that is evolving which helps us to overcome the cons of web mining. Though various algorithms and techniques have been proposed still work has to be done in discovering new tools to mine the web. Web mining is suitable an active concerning field of research because of its potential marketable benefits. It is further possible to analyze the visitor‘s performance by linking the Web logs with cookies and forms, and which could help e-services site to address several business questions. Its awareness in analyzing user‘s actions on the web after discovering access logs made its fame very rapidly specially in E-services areas. Details like user log files, request for resources etc. are uphold in web servers, which is the core mining area of web usage. The study of these gives the user browsing case and that can be utilized for target advertisement, improvement of web design, satisfaction of clientele and making market analysis. Most of the e-service suppliers understand the fact that they can apply this tool to keep their clientele as the web and its usage continues to grow, so too grows the chance to analyze web data and remove all manner of useful knowledge from it.

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Teaching Assistant, Karnataka College