UNIVERSITY UTARA MALAYSIA COLLEGE OF ARTS & SCIENCES

Research Methodology(SZRZ6014) web log mining for personalized information retrieval Prepared for:  Dr. Farzana binti Kabir Ahmad Prepared by: Ahmed Ghazi Hameed (812517)

Introduction Problem statement Significance of study Research questions or hypothesis Research objective Problem tree References Contents

The Web can be defined as the global, all includes space containment all Internet confiscated. Over the last decade, we have watched an exploder growth in the information plenteous on the Web. The Web users predicting more intelligent systems (or proxies) to collect the beneficial information from the large size of Web involved data sources to meet their information needs. Introduction

Web mining is utilized to automatically detecting and extract information from Web-involved data sources such as documents, log, services, and user profiles. Although analogical data mining methods can be applicative for mining on the Web, much specific algorithms need to be developed and applied for different purposes of Web based information processing in multiple Web resources, effectually and competently. In the paper, we suggest an epitomized Web mining model for extracting approximate concepts hidden in user profiles on the semantic Web. The epitome Web mining model idealizes knowledge on user profiles by using an ontology which includes of both “part-of” and “is-a” ties. In addition describe the details of using the abstract Web mining model for information gathering.

One of the essential issues regarding the competency of information gathering (IG) is “overload”. The problem of information overload happened when a hugely number of noninvolved documents may be regarded to be related. The finding approaches of information retrieval (IR) and information filtering (IF) can be used to solve this problem. The problem, however, is that most approaches of IR and IF cannot clearly translate user profiles (e.g., the user feedback, the user log data). problem statement

The importance of this study is to get rid of the overload of information that the user need not have when retrieving information as well as to extract information that are relevant and on the subject you are looking for. And also speed the retrieval of information and shorten the time. Significance of study

How can of information retrieval (IR) and information filtering (IF) from the web. How can gather relevant data from the Web or databases. How can obtain users’ log data or feedback in the system. How can the systems extract some attributes from the data. Research questions or hypothesis

To information retrieval (IR) and information filtering (IF) from the web To gather relevant data from the Web or databases. To obtain users’ log data or feedback in the system. To the systems extract some attributes from the data. The finding approaches of information retrieval (IR) and information filtering (IF) can be used to solve this problem. The problem, however, is that most approaches of IR and IF cannot clearly translate user profiles (e.g., the user feedback, the user log data). To web mining can be used data mining techniques. To detect the possibility useful knowledge, many typical approached have been presented. They are mining association rules, data classification and clustering, and data generalization and summarization. Research objectives

Problem tree Information Gathering overload Information Retrieval (IR ) Huge Size of Information feedback in the system Extract Information Information Filtering (IF) Gather Relevant Data

[1] R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval, Addison Wesley, [2] D. A. Grossman and O. Frieder, Information retrieval algorithms and heuristics, Kluwer Academic Publishers, Boston, [3] N. R. Jennings, K. Sycara and M. Wooldridge, A Roadmap of agent research and development,Autonomous Agents and Multi-Agent Systems, [4] E. A. Feigenbaum, How the “what” becomes “how”, Communications of the ACM, 1996, 39(5): [5]. Olston, C., Chi, E.H.: ScentTrails: Integrating browsing and searching on the web. ACM Transactions on Computer-Human Interaction 10(3) (2003) 177–197 References