Web Usage Mining

This paper introduces a new method for the session construction problem, which is the first main step of the web usage mining process. The proposed method is capable of extracting all possible maximal navigation sequences of web users. Through experiments, it is shown that when our new technique is used, it outperforms previous approaches in web usage mining applications such as next-page prediction.

With the emergence of the World Wide Web, analyzing and improving Web communication has become essential to adapt the Web content to the visitors’ expectations. Web communication analysis is traditionally performed by Web analytics software, which produce long lists of page-based audience metrics. These results suffer from page synonymy, page polysemy, page temporality, and page volatility. In addition, the metrics contain little semantics and are too detailed to be exploited by organization managers and chief editors, who need summarized and conceptual information to take high-level decisions. To obtain such metrics, we propose to classify the Web site pages into categories representing the Web site topics and to aggregate the page hits accordingly. In this paper, we show how to compute and visualize these metrics using OLAP tools. To solve the page-temporality issue, we propose to classify the versions of the pages using automatic classifiers.

In this paper, we present an overview of research issues in web mining. We discuss mining with respect to web data referred here as web data mining. In particular, our focus is on web data mining research in context of our web warehousing project called WHOWEDA (Warehouse of Web Data). We have categorized web data mining into threes areas; web content mining, web structure mining and web usage mining. We have highlighted and discussed various research issues involved in each of these web data mining category. We believe that web data mining will be the topic of exploratory research in near future.

Web usage mining has been used effectively as an underlying mechanism for Web personalization and recommender systems. A variety of recommendation frameworks have been proposed, including some based on non-sequential models, such as association rules and clusters, and some based on sequential models, such as sequential or navigational patterns. Our recent stud- ies have suggested that the structural characteristics of

User Navigation Behavior Mining (UNBM) mainly studies the problems of extracting the interesting user access patterns from user access sequences (UAS), which are usually used for user access prediction and web page recommendation. Through analyzing the real world web data, we find most of user access sequences carrying hybrid features of different patterns, rather than a single one. Therefore, the methods that categorize one access sequence into a single pattern, can hardly obtain good quality results. Due to this problem, we propose a multi-task learning approach based on multiple data domain description model (MDDD), which simultaneously captures correlations among patterns and allowing categorizing one UAS into more than one patterns.The experimental results show that our method achieves high performances in both precision and recall by virtue of using MDDD model.

Web Usage Mining (WUM) is the application of data mining techniques over Web server logs in order to extract navigation usage patterns. The analysis of mining patterns for assessing the knowledge they reveal is a critical phase in WUM. The main challenges are: (a) mining algorithms yield a huge number of patterns and (b) there is a significant semantic gap between URLs and events performed by users. In this paper, we describe the pattern analysis mechanisms integrated in O3R (Ontology-based Rules Retrieval and Rummaging), a human-centered environment for the analysis of navigation rules. O3R explores the synergy of mechanisms for retrieving and analyzing patterns. Filtering and clustering allow users to retrieve subsets of patterns with specific characteristics, in order to deal with the large volume of patterns. Rummaging mechanisms are targeted at assessing the meaning and relevance of pattern with regard to the domain, and it is particularly suitable for exploratory analysis. The distinctive feature of O3R is that is dynamically associates meaning to patterns using the concepts and relationships of a domain ontology, as a means of reducing the gap between syntactic URLs and semantic events performed by users. The paper describes the mechanisms in detail, and explores their synergic integration in the O3R prototype. It also reports two case studies that evaluate the use of O3R for the analysis of navigation patterns of a learning site.

Nowadays, due to the dramatic expansion of the Internet, in various spheres, and dependence on information resources it needs to create a secure environment, accessible to multiple users, human users and intelligent agents, soft feel be more. Web robots, there are plans for extracting knowledge from Web pages, that have begun their work, the number of pages, and views are all available -documentation available from these pages, recursively. Web robots, with destructive and non-destructive conduct, along with human users, are considered among the web visitors. These intelligent agents are designed for malicious purposes, such as collecting information, for the search engines, or malicious purposes, such as distributed attacks, which can leave the negative effects of the . In this paper, a method is proposed, based on fuzzy inference system for detection and classification of web visitors. The purpose of this paper is to introduce a hybrid fuzzy inference system, based on learning by example, and a decision tree that can provide, in the context of Web robot detection, acceptable performance compared to other methods, Class classification, such as neural networks, Supporter vector machines, Bayesian networks, and decision trees. Experiments have been carried out based on four main criteria, accuracy, call rate, F-measure and the curve ROC.

Determining similarity between web pages is a key factor for the success of many web mining applications such as recommendation systems and adaptive web sites. In this paper, we propose a new hybrid method of distributed learning automata and graph partitioning to determine similarity between web pages using the web usage data. The idea of the proposed method is that if different users request a couple of pages together, then these pages are likely to correspond to the same information needs therefore can be considered similar. In the proposed method, a learning automaton is assigned to each web page and tries to find the similarities between that page and other pages of a web site utilizing the results of a graph partitioning algorithm performed on the graph of the web site. Computer experiments show that the proposed method outperforms Hebbian algorithm and the only learning automata based method reported in the literature.

RapidMiner is a software for machine learning, data mining, predictive analytics, and business analytics. The server will record large web log files when user visits the website. Extracting knowledge from such huge data demands for new methods. In this paper, we propose a web usage mining method with RapidMiner. At first, the redundant files in log file are deleted by Matlab and then we mines web log which has been pretreated with RapidMiner, it obtains the custom of different user to visit the website by processing and analyzing log file, and mines unusual rules, and provides the reference for the policy decision and construction of website. Experimental result analysis show that, applying RapidMiner in web usage mining, will obtain frequent model which user visits the website, manage to optimize the website structure and recommends for users.

L'objectif de ce travail est la conception et la réalisation d'un outil logiciel, en utilisant les concepts du « Web usage mining », qui permettra au « Webmaster » d'avoir l'ensemble des connaissances sur le site web qu'il gère, en vue d'une amélioration et personnalisation. Il s'agit en fait, d'extraire de l'information à partir du fichier Log du serveur Web, hébergeant le site Web, et prendre les décisions pour découvrir les habitudes des internautes, et de répondre à leurs besoins en adaptant le contenu, la forme et l'agencement des pages web.

Mining and Data Warehousing are two most significant techniques for pattern detection and concentrated data management in present technology. ELearning is one of the most important applications of data mining. The foremost idea is to provide a proposal for a practical model and architecture. The standards and system structural design are analyzed here. This paper provides importance to the combination of Web Services on the e-Learning application domain, because Web Service is the most complex choice for distance education during these days. The process of e-Learning can be promising more efficiently by utilizing of Web usage mining. Mor07/e sophisticated tools are developed for internet customer's behaviour to boost sales and profit, but no such tools are developed to recognize learner's performance in e-Learning. In this paper, some data mining techniques are examined that could be used to improve web-based learning environments.

Recently, the web is becoming an important part of people’s life. The web is a very good place to run successful businesses. Selling products or services online plays an important role in the success of businesses that have a physical presence, like a retail business. Therefore, it is important to have a successful website to serve as a sales and marketing tool. One of the effective used technologies for that purpose is data mining. Data mining is the process of extracting interesting patterns from large databases. Web mining is the usage of data mining techniques to extract interesting information from web data. This paper presents the three components of web mining: web usage mining, web structure mining and web content mining and the main data preprocessing tasks for web usage mining.

Recently, the web is becoming an important part of people’s life. The web is a very good place to run successful businesses. Selling products or services online plays an important role in the success of businesses that have a physical presence, like a retail business. Therefore, it is important to have a successful website to serve as a sales and marketing tool. One of the effective used technologies for that purpose is data mining. Data mining is the process of extracting interesting patterns from large databases. Web mining is the usage of data mining techniques to extract interesting information from web data. This paper presents the three components of web mining: web usage mining, web structure mining and web content mining and the main data preprocessing tasks for web usage mining

Web server logs stores click stream data which can be useful for mining purposes. The data is stored as a result of user's access to a website. Web usage mining an application of data mining can be used to discover user access patterns from weblog data. The obtained results are used in different applications like, site modifications, business intelligence, system improvement and personalization. In this study, we have analyzed the log files of smart sync software web server to get information about visitors; top errors which can be utilized by system administrator and web designer to increase the effectiveness of the web site.

Since the web is always developing, and users' needs are constantly changing, organisations in recent decades have increasingly focused on developing information and communication technologies (ICTs). To introduce new e-services to their customers, they ...

Recommender systems are helpful tools which provide an adaptive Web environment for Web users. Recently, a number of Web page recommender systems have been developed to extract the user behavior from the user's navigational path and predict the next request as he/she visits Web pages. Web Usage Mining (WUM) is a kind of data mining method that can be used to discover this behavior of user and his/her access patterns from Web log data. This paper first presents an overview of the used concepts and techniques of WUM to design Web recommender systems. Then it is shown that how WUM can be applied to Web server logs for discovering access patterns. Afterward, we analyze some o f the problems and challenges in deploying recommender systems. Finally, we propose the solutions which address these problems.

We carried out a usability research based on the methodology of Software Usage Mining of the CRM system used in the Call Center of a major Hungarian telecommunication company. This paper describes the specific usability aspects of CRM systems used in Call Centers. By analyzing the communication model of the customer-company interaction, we identify the key areas of usability and data mining in the process. Our research has revealed how Web Usage Mining provides invaluable insights for identifying usability issues on a quantitative basis. Our results showed how a wizard-like interactional model would suit the process of customer care better, than the currently applied menu driven architecture. In the specific context of a Call Center, Web usage Mining is proposed to used as an evaluation tool to provide fairer assessment for Call Center operators.

Investigating user behaviour with a focus on the process by which users achieve their aims has the potential of discovering additional insight about their actions, improving the process advertently improves their use of the website. This study aims to develop a Process-Driven model that extracts the information contained within web server logs to identify the patterns hidden in user behaviour to improve website usability. Data for this study was collected from the ICT unit in southwest Nigeria. The data contained 879475 records of web server logs of users. The data was preprocessed, cleaned, and transformed from a spreadsheet format into an event data format (.xes). Process mining was applied to the dataset using five (5) algorithms, following which their performances were compared based on simplicity, fitness, precision, and generalization. The results showed that the most optimized process model was developed using an Inductive miner. A Petri net model was used to define the users' navigational behaviour on the website. The study concluded that information extracted from these patterns could improve website design, thus impacting website usability.

Prediction of web user behavior is the demand of today competitive edge of World Wide Web. Predicting the next web page is not sufficient, evaluation of prediction models is important because every model have its own pros and cons. Prediction results will be helpful if high prediction accuracy is achieved with minimum complexity, which are depended on the prediction model. Various models and their variations are proposed for predicting the next web page accessed by the web user. Markov model and their variations are found suitable for web prediction. In this research we have evaluated and compared various models for predicting next web page accessed by the web user. Experiments are conducted on three different real datasets.

The object of this research is to present a theoretical and empirical study on the uses and the appropriations of the ITC. The research carried out in this field is characterized by a large variety. Seven fields of investigation were counted :
- approach on the knowledge apprehending the currents on the informational societies and the acquisition of the knowledge ;
- approach of the canonical model "transmitting - receiver" or "offers - request" resulting from the vision economist from the uses and users ; 
- approach of the sociologists supporting the comprehension of the uses (dimensions social and cultural) and guided by two principal types of rationalities:  that of “sociotechnic” coherence and that of the performance “technosocial” ; 
- approach of the training in acquired skills or automatisms classifying the uses through the computer and the relationship between instruments, training and mediation; 
- directed approach "user" being based on the structure of the service and its various components; 
- approach of the societies processes and the ways of life analyzing the policy of the authorities and their interventions on the organization of spaces; 
- approach of fidelity customers being based on the concepts of perceived qualities and qualities offered.

These various approaches of the study of the uses are presented in the form of syntheses.

Data mining merupakan proses analisis terhadap sekumpulan data yang sangat besar untuk menemukan suatu hubungan dan keterkaitan antara data tersebut sehingga dihasilkan suatu pengetahuan baru yang berguna bagi masyarakat. Data mining menjadi salah satu konsep penggalian informasi yang terus berkembang hingga saat ini. Salah satu cabang dari data mining adalah web mining. Secara khusus web mining berusaha menggali informasi berdasarkan data yang tersedia di website. Berdasarkan jenis sumber datanya, web mining dapat dibagi menjadi tiga jenis, yaitu web content mining, web structure mining dan web usage mining. Dalam makalah ini dijelaskan mengenai teknik dan proses dasar dari web usage mining yang disertai contoh aplikasinya. Makalah ini menyajikan Google Analytics sebagai salah satu aplikasi web usage mining, serta contoh hasil analisisnya terhadap situs Achmatim.Net. Berbagai informasi penting dari suatu situs dapat dihasilkan dengan menggunakan aplikasi Google Analytics. Seminar Nasional Multidisiplin Ilmu (SENMI-2010) Universitas Budi Luhur

In this paper, we propose a novel framework called Smart-Miner for web usage mining problem which uses link information for producing accurate user sessions and frequent navigation patterns. Unlike the simple session concepts in the time and navigation based approaches, where sessions are sequences of web pages requested from the server or viewed in the browser, Smart Miner sessions are set of paths traversed in the web graph that corresponds to users' navigations among web pages. We have modeled session construction as a new graph problem and utilized a new algorithm, Smart-SRA, to solve this problem efficiently. For the pattern discovery phase, we have developed an efficient version of the Apriori-All technique which uses the structure of web graph to increase the performance. From the experiments that we have performed on both real and simulated data, we have observed that Smart-Miner produces at least 30% more accurate web usage patterns than other approaches including previous session construction methods. We have also studied the effect of having the referrer information in the web server logs to show that different versions of Smart-SRA produce similar results. Our another contribution is that we have implemented distributed version of the Smart Miner framework by employing Map/Reduce Paradigm. We conclude that we can efficiently process terabytes of web server logs belonging to multiple web sites by our scalable framework.

In this paper we presents study about how to extract the useful information on the web and also give the superficial knowledge and comparison about data mining. This paper describes the current, past and future of web mining. Here we introduce online resources for retrieval Information on the web i.e. web content mining, and the discovery of user access patterns from web servers, i.e. web usage mining that improve the data mining drawback. Furthermore, we also described web mining through cloud computing i.e. cloud mining. That can be seen as future of Web Mining.

Recibido el 1 de marzo de 2004, aceptado el 25 de septiembre de 2004 RESUMEN Los buscadores de páginas Web son herramientas indispensables de orientación para sus usuarios, lo cual refleja la gran cantidad de personas que los utilizan diariamente. Las bitácoras (logs) que registran el uso de un buscador son una valiosa fuente de información: nos hablan de las expectativas, preferencias y necesidades de las personas, es decir, qué esperan ellas encontrar en la Web. El resultado del análisis de logs de uso tiene un importante valor comercial, en particular para el comercio electrónico y la publicidad en Internet.

—Web mining involves the use of data mining procedures to extort valuable information from the web. The aim of this paper is to mine the visible web of SWAYAM portal and present the trends and patterns of the student enrolments for various courses. This paper also provides further scope for detailed web mining techniques in Indian context.

In this paper Knockout Refinement Algorithm (KRA) is proposed to refine original clusters obtained by applying SOM and K-Means clustering algorithms. KRA Algorithm is based on Contingency Table concepts. Metrics are computed for the Original and Refined Clusters. Quality of Original and Refined Clusters are compared in terms of metrics. The proposed algorithm (KRA) is tested in the educational domain and results show that it generates better quality clusters in terms of improved metric values.

A personalized product recommendation is an enabling mechanism to overcome information overload occurred when shopping in an Internet marketplace. Collaborative filtering has been known to be one of the most successful recommendation methods, but its application to e-commerce has exposed well-known limitations such as sparsity and scalability, which would lead to poor recommendations. This paper suggests a personalized recommendation methodology by which we are able to get further effectiveness and quality of recommendations when applied to an Internet shopping mall. The suggested methodology is based on a variety of data mining techniques such as web usage mining, decision tree induction, association rule mining and the product taxonomy. For the evaluation of the methodology, we implement a recommender system using intelligent agent and data warehousing technologies. q

Analysis of web site usage data involves two significant challenges: firstly the volume of data, arising from the growth of the web, and secondly, the structural complexity of web sites. In this paper we apply Data Mining and Information Visualization techniques to the web domain in order to benefit from the power of both human visual perception and computing; we term this Visual Web Mining. In response to the two challenges, we propose a generic framework, where we apply Data Mining techniques to large web data sets and use Information Visualization methods on the results. The goal is to correlate the outcomes of mining Web Usage Logs and the extracted Web Structure, by visually superimposing the results. We propose several new information visualization diagrams and analyze their utility and elaborate on the architecture of a prototype implementation.

Recommendation Systems are changing from novelties which were used by a few E-commerce sites, to tools that are almost-shaping the world of E-commerce. Many of the largest commerce web sites are already using recommendation systems to encourage their customers, locate products. A recommendation system learns from a customer and recommends products that he/she will investigate most necessary from the available products. This research work is to recommend a product using Apriori algorithm. This algorithm is mainly used to find frequently purchased items/products. Its aim is to detect association rules.

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented.

Computers promise that be as a repository of knowledge and wisdom, but instead, they sent us large amounts of data, web mining is the process of information discovery and knowledge from the Web data. The data is collected from the server, client, proxy server or database in Web mining. Web mining methods are divided into three categories: web content mining, web structure mining and web usage mining. There are several functional areas including e-commerce web mining, text mining, and management of customer behavior. Web mining research focuses on developing knowledge extraction techniques which are used for data analysis. 3 main methods that are used for data mining in web include: association or association rules, sequential patterns, and clustering requirements. The main objective of the web mining is to collect information about the user navigation patterns. Of course, web mining is faced with various challenges and constraints. And many researches are currently doing research in the field of web mining that aim to solve this problem.

With the advent of the World Wide Web and the emergence of e-commerce applications and social networks, organizations across the Web generate a large amount of data day-by-day. The abundant unstructured or semi-structured information on the Web leads a great challenge for both the users, who are seeking for effectively valuable information and for the business people, who needs to provide personalized service to the individual consumers, buried in the billions of web pages. To overcome these problems, data mining techniques must be applied on the Web. In this article, an attempt has been made to review the various web mining techniques to discover fruitful patterns from the Web, in detail. New concepts are also included in broad-sense for Optimal Web Mining. This paper also discusses the state of the art and survey on Web Mining that is used in knowledge discovery over the Web.

Purpose: The aim of this paper is to present the network visualization and SNA indicators as a way to explore and to analyse the web site design through the users' navigational paths. Design/methodology/approach: Web log files from the webometrics.info site were analysed. Web usage mining techniques were used to process these data. Several network graphs (navigational and query graph) were built to inspect the latent structure from the user view. SNA techniques and indicators were used to extract the main results. Findings: Results show networks with small-world and scale-free properties; the double structure of the two language versions; the central position of the search engines as hubs that distribute the access to every page; how the pages are grouped by their thematic and structural relationships; and which pages are less requested through search engines queries or are less visible. Research limitations: Results are based on the web performance of only a web site and its generalization has to be considered with caution. Originality/value: The application of network visualization and SNA indicators allows to explore the web site navigation by users and to test how it is used. The graph representation makes possible an intuitive way to observe the structure of a web site, the behaviour of the users and the information consumption.

There are many studies that dedicated to discovering and mining the Web usage in different countries, but none of these dedicated to the Arab countries. This paper aims at discovering the Web usage in six Arab countries (Jordan, Iraq, Lebanon, Palestinian territories, Saudi Arabia and Syria) through analyzing the top sites and top searches for these countries by comparing the collected data to find the percentages of similarities and differences within these countries. Data collection for Jordan and five other neighboring countries were recorded for the period of around 1 month (from 01/Oct/2010 to 31/ Oct /2010). The results show differences in Web usage between these countries and the tests show that these differences are statistically significant through the use of Chisquare test for independence.

Web mining is a new area of research in information technology. Its applications to teaching and research are addressed in this paper. The purpose of this paper is two-fold: (1) to provide insight into current techniques for teaching web mining in the classroom, and (2) to provide an overview of one selected type of web usage mining. The later is

Web Usage Mining (WUM) is the one of most researching area, it mostly focused on web users and their communication between web sites. The WUM attempts to determine useful knowledge about the web users from an obtained user interaction data. Different users have various requirements when they enter a query to a web search engine. The web search engine fine numerous web pages according to web user query, but most of the web pages are noisy or irrelevant content. Thus, in this paper review the WUM by applying different types of pattern analysis and pattern discovery techniques on web log data. Here WUM analysis divided into three phases such as parts Preprocessing, Pattern discovery and Pattern analysis. These phases can be accomplished by using different types of methods such as a CHAMELEON clustering algorithm, CHAMELEON clustering with Semantic analysis and CHAMELEON clustering with Semantic analysis combined with the Recommendation Rules Generation component. Further, this paper intended with the comparison results of these algorithms with the experimental work by using web log data. The performance results show that the CHAMELEON clustering with Semantic analysis combined with the Recommendation Rules Generation component performs better than the other two techniques.

Web usage mining exploits data mining techniques to discover valuable information from navigation behavior of World Wide Web (WWW) users. The required information is captured by web servers and stored in web usage data logs. The first phase of web usage mining is the data processing phase. In the data processing phase, first, relevant information is filtered from the logs. After that, sessions are reconstructed by using heuristics that select and group requests belonging to the same user session. If we are processing requests after they are handled by the web server, this technique is called "reactive" while in "proactive" techniques the same (pre)processing occurs during the interactive browsing of the web site by the user. Reactive session reconstruction uses "time" and "navigation" oriented heuristics. We propose to combine these heuristics with "site topology" information in order to increase the accuracy of the reconstructed sessions. In this work, we have implemented an agent simulator, which models behavior of web users and generates web user navigation as well as the log data kept by the web server. By this way we know the actual user sessions and we can accurately evaluate and compare the performances of alternative session reconstruction heuristics (which will use only the web server log data). To the best of our knowledge, this paper is the first work that uses such an agent simulator, and therefore, is able to accurately evaluate different session reconstruction heuristics. By using the agent simulator, we attempt to show that our new heuristic discovers more accurate sessions than previous heuristics.

Web usage mining has become the subject of exhaustive research, as its potential for Web-based personalized services, prediction of user near future intentions, adaptive Web sites, and customer profiling are recognized. Recently, a variety of recommendation systems to predict user future movements through Web usage mining have been proposed. However, the quality of recommendations in the current systems to predict user future requests in a particular Web site is below satisfaction. To effectively provide online prediction, we have developed a recommendation system called WebPUM, an online prediction using Web usage mining system and propose a novel approach for classifying user navigation patterns to predict users' future intentions. The approach is based on the new graph partitioning algorithm to model user navigation patterns for the navigation patterns mining phase. Furthermore, longest common subsequence algorithm is used for classifying current user activities to predict user next movement. The proposed system has been tested on CTI and MSNBC datasets. The results show an improvement in the quality of recommendations. Furthermore, experiments on scalability prove that the size of dataset and the number of the users in dataset do not significantly contribute to the percentage of accuracy.

Three common approaches to server-directed automatic Web personalization have been collaborative filtering, content-based filtering, and manual decision rule systems. Recent proposals have suggested Web usage mining as an enabling mechanism to overcome the problems associated with these more traditional techniques. These problems include, for example, the reliance on subjective user ratings or static profiles, and the inability of techniques based

Application of data mining techniques to the World Wide Web, referred to as Web mining, has been the focus of several recent research projects and papers. However, there is no established vocabulary, leading to confusion when comparing research e orts. The term Web mining has been used in two distinct ways. The rst, called Web content mining in this paper, is the process of information discovery from sources across the World Wide Web. The second, called Web usage mining, is the process of mining for user browsing and access patterns. In this paper we de ne Web mining and present an overview of the various research issues, techniques, and development e orts. We brie y describe WEBMINER, a system for Web usage mining, and conclude this paper by listing research issues.

Due to huge, unstructured and scattered amount of data available on web, it is very tough for users to get relevant information in less time. To achieve this, improvement in design of web site, personalization of contents, prefetching and caching activities are done according to user’s behavior analysis. User’s activities can be captured into a special file called log file. There are various types of log: Server log, Proxy server log, Client/Browser log. These log files are used by web usage mining to analyze and discover useful patterns. The process of web usage mining involves three interdependent steps: Data preprocessing, Pattern discovery and Pattern analysis. Among these steps, Data preprocessing plays a vital role because of unstructured, redundant and noisy nature of log data. To improve later phases of web usage mining like Pattern discovery and Pattern analysis several data preprocessing techniques such as Data Cleaning, User Identification, Session Identification, Path Completion etc. have been used. In this paper all these techniques are discussed in detail. Moreover these techniques are also categorized and incorporated with their advantage and disadvantage that will help scientist, researchers and academicians working in this direction.

An e-Government portal represents not only a reputable public image of the sovereignty of a region, it is responsible for serving many users from local citizens and beyond, in a reliable way. The requirement for robustness of e-Government portal becomes relatively stringent. In view of this, it would be very useful to have an independent server that serves as a 24/7 watch-guard to monitor the performance of the service portal. In this paper, we proposed a Web-based performance monitoring system (WMS) for checking the health status and some extras, of the service portal in real-time. Some basic server statuses are usually provided by the web hosting company at which the service portal is hosted. Usually they are simple statistics such as the number of hits and server loads over a selected period of time. Further details regarding the performance of the portal however may not be available or they come in expensive prices. This chargeable service is either by some third-party proprietary commercial software tools on the market or by provision of external analytic jobs of consultancy. In contrast, the advantages of WMS are additional performance checks and open source in Java for easy future development. It features web usage mining from web log files in addition to the computation of statistics offered by the hosting company. A higher level of analytic insights can be obtained too when the two combined. The functions of WMS include (1) Web Log Analysis, (2) Web Usability Analysis, (3) Website Performance Benchmarking, (4) Web Link Validation, and (5) Performance Reporting. WMS can be used as a generic monitoring tool for virtually all kinds of e-business models. Specifically, we discuss the context of applications of WMS to e-Government performance models.