Search results

The focus of this paper is a survey of web‐mining research related to areas of societal benefit. The article aims to focus particularly on web mining which may benefit societal areas by extracting new knowledge, providing support for decision making and empowering the effective management of societal issues.

E‐commerce and e‐business are two fields that have been empowered by web mining, having many applications for increasing online sales and doing intelligent business. Have areas of social interest also been empowered by web mining applications? What are the current ongoing research and trends in e‐services fields such as e‐learning, e‐government, e‐politics and e‐democracy? What other areas of social interest can benefit from web mining? This work will try to provide the answers by reviewing the literature for the applications and methods applied to the above fields.

There is a growing interest in applications of web mining that are of social interest. This reveals that one of the current trends of web mining is toward the connection between intelligent web services and societal benefit applications, which denotes the need for interdisciplinary collaboration between researchers from various fields.

On the one hand, this work presents to the web‐mining community an overview of research opportunities in societal benefit areas. On the other hand, it presents to web researchers from various disciplines an approach for improving their web studies by considering web mining as a powerful research tool.

With the proliferation of terrorist/extremist websites on the World Wide Web, it has become progressively more crucial to detect and analyze the content on these websites. Accordingly, the volume of previous research focused on identifying the techniques and activities of terrorist/extremist groups, as revealed by their sites on the so-called dark web, has also grown.

This study presents a review of the techniques used to detect and process the content of terrorist/extremist sites on the dark web. Forty of the most relevant data sources were examined, and various techniques were identified among them.

Based on this review, it was found that methods of feature selection and feature extraction can be used as topic modeling with content analysis and text clustering.

At the end of the review, present the current state-of-the- art and certain open issues associated with Arabic dark Web content analysis.

Behaviour in virtual learning environments (VLE), including travel, gaze, manipulate, gesture and conversation, offer considerable information about the user's implicit interest. The purpose of this study is to find an approach for user interest mining via behaviour analysis in a VLE.

According to research in psychology, any interaction in a VLE has implications for the user's implicit interest. In order to mine a user's implicit interest, an explicit interaction‐interest model needs to be established. This paper presents findings from the concept classification of behaviour in a VLE. Based on this classification, the paper proposes a hierarchical interaction model. In this model the relation between interaction and user interest can be described and used to improve system performance.

In the experimental prototype the authors found that user‐implicit interest could be mined via stages of web mining, i.e. capture the user's original gesture signal, data pre‐process, pattern discovery, interaction goal and interest mining. The mined user's interest information can be used to update the state of local interest, leading to a reduction in network traffic and promotion of better system performance.

This is an original study using behaviour analysis for interest mining in e‐learning. Research on interest mining in e‐learning focused on content mining or search engine and usage mining in web courses. The paper provides valuable clues regarding user interest mining in a VLE, in which the context is different from usual web courses. The research output can be implemented widely, including online learning, and especially in the VLE.

Web personalization has been studied in different streams of research such as Marketing, Human Computer Interaction and Computer Science. However, an information systems perspective of web personalization research is very scarcely visible in this body of knowledge. This research review seeks to address two important questions: how has web personalization evolved as an integrative discipline? How has web personalization been treated in IS literature and where should researchers focus next?

The paper intently follows an information systems perspective in its thematic classification of web personalization research which is consistent with the early conceptualization of information systems by logically mapping IS categories into web personalization research streams. Articles from 100+ journals were analyzed and important concepts related to web personalization were classified from an information systems perspective.

Surrounding the theme of web personalization two parallel streams of research evolved. First stream consisted of internet business models, computer science algorithms and web mining. Second stream focussed on human computer Interaction studies, user modelling and targeted marketing. Future information systems researchers in web personalization must focus on four important areas of social media, web development methodologies, emerging Internet accessing gadgets and domains other than e‐Commerce.

Web personalization has been studied previously in separate research streams. But no integrated view from different research streams exists. Although research interest in web mining has been growing as evidenced by growing number of publications an information systems perspective of web personalization research is very scarcely visible in the body of knowledge. The authors intently follow an information systems perspective in their thematic classification of web personalization research which is consistent with the early conceptualization of information systems by logically mapping IS categories into web personalization research streams. This thematic segregation of different research streams into IS framework makes our study distinct from other early reviews. They also identify four important areas where future IS researchers should focus on.

The purpose of this paper is to review and compare selected software for data mining, text mining (TM), and web mining that are not available as free open‐source software.

Selected softwares are compared with their common and unique features. The software for data mining are SAS^® Enterprise Miner™, Megaputer PolyAnalyst^® 5.0, NeuralWare Predict^®, and BioDiscovery GeneSight^®. The software for TM are CompareSuite, SAS^® Text Miner, TextAnalyst, VisualText, Megaputer PolyAnalyst^® 5.0, and WordStat. The software for web mining are Megaputer PolyAnalyst^®, SPSS Clementine^®, ClickTracks, and QL2.

This paper discusses and compares the existing features, characteristics, and algorithms of selected software for data mining, TM, and web mining, respectively. These softwares are also applied to available data sets.

The limitations are the inclusion of selected software and datasets rather than considering the entire realm of these. This review could be used as a framework for comparing other data, text, and web mining software.

This paper can be helpful for an organization or individual when choosing proper software to meet their mining needs.

Each of the software selected for this research has its own unique characteristics, properties, and algorithms. No other paper compares these selected softwares both visually and descriptively for all the three types of data, text, and web mining.

Advanced analytics‐driven data analyses allow enterprises to have a complete or “360 degrees” view of their operations and customers. The insight that they gain from such analyses is then used to direct, optimize, and automate their decision making to successfully achieve their organizational goals. Data, text, and web mining technologies are some of the key contributors to making advanced analytics possible. This paper aims to investigate these three mining technologies in terms of how they are used and the issues that are related to their effective implementation and management within the broader context of predictive or advanced analytics.

A range of recently published research literature on business intelligence (BI); predictive analytics; and data, text and web mining is reviewed to explore their current state, issues and challenges learned from their practice.

The findings are reported in two parts. The first part discusses a framework for BI using the data, text, and web mining technologies for advanced analytics; and the second part identifies and discusses the opportunities and challenges the business managers dealing with these technologies face for gaining competitive advantages for their businesses.

The study findings are intended to assist business managers to effectively understand the issues and emerging technologies behind advanced analytics implementation.

Higher education (HE) is becoming a big business, with huge investments in IT technology supporting online learning. With the awareness of the knowledge economy has come a growing consciousness that HE constitutes a large industry or economic sector in its own right. In a marketing fashion, we understand that some customers present much greater profit potential than others. But, how will we find those high‐potential customers in a database that contains hundreds of data items for each of millions of customers? Data mining software can help find the “high‐profit” gems buried in mountains of information. However, merely identifying the best prospects is not enough to improve customer value. One must somehow fit the data mining results into the execution of the content management system that enhances the profitability of customer relationships. However, data mining is not yet engaged into e‐learning systems. This paper describes how we can profit from the integration of data mining and the e‐learning technology.

The purpose of this paper is to anonymize web server log files used in e‐commerce web mining processes.

The paper has applied statistical disclosure control (SDC) techniques to achieve its goal. More precisely, it has introduced the micro‐aggregation of web access logs.

The experiments show that the proposed technique provides good results in general, but it is especially outstanding when dealing with relatively small websites.

As in all SDC techniques there is always a trade‐off between privacy and utility or, in other words, between disclosure risk and information loss. In this proposal, it has borne this issue in mind, providing k‐anonymity, while preserving acceptable information accuracy.

Web server logs are valuable information used nowadays for user profiling and general data‐mining analysis of a website in e‐commerce and e‐services. This proposal allows anonymizing such logs, so they can be safely outsourced to other companies for marketing purposes, stored for further analysis, or made publicly available, without risking customer privacy.

Current solutions to the problem presented here are very poor and scarce. They are normally reduced to the elimination of sensitive information from query strings of URLs in general. Moreover, to its knowledge, the use of SDC techniques has never been applied to the anonymization of web logs.

The purpose of this guest editorial is to introduce the papers in this special issue.

A brief introduction about the issue of web‐mining applications in e‐commerce and e‐services is provided, along with a summary of the main contributions of the papers that are included in the special issue.

The value of web‐mining techniques can be enhanced through applying them to real environments such as e‐commerce and e‐services. The research fields of web mining, e‐commerce and e‐services can also be expanded.

An overview of the special issue and related research is provided in this paper.

Web sites are typically designed attending to a variety of criteria. However, web site structure determines browsing behavior and way‐finding results. The aim of this study is to identify the main profiles of web sites' organizational structure by modeling them as graphs and considering several social network analysis features.

A case study based on 80 institutional Spanish universities' web sites has been used for this purpose. For each root domain, two different networks have been considered: the first is the domain network, and the second is the page network. In both cases, several indicators related to social network analysis have been evaluated to characterize the web site structure. Factor analysis provides the statistical methodology to adequately extract the main web site profiles in terms of their internal structure.

This paper allows the categorization of web site design styles and provides general guidelines to assist designers to better identify areas for creating and improving institutional web sites. The findings of this study offer practical implications to web site designers for creating and maintaining an effective web presence, and for improving usability.

The research is limited to 80 institutional Spanish universities' web sites. Other institutional university web sites from different countries can be analyzed, and the conclusions could be compared or enlarged.

This paper highlights the importance of the internal web sites structure, and their implications on usability and way‐finding results. As a difference to previous research, the paper is focused on the comparison of internal structure of institutional web sites, rather than analyzing the web as a whole or the interrelations among web sites.