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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.

In recent years, the World Wide Web (WWW) has become incredibly popular in homes and offices alike. Consumers need to search for relevant information to help solve purchasing problems on various Web sites. Although there is no question that great numbers of WWW users will continue using search engines for information retrieval, consumers still hesitate before making a final decision, often because only rough and limited information about the products is made available. Consequently, consumers need the help of data mining in order to help them make informed decisions. Herein we propose a new approach to integrating a search engine with data mining in an effort to help support customer‐oriented information search action. This approach also illustrates how to reduce the consumer’s information search perplexity.

This paper aims to examine the behaviour of the Egyptian scholars while accessing electronic resources through two federated search tools. The main purpose of this article is to provide guidance for federated search tool technicians and support teams about user issues, including the need for training.

Log files were exploited to examine the behaviour of users of information retrieval systems. This study examined two log files extracted from federated search tools available to the Egyptian scholars' community for accessing electronic resources. A data mining approach was implemented to investigate user behaviour through deep analysis of these logs.

Results show that: none of the available tools provide error messages for dummy queries; most of the Egyptian scholars had short queries; Boolean operators are not used in about 50 per cent of the queries; federated search tools do not provide techniques for query reformation; the optimal days for system maintenance are the non‐weekend vacations; and early morning is the best time for maintenance.

To maximise the value of the federated search tools by understanding user trends when utilising federated search tools. The study shows that more attention should be given to the search capabilities through ongoing training and awareness in order to maximise the benefit from the available resources and tools.

The hypothetical value of the federated search tools has not been previously examined and analysed to understand user trends.

This paper aims to develop a system, which would enable efficient management and exploitation of documentation in electronic form, related to mining projects, with information retrieval and information extraction (IE) features, using various language resources and natural language processing.

The system is designed to integrate textual, lexical, semantic and terminological resources, enabling advanced document search and extraction of information. These resources are integrated with a set of Web services and applications, for different user profiles and use-cases.

The use of the system is illustrated by examples demonstrating keyword search supported by Web query expansion services, search based on regular expressions, corpus search based on local grammars, followed by extraction of information based on this search and finally, search with lexical masks using domain and semantic markers.

The presented system is the first software solution for implementation of human language technology in management of documentation from the mining engineering domain, but it is also applicable to other engineering and non-engineering domains. The system is independent of the type of alphabet (Cyrillic and Latin), which makes it applicable to other languages of the Balkan region related to Serbian, and its support for morphological dictionaries can be applied in most morphologically complex languages, such as Slavic languages. Significant search improvements and the efficiency of IE are based on semantic networks and terminology dictionaries, with the support of local grammars.

Software developers extensively use stack overflow (SO) for knowledge sharing on software development. Thus, software engineering researchers have started mining the structured/unstructured data present in certain software repositories including the Q&A software developer community SO, with the aim to improve software development. The purpose of this paper is show that how academics/practitioners can get benefit from the valuable user-generated content shared on various online social networks, specifically from Q&A community SO for software development.

A comprehensive literature review was conducted and 166 research papers on SO were categorized about software development from the inception of SO till June 2016.

Most of the studies revolve around a limited number of software development tasks; approximately 70 percent of the papers used millions of posts data, applied basic machine learning methods, and conducted investigations semi-automatically and quantitative studies. Thus, future research should focus on the overcoming existing identified challenges and gaps.

The work on SO is classified into two main categories; “SO design and usage” and “SO content applications.” These categories not only give insights to Q&A forum providers about the shortcomings in design and usage of such forums but also provide ways to overcome them in future. It also enables software developers to exploit such forums for the identified under-utilized tasks of software development.

The study is the first of its kind to explore the work on SO about software development and makes an original contribution by presenting a comprehensive review, design/usage shortcomings of Q&A sites, and future research challenges.

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.

client/server(C/S) systems have revolutionized the systems development approach. Among the drivers of the C/S systems is the lower price/performance ratio compared to the mainframe‐based transaction processing systems. Data mining is a process of identifying patterns in corporate transactional and operational databases (also called data warehouses). As most Fortune 500 companies are moving quickly towards the client server systems, it is increasingly becoming important that a data mining approaches should be adapted for C/S systems. In the current paper, we describe different data mining approaches that are used in the C/S systems.

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.

This paper aims to presents Real-time Usage Mining (RUM), an approach that exploits the rich information provided by client logs to support the construction of adaptive Web applications. The main goal of RUM is to provide useful information about the behavior of users that are currently browsing a Web application. By consuming this information, the application is able to adapt its user interface in real-time to enhance the user experience. RUM provides two types of services as follows: support for the detection of struggling users; and user profiling based on the detection of behavior patterns.

RUM leverages the previous study on usability evaluation to provide a service that evaluates the usability of tasks performed by users while they browse applications. This evaluation is based on a metric that allows the detection of struggling users, making it possible to identify these users as soon as few logs from their interaction are processed. RUM also exploits log mining techniques to detect usage patterns, which are then associated with user profiles previously defined by the application specialist. After associating usage patterns to user profiles, RUM is able to classify users as they browse applications, allowing the application developer to tailor the user interface according to the users’ needs and preferences.

The proposed approach was exploited to improve user experience in real-world Web applications. Experiments showed that RUM was effective to provide support for struggling users to complete tasks. Moreover, it was also effective to detect usage patterns and associate them with user profiles.

Although the literature reports studies that explore client logs to support both the detection of struggling users and the user profiling based on usage patterns, no existing solutions provide support for detecting users from specific profiles or struggling users, in real-time, while they are browsing Web applications. RUM also provides a toolkit that allows the approach to be easily deployed in any Web application.

The purpose of this paper is to describe the development of a personalised information support system to help faculty members to search various portals and e‐resources without typing the search terms in different interfaces and to obtain results re‐ordered without human intervention.

After a careful survey of various tools and techniques available for computerised client‐centred information services, the study selected to apply web usage mining, proxy level data collection and a vector space retrieval model to develop the personalised information support for teaching and research in a higher education institution.

There are practical constraints in the implementation stage. There is considerable difficulty in getting real and correct user interests and mapping them effectively into the products and services offered by the library. Also the interests of users change continuously. If multiple users share the same PC, it is difficult to identify the user as there is no one‐to‐one mapping between user and IP address.

The paper has not considered cases for all the faculty members due to time constraints. The results obtained from the system also need correlation with the sources actually used by the faculty to test its efficacy in a highly fluid research situation like higher academics.

A pragmatic client‐centred information support prototype described in this paper may find use in other institutions needing similar information support.

This paper demonstrates the pragmatic application of ICT for linking users and e‐resources in an academic library.