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Indexed in the SCIE (2018 Impact Factor 0.854), and in Scopus

Journal of Web Engineering

Editors-in-Chief:
Martin Gaedke, Chemnitz University of Technology, Germany
Geert-Jan Houben, Delft University of Technology, The Netherlands
Bebo White, Stanford University, USA


ISSN: 1540-9589 (Print Version),

ISSN: 1544-5976 (Online Version)
Vol: 8   Issue: 2

Published In:   June 2009

Publication Frequency: 8 issues per year


Search Available Volume and Issue for Journal of Web Engineering


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WUM Approach to Detect Student's Collaborative Skills


Elena B. Duran 1and Analia Amandi2

1National University of Santiago del Estero, Argentina
2UNICEN University, CONICET, Argentina

Abstract: [+]    |    Download File [ 225KB ]

Abstract: An effective collaboration in learning environments involves a set of skills that students must learn and cultivate. Detecting the contexts in which students apply these skills facilitates personalized assistance in learning environments during the learning process. This work introduces a method to detect collaborative behavior patterns automatically. It is based on Web Usage Mining techniques and allows us to identify contexts in which collaborative skills are applied. The patterns are discovered using association rules and then are used to update a Collaborative Profile in a Collaborative and Dynamic Student Model. The method was validated with simulation techniques and the results obtained suggest that Web Usage Mining is an effective method for detecting collaborative profiles in distance learning environments.

Keywords: Usage Mining, association rules, collaborative learning, student model, collaborative profile.

Ontology-Driven Personalized Query Refinement


Sofia Stamou, Lefteris Kozanidis, Paraskevi Tzekou and Nikos Zotos

Computer Engineering and Informatics Department, Patras University, Greece

Abstract: [+]    |    Download File [ 559KB ]

Abstract: The most popular way for finding information on the Web is go to a search engine, submit a query that describes an information need and receive a list of results that relate to the information sought. As more and more topics are being discussed over the Web and our vocabulary remains relatively stable, it is increasingly difficult for Web users to select queries that express their varying information needs in a distinguishable by the engine manner. Query refinement is the process of providing information seekers with alternative wordings for expressing their search intentions. Although refined queries may contribute to the improvement of retrieval results, nevertheless their realization is intrinsically limited in that they consider nothing about the preferences of the user issuing that query. One way to go about selecting suitable query alternatives is to account for the user interests in the query refinement process. This task involves two great challenges. First we need to be able to effectively identify the user preferences and build a profile for every user. Second, once such a profile is available, we need to identify among a set of candidate query alternatives those that match the user interests. In this article, we present our work towards a personalized query refinement technique and we discuss how we address both of these challenges. Since Web users are reluctant to provide explicit information on their personal preferences, for the first challenge we attempt to determine them based on the analysis of the users’ click history. In particular, we leverage a topical ontology for estimating the user’s topic preferences based on her past searches. For the second challenge, we have developed a query refinement mechanism that uses the learnt user preferences in order to disambiguate the user’s current query and thereafter identify alternative query wordings that match both the initial query semantics and the user preferences. Our experiments show that user preferences can be learnt accurately through the use of the topical ontology and refined queries based on the user preferences yield significant improvements in the search quality over existing query improvement techniques.

Keywords: Personalized search, user preferences, topic-specific rankings, query refinement, topical ontology

Measures and Techniques for Effort Estimation of Web Applications: an Empirical Study Based on a Single-Company Dataset


Sergio Di Martino1, Filomena Ferrucci1, Carmine Gravino and Emilia Mendes2

1Dipartimento di Matematica e Informatica, University of Salerno, Italy
2The University of Auckland, Private Bag 92019

Abstract: [+]    |    Download File [ 298KB ]

Abstract: Effort estimation is a key management activity which goes on throughout a software project being fundamental for accurate project planning and for allocating resources adequately. Thus, it is important to identify techniques and measures that can support such project management activity during the development of Web applications. To this aim, empirical investigations should be performed using data coming from the industrial world. To address this issue, this paper reports on an empirical study based on data from 15 Web applications developed by an Italian software company. The objective of the study was two-fold. The first goal was to verify whether or not some size measures were good indicators of the effort spent to develop the Web applications taken into account. The second goal was to compare the effectiveness of some techniques to establish the relationships between the employed size measures and the development effort of the Web applications. The measures were organized in two sets, where the first one included some length measures while the second one consisted of the nine components which are used to estimate the Web Objects measure. The techniques taken into account were Stepwise Regression, Case- Based Reasoning, and Regression Tree. The results indicated that both the sets of size measures were good indicators of the effort for the analyzed dataset. Furthermore, the analysis also revealed that the first set presented significantly superior performance than the second set when using Stepwise Regression. No significant differences between the two sets of size measures were highlighted when using Case-Based Reasoning and Regression Tree.

Keywords: Web applications, Size measures, Effort estimation, Empirical validation

A Semantic Web Services-based Infrastructure for Ubiquitous Service Systems


Youngguk Ha1, Cheonshu Park2 and Sangseung Kang2

1Konkuk University, Seoul, Korea
2ETRI, Daejeon, Korea

Abstract: [+]    |    Download File [ 894KB ]

Abstract: The recent emergence of ubiquitous computing is rapidly changing computing environments and technologies. Based on the ubiquitous computing technologies, users can be provided with the services they need, anytime and anywhere through not only common computing devices but ubiquitous computing devices such as wireless sensor networks and embedded computers in their daily environments. There are requirements to be met for implementing such ubiquitous service systems. One of the essential requirements is that service applications must provide services dynamically based on the awareness of the current service environments, rather than statically for pre-programmed service environments. That is, service applications need to be aware of feasible service devices and sensors based on the user’s current location, and then interoperable with them automatically. In this paper, we present design and implementation of a service infrastructure for ubiquitous services by the use of Semantic Web Services technology.

Keywords: Semantic Web Services, service ontology, semantic service discovery, service composition, location-aware service, ubiquitous service

River Publishers: Journal of Web Engineering