Editorial

Citation

Khalil, I. (2008), "Editorial", International Journal of Web Information Systems, Vol. 4 No. 4. https://doi.org/10.1108/ijwis.2008.36204daa.001

Editorial

Article Type: Editorial From: International Journal of Web Information Systems, Volume 4, Issue 4

This is the final issue of Vol. 4 of the International Journal of Web Information Systems, which crowns the fourth year of this journal which has proved to be a premium journal publishing high quality papers, disseminating knowledge on an international basis in the wide and challenging areas of web information systems and serving as an excellent channel for communicating and exchanging ideas among researchers, academics, and practitioners who work, teach, study, and research in web technology and systems.

Continuing our tradition of publishing tutorial and survey papers in each issue of IJWIS on the state of the art research on web information systems contributed by well-known experts in the respective areas. The survey paper included in this issue is entitled “Access control and privacy in web-based social networks” contributed by Barbara Carminati, and Elena Ferrari from the University of Insubria, Italy. This paper thoroughly and extensively discusses the main requirements related to access control and privacy enforcement in Web-based Social Networks (WBSNs). It presents the protection functionalities provided by today WBSNs and examines the main research proposals defined so far, in view of the identified requirements.

The social networking paradigm is today used not only for recreational purposes; it is also used at the enterprise level as a means to facilitate knowledge sharing and information dissemination both at the internet and at the intranet level. As a result of the widespread use of WBSN services, millions of individuals can today easily share personal and confidential information with an incredible amount of (possible unknown) other users. Clearly, this huge amount of information and the ease with which it can be shared and disseminated pose serious security and privacy concerns.

The second paper “S²ProT: rank allocation by superpositioned propagation of topic-relevance” by OlaÅgren proposes a new approach for generating topic-specific rankings. The main idea of the approach is to use power iteration to assign topic-specific rating values (called relevance) to web pages, creating a ranking or partial order among these pages for each topic. The approach depends on a set of pages that are initially assumed to be relevant for a specific topic, the spatial link structure of the web pages, and a net-specific decay factor.

The third paper “A violent web filtering engine using textual and structural content-based analysis” by Mohamed Hammami, Radhouane Guermazi, and Abdelmajid Ben Hamadou proposes a violent Web content detection and filtering system which uses textual and structural content-based analysis. This analysis is based on a violent keyword dictionary focusing on the keyword dictionary preparation, and a comparative study of different data mining techniques to block violent content Web pages.

The fourth paper “DTD Schema: a simple but powerful XML schema language” by Mengchi Liu describes a novel XML schema language called DTD Schema that solves major limitations of DTD and supports features that XML Schema supports in a simple and concise way. The DTD Schema is designed based on DTD and data definition language of object-oriented and object-relational databases. It extends DTD with namespaces, richer built-in types and user-defined subtypes, local elements and attributes, complex types with nonmonotonic multiple element and attribute inheritance with overriding, blocking, conflict handling, and polymorphism.

The final paper in this issue “Arabic script language identification using letter frequency neural networks” by Ali Selamat and Choon-Ching Ng proposes an algorithm for Arabic script language identification based on letter frequency using back propagation neural networks. The algorithm uses windowing algorithm to capture the features from Arabic script document and then feed them to the back propagation neural network for language identification. It is found that feature selection using letter frequency performed better than the windowing algorithm. The average errors achieved by sliding windows; non-sliding windows and letter frequency are 0.0151, 0.1882 and 0.1926, respectively. It is a significant improvement from using windowing algorithm with back propagation neural network to letter frequency with neural network.

Ismail KhalilCo-Editor-in-Chief