Abdullah Al-Hamdani

Querying web metadata: Native score management and text support in databases

In this article, we discuss the issues involved in adding a native score management system to object-relational databases, to be used in querying Web metadata (that describes the semantic content of Web resources). The Web metadata model is based on topics (representing entities), relationships among topics (called metalinks), and importance scores (sideway values) of topics and metalinks. We extend database relations with scoring functions and importance scores. We add to SQL score-management clauses with well-defined semantics, and propose the sideway-value algebra (SVA), to evaluate the extended SQL queries. SQL extensions and the SVA algebra are illustrated through two Web resources, namely, the DBLP Bibliography and the SIGMOD Anthology.SQL extensions include clauses for propagating input tuple importance scores to output tuples during query processing, clauses that specify query stopping conditions, threshold predicates (a type of approximate similarity predicates for text comparisons), and user-defined-function-based predicates. The propagated importance scores are then used to rank and return a small number of output tuples. The query stopping conditions are propagated to SVA operators during query processing. We show that our SQL extensions are well-defined, meaning that, given a database and a query Q, under any query processing scheme, the output tuples of Q and their importance scores stay the same.To process the SQL extensions, we discuss two sideway value algebra operators, namely, sideway value algebra join and topic closure, give their implementation algorithms, and report their experimental evaluations.

Evaluating score and publication similarity functions in digital libraries

Digital libraries do not assign importance/relevance scores to their publications, authors, or publication venues, even though scores are potentially useful for (a) providing comparative assessment, or “importances”, of publications, authors, publication venues, (b) ranking publications returned in search outputs, and (c) using scores in locating similar publications. Using social networks and bibliometrics, one can define several score functions.

Web information resource discovery: Past, present, and future

In a time span of twelve years, the World Wide Web–only a computer and an internet connection away from anybody anywhere, and with abundant, diverse and sometimes incorrect, redundant, spam, and bad information–has become the major information repository for the masses and the world. The web is becoming all things to all people, totally oblivious to nation/country/continent boundaries, promising mostly free information to all, and quickly growing into a repository in all languages and all cultures. With large digital libraries and increasingly significant educational resources, the web is becoming an equalizer, a balancing force, and an opportunity for all, especially for underdeveloped/developing countries. The web is both exciting and overwhelming, changing the way the world communicates, from the way businesses are conducted to the way masses are educated, from the way research is performed to the way research results are disseminated. It is fair to say that the web will only get more diverse, larger and more chaotic in the near future.

Selecting Topics for Web Resource Discovery: Efficiency Issues in a Database Approach

This paper discusses algorithms for topic selection queries, designed to query a database containing metadata about web information resources. The metadata database contains topics and relationships, called metalinks, about topics. Topics in the database contain associated importance scores. The topic selection operator TSelection selects, within time T, topics that satisfy a given selection formula and having output importance scores above a given threshold value or in the top-k. The selection formula contains expensive predicates, in the form of user-defined functions.

Exploring the main building blocks of SOA method: SOA maturity model perspective

The most successful enterprises plan its SOA adoption process as a series of maturity stages, where each stage acts as a foundation for the next. Various SOA maturity models (SOAMMs) are available to judge the current level of SOA adoption maturity of an enterprise. However, only a very few models review the formal use of SOA methods in the maturity assessment process. In this work, the SOAMMs are evaluated in terms of how they assess the methodological aspects of SOA adoption at each level of maturity. Based on methodical features, we introduce a set of building blocks for different maturity levels to assist the method engineers in SOA method definition. Welke’s SOAMM is selected as a base model to derive maturity guidelines for SOA methods. This work establishes the role of SOAMM in realizing the true potential of SOA and discusses why CMMI models are inapplicable to assess SOA adoption maturity. The main intention of this work is to develop a conceptual framework that describes the building block of SOA methods at different levels of maturity, including their processes, tools and technologies, and sourcing mechanisms. We adopt a real case study to determine the maturity level of SOA methodology dimension and to demonstrate how SOA adoption maturity can be improved by focusing on the methodical building blocks identified.