Managing university academic resources is a complex administrative problem. Typically, the admission capacity for a degree program is decided based on the availability of lecturers, total enrolled students, and the available facilities in the university. The common formula used is the lecturer to student ratio. But often, the decision is either based on short term requirements which lacks the bird eye's view on the whole resource elements and their synergetic relations. Imperative actions should be taken by universities towards balancing the educational capacity based on the universities' main key performance index of academic activities. Accordingly, universities should efficiently manage their academic resources systematically and strategically. This paper studies the determining factors that form the basis of a decision support system for meeting the supply and demand of an academic program, which directly contributes to efficient resource management. A system dynamics (SD) model is constructed for this purpose.

Most healthcare institutions such as hospitals and clinics store their data in the form of databases of various formats. The Health Ontology System that we have developed provides a means to integrate these data with concepts and semantics in the form of a shared cumulative ontology for enabling machines to interpret them. This involves three major software tools, namely, Ontology Generator, Ontology Distiller and Ontology Accumulator. The Ontology Generator is used to create an ontology from a selected database using metadata provided by its database management system. In a reverse process, the Ontology Distiller enables a subset of data from an ontology to be distilled into a database for further analysis. The Ontology Accumulator integrates some similar types of ontology to be accumulated in the cumulative ontology. This Integrated Health Ontology System will pave the way for integrating existing data with ontologies that will be useful for developing semantic agents for healthcare domain.Keywords-Ontology encoding and generation; database schema; ontology viewing; ontology information extraction and integration; extracting ontology into database; knowledge sharing.

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Abstract. Caméléon# is a web data extraction and management tool that provides information aggregation with advanced capabilities that are useful for developing value-added applications and services for electronic business and electronic commerce. To illustrate its features, we use an airfare aggregation example that collects data from eight online sites, including Travelocity, Orbitz, and Expedia. This paper covers the integration of Caméléon# with commercial database management systems, such as MS SQL Server, and XML query languages, such as XQuery.

This review paper provides a comprehensive assessment of scheduling methods for cloud computing, with an emphasis on optimizing resource allocation in cloud computing systems. The PRISMA methodology was utilized to identify 2,487 articles for this comprehensive review of scheduling methods in cloud computing systems. Following a rigorous screening process, 30 papers published between 2018 and 2023 were selected for inclusion in the review. These papers were analyzed in-depth to provide an extensive overview of the current state of scheduling methods in cloud computing, along with the challenges and opportunities for improving resource allocation. The review evaluates various scheduling approaches, including heuristics, optimization, and machine learning-based methods, discussing their strengths and limitations and comparing results from multiple studies. The paper also highlights the latest trends and future directions in cloud computing scheduling research, offering insights for practitioners and researchers in this field.