Helmy, A., and M. H. Gheith, "An Enhanced Approach for Web Services Clustering using Supervised Machine Learning Techniques", International Journal of Scientific & Engineering Research, vol. 8, issue 1, pp. 158-170, 2016. an-enhanced-approach-for-web-services-clustering-using-supervised-machine-learning-techniques.pdf
Helmy, A., A. I. Salah, and M. H. Geith, "Web Services Clustering Approaches: A Review", International organization of Scientific Research (IOSR) Journal of Engineering (IOSRJEN), vol. 6, no. 9: International organization of Scientific Research (IOSR), pp. 38-44, Sep, 2016. AbstractWebsite
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Helmy, A., A. I. Salah, and M. H. Geith, "Similarity Analysis and Clustering for Web Services Discovery: A Review", International Journal of Computer Applications, vol. 152, no. 3, New York, USA, Foundation of Computer Science (FCS), NY, USA, pp. 34-38, Oct, 2016. AbstractWebsite
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Helmy, A. M., "Web Services Discovery Overview", Data Mining and Knowledge Engineering, vol. 8, no. 4, pp. 100–106, 2016. Abstract

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Enterprise Informatics II

Goal

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Enterprise Informatics I

Goal

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Project Management

Goal

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Knowledge Management

Knowledge management is a relatively new concept that has a multidisciplinary substance and influences many human activities not only in a business environment.

The main goal of this subject is to introduce to students basic concepts of knowledge society, knowledge economy, knowledge management and its systems environment.

Hafez, A. M., M. B. Riad, and N. M. El\_dein, "Building Web Ontology for Crop-Pests Domain to Allow Better Search on the Web", INFOS2005, Faculty of Computers and Information, Cairo University , 19-22 March 2005. Abstractbuilding_web_ontology_for_crop-pests_domain_to_allow_better_search_on_the_web.pdf

Ontology is the backbone of the semantic web. To apply
the semantic web concept, you should build ontology for
your domain. Building ontologies require cooperation
among domain experts to classify items in the domain,
specify properties, and determine rules for inferring new
information from the ontology. Once ontology was built,
stored in knowledge base system and published on the
web, an intelligent search engine or an inference engine
can query and make inference on this ontology. Also
ontology developers can reuse and extend this ontology. In
this paper we will show how we can build ontology for the
crop pests in the Agriculture domain, specify what the
benefits of this ontology are and how the query result is
more accurate than searching traditional web. In our
process we used the Ontology Web Language (OWL) [1]
as it is the most recent Ontology language proposed by
World Wide Web Consortium W3C (http://www.w3c.org)
and for its powerful features. Also we used protégé2000 as
Ontology Editor as it is suitable for OWL.

Advanced Technology and Technics in Programming

Objective:

 The aim is to get students acquainted with the newest technics and technology at present used in the area of software development

 Syllabus: