SACM - Canada
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9651
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Item Restricted General And Special Education Teachers’ Perceptions Of Knowledge And Beliefs About Dyslexia(Concordia University, 2013-11-07) Alsaig, Alaa; Alagar, Vangalur; Mohammad, MubarakModern societies have become very dependent on information and services. Technology is adapting to the increasing demands of people and businesses. Context-Aware Systems are becoming ubiquitous. These systems comprise mechanisms to acquire knowledge about the surrounding environment and adapt its behaviour and service provision accordingly. Service oriented computing is the main stream software development methodology. In Service-oriented Applications (SOA), service providers publish the services created by them in service registries. These services are accessed by service requesters during discovery process. For large scale SOA, the registry structure and the type of quires that it can handle are central to efficient service discovery. Moreover, the role of context in determining services and affecting execution is central. This thesis investigates the structure of a context-aware service registry in which context-aware services are stored by service producers and retrieved by service requesters in different contexts. The thesis builds on an existing rich theoretical service model in which contract, functionality, and contexts are bundled together. The thesis investigates generic models and structures for context, context history, and context-aware registry. Also, it studies state of the arts database technologies to analyse its suitability for implementing a registry for rich services. Specifically, the thesis provides a thorough study of the structures, implementation, performance, limitations, and features of Key-Value, Documented Oriented, and Column Oriented databases while considering options for implementing a rich service registry. Database models of contexts and context-aware services are discussed and implemented. The relative performance of the models are discussed after evaluating the test results run on large data sets. Based upon test results a justification for the selected model is given.7 0Item Restricted A Tight Coupling Context-Based Framework for Dataset Discovery(Concordia University, 2023-05-15) Alsaig, Alaa; Alagar, Vangalur; Ormandjieva, OlgaDiscovering datasets of relevance to meet research goals is at the core of different analysis tasks in order to prove proposed hypothesis and theories. In particular, researchers in Artificial Intelligence (AI) and Machine Learning (ML) research domains where relevant datasets are essential for precise predictions have identified how the absence of methods to discover quality datasets are leading to delay and in many cases failure, of ML projects. Many research reports have brought out the absence of dataset discovery methods that fills the gap between analysis requirements and available datasets, and have given statistics to show how it hinders the process of analysis, with completion rate less than 2\%. To the best of our knowledge, removing the above inadequacies remains “an open problem of great importance”. It is in this context that the thesis is making a contribution on context-based tightly coupled framework that will tightly couple dataset providers and data analytics teams. Through this framework, dataset providers publish the metadata descriptions of their datasets and analysts formulate and submit rich queries with goal specifications and quality requirements. The dataset search engine component tightly couples the query specification with metadata specifications datasets through a formal contextualized semantic matching and quality-based ranking and discover all datasets that are relevant to analyst requirements. The thesis gives a proof of concept prototype implementation and reports on its performance and efficiency through a case study.10 0