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- Creators: Davulcu, Hasan
- Creators: Coomber, Wesley Poblete
- Member of: Theses and Dissertations
Description
To facilitate the development of the Semantic Web, we propose in this thesis a general automatic ontology-building algorithm which, given a pool of potential terms and a set of relationships to include in the ontology, can utilize information gathered from Google queries to build a full ontology for a certain domain. We utilized this ontology-building algorithm as part of a larger system to tag computer tutorials for three systems with different kinds of metadata, and index the tagged documents into a search engine. Our evaluation of the resultant search engine indicates that our automatic ontology-building algorithm is able to build relatively good-quality ontologies and utilize this ontology to effectively apply metadata to documents.
ContributorsWalliman, Garret Greg (Author) / Davulcu, Hasan (Thesis director) / Liu, Huan (Committee member) / Bazzi, Rida (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-05
Description
Many organizational course design methodologies feature general guidelines for the chronological and time-management aspects of course design development. Proper course structure and instructional strategy pacing has been shown to facilitate student knowledge acquisition of novel material. These course-scheduling details influencing student learning outcomes implies the need for an effective and tightly coupled component of an instructional module. The Instructional Module Development System, or IMODS, seeks to improve STEM, or ‘science, technology, engineering, and math’, education, by equipping educators with a powerful informational tool that helps guide course design by providing information based on contemporary research about pedagogical methodology and assessment practices. This is particularly salient within the higher-education STEM fields because many instructors come from backgrounds that are more technical and most Ph.Ds. in science fields have traditionally not focused on preparing doctoral candidates to teach. This thesis project aims to apply a multidisciplinary approach, blending educational psychology and computer science, to help improve STEM education. By developing an instructional module-scheduling feature for the Web-based IMODS, Instructional Module Development System, system, we can help instructors plan out and organize their course work inside and outside of the classroom, while providing them with relevant helpful research that will help them improve their courses. This article illustrates the iterative design process to gather background research on pacing of workload and learning activities and their influence on student knowledge acquisition, constructively critique and analyze pre-existing information technology (IT) scheduling tools, synthesize graphical user interface, or GUI, mockups based on the background research, and then implement a functional-working prototype using the IMODs framework.
ContributorsCoomber, Wesley Poblete (Author) / Bansal, Srividya (Thesis director) / Lindquist, Timothy (Committee member) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The proliferation of semantic data in the form of RDF (Resource Description Framework) triples demands an efficient, scalable, and distributed storage along with a highly available and fault-tolerant parallel processing strategy. There are three open issues with distributed RDF data management systems that are not well addressed altogether in existing work. First is the querying efficiency, second is that solutions are optimized for certain types of query patterns and don’t necessarily work well for all types, and third is concerned with reducing pre-processing cost. Therefore, the rapid growth of RDF data raises the need for an efficient partitioning strategy over distributed data management systems to improve SPARQL (SPARQL Protocol and RDF Query Language) query performance regardless of its pattern shape with minimized pre-processing overhead. In this context, the first contribution of this work is a distributed RDF data partitioning schema called 3CStore that extends the existing VP (Vertical Partitioning) approach by using a subset of triples from the VP tables based on different join correlations. This approach speeds up queries at the cost of additional pre-processing overhead. To solve this, a relational partitioning schema called VPExp was developed by splitting predicates based on explicit type information of objects. This approach gains a significant query performance only for the specific type of query where the object is bound to a value for a particular predicate. To get efficient query performance on a wide range of query patterns, an improved solution is proposed by extending the existing Property Table approach to Subset-Property Table and combined with the VP approach. Further investigation on distributed RDF processing and querying systems based on typical use cases led to a novel relational partitioning schema called PTP (Property Table Partitioning) that further partitions the whole Property Table into the number of unique properties to minimize query input size and join operations during query evaluation. Finally, an RDF data management system based on the SPARQL-over-SQL approach called S3QLRDF is developed that generates the optimal query execution plan using statistics of PTP tables to provide efficient SPARQL query processing on a distributed system.
ContributorsHassan, P M Mahmudul Mahmudul (Author) / Bansal, Srividya (Thesis advisor) / Bansal, Ajay (Committee member) / Davulcu, Hasan (Committee member) / Sarwat Abdelghany Aly Elsayed, Mohamed (Committee member) / Arizona State University (Publisher)
Created2021