Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references. Also available in electronic version. Access restricted to campus users.

The purpose of this study was to explore the data-driven decision making process within the context of K-12 physical education. Although the topic has received extraordinary attention in other areas of education, it has yet to be investigated directly in physical education settings. A conceptual framework proposed by Mandinach, Honey, Light, and Brunner (2008) guided the investigation. Using a multi-site case study design, one school district previously awarded a Carol M. White Physical Education Program Grant served as the overarching case and eight schools within the district served as embedded cases. Eight physical education teachers, three district coordinators, one principal, and one school counselor participated in the study. Evidence was gathered through interviews, observations, documents, archival records, and artifacts. Analytic strategies such as pattern matching, examining rival explanations, and drawing diagrams were utilized to generate common themes within the data. Overall, findings indicated that physical education teachers collected substantial amounts of physical activity and fitness data aligned with policy requirements, often at the expense of data related to other important teaching domains. Evidence also indicated that teachers rarely transformed collected data into actionable knowledge. It seemed as though teachers were only collecting data because they were required to and held little value in the data once they were collected. Teachers reported that the data collection process was time-consuming and challenges associated with pedometers and information management systems served as barriers to the collection/organization process. In addition, professional development was not utilized to help teachers use data for effective teaching as district coordinators had limited access to teachers on designated professional development days. It is important to note that teachers had substantial concerns surrounding the validity and reliability of the data that were collected. This likely contributed to the low value that was placed upon data. Based upon the findings, ten recommendations for the enhancement of the DDDM process in physical education were generated. One of the most important recommendations is to provide physical education teachers with support in developing data literacy skills so they can take full advantage of the data they collect for the benefit of student learning. / text

published_or_final_version / Computer Science / Master / Master of Philosophy

RNA-seq sequencing has revolutionized the landscape of whole transcriptome sequencing and analysis. With its capacity of sequencing in a high-throughput and low-cost way, it produced ever increasingly amount of RNA-seq reads that are mines of treasure in biological and therapeutic studies. However, due to the complex nature and relatively un-developed knowledge base of transcription process, many challenges exist in the modeling and investigation of RNA-seq read data. It is of high importance to develop efficient computational tools to satisfy these needs. The first part of this thesis concentrates on algorithms for both upstream and downstream analysis of RNA-seq data. For the upstream, we aim to tackle down the problems of RNA-seq reads alignment where the segmental alignment causes the major difficulty. By employing a strategy of rigid extensive tries on read segmentations indices, we implemented an accurate algorithm for returning two-segmental alignments based on bi-directional BWT. For the downstream analysis, we study two types of gene fusion events which play a critical role in the formation of cancers. Unlike previous down-scoping-search methods, we applied a search-validate approach to design the framework. By introducing key techniques such as masking, two-segmental alignment and retention of multiple maps, we developed an efficient and robust tool for detecting gene fusions with high accuracy that proved by extensive simulation and real data tests. Optical mapping is a cutting edge technique for the study of genomic structural variations which address the defect and limitation of paired-end sequencing. It was designed with great improvement in accuracy, resolution and throughput than current techniques. Also, it produces much longer molecules which enables us to explore genomic regions rich in repetitive sequences. Optical mapping has the potential to enable us to draw a complete picture of the genome structure polymorphism and it is important for us to design tools for analysis of the data. The second part of the thesis is dedicated to the algorithms for both upstream and downstream analysis of optical map data. For the upstream, we formulated a robust scoring function, which combines the effectiveness of heuristic functions and the accuracy of statistical functions. Based on it, we implemented the high performance OMDP algorithm. For the downstream, we developed BP-OMDP which makes use of both split-mapping and disparity of coverage depth to call inversions in NA12878 human genome sample. / published_or_final_version / Computer science / Doctoral / Doctor of Philosophy

Not available / text

ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV / "Big Data" [1] and the security challenge of managing "Big Data" is a hot topic in the IT world. The term "Big Data" is used to describe very large data sets that cannot be processed by traditional database applications in "tractable" periods of time. Securing data in a conventional database is challenge enough; securing data whose size may exceed hundreds of terabytes or even petabytes is even more daunting! As the size of telemetry product and telemetry post-processed product continues to grow, "Big Data" management techniques and the securing of that data may have ever increasing application in the telemetry realm. After reviewing "Big Data", "Big Data" security and management basics, potential application to telemetry post-processed product will be explored.

ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California / In telemetric network systems, data extraction is often an after-thought. The data description frequently changes throughout the program so that last minute modifications of the data extraction approach are often required. This paper presents an alternative approach in which automation of measurement extraction is supported. The central key is a formal declarative language that can be used to configure instrumentation devices as well as measurement extraction devices. The Metadata Description Language (MDL) defined by the integrated Network Enhanced Telemetry (iNET) program, augmented with a generalized measurement extraction approach, addresses this issue. This paper describes the TmNS Data Extractor Tool, as well as lessons learned from commercial systems, the iNET program and TMATS.

published_or_final_version / Public Administration / Master / Master of Social Sciences

published_or_final_version / abstract / toc / Computer Science and Information Systems / Master / Master of Philosophy

published_or_final_version / abstract / toc / Computer Science and Information Systems / Master / Master of Philosophy