Data accesses consume considerable execution time during program execution. If we can improve the data access time at compile time, overall program execution time can be improved effectively. Hence, we designed a software for a data locality estimation method for estimating data locality in program optimizer. The program optimizer can then reduce the number of main memory block access and enhance the overall program performance effectively. In this research, we implemented the software design of the data locality estimation method. It includes two estimation models for estimating the number of access matches in memory blocks and for estimating the number of memory block access. We carried out experiments to verify accuracy of the locality estimation method.

Univ., Diss.--Frankfurt am Main, 2007.

Univ., Diss--Würzburg, 2006.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 70-73).

Thesis (M.S.)--Kutztown University of Pennsylvania, 2009. / Source: Masters Abstracts International, Volume: 47-05, page: 2936. Adviser: Randy Kaplan. Bibliographical references (p. 50-52)

Thesis--Auburn University, 2009. / Abstract. Includes bibliographic references (p.31-33).

published_or_final_version / Computer Science / Master / Master of Philosophy

Multi-faceted data contains different types of objects and relationships between them. With rapid growth of web-based services, multi-faceted data are increasing (e.g. Flickr, Yago, IMDB), which offers us richer information to infer users’ preferences and provide them better services. In this study, we look at two types of multi-faceted data: social tagging system and heterogeneous information network and how to improve service such as resources retrieving and classification on them. In social tagging systems, resources such as images and videos are annotated with descriptive words called tags. It has been shown that tag-based resource searching and retrieval is much more effective than content-based retrieval. With the advances in mobile technology, many resources are also geo-tagged with location information. We observe that a traditional tag (word) can carry different semantics at different locations. We study how location information can be used to help distinguish the different semantics of a resource’s tags and thus to improve retrieval accuracy. Given a search query, we propose a location-partitioning method that partitions all locations into regions such that the user query carries distinguishing semantics in each region. Based on the identified regions, we utilize location information in estimating the ranking scores of resources for the given query. These ranking scores are learned using the Bayesian Personalized Ranking (BPR) framework. Two algorithms, namely, LTD and LPITF, which apply Tucker Decomposition and Pairwise Interaction Tensor Factorization, respectively for modeling the ranking score tensor are proposed. Through experiments on real datasets, we show that LTD and LPITF outperform other tag-based resource retrieval methods. A heterogeneous information network (HIN) is used to model objects of different types and their relationships. Meta-paths are sequences of object types. They are used to represent complex relationships between objects beyond what links in a homogeneous network capture. We study the problem of classifying objects in an HIN. We propose class-level meta-paths and study how they can be used to (1) build more accurate classifiers and (2) improve active learning in identifying objects for which training labels should be obtained. We show that class-level meta-paths and object classification exhibit interesting synergy. Our experimental results show that the use of class-level meta-paths results in very effective active learning and good classification performance in HINs. / published_or_final_version / Computer Science / Master / Master of Philosophy

ITC/USA 2014 Conference Proceedings / The Fiftieth Annual International Telemetering Conference and Technical Exhibition / October 20-23, 2014 / Town and Country Resort & Convention Center, San Diego, CA / This paper reflects the principal results of a study performed internally by Airbus's flight test centers. The purpose of this study was to share the body of knowledge concerning data consistency checks between all Airbus business units. An analysis of the test process is followed by the identification of the process stakeholders involved in ensuring data consistency. In the main part of the paper several different possibilities for improving data consistency are listed; it is left to the discretion of the reader to determine the appropriateness these methods.

published_or_final_version / abstract / toc / Electrical and Electronic Engineering / Master / Master of Philosophy