ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Davulcu, Hasan
Description
Multidimensional data have various representations. Thanks to their simplicity in modeling multidimensional data and the availability of various mathematical tools (such as tensor decompositions) that support multi-aspect analysis of such data, tensors are increasingly being used in many application domains including scientific data management, sensor data management, and social network data analysis. Relational model, on the other hand, enables semantic manipulation of data using relational operators, such as projection, selection, Cartesian-product, and set operators. For many multidimensional data applications, tensor operations as well as relational operations need to be supported throughout the data life cycle. In this thesis, we introduce a tensor-based relational data model (TRM), which enables both tensor- based data analysis and relational manipulations of multidimensional data, and define tensor-relational operations on this model. Then we introduce a tensor-relational data management system, so called, TensorDB. TensorDB is based on TRM, which brings together relational algebraic operations (for data manipulation and integration) and tensor algebraic operations (for data analysis). We develop optimization strategies for tensor-relational operations in both in-memory and in-database TensorDB. The goal of the TRM and TensorDB is to serve as a single environment that supports the entire life cycle of data; that is, data can be manipulated, integrated, processed, and analyzed.
ContributorsKim, Mijung (Author) / Candan, K. Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sundaram, Hari (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2014
Efficient processing of skyline queries on static data sources, data streams and incomplete datasets
Description
Skyline queries extract interesting points that are non-dominated and help paint the bigger picture of the data in question. They are valuable in many multi-criteria decision applications and are becoming a staple of decision support systems.
An assumption commonly made by many skyline algorithms is that a skyline query is applied to a single static data source or data stream. Unfortunately, this assumption does not hold in many applications in which a skyline query may involve attributes belonging to multiple data sources and requires a join operation to be performed before the skyline can be produced. Recently, various skyline-join algorithms have been proposed to address this problem in the context of static data sources. However, these algorithms suffer from several drawbacks: they often need to scan the data sources exhaustively to obtain the skyline-join results; moreover, the pruning techniques employed to eliminate tuples are largely based on expensive tuple-to-tuple comparisons. On the other hand, most data stream techniques focus on single stream skyline queries, thus rendering them unsuitable for skyline-join queries.
Another assumption typically made by most of the earlier skyline algorithms is that the data is complete and all skyline attribute values are available. Due to this constraint, these algorithms cannot be applied to incomplete data sources in which some of the attribute values are missing and are represented by NULL values. There exists a definition of dominance for incomplete data, but this leads to undesirable consequences such as non-transitive and cyclic dominance relations both of which are detrimental to skyline processing.
Based on the aforementioned observations, the main goal of the research described in this dissertation is the design and development of a framework of skyline operators that effectively handles three distinct types of skyline queries: 1) skyline-join queries on static data sources, 2) skyline-window-join queries over data streams, and 3) strata-skyline queries on incomplete datasets. This dissertation presents the unique challenges posed by these skyline queries and addresses the shortcomings of current skyline techniques by proposing efficient methods to tackle the added overhead in processing skyline queries on static data sources, data streams, and incomplete datasets.
An assumption commonly made by many skyline algorithms is that a skyline query is applied to a single static data source or data stream. Unfortunately, this assumption does not hold in many applications in which a skyline query may involve attributes belonging to multiple data sources and requires a join operation to be performed before the skyline can be produced. Recently, various skyline-join algorithms have been proposed to address this problem in the context of static data sources. However, these algorithms suffer from several drawbacks: they often need to scan the data sources exhaustively to obtain the skyline-join results; moreover, the pruning techniques employed to eliminate tuples are largely based on expensive tuple-to-tuple comparisons. On the other hand, most data stream techniques focus on single stream skyline queries, thus rendering them unsuitable for skyline-join queries.
Another assumption typically made by most of the earlier skyline algorithms is that the data is complete and all skyline attribute values are available. Due to this constraint, these algorithms cannot be applied to incomplete data sources in which some of the attribute values are missing and are represented by NULL values. There exists a definition of dominance for incomplete data, but this leads to undesirable consequences such as non-transitive and cyclic dominance relations both of which are detrimental to skyline processing.
Based on the aforementioned observations, the main goal of the research described in this dissertation is the design and development of a framework of skyline operators that effectively handles three distinct types of skyline queries: 1) skyline-join queries on static data sources, 2) skyline-window-join queries over data streams, and 3) strata-skyline queries on incomplete datasets. This dissertation presents the unique challenges posed by these skyline queries and addresses the shortcomings of current skyline techniques by proposing efficient methods to tackle the added overhead in processing skyline queries on static data sources, data streams, and incomplete datasets.
ContributorsNagendra, Mithila (Author) / Candan, Kasim Selcuk (Thesis advisor) / Chen, Yi (Committee member) / Davulcu, Hasan (Committee member) / Silva, Yasin N. (Committee member) / Sundaram, Hari (Committee member) / Arizona State University (Publisher)
Created2014
Description
Templates are wildly used in Web sites development. Finding the template for a given set of Web pages could be very important and useful for many applications like Web page classification and monitoring content and structure changes of Web pages. In this thesis, two novel sequence-based Web page template detection algorithms are presented. Different from tree mapping algorithms which are based on tree edit distance, sequence-based template detection algorithms operate on the Prüfer/Consolidated Prüfer sequences of trees. Since there are one-to-one correspondences between Prüfer/Consolidated Prüfer sequences and trees, sequence-based template detection algorithms identify the template by finding a common subsequence between to Prüfer/Consolidated Prüfer sequences. This subsequence should be a sequential representation of a common subtree of input trees. Experiments on real-world web pages showed that our approaches detect templates effectively and efficiently.
ContributorsHuang, Wei (Author) / Candan, Kasim Selcuk (Thesis advisor) / Sundaram, Hari (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Navigating within non-linear structures is a challenge for all users when the space is large but the problem is most pronounced when the users are blind or visually impaired. Such users access digital content through screen readers like JAWS which read out the text on the screen. However presentation of non-linear narratives in such a manner without visual cues and information about spatial dependencies is very inefficient for such users. The NSDL Science Literacy StrandMaps are visual layouts to help students and teachers browse educational resources. A Strandmap shows relationships between concepts and how they build upon one another across grade levels. NSDL Strandmaps are non-linear narratives which need to be presented to users who are blind in an effective way. A good summary of the Strandmap can give the users an idea about the concepts that are explained in it. This can help them decide whether to view the map or not. In addition, a preview-based navigation mechanism can help users decide which direction they want to take, based on a preview of upcoming content in each direction. Given a non-linear narrative like a Strandmap which has both text and structure, and a word limit w, the goal of this thesis is to find the best way to create its summary. The following approaches are considered: – Purely Text-based Approach using a Multi-document Text Summarizer – Purely Structure-based Approach using PageRank – Approaches Combining both Text and Structure → CUTS-Based Approach (Topic Segmentation) → PageRank with Content Since no reference summaries for such structures were available, user studies were conducted to evaluate these algorithms. PageRank with Content approach performed the best. Another important conclusion was that text and structure are intertwined in a Strandmap by design.
ContributorsGaur, Shruti (Author) / Candan, Kasim Selcuk (Thesis advisor) / Sundaram, Hari (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2011