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- All Subjects: Computer Science
- Genre: Doctoral Dissertation
- Member of: ASU Electronic Theses and Dissertations
- Resource Type: Text
Description
As pointed out in the keynote speech by H. V. Jagadish in SIGMOD'07, and also commonly agreed in the database community, the usability of structured data by casual users is as important as the data management systems' functionalities. A major hardness of using structured data is the problem of easily retrieving information from them given a user's information needs. Learning and using a structured query language (e.g., SQL and XQuery) is overwhelmingly burdensome for most users, as not only are these languages sophisticated, but the users need to know the data schema. Keyword search provides us with opportunities to conveniently access structured data and potentially significantly enhances the usability of structured data. However, processing keyword search on structured data is challenging due to various types of ambiguities such as structural ambiguity (keyword queries have no structure), keyword ambiguity (the keywords may not be accurate), user preference ambiguity (the user may have implicit preferences that are not indicated in the query), as well as the efficiency challenges due to large search space. This dissertation performs an expansive study on keyword search processing techniques as a gateway for users to access structured data and retrieve desired information. The key issues addressed include: (1) Resolving structural ambiguities in keyword queries by generating meaningful query results, which involves identifying relevant keyword matches, identifying return information, composing query results based on relevant matches and return information. (2) Resolving structural, keyword and user preference ambiguities through result analysis, including snippet generation, result differentiation, result clustering, result summarization/query expansion, etc. (3) Resolving the efficiency challenge in processing keyword search on structured data by utilizing and efficiently maintaining materialized views. These works deliver significant technical contributions towards building a full-fledged search engine for structured data.
ContributorsLiu, Ziyang (Author) / Chen, Yi (Thesis advisor) / Candan, Kasim S (Committee member) / Davulcu, Hasan (Committee member) / Jagadish, H V (Committee member) / Arizona State University (Publisher)
Created2011
Description
Most current database management systems are optimized for single query execution.
Yet, often, queries come as part of a query workload. Therefore, there is a need
for index structures that can take into consideration existence of multiple queries in a
query workload and efficiently produce accurate results for the entire query workload.
These index structures should be scalable to handle large amounts of data as well as
large query workloads.
The main objective of this dissertation is to create and design scalable index structures
that are optimized for range query workloads. Range queries are an important
type of queries with wide-ranging applications. There are no existing index structures
that are optimized for efficient execution of range query workloads. There are
also unique challenges that need to be addressed for range queries in 1D, 2D, and
high-dimensional spaces. In this work, I introduce novel cost models, index selection
algorithms, and storage mechanisms that can tackle these challenges and efficiently
process a given range query workload in 1D, 2D, and high-dimensional spaces. In particular,
I introduce the index structures, HCS (for 1D spaces), cSHB (for 2D spaces),
and PSLSH (for high-dimensional spaces) that are designed specifically to efficiently
handle range query workload and the unique challenges arising from their respective
spaces. I experimentally show the effectiveness of the above proposed index structures
by comparing with state-of-the-art techniques.
Yet, often, queries come as part of a query workload. Therefore, there is a need
for index structures that can take into consideration existence of multiple queries in a
query workload and efficiently produce accurate results for the entire query workload.
These index structures should be scalable to handle large amounts of data as well as
large query workloads.
The main objective of this dissertation is to create and design scalable index structures
that are optimized for range query workloads. Range queries are an important
type of queries with wide-ranging applications. There are no existing index structures
that are optimized for efficient execution of range query workloads. There are
also unique challenges that need to be addressed for range queries in 1D, 2D, and
high-dimensional spaces. In this work, I introduce novel cost models, index selection
algorithms, and storage mechanisms that can tackle these challenges and efficiently
process a given range query workload in 1D, 2D, and high-dimensional spaces. In particular,
I introduce the index structures, HCS (for 1D spaces), cSHB (for 2D spaces),
and PSLSH (for high-dimensional spaces) that are designed specifically to efficiently
handle range query workload and the unique challenges arising from their respective
spaces. I experimentally show the effectiveness of the above proposed index structures
by comparing with state-of-the-art techniques.
ContributorsNagarkar, Parth (Author) / Candan, Kasim S (Thesis advisor) / Davulcu, Hasan (Committee member) / Sapino, Maria Luisa (Committee member) / Sarwat, Mohamed (Committee member) / Arizona State University (Publisher)
Created2017
Description
Many real-world problems, such as model- and data-driven computer simulation analysis, social and collaborative network analysis, brain data analysis, and so on, benefit from jointly modeling and analyzing the underlying patterns associated with complex, multi-relational data. Tensor decomposition is an ideal mathematical tool for this joint modeling, due to its simultaneous analysis of such multi-relational data, which is made possible by the data's multidimensional, array-based nature. A major challenge in tensor decomposition lies with its computational and space complexity, especially for dense datasets. While the process is comparatively faster for sparse tensors, decomposition is still a major bottleneck for many applications. The tensor decomposition process results in dense (hence, large) intermediate results, even when the input tensor is sparse (or small). Noise is another challenge for most data mining techniques, and many tensor decomposition schemes are sensitive to noisy datasets; this is an inevitable problem for real-world data, which can lead to false conclusions. In this dissertation, I develop innovative tensor decomposition algorithms for mining both sparse and dense multi-relational data in a noise-resistant way. I present novel, scalable, parallelizable tensor decomposition algorithms, specifically tuned to be effective for dense, noisy tensors, and which maintain the quality of the resulting analysis. Furthermore, I present results on multi-relational data applications focusing on model- and data-driven computer simulation analysis, as well as social network and web mining, which demonstrate the effectiveness of these tensor decompositions.
ContributorsLi, Xinsheng (Author) / Candan, Kasim S (Thesis advisor) / Davulcu, Hasan (Committee member) / Sapino, Maria L (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2019
Description
Spatial data is fundamental in many applications like map services, land resource management, etc. Meanwhile, spatial data inherently comes with abundant context information because spatial entities themselves possess different properties, e.g., graph or textual information, etc. Among all these compound spatial data, geospatial graph data is one of the most challenging for the complexity of graph data. Graph data is commonly used to model real scenarios and searching for the matching subgraphs is fundamental in retrieving and analyzing graph data. With the ubiquity of spatial data, vertexes or edges in graphs are enriched with spatial location attributes side by side with other non-spatial attributes. Graph-based applications integrate spatial data into the graph model and provide more spatial-aware services. The co-existence of the graph and spatial data in the same geospatial graph triggers some new applications. To solve new problems in these applications, existing solutions develop an integrated system that incorporates the graph database and spatial database engines. However, existing approaches suffer from the architecture where graph data and spatial data are isolated. In this dissertation, I will explain two indexing frameworks, GeoReach and RisoTree, which can significantly accelerate the queries in geospatial graphs. GeoReach includes a query operator that adds spatial data awareness to a graph database management system. In GeoReach, the neighborhood spatial information is summarized and stored on each vertex in the graph. The summarization includes three different structures according to the location distribution. These spatial summaries are utilized to terminate the graph search early.RisoTree is a hierarchical tree structure where each node is represented by a minimum bounding rectangle (MBR). The MBR of a node is a rectangle that encloses all its children. A key difference between RisoTree and RTree is that RisoTree contains pre-materialized subgraph information to each index node. The subgraph information is utilized during the spatial index search phase to prune search paths that cannot satisfy the query graph pattern. The RisoTree index reduces the search space when the spatial filtering phase is performed with relatively light cost.
ContributorsSun, Yuhan (Author) / Sarwat, Mohamed (Thesis advisor) / Tong, Hanghang (Committee member) / Candan, Kasim S (Committee member) / Zhao, Ming (Committee member) / Arizona State University (Publisher)
Created2021