Due to the unstructuredness and the lack of schema information of knowledge graphs, social networks and RDF graphs, keyword search has been proposed for querying such graphs/networks. Recently, various keyword search semantics have been designed. However, these keyword search semantics and algorithms encounter efficiency or scalability issues. In this thesis, we propose new three generic frameworks or index techniques to address these issues. The thesis results show that the keyword search on massive graphs under different scenarios can be effective and efficient, which would facilitate keyword search services on graphs in the real world. First, we study the keyword search on massive knowledge graphs. In particular, we propose a generic ontology- based indexing framework for keyword search, called Bisimulation of Generalized Graph Index (BiG-index), to enhance the search performance. The novelties of BiG-index reside in using an ontology graph GOnt to summarize and index a data graph G iteratively, to form a hierarchical index structure G. Regarding query evaluation, we transform a keyword search q into Q according to GOnt in runtime. The transformed query is searched on the summary graphs in G. The efficiency is due to the small sizes of the summary graphs and the early pruning of semantically irrelevant subgraphs. To illustrate BiG-index's applicability, we show popular indexing techniques for keyword search can be easily implemented on top of BiG-index. Our extensive experiments show that BiG-index clearly reduced the runtimes of popular keyword search algorithms. Second, we study the problem of keyword search on public-private graph. In many applications (e.g., social networks), users may prefer to hide parts or all of her/his data graphs (e.g., private friendships) from the public. This leads to a recent graph model, namely the public-private network model, in which each user has his/her own network. While there have been studies on public-private network analysis, keyword search on public-private networks has not yet been studied. Hence, we propose a new keyword search framework, called public-private keyword search (PPKWS). PPKWS consists of three major steps: partial evaluation, answer refinement, and answer completion. We select three representative ones and show that they can be implemented on the model with minor modifications. We propose indexes and optimizations for PPKWS. We have verified through experiments that, on average, the algorithms implemented on top of PPKWS run 113 times faster than the original algorithms directly running on the public network attached to the private network for retrieving answers that span through them. Third, we study the keyword search in distributed graph evaluation systems. In the recent research on query evaluation, parallel evaluation has attracted much interest. However, the study on keyword search on distributed graphs has still been limited. We propose a novel distributed keyword search framework called DKWS. We propose a notify-push paradigm which can exchange the upper bounds of answers across all the workers asynchronously. In particular, the workers notify the coordinator when the local upper bound is refined. The coordinator pushes the refined global upper bound to all the workers. Moreover, we propose an efficient and generic keyword search algorithm for the workers. We have implemented DKWS on top of GRAPE, a distributed graph process system from our previous research collaboration. Extensive experimental results show that DKWS outperforms current-state-of-art techniques

Due to the unstructuredness and the lack of schema information of knowledge graphs, social networks and RDF graphs, keyword search has been proposed for querying such graphs/networks. Recently, various keyword search semantics have been designed. However, these keyword search semantics and algorithms encounter efficiency or scalability issues. In this thesis, we propose new three generic frameworks or index techniques to address these issues. The thesis results show that the keyword search on massive graphs under different scenarios can be effective and efficient, which would facilitate keyword search services on graphs in the real world. First, we study the keyword search on massive knowledge graphs. In particular, we propose a generic ontology- based indexing framework for keyword search, called Bisimulation of Generalized Graph Index (BiG-index), to enhance the search performance. The novelties of BiG-index reside in using an ontology graph GOnt to summarize and index a data graph G iteratively, to form a hierarchical index structure G. Regarding query evaluation, we transform a keyword search q into Q according to GOnt in runtime. The transformed query is searched on the summary graphs in G. The efficiency is due to the small sizes of the summary graphs and the early pruning of semantically irrelevant subgraphs. To illustrate BiG-index's applicability, we show popular indexing techniques for keyword search can be easily implemented on top of BiG-index. Our extensive experiments show that BiG-index clearly reduced the runtimes of popular keyword search algorithms. Second, we study the problem of keyword search on public-private graph. In many applications (e.g., social networks), users may prefer to hide parts or all of her/his data graphs (e.g., private friendships) from the public. This leads to a recent graph model, namely the public-private network model, in which each user has his/her own network. While there have been studies on public-private network analysis, keyword search on public-private networks has not yet been studied. Hence, we propose a new keyword search framework, called public-private keyword search (PPKWS). PPKWS consists of three major steps: partial evaluation, answer refinement, and answer completion. We select three representative ones and show that they can be implemented on the model with minor modifications. We propose indexes and optimizations for PPKWS. We have verified through experiments that, on average, the algorithms implemented on top of PPKWS run 113 times faster than the original algorithms directly running on the public network attached to the private network for retrieving answers that span through them. Third, we study the keyword search in distributed graph evaluation systems. In the recent research on query evaluation, parallel evaluation has attracted much interest. However, the study on keyword search on distributed graphs has still been limited. We propose a novel distributed keyword search framework called DKWS. We propose a notify-push paradigm which can exchange the upper bounds of answers across all the workers asynchronously. In particular, the workers notify the coordinator when the local upper bound is refined. The coordinator pushes the refined global upper bound to all the workers. Moreover, we propose an efficient and generic keyword search algorithm for the workers. We have implemented DKWS on top of GRAPE, a distributed graph process system from our previous research collaboration. Extensive experimental results show that DKWS outperforms current-state-of-art techniques

Modern stream applications necessitate the handling of large numbers of continuous queries specified over high volume data streams. This dissertation proposes novel solutions to continuous query optimization in three core areas of stream query processing, namely state-slice based multiple continuous query sharing, ring-based multi-way join query distribution and scalable distributed multi-query optimization. The first part of the dissertation proposes efficient optimization strategies that utilize the novel state-slicing concept to achieve maximum memory and computation sharing for stream join queries with window constraints. Extensive analytical and experimental evaluations demonstrate that our proposed strategies is capable to minimize the memory or CPU consumptions for multiple join queries. The second part of this dissertation proposes a novel scheme for the distributed execution of generic multi-way joins with window constraints. The proposed scheme partitions the states into disjoint slices in the time domain, and then distributes the fine-grained states in the cluster, forming a virtual computation ring. New challenges to support this distributed state-slicing processing are answered by numerous new techniques. The extensive experimental evaluations show that the proposed strategies achieve significant performance improvements in terms of response time and memory usages for a wide range of configurations and workloads on a real system. Ring based distributed stream query processing and multi-query sharing both are based on the state-slice concept. The third part of this dissertation combines the first two parts of this dissertation work and proposes a novel distributed multi-query optimization technique.

Luo, Yiwen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (p. 38-41). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Image Search by Multi-Class Query --- p.1 / Chapter 1.1.1 --- Related Work --- p.2 / Chapter 1.1.2 --- Our Method --- p.4 / Chapter 1.2 --- Image and Video Quality Assessment --- p.5 / Chapter 1.2.1 --- Related Work --- p.6 / Chapter 1.2.2 --- Our Method --- p.6 / Chapter 2 --- Multi-Class Query Image Search System --- p.8 / Chapter 2.1 --- System Description --- p.8 / Chapter 2.1.1 --- Multi-Query Search --- p.8 / Chapter 2.1.2 --- Image Annotation --- p.9 / Chapter 2.1.3 --- Image Re-ranking --- p.10 / Chapter 2.2 --- Algorithm Description --- p.10 / Chapter 2.3 --- Evaluation and Results --- p.12 / Chapter 3 --- Image and Video Quality Assessment --- p.15 / Chapter 3.1 --- Criteria for Assessing Photo Quality --- p.15 / Chapter 3.1.1 --- Composition --- p.15 / Chapter 3.1.2 --- Lighting --- p.16 / Chapter 3.1.3 --- Focus Controlling --- p.16 / Chapter 3.1.4 --- Color --- p.17 / Chapter 3.2 --- Features for Photo Quality Assessment --- p.18 / Chapter 3.2.1 --- Subject Region Extraction --- p.18 / Chapter 3.2.2 --- Clarity Contrast Feature --- p.20 / Chapter 3.2.3 --- Lighting Feature --- p.21 / Chapter 3.2.4 --- Simplicity Feature --- p.22 / Chapter 3.2.5 --- Composition Geometry Feature --- p.22 / Chapter 3.2.6 --- Color Harmony Feature --- p.23 / Chapter 3.3 --- Features for Video Quality Assessment --- p.24 / Chapter 3.3.1 --- Length of Subject Region Motion --- p.24 / Chapter 3.3.2 --- Motion Stability --- p.26 / Chapter 3.4 --- Classification --- p.26 / Chapter 3.5 --- Experiments --- p.27 / Chapter 3.5.1 --- Photo Assessment --- p.27 / Chapter 3.5.2 --- Video Assessment --- p.28 / Chapter 3.5.3 --- Web Image Ranking --- p.31 / Chapter 4 --- Conclusion --- p.36 / Bibliography --- p.38 / Chapter A --- Supplementary Materials of Photo Quality Assessment --- p.42 / Chapter A.l --- Photo Database --- p.42 / Chapter A.2 --- Web Image Ranking

Thesis (M.S.) -- Worcester Polytechnic Institute. / Keywords: optimization; XML; query evaluation; stream processing; database. Includes bibliographical references (p.66-67).

Thesis ( Ph.D.) -- University of Texas at Arlington, 2008.

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2006. / Includes bibliographical references (p. 81-85).

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 169-179).

Thesis (Ph.D.) in Spatial Information Science and Engineering--University of Maine, 2009. / Includes vita. Includes bibliographical references (leaves 185-202).