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This paper investigates a new class of database systems loosely referred to as "non-relational databases," which offer a subset of traditional relational database functionality, in exchange for improved scalability, performance, and / or simplicity. We explore the differences in conceptual modeling techniques, and examine both the advantages and limitations of several classes of currently available systems, using running examples of real-world problems as implemented in both a traditional relational database model, as well as several non-relational models. / text

The analysis of networks is used in many fields of study including statistics, social science, computer sciences, physics, and biology. The interest in networks is diverse as it usually depends on the field of study. For instance, social scientists are interested in interpreting how edges arise, while biologists seek to understand underlying biological processes. Among the problems being explored in network analysis, community detection stands out as being one of the most important. Community detection seeks to find groups of nodes with a large concentration of links within but few between. Inferring groups are important in many applications as they are used for further downstream analysis. For example, identifying clusters of consumers with similar purchasing behavior in a customer and product network can be used to create better recommendation systems. Finding a node with a high concentration of its edges to other nodes in the community may give insight into how the community formed. Many statistical models for networks implicitly define the notion of a community. Statistical inference aims to fit a model that posits how vertices are connected to each other. One of the most common models for community detection is the stochastic block model (SBM) [Holland et al., 1983]. Although simple, it is a highly expressive family of random graphs. However, it does have its drawbacks. First, it does not capture the degree distribution of real-world networks. Second, it allows nodes to only belong to one community. In many applications, it is useful to consider overlapping communities. The Mixed Membership Stochastic Blockmodel (MMSB) is a Bayesian extension of the SBM that allows nodes to belong to multiple communities. Fitting large Bayesian network models quickly become computationally infeasible when the number of nodes grows into the hundred of thousands and millions. In particular, the number of parameters in the MMSB grows as the number of nodes squared. This thesis introduces an efficient method for fitting a Bayesian model to massive networks through use of aggregated relational data. Our inference method converges faster than existing methods by leveraging nodal information that often accompany real world networks. Conditioning on this extra information leads to a model that admits a parallel variational inference algorithm. We apply our method to a citation network with over three million nodes and 25 million edges. Our method converges faster than existing posterior inference algorithms for the MMSB and recovers parameters better on simulated networks generated according to the MMSB.

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Thesis (M.S. in Information Systems)--Naval Postgraduate School, June 1990. / Thesis Advisor(s): Kamel, Magdi N. Second Reader: Bhargava, Hemant K. "June 1990." Description based on signature page as viewed on October 19, 2009. Author(s) subject terms: Ammunition, database design, relational database. Includes bibliographical references (p. 163-166). Also available online.

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Jessie Ching. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1989. / Bibliography: leaves 98-101.

by Yan, Men-Hin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 114-119). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgments --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Storing XML in Database Systems --- p.2 / Chapter 1.2 --- Outline of the Thesis --- p.4 / Chapter 2 --- Related Work --- p.5 / Chapter 2.1 --- Overview of XML --- p.5 / Chapter 2.1.1 --- Extensible Markup Language (XML) --- p.5 / Chapter 2.1.2 --- Data Type Definition (DTD) --- p.6 / Chapter 2.1.3 --- "ID, IDREF and IDREFS" --- p.9 / Chapter 2.2 --- Using Special-Purpose Database to Store XML Data --- p.10 / Chapter 2.3 --- Using Relational Databases to Store XML Data --- p.11 / Chapter 2.3.1 --- Extracting Schemas with STORED --- p.11 / Chapter 2.3.2 --- Using Simple Schemes Based on Labeled Graph --- p.12 / Chapter 2.3.3 --- Generating Schemas from DTDs --- p.12 / Chapter 2.3.4 --- Commercial Approaches --- p.13 / Chapter 2.4 --- Discovering Functional Dependencies --- p.14 / Chapter 2.4.1 --- Functional Dependency --- p.14 / Chapter 2.4.2 --- Finding Functional Dependencies --- p.14 / Chapter 2.4.3 --- TANE and Partition Refinement --- p.15 / Chapter 2.5 --- Multivalued Dependencies --- p.17 / Chapter 2.5.1 --- Example of Multivalued Dependency --- p.18 / Chapter 3 --- Using RDBMS to Store XML Data --- p.20 / Chapter 3.1 --- Global Schema Extraction Algorithm --- p.22 / Chapter 3.1.1 --- Step 1: Simplify DTD --- p.22 / Chapter 3.1.2 --- Step 2: Construct Schema Prototype Trees --- p.24 / Chapter 3.1.3 --- Step 3: Generate Relational Schema Prototype --- p.29 / Chapter 3.1.4 --- Step 4: Discover Functional Dependencies and Candidate Keys --- p.31 / Chapter 3.1.5 --- Step 5: Normalize the Relational Schema Prototypes --- p.32 / Chapter 3.1.6 --- Discussion --- p.32 / Chapter 3.2 --- DTD-splitting Schema Extraction Algorithm --- p.34 / Chapter 3.2.1 --- Step 1: Simplify DTD --- p.35 / Chapter 3.2.2 --- Step 2: Construct Schema Prototype Trees --- p.36 / Chapter 3.2.3 --- Step 3: Generate Relational Schema Prototype --- p.45 / Chapter 3.2.4 --- Step 4: Discover Functional Dependencies and Candidate Keys --- p.46 / Chapter 3.2.5 --- Step 5: Normalize the Relational Schema Prototypes --- p.47 / Chapter 3.2.6 --- Discussion --- p.49 / Chapter 3.3 --- Experimental Results --- p.50 / Chapter 3.3.1 --- Real Life XML Data: SIGMOD Record XML --- p.50 / Chapter 3.3.2 --- Synthetic XML Data --- p.58 / Chapter 3.3.3 --- Discussion --- p.68 / Chapter 4 --- Finding Multivalued Dependencies --- p.75 / Chapter 4.1 --- Validation of Multivalued Dependencies --- p.77 / Chapter 4.2 --- Search Strategy and Pruning --- p.80 / Chapter 4.2.1 --- Search Strategy for Left-hand Sides Candidates --- p.81 / Chapter 4.2.2 --- Search Strategy for Right-hand Sides Candidates --- p.82 / Chapter 4.2.3 --- Other Pruning --- p.85 / Chapter 4.3 --- Computing with Partitions --- p.87 / Chapter 4.3.1 --- Computing Partitions --- p.88 / Chapter 4.4 --- Algorithm --- p.89 / Chapter 4.4.1 --- Generating Next Level Candidates --- p.92 / Chapter 4.4.2 --- Computing Partitions --- p.93 / Chapter 4.5 --- Experimental Results --- p.94 / Chapter 4.5.1 --- Results of the Algorithm --- p.95 / Chapter 4.5.2 --- Evaluation on the Results --- p.96 / Chapter 4.5.3 --- Scalability of the Algorithm --- p.98 / Chapter 4.5.4 --- Using Multivalued Dependencies in Schema Extraction Al- gorithms --- p.101 / Chapter 5 --- Conclusion --- p.108 / Chapter 5.1 --- Discussion --- p.108 / Chapter 5.2 --- Future Work --- p.110 / Chapter 5.2.1 --- Translate Semistructured Queries to SQL --- p.110 / Chapter 5.2.2 --- Improve the Multivalued Dependency Discovery Algorithm --- p.112 / Chapter 5.2.3 --- Incremental Update of Resulting Schema --- p.113 / Bibliography --- p.113 / Appendix --- p.120 / Chapter A --- Simple Proof for Minimality in Multivalued Dependencies --- p.120 / Chapter B --- Third and Fourth Normal Form Decompositions --- p.122 / Chapter B.1 --- 3NF Decomposition Algorithm --- p.123 / Chapter B.2 --- 4NF Decomposition Algorithm --- p.124

In this thesis, for the schema-based approaches, we propose an efficient algorithm to general all relational algebra expressions in order to find all the connected trees in an RDB. We also study an efficient algorithm to evaluate all the expressions using semijoins in RDBMS . We show that our method can also be extended to answer continuous keyword queries in a relational data stream. We further propose novel algorithms that find sets of tuples that are reachable from a root tuple within a radius, and algorithms that find multi-center subgraphs within a radius. Our algorithms use SQL queries only in order to make fully use of RDBMS. We show that the current commercial RDBMSs are powerful enough to support such keyword queries in RDBs efficiently without any additional new indexing to be built and maintained. The main idea behind our approach is tuple reduction. For the graph-based approaches, we propose an efficient algorithm to find all/top- K multi-center subgraphs in polynomial delay. We also introduce a new kind of keyword query, namely, structural statistics by keywords, to summarize keyword search results into several dimensions. We conducted extensive performance studies using two large real datasets IMDB and DBLP to show the efficiency and effectiveness of all our approaches. / Keyword search in relational databases (RDBs) has been extensively studied recently. A keyword search (or a keyword query) in RDBs is specified by a set of keywords to explore the interconnected tuple structures in an RDB that cannot be easily identified using SQL on RDBMSs. In brief, it finds how the tuples containing the given keywords are connected via sequences of connections (foreign key references) among tuples in an RDB. Such interconnected tuple structures can be found as connected trees up to a certain size, sets of tuples that are reachable from a root tuple within a radius, or even multi-center subgraphs within a radius. In the literature, there are two main approaches, namely schema-based approaches and graph-based approaches. The schema-based approaches are to generate a set of relational algebra expressions and evaluate every such expression using SQL on an RDBMS directly or in a middleware on top of an RDBMS indirectly. Due to a large number of relational algebra expressions needed to process, most of the existing works take a middleware approach without fully utilizing RDBMSs. The graph-based approaches are to materialize an RDB as a graph and find the interconnected tuple structures using graph-based algorithms in memory. / Qin, Lu. / Adviser: Jeffrey Xu Yu. / Source: Dissertation Abstracts International, Volume: 73-02, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 133-138). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Thesis (M. Phil.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.