Inconsistency often arises in real-world databases and, as a result, critical queries over dirty data may lead users to make ill-informed decisions. Functional dependencies (FDs) can be used to specify intended semantics of the underlying data and aid with the cleaning task. Enumerating and evaluating all the possible repairs to FD violations is infeasible, while approaches that produce a single repair or attempt to isolate the dirty portion of data are often too destructive or constraining. In this thesis, we leverage a recent advance in data cleaning that allows sampling from a well-defined space of reasonable repairs, and provide the user with a data management tool that gives uncertain query answers over this space. We propose a framework to compute probabilistic query answers as though each repair sample were a possible world. We show experimentally that queries over many possible repairs produce results that are more useful than other approaches and that our system can scale to large datasets.

In an advanced avionics system, the demand of high reliability and availability is of great importance. Testability Analysis is a method of examining this. In the project RWE Tornado GE at Saab Avitronics, they use Built-In Test (BIT), for the purpose of detecting and isolating possible failures in the equipment in question. There is however the need of verification of BIT functionality. Some of the verification tests are requested by customer EADS to be simulated and demonstrated. The objective of this thesis is to understand the Testability Analysis process as well as develop a tool to assist the Testability Demonstration preparations and result recording. / Kravet på hög tillförlitlighet samt tillgänglighet är av yttersta vikt inom ett avancerat avioniksystem. Testbarhetsanalys är en undersökningsmetod som man kan tillämpa. I projektet RWE Tornado GE på Saab Avitronics använder man sig av inbyggda tester - Built-In Test (BIT) - för att kunna upptäcka och isolera eventuella felmoder i en utrustning. Utöver detta vill man verifiera att BIT-funktionen faktiskt fungerar. Vissa av dessa verifieringstester måste simuleras och demonstreras i enlighet med kunden EADS begäran. Målet med examensarbetet är att förstå testbarhetsanalysprocessen samt utveckla ett verktyg för att kunna göra nödvändiga förberedelser för samt resultatinsamling under testbarhetsdemonstrationen (T-Demo).

The problem of records retention is often viewed as simply deleting records when they have outlived their purpose. However, in the world of relational databases there is no standardized notion of a business record and its retention obligations. Unlike physical documents such as forms and reports, information in databases is organized such that one item of data may be part of various legal records and consequently subject to several (and possibly conflicting) retention policies. This thesis proposes a framework for records retention in relational database systems. It presents a mechanism through which users can specify a broad range of protective and destructive data retention policies for relational records. Compared to naïve solutions for enforcing records management policies, our framework is not only significantly more efficient but it also addresses several unanswered questions about how policies can be mapped from given legal requirements to actions on relational data. The novelty in our approach is that we defined a record in a relational database as an arbitrary logical view, effectively allowing us to reduce several challenges in enforcing data retention policies to well-studied problems in database theory. We argue that our expression based approach of tracking records management obligations is not only easier for records managers to use but also far more space/time efficient compared to traditional metadata approaches discussed in the literature. The thesis concludes with a thorough examination of the limitations of the proposed framework and suggestion for future research in the area of records management for relational database management systems.

This is a study of the history of the architectural guidebook, and a proposal for an architectural information system which utilizes a relational database system to aid in the creation of future architectural guides. Chapter 1 explores the reasons why individuals may choose to visit buildings. Chapters 2 and 3 trace the history of the architectural guidebook through the development of travel writing from the Greeks to the present day. Chapter 4 provides an analysis of various characteristics of architectural guides since 1900, and explores the use of descriptions, images, and maps, as well as how guides subdivide the areas which they deal with. Chapter 5 introduces the reader to relational databases and examines how the present relational database system was constructed; chapter 5 also includes several proposals for a future architectural guide based on the analysis of previous guides contained in chapters 2 through 4.

Proteins are the structural components of living cells and tissues, and thus an important building block in all living organisms. Patterns in proteins sequences are some subsequences which appear frequently. Patterns often denote important functional regions in proteins and can be used to characterize a protein family or discover the function of proteins. Moreover, it provides valuable information about the evolution of species. Grouping protein sequences that share similar structure helps in identifying sequences with similar functionality. Many algorithms have been proposed for clustering proteins according to their similarity, i.e., sequential patterns in protein databases, for example, feature-based clustering algorithms of the global approach and the local approach. They use the algorithm of mining sequential patterns to solve the no-gap-limit sequential pattern problem in a protein sequences database, and then find global features and local features separately for clustering. Feature-based clustering algorithms are entirely different approaches to protein clustering that do not require an all-against-all analysis and use a near-linear complexity K-means based clustering algorithm. Although feature-based clustering algorithms are scalable and lead to reasonably good clusters, they consume time on performing the global approach and the local approach separately. Therefore, in this thesis, we propose hybrid algorithms to find and mark features for feature-based clustering algorithms. We observe an interesting result from the relation between the local features and the closed frequent sequential patterns. The important observation which we find is that some features in the closed frequent sequential patterns can be taken apart to several features in the local selected features and the total support number of these features in the local selected features is equal to the support number of the corresponding feature in the closed frequent sequential patterns. There are two phases, find-feature and mark-feature, in the global approach and the local approach after mining sequential patterns. In our hybrid algorithms of Method 1 (LocalG), we first find and mark the local features. Then, we find the global features. Finally, we mark the bit vectors of the global features efficiently from the bit vector of the local features. In our hybrid algorithms of Method 2 (CLoseLG), we first find the closed frequent sequential patterns directly. Next, we find local candidate features efficiently from the closed frequent sequential patterns and then mark the local features. Finally, we find and mark the global features. From our performance study based on the biological data and the synthetic data, we show that our proposed hybrid algorithms are more efficient than the feature-based algorithm.

Proteins are the structural components of living cells and tissues, and thus an important building block in all living organisms. Patterns in proteins sequences are some subsequences which appear frequently. Patterns often denote important functional regions in proteins and can be used to characterize a protein family or discover the function of proteins. Moreover, it provides valuable information about the evolution of species. Patterns contain gaps of arbitrary size. Considering the no--gap--limit sequential pattern problem in a protein database, we may use the algorithm of mining sequential patterns to solve it. However, in a protein database, the order of segment appearing in protein sequences is important and it may appear many times repeatedly in a protein sequence. Therefore, we can not directly use the traditional sequential pattern mining algorithms to mine them. Many algorithms have been proposed to mine sequential patterns in protein databases, for example, the SP-index algorithm. They enumerate patterns of limited sizes (segments) in the solution space and find all patterns. The SP-index algorithm is based on the traditional sequential pattern mining algorithms and considers the the problem of the multiple--appearances of segments in a protein sequence. Although the SP-index algorithm considers the characteristics of bioinformatics, it still contains a time--consuming step which constructs the SP-tree to find the frequent patterns. In this step, it has to trace many nodes to get the result. Therefore, in this thesis, we propose a Bit--Pattern--based (BP) algorithm to improve the disadvantages of the SP-index algorithm. First, we transform the protein sequences into bit sequences. Second, we construct the frequent segments by using the AND operator. Because we use the bit operator, it is efficient to get the frequent segments. Then, we prune unnecessary frequent segments, which results in the case that we do not have to test many frequent segments in the following step. Third, we use the OR operator to get the longest pattern. In this step, we test whether two segments can be linked together to construct a long segment, and we get the result by testing once. Because we focus on which position the segment appears on, we can use the OR operator and then judge the bit sequences to get the result. Thus, we can avoid many testing processes. From our performance study based on the biological data, we show that we can improve the efficiency of the SP-index algorithm. Moreover, from our simulation results, we show that our proposed algorithm can improve the processing time up to 50\% as compared to the SP-index algorithm, since the SP--index algorithm has to trace many nodes to construct the longest pattern.

Thesis (M.S.)--University of Florida, 2001. / Title from first page of PDF file. Document formatted into pages; contains x, 54 p.; also contains graphics. Vita. Includes bibliographical references (p. 50-53).

Thesis (M.S.) in Spatial Information Science and Engineering--University of Maine, 2001. / Includes vita. Includes bibliographical references (leaves 160-166).

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002. / Includes bibliographical references (leaves 208-215). Also available in electronic version. Access restricted to campus users.