Introduction: The Office of Arid Lands Studies (OALS), the Water Resources Research Center (WRRC), of the University of Arizona, and the Arizona Water Commission (AWC) have developed a generalized data storage and retrieval system on the DEC-10 computer system at the University of Arizona for systematic reporting of water information (quality and quantity) within the State of Arizona. The objective of the on-line system is to expand the AWC's efficiency in answering routine questions regarding Arizona's water resources easily and on short notice through remote terminal access to hydrologic information. Currently, two files reside on AWIS whereby water-related information can be extracted for the entire State on either quarter- township basis or regionally by drainage basin. AWC is given authority to "collect and investigate information upon and prepare and devise means and plans for the development, conservation, and utilization of all waterways, watersheds, subterranean waters, groundwater basins and water resources in the State. . ." To this end, AWC is developing a State Water Plan, and the data residing on the two files in AWLS are part of the Plan's first phase--an inventory of the resource, current uses and associated problems as presented in Arizona Water Commission Report "Arizona State Water Plan, Inventory of Resources and Uses, Phase I, July, 1975."

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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.

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Context: Multi Mediation is a process of collecting data from network(s) &amp; network elements, pre-processing this data and distributing it to various systems like Big Data analysis, Billing Systems, Network Monitoring Systems, and Service Assurance etc. With the growing demand for networks and emergence of new services, data collected from networks is growing. There is need for efficiently organizing this data and this can be done using databases. Although RDBMS offers Scale-up solutions to handle voluminous data and concurrent requests, this approach is expensive. So, alternatives like distributed databases are an attractive solution. Suitable distributed database for Multi Mediation, needs to be investigated. Objectives: In this research we analyze two distributed databases in terms of performance, scalability and availability. The inter-relations between performance, scalability and availability of distributed databases are also analyzed. The distributed databases that are analyzed are MySQL Cluster 7.4.4 and Apache Cassandra 2.0.13. Performance, scalability and availability are quantified, measurements are made in the context of Multi Mediation system. Methods: The methods to carry out this research are both qualitative and quantitative. Qualitative study is made for the selection of databases for evaluation. A benchmarking harness application is designed to quantitatively evaluate the performance of distributed database in the context of Multi Mediation. Several experiments are designed and performed using the benchmarking harness on the database cluster. Results: Results collected include average response time &amp; average throughput of the distributed databases in various scenarios. The average throughput &amp; average INSERT response time results favor Apache Cassandra low availability configuration. MySQL Cluster average SELECT response time is better than Apache Cassandra for greater number of client threads, in high availability and low availability configurations.Conclusions: Although Apache Cassandra outperforms MySQL Cluster, the support for transaction and ACID compliance are not to be forgotten for the selection of database. Apart from the contextual benchmarks, organizational choices, development costs, resource utilizations etc. are more influential parameters for selection of database within an organization. There is still a need for further evaluation of distributed databases. / <p>I am indebted to my advisor Prof. Lars Lundberg and his valuable ideas which helped in the completion of this work. In fact he has guided on every crucial and important stages of this research work.</p><p>I sincerely thank Prof. Markus Fiedler &amp; Prof. Kurt Tutschku for their endless support during the work.</p><p>I am grateful to Neeraj Garg, Sourab, Saket &amp; Kulbir at Ericsson, for providing me necessary equipment and helping me financially during my work.</p><p>To my family members and friends who one way or the other shared their support. Thank you.</p><p>Above all I would like to thank the Supreme Personality of Godhead, the author of everything.</p>

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Pagrindinis šio darbo tikslas yra išnagrinėti judančių objektų indeksavimo duomenų bazėse problemas, siūlomus sprendimus bei palyginti keleto iš jų veiksmingumą. Įvairiais pjūviais buvo lyginami praeities duomenis indeksuojantys R ir iš jo išvesti STR bei TB medžiai. Eksperimentai atlikti naudojant sugeneruotus judančių objektų duomenis. Gauti rezultatai parodė, kad indeksų veiksmingas priklauso nuo tam tikrų sąlygų ir aplinkybių, kuriomis jie naudojami. / Over the past few years, there has been a continuous improvement in the wireless communications and the positioning technologies. As a result, tracking the changing positions of continuously moving objects is becoming increasingly feasible and necessary. Databases that deal with objects that change their location and/or shape over time are called spatio-temporal databases. Traditional database approaches for effective information retrieval cannot be used as the moving objects database is highly dynamic. A need for so called spatio-temporal indexing techniques comes to scene. Mainly, by the problem they are addressed to, indices are divided into two groups: a) indexing the past and b) indexing the current and predicted future positions. Also the have been proposed techniques covering both problems. This work is a survey for well known and used indices. Also there is a performance comparison between several past indexing methods. STR Tree, TB Tree and the predecessor of many indices, the R Tree are compared in various aspects using generated datasets of simulated objects movement.