In den letzten Jahren haben sich spezielle Prinzipien zur Veröffentlichung strukturierter Daten im World Wide Web (WWW) etabliert. Diese Prinzipien erlauben es, von den jeweils angebotenen Daten auf weitere, nach den selben Prinzipien veröffentlichten Daten zu verweisen. Die daraus resultierende Form von Web-Daten wird entsprechend als Linked Data bezeichnet. Mit der Veröffentlichung von Linked Data im WWW entsteht ein sehr großer Datenraum, welcher Daten verschiedenster Anbieter miteinander verbindet und neuartige Möglichkeiten für Web-basierte Anwendungen bietet. Als Basis für die Entwicklung solcher Anwendungen haben mehrere Forschungsgruppen begonnen, Ansätze zu untersuchen, welche diesen Datenraum als eine Art verteilte Datenbank auffassen und die Ausführung deklarativer Anfragen über dieser Datenbank ermöglichen. Forschungsarbeit zu theoretischen Grundlagen der untersuchten Ansätze fehlt jedoch nahezu vollständig. Die vorliegende Dissertation schließt diese Lücke. / During recent years a set of best practices for publishing and connecting structured data on the World Wide Web (WWW) has emerged. These best practices are referred to as the Linked Data principles and the resulting form of Web data is called Linked Data. The increasing adoption of these principles has lead to the creation of a globally distributed space of Linked Data that covers various domains such as government, libraries, life sciences, and media. Approaches that conceive this data space as a huge distributed database and enable an execution of declarative queries over this database hold an enormous potential; they allow users to benefit from a virtually unbounded set of up-to-date data. As a consequence, several research groups have started to study such approaches. However, the main focus of existing work is to address practical challenges that arise in this context. Research on the foundations of such approaches is largely missing. This dissertation closes this gap.

Daten werden noch immer groesstenteils in Dateien und nicht in Datenbanken gespeichert. Dieser Trend wird durch den Internetboom der 90er Jahre nur noch verstaerkt. Daraus ist das Forschungsgebiet der semistrukturierten Daten entstanden. Semistrukturierte Daten sind Daten, die meist in Dokumenten gespeichert sind und eine implizite und irregulaere Struktur aufweisen. HTML- oder BibTeX-Dateien oder in ASCII-Dateien gespeicherte Genomdaten sind Beispiele. Traditionelles Datenbankmanagement erfordert Design und sichert Deklarativitaet zu. Dies ist im Umfeld der semistrukturierten Daten nicht gegeben, ein flexiblerer Ansatz wird gebraucht. In dieser Arbeit wird ein neuer Ansatz des Abfragens semistrukturierter Daten praesentiert. Wir schlagen vor, semistrukturierte Daten durch eine Menge von partiellen Schemata zu beschreiben, anstatt zu versuchen, ein globales Schema zu definieren. Letzteres ist zwar geeignet, einen effizienten Zugriff auf Daten zu ermoeglichen; ein globales Schema fuer semistrukturierte Daten leidet aber zwangslaeufig an der Irregularitaet der Struktur der Daten. Wegen der vielen Ausnahmen vom intendierten Schema wird ein globales Schema schnell sehr gross und wenig repraesentativ. Damit wird dem Nutzer ein verzerrtes Bild ueber die Daten gegeben. Hingegen koennen partielle Schemata eher ein repraesentatives Bild eines Teils der Daten darstellen. Mit Hilfe statistischer Methoden kann die Guete eines partiellen Schemas bewertet werden, ebenso koennen irrelevante Teile der Datenbank identifiziert werden. Ein Datenbanksystem, das auf partiellen Schemata basiert, ist flexibler und reflektiert den Grad der Strukturierung auf vielen Ebenen. Seine Benutzbarkeit und seine Performanz steigen mit einem hoeheren Grad an Struktur und mit seiner Nutzungsdauer. Partielle Schemata koennen auf zwei Arten gewonnen werden. Erstens koennen sie durch einen Datenbankdesigner bereitgestellt werden. Es ist so gut wie unmoeglich, eine semistrukturierte Datenbank komplett zu modellieren, das Modellieren gewisser Teile ist jedoch denkbar. Zweitens koennen partielle Schemata aus Benutzeranfragen gewonnen werden, wenn nur die Anfragesprache entsprechend entworfen und definiert wird. Wir schlagen vor, eine Anfrage in einen ``Was''- und einen ``Wie''-Teil aufzuspalten. Der ``Was''-Teil wird durch partielle Schemata repraesentiert. Partielle Schemata beinhalten reiche semantische Konzepte, wie Variablendefinitionen und Pfadbeschreibungen, die an Konzepte aus Anfragesprachen angelehnt sind. Mit Variablendefinitionen koennen verschiedene Teile der Datenbank miteinander verbunden werden. Pfadbeschreibungen helfen, durch das Zulassen einer gewissen Unschaerfe, die Irregularitaet der Struktur der Daten zu verdecken. Das Finden von Stellen der Datenbank, die zu einem partiellen Schema passen, bildet die Grundlage fuer alle Arten von Anfragen. Im ``Wie''-Teil der Anfrage werden die gefundenen Stellen der Datenbank fuer die Antwort modifiziert. Dabei koennen Teile der gefundenen Entsprechungen des partiellen Schemas ausgeblendet werden oder auch die Struktur der Antwort voellig veraendert werden. Wir untersuchen die Ausdrucksstaerke unserer Anfragesprache, in dem wir einerseits die Operatoren der relationalen Algebra abbilden und andererseits das Abfragen von XML-Dokumenten demonstrieren. Wir stellen fest, dass das Finden der Entsprechungen eines Schemas (wir nennen ein partielles Schema in der Arbeit nur Schema) den aufwendigsten Teil der Anfragebearbeitung ausmacht. Wir verwenden eine weitere Abstraktionsebene, die der Constraint Satisfaction Probleme, um die Entsprechungen eines Schemas in einer Datenbank zu finden. Constraint Satisfaction Probleme bilden eine allgemeine Klasse von Suchproblemen. Fuer sie existieren bereits zahlreiche Optimierungsalgorithmen und -heuristiken. Die Grundidee besteht darin, Variablen mit zugehoerigen Domaenen einzufuehren und dann die Werte, die verschiedene Variablen gleichzeitig annehmen koennen, ueber Nebenbedingungen zu steuern. In unserem Ansatz wird das Schema in Variablen ueberfuehrt, die Domaenen werden aus der Datenbank gebildet. Nebenbedingungen ergeben sich aus den im Schema vorhandenen Praedikaten, Variablendefinitionen und Pfadbeschreibungen sowie aus der Graphstruktur des Schemas. Es werden zahlreiche Optimierungstechniken fuer Constraint Satisfaction Probleme in der Arbeit vorgestellt. Wir beweisen, dass die Entsprechungen eines Schemas in einer Datenbank ohne Suche und in polynomialer Zeit gefunden werden koennen, wenn das Schema ein Baum ist, keine Variablendefinitionen enthaelt und von der Anforderung der Injektivitaet einer Einbettung abgesehen wird. Zur Optimierung wird das Enthaltensein von Schemata herangezogen. Das Enthaltensein von Schemata kann auf zwei Weisen, je nach Richtung der Enthaltenseinsbeziehung, genutzt werden: Entweder kann der Suchraum fuer ein neues Schema reduziert werden oder es koennen die ersten passenden Stellen zu einem neuen Schema sofort praesentiert werden. Der gesamte Anfrageansatz wurde prototypisch zunaechst in einem Public-Domain Prolog System, spaeter im Constraintsystem ECLiPSe implementiert und mit Anfragen an XML-Dokumente getestet. Dabei wurden die Auswirkungen verschiedener Optimierungen getestet. Ausserdem wird eine grafische Benutzerschnittstelle zur Verfuegung gestellt. / Most of today's data is still stored in files rather than in databases. This fact has become even more evident with the growth of the World Wide Web in the 1990s. Because of that observation, the research area of semistructured data has evolved. Semistructured data is typically stored in documents and has an irregular, partial, and implicit structure. The thesis presents a new framework for querying semistructured data. Traditional database management requires design and ensures declarativity. The possibilities to design are limited in the field of semistructured data, thus, a more flexible approach is needed. We argue that semistructured data should be represented by a set of partial schemata rather than by one complete schema. Because of irregularities of the data, a complete schema would be very large and not representative. Instead, partial schemata can serve as good representations of parts of the data. While finding a complete schema turns out to be difficult, a database designer may be able to provide partial schemata for the database. Also, partial schemata can be extracted from user queries if the query language is designed appropriately. We suggest to split the notion of query into a ``What''- and a ``How''-part. Partial schemata represent the ``What''-part. They cover semantically richer concepts than database schemata traditionally do. Among these concepts are predicates, variable definitions, and path descriptions. Schemata can be used for query optimization, but they also give users hints on the content of the database. Finding the occurrences (matches) of such a schema forms the most important part of query execution. All queries of our approach, such as the focus query or the transformation query, are based on this matching. Query execution can be optimized using knowledge about containment relationships between different schemata. Our approach and the optimization techniques are conceptually modeled and implemented as a prototype on the basis of Constraint Satisfaction Problems (CSPs). CSPs form a general class of search problems for which many techniques and heuristics exist. A CSP consists of variables that have a domain associated to them. Constraints restrict the values that variables can simultaneously take. We transform the problem of finding the matches of a schema in a database to a CSP. We prove that under certain conditions the matches of a schema can be found without any search and in polynomial time. For optimization purposes the containment relationship between schemata is explored. We formulate a sufficient condition for schema containment and test it again using CSP techniques. The containment relationship can be used in two ways depending on the direction of the containment: It is either possible to reduce the search space when looking for matches of a schema, or it is possible to present the first few matches immediately without any search. Our approach has been implemented into the constraint system ECLiPSe and tested using XML documents.

Die parallele Anfragebearbeitung für relationale Datenbankmanagementsysteme (RDBMS) ist wegen ihrer unterschiedlichen Arten der Ausführungsparallelität und den Eigenschaften der zugrunde liegenden parallelen Architektur ein äusserst komplexes Problem. Systemänderungen zur Laufzeit der Anfrage können zusätzlich ein dynamisches Verhalten der ausführenden Komponenten erfordern, um eine nahezu optimale Antwortzeit zu gewährleisten. Diese Arbeit stellt einen neuen, flexiblen Ansatz für die Optimierung und Abarbeitung von komplexen Anfragen vor, der besonders die dynamische Optimierung berücksichtigt. Insbesondere werden in der Arbeit folgende Teile präsentiert: 1. die Architektur eines neuen, verteilt-kooperierenden Komponentensystems beeinflusst von agenten-orientierten Konzepten; 2. der Entwurf und die Realisierung einer neuen Kommunikationsinfrastruktur für die identifizierten Systemkomponenten; 3. der Entwurf und die Implementierung eines flexiblen Anfrageoptimierers mit einem neuen, zufallsbasierten Algorithmus; und 4. der Entwurf und die Realisierung einer parallel arbeitenden Ausführungskomponente unter besonderer Berücksichtigung der dynamischen Anfrageoptimierung. Bei der Entwicklung der Konzepte standen neben den spezifischen Anforderungen für RDBMS besonders die Konfigurierbarkeit und die Erweiterbarkeit des verteilten Systems im Vordergrund. / Parallel query evaluation for relational database management systems (RDBSM) still remains a challenging problem. Modern systems must show near optimal performance in spite of running in a heterogeneous hardware environment, exploiting different ways of parallelism and dealing with unpredictable system load. This thesis paper presents a dynamic and flexible system addressing the issues of optimization and evaluation of relational queries for a distributed and dynamic environment. In particular, this work consists of: 1) the architecture of a distributed system which was inspired by the concepts of software agents, 2) the architecture and the implementation of a communication infrastructure for the system components, 3) the architecture and the implementation of a new query optimization algorithm, and 4) the concept and the implementation of a new query evaluation engine for parallel execution, which enables runtime optimization of queries. Furthermore, the design supports the extension and the configuration of the system and its components.

Submitted by Kamila Costa (kamilavasconceloscosta@gmail.com) on 2015-06-11T19:18:34Z No. of bitstreams: 1 Dissertação-Lídia L S de Carvalho.pdf: 837456 bytes, checksum: 620d89f05fc84dc2af7b89b6b6e587a0 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-06-15T17:53:19Z (GMT) No. of bitstreams: 1 Dissertação-Lídia L S de Carvalho.pdf: 837456 bytes, checksum: 620d89f05fc84dc2af7b89b6b6e587a0 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-06-15T17:57:19Z (GMT) No. of bitstreams: 1 Dissertação-Lídia L S de Carvalho.pdf: 837456 bytes, checksum: 620d89f05fc84dc2af7b89b6b6e587a0 (MD5) / Made available in DSpace on 2015-06-15T17:57:19Z (GMT). No. of bitstreams: 1 Dissertação-Lídia L S de Carvalho.pdf: 837456 bytes, checksum: 620d89f05fc84dc2af7b89b6b6e587a0 (MD5) Previous issue date: 2015-03-11 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Several research efforts have been conducted in the literature to develop methods to reduce the cost of query processing in search engines. This research aims to propose modifications to improve the performance of the block-Max WAND (BMW) algorithm, one of the most efficient algorithms proposed previously. The BMW algorithm uses heuristics to discard the documents entries at query processing, which makes it extremely fast. In this dissertation, we propose and evaluate additional heuristics to improve the perfomance of BMW and your variant BMW-CS in an attempt to both further reduces query processing times and the amount of memory required for processing queries. / Nos últimos anos, pesquisas relacionadas ao processamento de consultas em máquinas de busca têm sido realizadas com o objetivo de desenvolver métodos que reduzam o seu custo. Este trabalho visa propor modificações para melhorar o desempenho do algoritmo Block-Max WAND (BMW), um dos algoritmos mais eficientes propostos na literatura. O algoritmo BMW utiliza heurísticas para descartar documentos da resposta durante o processamento de consultas, o que torna sua execução extremamente veloz. Nesta dissertação, serão propostas e experimentadas modificações nas heurísticas de descarte de documentos e redução na quantidade de memória utilizada para processar consultas pelo algoritmo BMW e suas variantes, buscando-se assim ganhos de desempenho.

Modern database systems use a query optimizer to identify the most efficient strategy, called “query execution plan”, to execute declarative SQL queries. The role of the query optimizer is especially critical for the complex decision-support queries featured in current data warehousing and data mining applications. Given an SQL query template that is parametrized on the selectivities of the participating base relations and a choice of query optimizer, a plan diagram is a color-coded pictorial enumeration of the execution plan choices of the optimizer over the query parameter space. Complementary to the plan-diagrams are cost and cardinality diagrams which graphically plot the estimated execution costs and cardinalities respectively, over the query parameter space. These diagrams are collectively known as optimizer diagrams. Optimizer diagrams have proved to be a powerful tool for the analysis and redesign of modern optimizers, and are gaining interest in diverse industrial and academic institutions. However, their utility is adversely impacted by the impractically large computational overheads incurred when standard brute-force approaches are used for producing fine-grained diagrams on high-dimensional query templates. In this thesis, we investigate strategies for efficiently producing close approximations to complex optimizer diagrams. Our techniques are customized for different classes of optimizers, ranging from the generic Class I optimizers that provide only the optimal plan for a query, to Class II optimizers that also support costing of sub-optimal plans and Class III optimizers which offer enumerated rank-ordered lists of plans in addition to both the former features. For approximating plan diagrams for Class I optimizers, we first present database oblivious techniques based on classical random sampling in conjunction with nearest neighbor (NN) inference scheme. Next we propose grid sampling algorithms which consider database specific knowledge such as(a) the structural differences between the operator trees of plans on the grid locations and (b) parametric query optimization principle. These algorithms become more efficient when modified to exploit the sub-optimal plan costing feature available with Class II optimizers. The final algorithm developed for Class III optimizers assume plan cost monotonicity and utilize the rank-ordered lists of plans to efficiently generate completely accurate optimizer diagrams. Subsequently, we provide a relaxed variant, which trades quality of approximation, for reduction in diagram generation overhead. Our proposed algorithms are capable of terminating according to user given error bound for plan diagram approximation. For approximating cost diagrams, our strategy is based on linear least square regression performed on a mathematical model of plan cost behavior over the parameter space, in conjunction with interpolation techniques. Game theoretic and linear programming approaches have been employed to further reduce the error in cost approximation. For approximating cardinality diagrams, we propose a novel parametrized mathematical model as a function of selectivities for characterizing query cardinality behavior. The complete cardinality model is constructed by clustering the data points according to their cardinality values and subsequently fitting the model through linear least square regression technique separately for each cluster. For non-sampled data points the cardinality values are estimated by first determining the cluster they belong to and then interpolating the cardinality value according to the suitable model. Extensive experimentation with a representative set of TPC-H and TPC-DS-based query templates on industrial-strength optimizers indicates that our techniques are capable of delivering 90% accurate optimizer diagrams while incurring no more than 20% of the computational overheads of the exhaustive approach. Infact, for full-featured optimizers, we can guarantee zero error optimizer diagrams which usually require less than 10% overheads. Our results exhibit that (a) the approximation is materially faithful to the features of the exact optimizer diagram, with the errors thinly spread across the picture and Largely confined to the plan transition boundaries and (b) the cost increase at the non-sampled point due to assignment of sub-optimal plan is also limited. These approximation techniques have been implemented in the publicly available Picasso optimizer visualizer tool. We have also modified PostgreSQL’s optimizer to incorporate costing of sub-optimal plans and enumerating rank-ordered lists of plans. In addition to these, we have designed estimators for predicting the time overhead involved in approximating optimizer diagrams with regard to user given error bounds. In summary, this thesis demonstrates that accurate approximations to exact optimizer diagrams can indeed be obtained cheaply and consistently, with typical overheads being an order of magnitude lower than the brute-force approach. We hope that our results will encourage database vendors to incorporate the foreign-plan-costing and plan-rank-list features in their optimizer APIs.

Processing and optimizing ad-hoc and continual queries in an open environment with distributed, autonomous, and heterogeneous data servers (e.g., the Internet) pose several technical challenges. First, it is well known that optimized query execution plans constructed at compile time make some assumptions about the environment (e.g., network speed, data sources' availability). When such assumptions no longer hold at runtime, how can I guarantee the optimized execution of the query? Second, it is widely recognized that runtime adaptation is a complex and difficult task in terms of cost and benefit. How to develop an adaptation methodology that makes the runtime adaptation beneficial at an affordable cost? Last, but not the least, are there any viable performance metrics and performance evaluation techniques for measuring the cost and validating the benefits of runtime adaptation methods? To address the new challenges posed by Internet query and search systems, several areas of computer science (e.g., database and operating systems) are exploring the design of systems that are adaptive to their environment. However, despite the large number of adaptive systems proposed in the literature up to now, most of them present a solution for adapting the system to a specific change to the runtime environment. Typically, these solutions are not easily ``extendable' to allow the system to adapt to other runtime changes not predicted in their approach. In this dissertation, I study the problem of how to construct a framework where I can catalog the known solutions to query processing adaptation and how to develop an application that makes use of this framework. I call the solution to these two problems the Ginga approach. I provide in this dissertation three main contributions: The first contribution is the adoption of the Adaptation Space concept combined with feedback-based control mechanisms for coordinating and integrating different kinds of query adaptations to different runtime changes. The second contribution is the development of a systematic approach, called Ginga, to integrate the adaptation space with feedback control that allows me to combine the generation of predefined query plans (at compile-time) with reactive adaptive query processing (at runtime), including policies and mechanisms for determining when to adapt, what to adapt, and how to adapt. The third contribution is a detailed study on how to adapt to two important runtime changes, and their combination, encountered during the execution of distributed queries: memory constraints and end-to-end delays.

Increasing performance of computers and ability to connect computers with high speed communication networks make distributed databases systems an attractive research area. In this study, we evaluate communication and data processing capabilities of a HPC machine. We calculate accurate cost formulas for high volume data communication between processing nodes and experimentally measure sorting times. A left deep query plan executer has been implemented and experimentally used for executing plans generated by two different genetic algorithms for a distributed database environment using message passing paradigm to prove that a parallel system can provide scalable performance by increasing the number of nodes used for storing database relations and processing nodes. We compare the performance of plans generated by genetic algorithms with optimal plans generated by exhaustive search algorithm. Our results have verified that optimal plans are better than those of genetic algorithms, as expected.

Cluster computer systems assembled from commodity off-the-shelf components have emerged as a viable and cost-effective alternative to high-end custom parallel computer systems.In this thesis, we investigate how scalable performance can be achieved for database systems on clusters. In this context we specfically considered database query processing for evaluation of botlenecks and suggest optimization techniques for obtaining scalable application performance. First we systematically demonstrated that in a large cluster with high disk bandwidth, the processing capability and the I/O bus bandwidth are the two major performance bottlenecks in database systems. To identify and assess bottlenecks, we developed a Petri net model of parallel query execution on a cluster. Once identified and assessed,we address the above two performance bottlenecks by offoading certain application related tasks to the processor in the network interface card. Offoading application tasks to the processor in the network interface cards shifts the bottleneck from cluster processor to I/O bus. Further, we propose a hardware scheme,network attached disk ,and a software scheme to achieve a balanced utilization of re-sources like host processor, I/O bus, and processor in the network interface card. The proposed schemes result in a speedup of upto 1.47 compared to the base scheme, and ensures scalable performance upto 64 processors. Encouraged by the benefits of offloading application tasks to network processors, we explore the possibilities of performing the bloom filter operations in network processors. We combine offloading bloom filter operations with the proposed hardware schemes to achieve upto 50% reduction in execution time. The later part of the thesis provides introductory experiments conducted in Community At-mospheric Model(CAM), a large scale parallel application used for global weather and climate prediction. CAM is a communication intensive application that involves collective communication of large messages. In our limited experiment, we identified CAM to see the effect of compression techniques and offloading techniques (as formulated for database) on the performance of communication intensive applications. Due to time constraint, we considered only the possibility of compression technique for improving the application performance. However, offloading technique could be taken as a full-fledged research problem for further investigation In our experiment, we found compression of messages reduces the message latencies, and hence improves the execution time and scalability of the application. Without using compression techniques, performance measured on 64 processor cluster resulted in a speed up of only 15.6. While lossless compression retains the accuracy and correctness of the program, it does not result in high compression. We therefore propose lossy compression technique which can achieve a higher compression, yet retain the accuracy and numerical stability of the application while achieving a scalable performance. This leads to speedup of 31.7 on 64 processors compared to a speedup of 15.6 without message compression. We establish that the accuracy within prescribed limit of variation and numerical stability of CAM is retained under lossy compression.

Aktuelle Studien belegen ein rasantes, mehrdimensionales Wachstum in analytischen Datenbanken: Das Datenvolumen verzehnfachte sich in den letzten vier Jahren, die Anzahl der Nutzer wuchs um durchschnittlich 25% pro Jahr und die Anzahl der Anfragen verdoppelte sich seit 2004 jährlich. Bei den Anfragen handelt es sich zunehmend um komplexe Verbundanfragen mit Aggregationen; sie sind häufig explorativer Natur und werden interaktiv an das System gestellt. Eine Möglichkeit, der Forderung nach Interaktivität bei diesem starken, mehrdimensionalen Wachstum nachzukommen, stellen Stichproben und eine darauf aufsetzende näherungsweise Anfrageverarbeitung dar. Diese Lösung bietet signifikant kürzere Antwortzeiten sowie Schätzungen mit probabilistischen Fehlergrenzen. Mit den Operationen Verbund, Gruppierung und Aggregation als Hauptbestandteile analytischer Anfragen ergeben sich folgende Anforderungen an das Design von Stichproben in analytischen Datenbanken: Zwischen den Stichproben fremdschlüsselverbundener Relationen ist die referenzielle Integrität zu gewährleisten, sämtliche Gruppen sind angemessen zu repräsentieren und Aggregationsattribute sind auf extreme Werte zu untersuchen. In dieser Dissertation wird für jedes dieser Teilprobleme ein Stichprobenverfahren vorgestellt, das sich durch speicherplatzbeschränkte Stichproben und geringe Schätzfehler auszeichnet. Im ersten der vorgestellten Verfahren wird durch eine korrelierte Stichprobenerhebung die referenzielle Integrität bei minimalem zusätzlichen Speicherplatz gewährleistet. Das zweite vorgestellte Stichprobenverfahren hat durch eine Berücksichtigung der Streuung der Daten eine angemessene Repräsentation sämtlicher Gruppen zur Folge und unterstützt damit beliebige Gruppierungen, und im dritten Verfahren ermöglicht eine mehrdimensionale Ausreißerbehandlung geringe Schätzfehler für beliebig viele Aggregationsattribute. Für jedes dieser Verfahren wird die Qualität der resultierenden Stichprobe diskutiert und bei der Berechnung speicherplatzbeschränkter Stichproben berücksichtigt. Um den Berechnungsaufwand und damit die Systembelastung gering zu halten, werden für jeden Algorithmus Heuristiken vorgestellt, deren Kennzeichen hohe Effizienz und eine geringe Beeinflussung der Stichprobenqualität sind. Weiterhin werden alle möglichen Kombinationen der vorgestellten Stichprobenverfahren betrachtet; diese Kombinationen ermöglichen eine zusätzliche Verringerung der Schätzfehler und vergrößern gleichzeitig das Anwendungsspektrum der resultierenden Stichproben. Mit der Kombination aller drei Techniken wird ein Stichprobenverfahren vorgestellt, das alle Anforderungen an das Design von Stichproben in analytischen Datenbanken erfüllt und die Vorteile der Einzellösungen vereint. Damit ist es möglich, ein breites Spektrum an Anfragen mit hoher Genauigkeit näherungsweise zu beantworten. / Recent studies have shown the fast and multi-dimensional growth in analytical databases: Over the last four years, the data volume has risen by a factor of 10; the number of users has increased by an average of 25% per year; and the number of queries has been doubling every year since 2004. These queries have increasingly become complex join queries with aggregations; they are often of an explorative nature and interactively submitted to the system. One option to address the need for interactivity in the context of this strong, multi-dimensional growth is the use of samples and an approximate query processing approach based on those samples. Such a solution offers significantly shorter response times as well as estimates with probabilistic error bounds. Given that joins, groupings and aggregations are the main components of analytical queries, the following requirements for the design of samples in analytical databases arise: 1) The foreign-key integrity between the samples of foreign-key related tables has to be preserved. 2) Any existing groups have to be represented appropriately. 3) Aggregation attributes have to be checked for extreme values. For each of these sub-problems, this dissertation presents sampling techniques that are characterized by memory-bounded samples and low estimation errors. In the first of these presented approaches, a correlated sampling process guarantees the referential integrity while only using up a minimum of additional memory. The second illustrated sampling technique considers the data distribution, and as a result, any arbitrary grouping is supported; all groups are appropriately represented. In the third approach, the multi-column outlier handling leads to low estimation errors for any number of aggregation attributes. For all three approaches, the quality of the resulting samples is discussed and considered when computing memory-bounded samples. In order to keep the computation effort - and thus the system load - at a low level, heuristics are provided for each algorithm; these are marked by high efficiency and minimal effects on the sampling quality. Furthermore, the dissertation examines all possible combinations of the presented sampling techniques; such combinations allow to additionally reduce estimation errors while increasing the range of applicability for the resulting samples at the same time. With the combination of all three techniques, a sampling technique is introduced that meets all requirements for the design of samples in analytical databases and that merges the advantages of the individual techniques. Thereby, the approximate but very precise answering of a wide range of queries becomes a true possibility.

This Thesis addresses how Semantic Web representations, in particular RDF, can enable flexible and scalable preservation, recreation, and querying of databases. An approach has been developed for selective scalable long-term archival of relational databases (RDBs) as RDF, implemented in the SAQ (Semantic Archive and Query) system. The archival of user-specified parts of an RDB is specified using an extension of SPARQL, A-SPARQL. SAQ automatically generates an RDF view of the RDB, the RD-view. The result of an archival query is RDF triples stored in: i) a data archive file containing the preserved RDB content, and ii) a schema archive file containing sufficient meta-data to reconstruct the archived database. To achieve scalable data preservation and recreation, SAQ uses special query rewriting optimizations for the archival queries. It was experimentally shown that they improve query execution and archival time compared with naïve processing. The performance of SAQ was compared with that of other systems supporting SPARQL queries to views of existing RDBs. When an archived RDB is to be recreated, the reloader module of SAQ first reads the schema archive file and executes a schema reconstruction algorithm to automatically construct the RDB schema. The thus created RDB is populated by reading the data archive and converting the read data into relational attribute values. For scalable recreation of RDF archived data we have developed the Triple Bulk Load (TBL) approach where the relational data is reconstructed by using the bulk load facility of the RDBMS. Our experiments show that the TBL approach is substantially faster than the naïve Insert Attribute Value (IAV) approach, despite the added sorting and post-processing. To view and query semi-structured Topic Maps data as RDF the prototype system TM-Viewer was implemented. A declarative RDF view of Topic Maps, the TM-view, is automatically generated by the TM-viewer using a developed conceptual schema for the Topic Maps data model. To achieve efficient query processing of SPARQL queries to the TM-view query rewrite transformations were developed and evaluated. It was shown that they significantly improve the query execution time. / eSSENCE