Integrating the UB-Tree into a Database System Kernel

Multidimensional access methods have shown high potential for significant performance improvements in various application domains

The periodic table of data structures

http://sites.computer.org/debull/A18sept/p64.pdfPublished versio

Optimizing spatial cache performance for mobile applications

Digital maps are available on a large range of devices, many of them mobile. Because of the size of map data, they are often stored on a central server. Mobile devices have limited network bandwidth, and the traffic costs may be high. Local caching is a way to reduce the amount of data transmitted between the server and the client. This thesis presents some theory related to digital map systems and caching in general and discusses some issues specific for caching spatial data. A prototype implementation of a spatial cache is used to study the effects of cache size and tile size on the performance of the cache. The results are not as significant as expected. This is assumed to be because the usage pattern in the tests is random and an efficient cache implementation depends on predictability in the usage pattern. However, the results indicate that careful selection of tile size is important to maximize the performance of the cache

Extensibility in ORDBMS databases : an exploration of the data cartridge mechanism in Oracle9i

To support current and emerging database applications, Object-Relational Database Management Systems (ORDBMS) provide mechanisms to extend the data storage capabilities and the functionality of the database with application-specific types and methods. Using these mechanisms, the database may contain user-defined data types, large objects (LOBs), external procedures, extensible indexing, query optimisation techniques and other features that are treated in the same way as built-in database features . The many extensibility options provided by the ORDBMS, however, raise several implementation challenges that are not always obvious. This thesis examines a few of the key challenges that arise when extending Oracle database with new functionality. To realise the potential of extensibility in Oracle, the thesis used the problem area of image retrieval as the main test domain. Current research efforts in image retrieval are lagging behind the required retrieval, but are continuously improving. As better retrieval techniques become available, it is important that they are integrated into the available database systems to facilitate improved retrieval. The thesis also reports on the practical experiences gained from integrating an extensible indexing scenario. Sample scenarios are integrated in Oracle9i database using the data cartridge mechanism, which allows Oracle database functionality to be extended with new functional components. The integration demonstrates how additional functionality may be effectively applied to both general and specialised domains in the database. It also reveals alternative design options that allow data cartridge developers, most of who are not database server experts, to extend the database. The thesis is concluded with some of the key observations and options that designers must consider when extending the database with new functionality. The main challenges for developers are the learning curve required to understand the data cartridge framework and the ability to adapt already developed code within the constraints of the data cartridge using the provided extensibility APls. Maximum reusability relies on making good choices for the basic functions, out of which specialised functions can be built.KMBT_363Adobe Acrobat 9.54 Paper Capture Plug-i

Design of a performance evaluation tool for multimedia databases with special reference to Oracle

Increased production and use of multimedia data has led to the development of a more advanced Database Management System (DBMS), like an Object Relational Database Management System (ORDBMS). These advanced databases are necessitated by the complexity in structure and the functionality required by multimedia data. Unfortunately, no suitable benchmarks exist with which to test the performance of databases when handling multimedia data. This thesis describes the design of a benchmark to measure the performance of basic functionality found in multimedia databases. The benchmark, called MORD (Multimedia Object Relational Databases), targets Oracle, a well known commercial Object Relational Database Management System (ORDBMS) that can handle multimedia data. Although MORD targets Oracle, it can easily be applied to other Multimedia Database Management System (MMDBMS) as a result of a design that stressed its portability, and simplicity. MORD consists of a database schema, test data, and code to simulate representative queries on multimedia databases. A number of experiments are described that validate MORD and ensure its correct design and that its objectives are met. A by-product of these experiments is an initial understanding of the performance of multimedia databases. The experiments show that with multimedia data the buffer cache should be at least large enough to hold the largest dataset, a bigger block size improves the performance, and turning off logging and caching for bulk loading improves the performance. MORD can be used to compare different ORDBMS or to assist in the configuration of a specific database

A disk-resident suffix tree index and generic framework for managing tunable indexes

This thesis introduces two related technologies. The first is a disk-resident index for biological sequence data, and the second is a framework and toolkit for the management of operational parameters for applications of which this index is typical. The Top-Compressed Suffix Tree is a novel data structure that can be used to provide a scalable, disk-resident index for large sequences. This data structure is based on the suffix tree, but has been designed to overcome the problems associated with using such structures on secondary memory. Top-Compressed Suffix Trees can be constructed incrementally, allowing indexes to be created that are larger than the amount of available main memory. Correspondingly, querying such an index only requires part of the data structure to be resident in main memory, thus allowing support for on-demand faulting and eviction of index sections during search. Such an index may be of great benefit to scientists requiring efficient access to vast repositories of genomic data. The Generic Index Development and Operation Framework (GIDOF) is a framework and toolkit that supports various tasks relating to the management of operational parameters. The performance of an index's implementation is typically influenced by several operational parameters parameters that must be tuned carefully if optimum performance is to be obtained. Indexes implemented using GIDOF can be structured in such a way that values of selected operational parameters can be adjusted; resulting in an index implementation that can be tuned to suit a given workload or system environment. This thesis presents a detailed description of the design of both the Top-Compressed Suffix Tree and the algorithms that operate over it. Extensive performance measurements are then presented and discussed, covering such aspects of index performance as construction time, average query performance and the size of the completed index. An overview of the GIDOF parameter model and toolkit is then given together with examples of how this framework can be used to manage tunable indexes, such as the Top-Compressed Suffix Tree

Spatial Database Support for Virtual Engineering

The development, design, manufacturing and maintenance of modern engineering products is a very expensive and complex task. Shorter product cycles and a greater diversity of models are becoming decisive competitive factors in the hard-fought automobile and plane market. In order to support engineers to create complex products when being pressed for time, systems are required which answer collision and similarity queries effectively and efficiently. In order to achieve industrial strength, the required specialized functionality has to be integrated into fully-fledged database systems, so that fundamental services of these systems can be fully reused, including transactions, concurrency control and recovery. This thesis aims at the development of theoretical sound and practical realizable algorithms which effectively and efficiently detect colliding and similar complex spatial objects. After a short introductory Part I, we look in Part II at different spatial index structures and discuss their integrability into object-relational database systems. Based on this discussion, we present two generic approaches for accelerating collision queries. The first approach exploits available statistical information in order to accelerate the query process. The second approach is based on a cost-based decompositioning of complex spatial objects. In a broad experimental evaluation based on real-world test data sets, we demonstrate the usefulness of the presented techniques which allow interactive query response times even for large data sets of complex objects. In Part III of the thesis, we discuss several similarity models for spatial objects. We show by means of a new evaluation method that data-partitioning similarity models yield more meaningful results than space-partitioning similarity models. We introduce a very effective similarity model which is based on a new paradigm in similarity search, namely the use of vector set represented objects. In order to guarantee efficient query processing, suitable filters are introduced for accelerating similarity queries on complex spatial objects. Based on clustering and the introduced similarity models we present an industrial prototype which helps the user to navigate through massive data sets.Ein schneller und reibungsloser Entwicklungsprozess neuer Produkte ist ein wichtiger Faktor für den wirtschaftlichen Erfolg vieler Unternehmen insbesondere aus der Luft- und Raumfahrttechnik und der Automobilindustrie. Damit Ingenieure in immer kürzerer Zeit immer anspruchsvollere Produkte entwickeln können, werden effektive und effiziente Kollisions- und Ähnlichkeitsanfragen auf komplexen räumlichen Objekten benötigt. Um den hohen Anforderungen eines produktiven Einsatzes zu genügen, müssen entsprechend spezialisierte Zugriffsmethoden in vollwertige Datenbanksysteme integriert werden, so dass zentrale Datenbankdienste wie Trans-aktionen, kontrollierte Nebenläufigkeit und Wiederanlauf sichergestellt sind. Ziel dieser Doktorarbeit ist es deshalb, effektive und effiziente Algorithmen für Kollisions- und Ähnlichkeitsanfragen auf komplexen räumlichen Objekten zu ent-wickeln und diese in kommerzielle Objekt-Relationale Datenbanksysteme zu integrieren. Im ersten Teil der Arbeit werden verschiedene räumliche Indexstrukturen zur effizienten Bearbeitung von Kollisionsanfragen diskutiert und auf ihre Integrationsfähigkeit in Objekt-Relationale Datenbanksysteme hin untersucht. Daran an-knüpfend werden zwei generische Verfahren zur Beschleunigung von Kollisionsanfragen vorgestellt. Das erste Verfahren benutzt statistische Informationen räumlicher Indexstrukturen, um eine gegebene Anfrage zu beschleunigen. Das zweite Verfahren beruht auf einer kostenbasierten Zerlegung komplexer räumlicher Datenbank- Objekte. Diese beiden Verfahren ergänzen sich gegenseitig und können unabhängig voneinander oder zusammen eingesetzt werden. In einer ausführlichen experimentellen Evaluation wird gezeigt, dass die beiden vorgestellten Verfahren interaktive Kollisionsanfragen auf umfangreichen Datenmengen und komplexen Objekten ermöglichen. Im zweiten Teil der Arbeit werden verschiedene Ähnlichkeitsmodelle für räum-liche Objekte vorgestellt. Es wird experimentell aufgezeigt, dass datenpartitionierende Modelle effektiver sind als raumpartitionierende Verfahren. Weiterhin werden geeignete Filtertechniken zur Beschleunigung des Anfrageprozesses entwickelt und experimentell untersucht. Basierend auf Clustering und den entwickelten Ähnlichkeitsmodellen wird ein industrietauglicher Prototyp vorgestellt, der Benutzern hilft, durch große Datenmengen zu navigieren