Performance issues in mid-sized relational database machines

Relational database systems have provided end users and application programmers with an improved working environment over older hierarchial and networked database systems. End users now use interactive query languages to inspect and manage their data. And application programs are easier to write and maintain due to the separation of physical data storage information from the application program itself. These and other benefits do not come without a price however. System resource consumption has long been the perceived problem with relational systems. The additional resource demands usually force computing sites to upgrade existing systems or add additional facilities. One method of protecting the current investment in systems is to use specialized hardware designed specifically for relational database processing. \u27Database Machines\u27 provide that alternative. Since the commercial introduction of database machines in the early 1980\u27s, both software and hardware vendors of relational database systems have claimed superior performance over competing products. Without a STANDARD performance measurement technique, the database user community has been flooded with benchmarks and claims from vendors which are immediately discarded by some competitors as being biased towards a particular system design. This thesis discusses the issues of relational database performance measurement with an emphasis on database machines, however; these performance issues are applicable to both hardware and software systems. A discussion of hardware design, performance metrics, software and database design is included. Also provided are recommended guidelines to use in evaluating relational database systems in lieu of a standard benchmark methodology

Storage and Querying of Large Persistent Arrays

The scientic and analytical applications today are increasingly becoming data in- tensive. Many such applications deal with data that is multidimensional in nature. Traditionally, relational database systems have been used by many data intensive application, and relational paradigm has proved to be both natural and ecient. However, for multidimensional data, when the number of dimensions becomes large, relational databases are inecient both in terms of storage and query response time. In this thesis, we explore linearised storage, and indexed and skiplist based retrieval on persistent arrays. The application programs are provided with a logical view of multidimensional array. The techniques have been implemented in a home-grown database management system called MuBase

Space station data system analysis/architecture study. Task 2: Options development, DR-5. Volume 2: Design options

The primary objective of Task 2 is the development of an information base that will support the conduct of trade studies and provide sufficient data to make key design/programmatic decisions. This includes: (1) the establishment of option categories that are most likely to influence Space Station Data System (SSDS) definition; (2) the identification of preferred options in each category; and (3) the characterization of these options with respect to performance attributes, constraints, cost and risk. This volume contains the options development for the design category. This category comprises alternative structures, configurations and techniques that can be used to develop designs that are responsive to the SSDS requirements. The specific areas discussed are software, including data base management and distributed operating systems; system architecture, including fault tolerance and system growth/automation/autonomy and system interfaces; time management; and system security/privacy. Also discussed are space communications and local area networking

Prefetching techniques for client server object-oriented database systems

The performance of many object-oriented database applications suffers from the page fetch latency which is determined by the expense of disk access. In this work we suggest several prefetching techniques to avoid, or at least to reduce, page fetch latency. In practice no prediction technique is perfect and no prefetching technique can entirely eliminate delay due to page fetch latency. Therefore we are interested in the trade-off between the level of accuracy required for obtaining good results in terms of elapsed time reduction and the processing overhead needed to achieve this level of accuracy. If prefetching accuracy is high then the total elapsed time of an application can be reduced significantly otherwise if the prefetching accuracy is low, many incorrect pages are prefetched and the extra load on the client, network, server and disks decreases the whole system performance. Access pattern of object-oriented databases are often complex and usually hard to predict accurately. The ..

Data Management for Dynamic Multimedia Analytics and Retrieval

Multimedia data in its various manifestations poses a unique challenge from a data storage and data management perspective, especially if search, analysis and analytics in large data corpora is considered. The inherently unstructured nature of the data itself and the curse of dimensionality that afflicts the representations we typically work with in its stead are cause for a broad range of issues that require sophisticated solutions at different levels. This has given rise to a huge corpus of research that puts focus on techniques that allow for effective and efficient multimedia search and exploration. Many of these contributions have led to an array of purpose-built, multimedia search systems. However, recent progress in multimedia analytics and interactive multimedia retrieval, has demonstrated that several of the assumptions usually made for such multimedia search workloads do not hold once a session has a human user in the loop. Firstly, many of the required query operations cannot be expressed by mere similarity search and since the concrete requirement cannot always be anticipated, one needs a flexible and adaptable data management and query framework. Secondly, the widespread notion of staticity of data collections does not hold if one considers analytics workloads, whose purpose is to produce and store new insights and information. And finally, it is impossible even for an expert user to specify exactly how a data management system should produce and arrive at the desired outcomes of the potentially many different queries. Guided by these shortcomings and motivated by the fact that similar questions have once been answered for structured data in classical database research, this Thesis presents three contributions that seek to mitigate the aforementioned issues. We present a query model that generalises the notion of proximity-based query operations and formalises the connection between those queries and high-dimensional indexing. We complement this by a cost-model that makes the often implicit trade-off between query execution speed and results quality transparent to the system and the user. And we describe a model for the transactional and durable maintenance of high-dimensional index structures. All contributions are implemented in the open-source multimedia database system Cottontail DB, on top of which we present an evaluation that demonstrates the effectiveness of the proposed models. We conclude by discussing avenues for future research in the quest for converging the fields of databases on the one hand and (interactive) multimedia retrieval and analytics on the other

Growth of relational model: Interdependence and complementary to big data

A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval techniques like indexing, query processing and concurrency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructured environment, particularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system