Adaptive Query Processing for Improving Responsiveness of Wide-area Queries PDF Download
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Author: Tolga Urhan
Publisher:
ISBN:
Category : Data flow computing
Languages : en
Pages : 342
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Book Description
Author: Tolga Urhan
Publisher:
ISBN:
Category : Data flow computing
Languages : en
Pages : 342
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Book Description
Author: Amol Deshpande
Publisher: Now Publishers Inc
ISBN: 1601980345
Category : Computers
Languages : en
Pages : 156
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Book Description
Adaptive Query Processing surveys the fundamental issues, techniques, costs, and benefits of adaptive query processing. It begins with a broad overview of the field, identifying the dimensions of adaptive techniques. It then looks at the spectrum of approaches available to adapt query execution at runtime - primarily in a non-streaming context. The emphasis is on simplifying and abstracting the key concepts of each technique, rather than reproducing the full details available in the papers. The authors identify the strengths and limitations of the different techniques, demonstrate when they are most useful, and suggest possible avenues of future research. Adaptive Query Processing serves as a valuable reference for students of databases, providing a thorough survey of the area. Database researchers will benefit from a more complete point of view, including a number of approaches which they may not have focused on within the scope of their own research.
Author: Clayton Maciel Costa
Publisher: Simplíssimo
ISBN: 658624983X
Category : Computers
Languages : en
Pages : 147
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Book Description
Nowadays, many companies are migrating their applications and data to cloud service providers, mainly because of their ability to answer quickly to business requirements. Thereby, the performance is an important requirement for most customers when they wish to migrate their applications to the cloud. Therefore, in cloud environments, resources should be acquired and released automatically and quickly at runtime. Moreover, the users and service providers expect to get answers in time to ensure the service SLA (Service Level Agreement). Consequently, ensuring the QoS (Quality of Service) is a great challenge and it increases when we have large amounts of data to be manipulated in this environment. To resolve this kind of problems, several researches have been focused on shorter execution time using adaptive query processing and/or prediction of resources based on current system status. However, they present important limitations. For example, most of these works does not use monitoring during query execution and/or presents intrusive solutions, i.e. applied to the particular context. The aim of this book is to present the development of new solutions/strategies to efficient adaptive query processing on large databases available in a cloud environment. It must integrate adaptive re-optimization at query runtime and their costs are based on the SRT (Service Response Time – SLA QoS performance parameter). Finally, the proposed solution will be evaluated on large scale with large volume of data, machines and queries in a cloud computing infrastructure. Finally, this work also proposes a new model to estimate the SRT for different request types (database access requests). This model will allow the cloud service provider and its customers to establish an appropriate SLA relative to the expected performance of the services available in the cloud.
Author: Harshit Ashokkumar Modi
Publisher:
ISBN:
Category :
Languages : en
Pages : 92
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Book Description
The utility and widespread use of Relational Database Management Systems(RDBMSs) comes not only from its simple, easy-to-understand data model (a rela-tion or a set) but mainly from the ability to write non-procedural queries and their optimization by the system. Queries produce exact answers that match the contents of the database. Query processing of RDBMSs has been researched for more than 4 decades and includes extensions to more complex analysis on data warehouses. In contrast, search has not been addressed by RDBMSs. As the use of other other data types (key-value store, column-store, and graphs to name a few) are becoming popular for modeling to match the data set characteristics, query processing and optimization are becoming important again. The approaches used in RDBMSs, such as cost-based, I/O focused may not be applicable in the same way to new models and queries. Hence, new approaches need to be developed that are suited for the data model used and the expressiveness of the queries to be supported. This thesis addresses query processing of large graphs (or forest) and develops algorithms for query processing as well as develops heuristics for improving the response time using graph characteristics. Although search (unlike RDBMS) has received a lot of attention for graphs, query processing, in contrast, has received very little attention. With the advent of large social networks and other large graphs(e.g., freebase, knowledge and entity graphs), querying to understand the data set and retrieve relevant/exact information becoming critical. This thesis builds on the previous work at the Information Technology laboratory at UTA (IT Lab) to scale query processing to arbitrary-size graphs (or forests)and to exploit parallelism as much as possible. Partitioning (a form of divide and conquer) and Map/Reduce (for parallel processing) are used as basic ingredients for scalability. Partitioning a graph for query processing and computing all answers poses a number of challenges: i) partitioning schemes, ii) scheduling or choosing which partition or partitions to schedule for processing, iii) developing heuristics for reducing the total response time exploiting query and graph characteristics, and iv) importantly,correctness of results. This thesis address all of the above challenges using the map/reduce framework.The choice of map/reduce framework allows us to make partitions based on available resources and optimize parallelism based on the number of partitions to schedule at a time. We use a partitioning strategy that has been shown to be good for substructure discovery. We develop a number of heuristics that are based on query and graph characteristics. The query itself is expressed as a graph without having to cast in some other language. Relational comparison operators, Boolean operators, wild cards, and union queries are supported. There is no restriction on node and edge labels, and uniquely labeled multiple edges are supported. Extensive experimental analysis of the approach (partitioning sizes, algorithm, and heuristics) using large data sets (real-world and synthetic) are shown for speedup, scalability, and ecacy of the heuristicsproposed.
Author: Petrus Maria Gerardus Apers
Publisher: Morgan Kaufmann
ISBN:
Category : Data structures (Computer science)
Languages : en
Pages : 778
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Book Description
Author:
Publisher:
ISBN: 9781604239577
Category : Database management
Languages : en
Pages : 516
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Book Description
Author: Henrique Wiermann Paques
Publisher:
ISBN:
Category : Adaptive computer systems
Languages : en
Pages :
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Book Description
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.
Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 946
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Book Description
Author: Michael J. Franklin
Publisher: Springer Science & Business Media
ISBN: 1461313635
Category : Computers
Languages : en
Pages : 227
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Book Description
Despite the significant ongoing work in the development of new database systems, many of the basic architectural and performance tradeoffs involved in their design have not previously been explored in a systematic manner. The designers of the various systems have adopted a wide range of strategies in areas such as process structure, client-server interaction, concurrency control, transaction management, and memory management. This monograph investigates several fundamental aspects of the emerging generation of database systems. It describes and investigates implementation techniques to provide high performance and scalability while maintaining the transaction semantics, reliability, and availability associated with more traditional database architectures. The common theme of the techniques developed here is the exploitation of client resources through caching-based data replication. Client Data Caching: A Foundation for High Performance Object Database Systems should be a value to anyone interested in the performance and architecture of distributed information systems in general and Object-based Database Management Systems in particular. It provides useful information for designers of such systems, as well as for practitioners who need to understand the inherent tradeoffs among the architectural alternatives in order to evaluate existing systems. Furthermore, many of the issues addressed in this book are relevant to other systems beyond the ODBMS domain. Such systems include shared-disk parallel database systems, distributed file systems, and distributed virtual memory systems. The presentation is suitable for practitioners and advanced students in all of these areas, although a basic understanding of database transaction semantics and techniques is assumed.
Author: Keng Siau
Publisher: IGI Global
ISBN: 1591400988
Category : Computers
Languages : en
Pages : 359
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Book Description
Advanced Topics in Database Research features the latest, cutting-edge research findings dealing with all aspects of database management, systems analysis and design and software engineering. This book provides information that is instrumental in the improvement and development of theory and practice related to information technology and management of information resources.
