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RPE query processing and optimization Techniques for XML Databases

Journal of Computer Science & Technology 2004年第2期19卷 224-237页

作者： Guo-RenWang BingSun Jian-HuaLv GeYu DepartmentofComputerScience NortheasternUniversityShenyang110004P.R.China

An extent join to compute path expressions containing parent-children andancestor-descendent operations and two path expression optimization rules, path-shortening andpath-complementing, are presented in this paper. Path-shortening reduces the number of joins byshortening the path while path-complementing optimizes the path execution by using an equivalentcomplementary path expression to compute the original one. Experimental results show that thealgorithms proposed are more efficient than traditional algorithms.

关键词： XML regular path expressions query processing and optimization

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Graph-based parallel query processing and optimization strategies for object-oriented databases

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DISTRIBUTED AND PARALLEL DATABASES 1998年第3期6卷 247-285页

作者： Su, SYW Huang, Y Akaboshi, N Univ Florida Database Syst Res & Dev Ctr Dept Comp & Informat Sci & Engn Dept Elect & Comp Engn Gainesville FL 32611 USA

Much work has been accomplished in the past on the subject of parallel query processing and optimization in parallel relational database systems;however, little work on the same subject has been done in parallel object-oriented database systems. Since the object-oriented view of a database and its processing are quite different from those of a relational system, it can be expected that techniques of parallel query processing and optimization for the latter can be different from the former. In this paper, we present a general framework for parallel object-oriented database systems and several implemented query processing and optimization strategies together with some performance evaluation results. In this work, multiwavefront algorithms are used in query processing to allow a higher degree of parallelism than the traditional tree-based query processing. Four optimization strategies, which are designed specifically for the multiwavefront algorithms and for the optimization of single as well as multiple queries, are introduced. The query processing algorithms and optimization strategies have been implemented on a parallel computer, nCUBE2;and the results of a performance evaluation are presented in this paper. The main emphases and the intended contributions of this paper are (1) data partitioning, query processing and optimization strategies suitable for parallel OODBMSs, (2) the implementation of the multiwavefront algorithms and optimization strategies, and (3) the performance evaluation results.

关键词： query processing and optimization object-oriented databases parallel algorithms data partitioning strategies

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Advanced query processing and its optimization for Mobile Computing Environment 2

Advanced Query Processing and its Optimization for Mobile Co...

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2nd International Conference on Inventive Systems and Control (ICISC)

作者： Chendke, A. P. Sherekar, S. S. Thakare, V. M. HVPM DCPE Dept Comp Sci Amravati India SGB Amravati Univ PG Dept Comp Sci & Engn Amravati India

ISBN: (纸本)9781538608074

query processing is that method or technique in area of mobile atmosphere which deal using mobile environment. This research in that way user can think advancement using query processing and it takes more use for speedily accessing global query processing and it is joint venture of query processing between dissimilar sites includes fixed server movable computer. The necessity saving of energy and also the existence of lopsided features in a moveable computing atmosphere, the predictable query processing for a scattered database cannot be straightly genuine to a moveable computing organization. The mobile environment is a collection of mobile diverse hosts, which are enabled to communicate using wireless links. These wireless links are change giving to the natures of mobile networks. Moreover, nodes in the ad-hoc network have to link without any centralized or help. The usability for the user also changes using query processing tool. Thus, this mechanism that allows the sharing of functionality among different devices in same environment change the way of user communication for searching fast query time in mobile computing environment. The aim of this research is to make innovation in query optimization technique in mobile computing environment. This research focuses on improvement of various query optimization techniques for highly effective and trustworthy query optimization methods.

关键词： Mobile Computing query processing and optimization Conventional query processing query Time

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INFORMATION SYSTEMS 2015年 52卷 149-162页

作者： Silva, Yasin N. Pearson, Spencer S. Chon, Jaime Roberts, Ryan Arizona State Univ Glendale AZ 85306 USA

Similarity Joins are extensively used in multiple application domains and are recognized among the most useful data processing and analysis operations. They retrieve all data pairs whose distances are smaller than a predefined threshold epsilon. While several standalone implementations have been proposed, very little work has addressed the implementation of Similarity Joins as physical database operators. In this paper, we focus on the study, design, implementation, and optimization of a Similarity Join database operator for metric spaces. We present DBSimJoin, a physical database operator that integrates techniques to: enable a non-blocking behavior, prioritize the early generation of results, and fully support the database iterator interface. The proposed operator can be used with multiple distance functions and data types. We describe the changes in each query engine module to implement DBSimJoin and provide details of our implementation in PostgreSQL. We also study ways in which DBSimJoin can be combined with other similarity and non-similarity operators to answer more complex queries, and how DBSimJoin can be used in query transformation rules to improve query performance. The extensive performance evaluation shows that DBSimJoin significantly outperforms alternative approaches and scales very well when important parameters like epsilon, data size, and number of dimensions increase. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Similarity Join Database operator Similarity queries PostgreSQL query processing and optimization

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3SEPIAS: A Semi-Structured Search Engine for Personal Information in dAtaspace System

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INFORMATION SCIENCES 2013年 218卷 31-50页

作者： Zhong, Ming Liu, Mengchi He, Yanxiang Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China Carleton Univ Sch Comp Sci Ottawa ON K1S 5B6 Canada

Nowadays, personal information is being distributed into more and more heterogeneous sources, which presents a huge obstacle to management and retrieval of personal information. To address this problem, this paper presents the blueprint of a novel Personal Information Management (PIM) system named 3SEPIAS (short for Semi-Structured Search Engine for Personal Information in dAtaspace System). 3SEPIAS has three main features, data integration without upfront semantic reconciliation, flexible query model for data having sparse and evolving schema, and efficient best-effort proximity search approach on graphs. For that, we first propose a semi-structured graph data model called Interpreted Object Model (IOM) to uniformly represents a user's heterogeneous personal information and loosely integrates it into a dataspace in a schema-later way. Then, a Semi-Structured Search Engine (3SE) can be used to search over the personal dataspaces. We propose an intuitive 3SE query Language (3SQL) that enables users to query in a varying degree of structural constraint according to their knowledge of underlying schemas. Moreover, a best-effort top-k proximity search optimization strategy and corresponding graph index structures are proposed to improve the efficiency of query processing. We perform comprehensive experiments to test both effectiveness and efficiency of our proximity search approach. The results reveal that 3SE can beat the previous proximity search systems by a large margin with only a little or even no loss of result quality, especially for large graphs. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Personal information management Dataspace Graph data model Semi-structured query query processing and optimization

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A General Technique for Top-k Geometric Intersection query Problems

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2014年第12期26卷 2859-2871页

作者： Rahul, Saladi Janardan, Ravi Univ Minnesota Twin Cities Dept Comp Sci & Engn Minneapolis MN 55455 USA

In a top-k Geometric Intersection query (top-k GIQ) problem, a set of n weighted, geometric objects in R-d is to be preprocessed into a compact data structure so that for any query geometric object, q, and integer k > 0, the k largest-weight objects intersected by q can be reported efficiently. While the top-k problem has been studied extensively for non-geometric problems (e. g., recommender systems), the geometric version has received little attention. This paper gives a general technique to solve any top-k GIQ problem efficiently. The technique relies only on the availability of an efficient solution for the underlying (non-top-k) GIQ problem, which is often the case. Using this, asymptotically efficient solutions are derived for several top-k GIQ problems, including top-k orthogonal and circular range search, point enclosure search, halfspace range search, etc. Implementations of some of these solutions, using practical data structures, show that they are quite efficient in practice. This paper also does a formal investigation of the hardness of the top-k GIQ problem, which reveals interesting connections between the top-k GIQ problem and the underlying (non-top-k) GIQ problem.

关键词： Range search aggregation Top-k query processing and optimization geometric algorithms and data structures

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The Collective Index: A Technique for Efficient processing of Progressive Queries

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COMPUTER JOURNAL 2008年第6期51卷 662-676页

作者： Zhu, Qiang Medjahed, Brahim Sharma, Anshuman Huang, Henry Univ Michigan Dept Comp & Informat Sci Dearborn MI 48128 USA Res & Adv Engn Ford Motor Co Dearborn MI 48121 USA

The emergence of modern data-intensive applications requires sophisticated database techniques for processing advanced types of user queries on massive data. In this paper, we study such a new type of query, called progressive queries. A progressive query is defined as a set of inter-related and incrementally formulated step-queries. A step-query in a progressive query PQ is specified on the fly based on the results of previously-executed step-queries in PQ. Hence, a progressive query cannot be formulated in advance before its execution, which raises challenges for its processing and optimization. We introduce a query model to characterize different types of progressive queries. We then present a new index structure, called the collective index, to efficiently process progressive queries. The collective index technique incrementally evaluates step-queries via dynamically maintained member indexes. Utilizing the special structure of a collective index, the (member) indexes on the input relation(s) of a step-query are efficiently transformed into indexes on the result relation. Algorithms to efficiently process single-input (unary) linear and multiple-input (join) linear progressive queries based on the collective index are presented. Our experiment results show that the proposed collective index technique outperforms the conventional query processing methods in processing progressive queries.

关键词： progressive query query processing and optimization index structure index maintenance algorithm performance

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Exploiting early sorting and early partitioning for decision support query processing

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VLDB JOURNAL 2000年第3期9卷 190-213页

作者： Claussen, J Kemper, A Kossmann, D Wiesner, C Univ Passau Lehrstuhl Informat D-94030 Passau Germany

Decision support queries typically involve several joins, a grouping with aggregation, and/or sorting of the result tuples. We propose two new classes of query evaluation algorithms that can be used to speed up the execution of such queries. The algorithms are based on (1) early sorting and (2) early partitioning - or a combination of both. The idea is to push the sorting and/or the partitioning to the leaves, i.e., the base relations, of the query evaluation plans (QEPs) and thereby avoid sorting or partitioning large intermediate results generated by the joins. Both early sorting and early partitioning are used in combination with hash-based algorithms for evaluating the joints) and the grouping. To enable early sorting, the sort order generated at an early stage of the QEP is retained through an arbitrary number of so-called order-preserving hashjoins. To make early partitioning applicable to a large class of decision support queries, we generalize the so-called hash teams proposed by Graefe et al. [GBC98]. Hash teams allow to perform several hash-based operations (join and grouping! on the same attribute in one pass without repartitioning intermediate results. Our generalization consists of indirectly partitioning the input data. indirect partitioning means partitioning the input data on an attribute that is not directly needed for the next hash-based operation, and it involves the construction of bitmaps to approximate the partitioning for the attribute that is needed in the next hash-based operation. Our performance experiments show that such QEPs based on early sorting, early partitioning, or both in combination perform significantly better than conventional strategies for many common classes of decision support queries.

关键词： Decision Support Systems query processing and optimization early sorting and partitioning hash joins and hash teams performance evaluation

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MONSOON: Multi-Step optimization and Execution of Queries with Partially Obscured Predicates 20

MONSOON: Multi-Step Optimization and Execution of Queries wi...

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ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Sikdar, Sourav Jermaine, Chris Rice Univ Houston TX 77251 USA

ISBN: (纸本)9781450367356

User-defined functions (UDFs) in modern SQL database systems and Big Data processing systems such as Spark-that offer API bindings in high-level languages such as Python or Scala-make automatic optimization challenging. The foundation of modern database query optimization is the collection of statistics describing the data to be processed, but when a database or Big Data computation is partially obscured by UDFs, good statistics are often unavailable. In this paper, we describe a query optimizer called the Monsoon optimizer. In the presence of UDFs, the Monsoon optimizer may choose to collect statistics on the UDFs, and then run the computation. Or, it may optimize and execute part of the plan, collecting statistics on the result of the partial plan, followed by a re-optimization step, with the process repeated as needed. Monsoon decides how to interleave execution and statistics collection in a principled fashion by formalizing the problem as a Markov decision process.

关键词： query processing and optimization machine learning for data management

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RID: Deduplicating Snapshot Computations 20

RID: Deduplicating Snapshot Computations

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ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Tsikoudis, Nikos Shrira, Liuba Brandeis Univ Waltham MA 02453 USA

ISBN: (纸本)9781450367356

One can audit SQL applications by running SQL programs over sequences of persistent snapshots, but care is needed to avoid wasteful duplicate computation. This paper describes the design, implementation, and performance of RID, the first language-independent optimization framework that eliminates duplicate computations in SQL programs running over low-level snapshots by exploiting snapshot metadata efficiently.

关键词： Temporal Databases query processing and optimization

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