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3rd International Conference on Mobile Ad-Hoc and Sensor Networks

作者： Sun, Jun-Zhao Oulu Univ Dept Elect & Informat Engn Acad Finland FIN-90014 Oulu Finland

ISBN: (纸本)9783540770237

In sensor networks, queries need to be jointly designed, in order to minimize the power consumption and maximize the lifetime. Data reduction techniques can be employed to decrease the size of data to be transferred in the network, and therefore save energy of sensor nodes. This paper presents a novel method for optimizing multi-query in sensor networks. Our approach is, by using packet combination techniques, to reduce the data size of multiple simultaneous queries, so that the energy for data transmission can be saved to the best extent. A delay item is specified together with the query by the application. Then an optimal query plan can be obtained by studying the best time of sending local data to sink that can lead to the minimum cost. Algorithm is described in detail. Performance analysis is performed to validate the effectiveness of the proposed method.

关键词： sensor networks multi-query optimization delay-aware data collection energy-efficient

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SPARQL multi-query optimization 17

SPARQL Multi-Query Optimization

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17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom) / 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE)

作者： Chen, Jiaqi Zhang, Fan Zou, Lei Peking Univ Inst Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781538643877

With RDF knowledge base and SPARQL have been widely used, the performance of query engine gets more attention. In the actual complicated application scenarios, query engine may receive intensive query requests with similar structure in a short time, as usual these queries will be evaluated independently. multi-query optimization evaluation approach can mine feasible common query patterns deeply, choose preferable combination of common query patterns according to the cost model, and reduce the total time consumption by taking advantage of the common query pattern evaluation results. The experiments on LUBM dataset indicate that the total evaluation time of multi-query optimization evaluation approach is shorter than sequential evaluation approach and making the throughput of query engine improve.

关键词： rdf sparql multi-query optimization common query pattern mining and selecting

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Efficient and Provable multi-query optimization 17

Efficient and Provable Multi-Query Optimization

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36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS)

作者： Kathuria, Tarun Sudarshan, S. Microsoft Res Bengaluru Karnataka India Indian Inst Technol Bombay Maharashtra India

ISBN: (纸本)9781450341981

Complex queries for massive data analysis jobs have become increasingly commonplace. Many such queries contain common subexpressions, either within a single query or among multiple queries submitted as a batch. Conventional query optimizers do not exploit these subexpressions and produce sub-optimal plans. The problem of multi-query optimization (MQO) is to generate an optimal combined evaluation plan by computing common subexpressions once and reusing them. Exhaustive algorithms for MQO explore an O(n(n)) search space. Thus, this problem has primarily been tackled using various heuristic algorithms, without providing any theoretical guarantees on the quality of their solution. In this paper, instead of the conventional cost minimization problem, we treat the problem as maximizing a linear transformation of the cost function. We propose a greedy algorithm for this transformed formulation of the problem, which under weak, intuitive assumptions, provides an approximation factor guarantee for this formulation. We go on to show that this factor is optimal, unless P = NP. Another noteworthy point about our algorithm is that it can be easily incorporated into existing transformation-based optimizers. We finally propose optimizations which can be used to improve the efficiency of our algorithm.

关键词： Approximation algorithms hardness of approximation multi-query optimization

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What Happens When Two multi-query optimization Paradigms Combine? A Hybrid Shared Sub-Expression (SSE) and Materialized View Reuse (MVR) Study 27th

What Happens When Two Multi-Query Optimization Paradigms Com...

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27th European Conference on Advances in Databases and Information Systems (ADBIS)

作者： Gurumurthy, Bala Bidarkar, Vasudev Raghavendra Broneske, David Pionteck, Thilo Saake, Gunter Otto Von Guericke Univ Magdeburg Germany German Ctr Higher Educ Res & Sci Studies Hannover Germany

ISBN: (纸本)9783031429132;9783031429149

querying in isolation lacks the potential of reusing results, that ends up wasting computational resources. multi-query optimization (MQO) addresses this challenge by devising a shared execution strategy across queries, with two generally used strategies: batched or cached. These strategies are shown to improve performance, but hardly any study explores the combination of both. In this work we explore such a hybrid MQO, combining batching (Shared Sub-Expression) and caching (Materialized View Reuse) techniques. Our hybrid-MQO system merges batched query results as well as caches the intermediate results, thereby any new query is given a path within the previous plan as well as reusing the results. To study the influence of batching, we vary the factor - derivability which represents the similarity of the results within a query batch. Similarly, we vary the cache sizes to study the influence of caching. Moreover, we also study the role of different database operators in the performance of our hybrid system. The results suggest that, depending on the individual operators, our hybrid method gains a speed-up between 4x to a slowdown of 2x from using MQO techniques in isolation. Furthermore, our results show that workloads with a generously sized cache that contain similar queries benefit from using our hybrid method, with an observed speed-up of 2x over sequential execution in the best case.

关键词： multi-query optimization batched query execution materialized view reuse

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query grouping-based multi-query optimization framework for interactive SQL query engines on Hadoop

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2018年第19期30卷

作者： Chen, Ling Lin, Yan Wang, Jingchang Huang, Heqing Chen, Donghui Wu, Yong Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Zhejiang Peoples R China Alibaba Zhejiang Univ Joint Inst Frontier Technol Hangzhou 310027 Zhejiang Peoples R China Zhejiang Hongcheng Comp Syst Co Ltd Hangzhou 310053 Zhejiang Peoples R China

In the past few years, executing high-concurrency queries with interactive SQL query engines on Hadoop has become an important activity for many organizations. However, these systems do not adopt multi-query optimization (MQO) to accelerate the process. There are two major concerns. Firstly, traditional MQO researches assume that multiple queries have high similarity. However, these systems usually serve a variety of applications. Although queries from the same application have high similarity, queries from different applications may have low similarity, so using traditional MQO will be inefficient and time consuming. Secondly, integrating MQO may lead to lots of system modifications. To integrate MQO into interactive SQL query engines on Hadoop efficiently, a query grouping-based MQO framework is proposed. A lightweight mechanism is used to represent SQL queries, on which a grouping method is exploited to speed up the optimization process. A cost model is integrated to estimate the execution cost of interactive SQL query engines on Hadoop. By using the proposed framework, we modify Impala system to support MQO, and the experimental results on TPC-DS show significant performance improvements.

关键词： grouping method Impala system multi-query optimization

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Coupling Materialized View Selection to multi query optimization: Hyper Graph Approach

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International Journal of Data Warehousing and Mining 2015年第2期11卷 62-84页

作者： Boukorca, Ahcene Bellatreche, Ladjel Senouci, Sid-Ahmed Benali Faget, Zoe Univ Poitiers LIAS ISAE ENSMA Poitiers France Mentors Graph Montbonnot St Martin France

Materialized views are queries whose results are stored and maintained in order to facilitate access to data in their underlying base tables of extremely large databases. Selecting the best materialized views for a given query workload is a hard problem. Studies on view selection have considered sharing common sub expressions and other multi-query optimization techniques. multi-query optimization is a well-studied domain in traditional and advanced databases. It aims at optimizing a workload of queries by finding and reusing common subexpression between queries. Finding the best shared expression is known as a NP-hard problem. The shared expressions usually identified by graph structure have been used to be candidate for materialized views. This shows the strong interdependency between the problems of materialized view selection (PVS) and multi query optimization (PMQO), since the PVS uses the graph structure of the PMQO. Exploring the existing works on PVS considering the interaction between PVS and PMQO figures two main categories of studies: (i) those considering the PMQO as a black box where the output is the graph and (ii) those preparing the graph to guide the materialized view selection process. In this category, the graph generation is based on individual query plans, an approach that does not scale, especially with the explosion of Big Data applications requiring large number of complex queries with high interaction. To ensure a scalable solution, this work proposes a new technique to generate a global processing plan without using individual plans by borrowing techniques used in the electronic design automation (EDA) domain. This paper first presents a rich state of art regarding the PVS and a classification of the most important existing work. Secondly, an analogy between the MQO problem and the EDA domain, in which large circuits are manipulated, is established. Thirdly, it proposes to model the problem with hypergraphs which are massively used to design

关键词： Hypergraph Materialized Views multi-query optimization query Interaction VLSI

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multi-query optimization for Subgraph Isomorphism Search

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PROCEEDINGS OF THE VLDB ENDOWMENT 2016年第3期10卷 121-132页

作者： Ren, Xuguang Wang, Junhu Griffith Univ Sch Informat & Commun Technol Gold Coast Campus Nathan Qld 4111 Australia

Existing work on subgraph isomorphism search mainly focuses on a-query-at-a-time approaches: optimizing and answering each query separately. When multiple queries arrive at the same time, sequential processing is not always the most efficient. In this paper, we study multi-query optimization for subgraph isomorphism search. We first propose a novel method for efficiently detecting useful common sub graphs and a data structure to organize them. Then we propose a heuristic algorithm based on the data structure to compute a query execution order so that cached intermediate results can be effectively utilized. To balance memory usage and the time for cached results retrieval, we present a novel structure for caching the intermediate results. We provide strategies to revise existing single-query subgraph isomorphism algorithms to seamlessly utilize the cached results, which leads to significant performance improvement. Extensive experiments verified the effectiveness of our solution.

关键词： multi-query optimization subgraph isomorphism

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Communication-efficient processing of multiple continuous aggregate queries

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INFORMATION SCIENCES 2014年 284卷 1-17页

作者： Jeon, Joo Hyuk Lee, Ki Yong Kim, Myoung Ho Korea Adv Inst Sci & Technol Taejon 305701 South Korea Sookmyung Womens Univ Seoul 140742 South Korea

Recently, great attention has been paid to distributed stream processing systems (DSPSs), where a number of distributed source nodes generate data streams and queries are continuously executed over those streams. An emerging challenge in large-scale DSPSs is to efficiently process multiple continuous aggregate queries. To process a continuous aggregate query, each source node periodically sends partial aggregates computed over its local stream to the sink node, where the final results are computed. Since sending partial aggregates for each query separately can lead to significant communication overhead, lowering the overall performance and scalability, multiple aggregate queries must be processed collectively, rather than separately. In this paper, we propose an efficient method for collectively processing multiple continuous aggregate queries, which minimizes the total communication costs from source nodes to the sink node. The proposed method finds the smallest set of partial aggregates that each source node needs to send to the sink node to correctly answer all the queries, and thus minimizes the number of message transmissions. Furthermore, since the proposed method operates on-the-fly, it can efficiently handle registration or deregistration of queries at any time. Through theoretical analysis and experiments, we show that the proposed method outperforms the existing ones in terms of communication costs, (C) 2014 Elsevier Inc. All rights reserved.

关键词： Aggregate query multi-query optimization Distributed data stream Sliding window

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Enhancing the Scalability of Non-Spatial Concurrent Queries in Wireless Sensor Networks

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COMPUTER JOURNAL 2014年第12期57卷 1912-1924页

作者： Enigo, V. S. Felix Ramachandran, V. SSN Coll Engn Dept Comp Sci & Engn Chennai Tamil Nadu India Anna Univ Dept Informat Sci & Technol Chennai 600025 Tamil Nadu India

Handling large concurrent queries in an energy-efficient way always has been a critical problem in wireless sensor networks (WSNs) due to its resource-constrained nature. Many sensor applications use non-spatial queries for accessing precise sensor data at specific locations rather than spatial queries that retrieve data over a region. Though performing pre-optimization of queries externally is a common technique followed for sensor query optimization before it is optimized in-network;a detailed cost-based model for optimizing non-spatial queries has not yet been carried out effectively. This paper focuses on optimizing non-spatial queries through a bi-level multi-query optimization scheme that uses an elaborate cost model to compute the execution cost of different query plans. Further, in-network optimization is implemented to reduce the energy consumption due to query result transmissions. Experimental analysis shows that the proposed optimization reduces the overall energy consumption and increases the scalability of non-spatial concurrent queries.

关键词： concurrent queries wireless sensor networks non-spatial queries multi-query optimization in-network

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query Interaction Based Approach for Horizontal Data Partitioning

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INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING 2015年第2期11卷 44-61页

作者： Bellatreche, Ladjel Kerkad, Amira Univ Poitiers LIAS ISAE ENSMA Poitiers France

With the explosion of data, several applications are designed around analytical aspects, with data warehousing technology at the heart of the construction chain. The exploitation of this data warehouse is usually performed by the use of complex queries involving selections, joins and aggregations. These queries bring the following characteristics: (1) their routinely aspects, (2) their large number, and (3) the high operation sharing between queries. This interaction has been largely identified in the context of multi-query optimization, where graph data structures were proposed to capture it. Also during the physical design, the structures have been used to select redundant optimization structures such as materialized views and indexes. Horizontal data partitioning (HDP) is another non-redundant optimization structure that can be selected in the physical design phase. It is a pre-condition for designing extremely large databases in several environments: centralized, distributed, parallel and cloud. It aims to reduce the cost of the above operations. In HDP, the optimization space of potential candidates for partitioning grows exponentially with the problem size making the problem NP-hard. This paper proposes a new approach based on query interactions to select a partitioning schema of a data warehouse in a divide and conquer manner to achieve an improved trade-off between the optimization algorithm's speed and the quality of the solution. The effectiveness of our approach is proven through a validation using the Star Schema Benchmark (100 GB) on Oracle11g.

关键词： Database Horizontal Data Partitioning multi-query optimization optimization Structure query Interaction

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