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Estimating communication costs for distributed XML query processing

IET COMMUNICATIONS 2013年第8期7卷 766-773页

作者： Park, Jong-Hyun Kang, Ji-Hoon Chungnam Natl Univ Dept Comp Sci & Engn Taejon South Korea

In distributed query processing, good estimation algorithms of communication costs are critical for query processing, including distributed XML queries. There are techniques that estimate a communication cost for distributed SQL query processing, and some of techniques are adopted in numerous distributed SQL processors. Therefore adopting the processing techniques for SQL queries for the communication cost-based processing of the distributed XML queries seems natural. Unfortunately, however, the tree-structured XML document is different from the table-shaped relational data. These structural differences make adopting the techniques for SQL queries difficult. This study defines some of the considerations for estimating the communication cost of distributed XML queries, and proposes a method for communication cost-based query processing. The experiments show that the proposed algorithm is reasonable for estimating the communication cost for distributed XML queries.

关键词： distributed processing estimation theory query processing SQL tree data structures XML communication cost-based query processing structural differences table-shaped relational data tree-structured XML document communication cost-based processing processing techniques distributed SQL processors distributed SQL query processing estimation algorithms distributed query processing distributed XML query processing communication cost estimation distributed processing estimation theory query processing SQL tree data structures XML communication cost-based query processing structural differences table-shaped relational data tree-structured XML document communication cost-based processing processing techniques distributed SQL processors distributed SQL query processing estimation algorithms distributed query processing distributed XML query processing communication cost estimation

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Improved Centralized XML query processing Using distributed query Workload

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IEEE ACCESS 2021年 9卷 29127-29142页

作者： Subramaniam, Samini Haw, Su-Cheng Soon, Lay-Ki Multimedia Univ Fac Comp & Informat Cyberjaya 63000 Malaysia Monash Univ Malaysia Sch Informat Technol Bandar Sunway 47500 Malaysia

eXtensible Markup Language (XML) is used widely to transfer data among a wide variety of systems. Due to an increase in query workloads and management of larger datasets, centralized processing is no longer feasible for XML query processing. To address this issue, we propose a technique that improves XML query processing through query workload distribution. An effective distributed XML query processing can be affected by several criteria such as indexing, fragmentation, distribution strategy, and well as the query handling in the distributed servers. However, we believe that an efficient labeling mechanism and an inexpensive centralized query processors or a pruning method at dedicated servers contribute greatly to the overall performance of a distributed query processor. In this paper, we present an effective centralized pruning technique that is adopted into our proposed distributed XML query processing technique to process XML queries robustly. Experimental evaluations showed that the proposed distributed query processor superseded the performance of centralized query processor.

关键词： XML query processing Servers Indexing Vegetation Program processors Merging XML labeling XML indexing XML query processing XML database centralized query processing distributed query processing

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An intelligent query processing for distributed ontologies

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JOURNAL OF SYSTEMS AND SOFTWARE 2010年第1期83卷 85-95页

作者： Lee, Jihyun Park, Jeong-Hoon Park, Myung-Jae Chung, Chin-Wan Min, Jun-Ki Korea Adv Inst Sci & Technol Div Comp Sci Dept Elect Engn & Comp Sci Taejon 305701 South Korea Korea Univ Technol & Educ Sch Internet Media Engn Cheonan 330708 Chungnam South Korea

In this paper, we propose an intelligent distributed query processing method considering the characteristics of a distributed ontology environment. We suggest more general models of the distributed ontology query and the semantic mapping among distributed ontologies compared with the previous works. Our approach rewrites a distributed ontology query into multiple distributed ontology queries using the semantic mapping, and we can obtain the integrated answer through the execution of these queries. Furthermore, we propose a distributed ontology query processing algorithm with several query optimization techniques: pruning rules to remove unnecessary queries, a cost model considering site load balancing and caching, and a heuristic strategy for scheduling plans to be executed at a local site. Finally, experimental results show that our optimization techniques are effective to reduce the response time. (C) 2009 Elsevier Inc. All rights reserved.

关键词： distributed query processing Semantic mapping query optimization distributed ontologies Semantic Web

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Probabilistic nearest neighbor query processing on distributed uncertain data

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distributed AND PARALLEL DATABASES 2016年第2期34卷 259-287页

作者： Amagata, Daichi Sasaki, Yuya Hara, Takahiro Nishio, Shojiro Osaka Univ Grad Sch Informat Sci & Technol Dept Multimedia Engn 1-5 Yamadaoka Suita Osaka 5650871 Japan Nagoya Univ Grad Sch Informat Sci Dept Syst & Social Informat Chikusa Ku Furo Cho Nagoya Aichi 4648601 Japan

A nearest neighbor (NN) query, which returns the most similar object to a user-specified query object, plays an important role in a wide range of applications and hence has received considerable attention. In many such applications, e.g., sensor data collection and location-based services, objects are inherently uncertain. Furthermore, due to the ever increasing generation of massive datasets, the importance of distributed databases, which deal with such data objects, has been growing. One emerging challenge is to efficiently process probabilistic NN queries over distributed uncertain databases. The straightforward approach, that each local site forwards its own database to the central server, is communication-expensive, so we have to minimize communication cost for the NN object retrieval. In this paper, we focus on two important queries, namely top-k probable NN queries and probabilistic star queries, and propose efficient algorithms to process them over distributed uncertain databases. Extensive experiments on both real and synthetic data have demonstrated that our algorithms significantly reduce communication cost.

关键词： Probabilistic nearest neighbor query Uncertain databases distributed query processing

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Constrained Skyline query processing against distributed Data Sites

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2011年第2期23卷 204-217页

作者： Chen, Lijiang Cui, Bin Lu, Hua Peking Univ Key Lab High Confidence Software Technol Minist Educ Beijing 100871 Peoples R China Peking Univ Dept Comp Sci Beijing 100871 Peoples R China Aalborg Univ Dept Comp Sci DK-9220 Aalborg Denmark

The skyline of a multidimensional point set is a subset of interesting points that are not dominated by others. In this paper, we investigate constrained skyline queries in a large-scale unstructured distributed environment, where relevant data are distributed among geographically scattered sites. We first propose a partition algorithm that divides all data sites into incomparable groups such that the skyline computations in all groups can be parallelized without changing the final result. We then develop a novel algorithm framework called PaDSkyline for parallel skyline query processing among partitioned site groups. We also employ intragroup optimization and multifiltering technique to improve the skyline query processes within each group. In particular, multiple (local) skyline points are sent together with the query as filtering points, which help identify unqualified local skyline points early on a data site. In this way, the amount of data to be transmitted via network connections is reduced, and thus, the overall query response time is shortened further. Cost models and heuristics are proposed to guide the selection of a given number of filtering points from a superset. A cost-efficient model is developed to determine how many filtering points to use for a particular data site. The results of an extensive experimental study demonstrate that our proposals are effective and efficient.

关键词： Constrained skyline query filtering point distributed query processing

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An efficient index structure for distributed k-nearest neighbours query processing

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SOFT COMPUTING 2020年第8期24卷 5539-5550页

作者： Yang, Min Ma, Kun Yu, Xiaohui Shandong Univ Sch Comp Sci & Technol Jinan Peoples R China Univ Jinan Sch Informat Sci & Engn Jinan Peoples R China Univ York Sch Informat Technol Toronto ON M3J 1P3 Canada

Many location-based services are supported by the moving k-nearest neighbour (k-NN) query, which continuously returns the k-nearest data objects for a query point. Most of existing approaches to this problem have focused on a centralized setting, which show poor scalability to work around massive-scale and distributed data sets. In this paper, we propose an efficient distributed solution for k-NN query over moving objects to tackle the increasingly large scale of data. This approach includes a new grid-based index called Block Grid Index (BGI), and a distributed k-NN query algorithm based on BGI. There are three advantages of our approach: (1) BGI can be easily constructed and maintained in a distributed setting;(2) the algorithm is able to return the results set in only two iterations. (3) the efficiency of k-NN query is improved. The efficiency of our solution is verified by extensive experiments with millions of nodes.

关键词： k-Nearest neighbour query distributed query processing Moving objects

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Index-based query processing on distributed multidimensional data

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GEOINFORMATICA 2013年第3期17卷 489-519页

作者： Tsatsanifos, George Sacharidis, Dimitris Sellis, Timos Natl Tech Univ Athens Sch Elect & Comp Engn Athens Greece RC Athena Inst Management Informat Syst Athens Greece

This work introduces decentralized query processing techniques based on MIDAS, a novel distributed multidimensional index. In particular, MIDAS implements a distributed k-d tree, where leaves correspond to peers, and internal nodes dictate message routing. MIDAS requires that peers maintain little network information, and features mechanisms that support fault tolerance and load balancing. The proposed algorithms process point and range queries over the multidimensional indexed space in only O(log n) hops in expectance, where n is the network size. For nearest neighbor queries, two processing alternatives are discussed. The first, termed eager processing, has low latency (expected value of O(log n) hops) but may involve a large number of peers. The second, termed iterative processing, has higher latency (expected value of O(log(2) n) hops) but involves far fewer peers. A detailed experimental evaluation demonstrates that our query processing techniques outperform existing methods for settings involving real spatial data as well as in the case of high dimensional synthetic data.

关键词： distributed index distributed query processing Peer-to-peer networks Nearest neighbor query

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SIMULATION OF JOIN query-processing ALGORITHMS FOR A TRUSTED distributed DATABASE-MANAGEMENT SYSTEM

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INFORMATION AND SOFTWARE TECHNOLOGY 1993年第5期35卷 287-299页

作者： RUBINOVITZ, H THURAISINGHAM, B The MITRE Corporation Burlington Road Bedford MA 01730 USA

The paper describes the simulation of join query processing algorithms for a trusted distributed database management system (TDDBMS). In a TDDBMS, users cleared at different security levels access and share a distributed database with data at different sensitivity levels without compromising security. The query processing algorithms should ensure that users only obtain the information classified at or below their security levels. The paper also describes two algorithms for the join operation and discusses the simulation of these algorithms. Some of the results obtained from the simulation with those obtained from a proto-type implementation are then compared, and finally, the authors recommend an algorithm for join query processing in a TDDBMS.

关键词： JOIN query processing ALGORITHMS TRUSTED DATABASE MANAGEMENT SYSTEMS distributed query processing MULTILEVEL DATA DISTRIBUTION

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query-processing IN A FRAGMENTED RELATIONAL distributed SYSTEM - MERMAID

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 1985年第8期11卷 795-810页

作者： YU, CT CHANG, CC TEMPLETON, M BRILL, D LUND, E IOWA STATE UNIV SCI & TECHNOL DEPT COMP SCIAMESIA 50010 SYST DEV CORP MERMAID PROJECTSANTA MONICACA 90406

The query optimizer of the Mermaid system by SDC is described that provides a user with a unified view of multiple preexisting databases that may be stored under different database management systems (DBMS). The algorithm is designed for databases that may contain replicated or fragmented relations and for users who are mainly making interactive, ad hoc queries. Although implementation of the algorithm is a front-end system, not an integrated distributed DBMS, it should also be applicable to a distributed DBMS. Among the primary features of the algorithm are: 1. It employs redundant relations to reduce communication cost and allows parallel processing. 2. Useless semijoins are discarded and worse semijoins are replaced by better ones. 3. An efficient algorithm is provided to process fragments. 4. A copy of each relation referenced by the query is selected so that the set of relations are contained in the minimum number of sites.

关键词： Semijoin Algorithm distributed query processing Dynamic Estimation

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Connectivity-Oriented Property Graph Partitioning for distributed Graph Pattern query processing

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Proceedings of the ACM on Management of Data 2024年第6期2卷 1-26页

作者： Min Shi Peng Peng Xu Zhou Jiayu Liu Guoqing Xiao Kenli Li Hunan University Changsha Hunan China

Graph pattern query is a powerful tool for extracting crucial information from property graphs. With the exponential growth of sizes, property graphs are typically divided into multiple subgraphs (referred to as partitions) and stored across various sites in distributed environments. Existing graph partitioning methods have not been efficiently optimized for pattern queries, resulting in numerous query matches across multiple partitions, called crossing matches. Identifying these matches requires much inter-partition communication, which is the primary performance bottleneck in distributed query processing. To address this issue, this paper introduces a novel connectivity-oriented relationship-disjoint partitioning method, namely RCP (Relationship Connectivity Partitioning), aimed at enhancing the efficiency of graph pattern query processing by reducing crossing matches. By employing each weakly connected component of the subgraph, which is induced by different relationship labels, as a basic unit of partition, RCP ensures that matches for both variable-length path and labeled graph pattern queries are not crossing matches. Here, variable-length path and labeled graph pattern are two common components in graph pattern queries to identify paths meeting specific label constraints and retrieve subgraphs with consistent relationship types, respectively. Moreover, in the query processing phase, we further demonstrate that all graph pattern queries, belonging to these two basic queries or their extensions, can be executed independently under RCP, thereby avoiding crossing matches. In experiments, we implemented two prototype distributed property graph systems based on Neo4j and JanusGraph, which use declarative and functional query language, respectively. Experimental results on billion-scale datasets demonstrate that our approach brings a performance improvement of nearly two orders of magnitude over state-of-the-art partitioning methods.

关键词： distributed query processing graph pattern query property graph partitioning

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