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ACM SIGMOD International Conference on Management of Data

作者： Barthels, Claude Loesing, Simon Alonso, Gustavo Kossmann, Donald Swiss Fed Inst Technol Dept Comp Sci Syst Grp Zurich Switzerland

ISBN: (纸本)9781450327589

Database systems running on a cluster of machines, i.e. rack-scale databases, are a common architecture for many large databases and data appliances. As the data movement across machines is often a significant bottleneck, these systems typically use a low-latency, high-throughput network such as InfiniBand. To achieve the necessary performance, parallel join algorithms must take advantage of the primitives provided by the network to speed up data transfer. In this paper we focus on implementing parallel in-memory joins using Remote Direct Memory Access (RDMA), a communication mechanism to transfer data directly into the memory of a remote machine. The results of this paper are, to our knowledge, the first detailed analysis of parallel hash joins using RDMA. TO capture their behavior independently of the network characteristics, we develop an analytical model and test our implementation on two different types of networks. The experimental results show that the model is accurate and the resulting distributed join exhibits good performance.

关键词： distributed join rack scale databases join processing with rdma distributed query processing

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HRDBMS: A NewSQL Database for Analytics

HRDBMS: A NewSQL Database for Analytics

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IEEE International Conference on Cluster Computing (CLUSTER)

作者： Arnold, Jason Glavic, Boris Raicu, Ioan IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9781467365987

HRDBMS is a novel distributed relational database that uses a hybrid model combining the best of traditional distributed relational databases and Big Data analytics platforms such as Hive. This allows HRDBMS to leverage years worth of research regarding query optimization, while also taking advantage of the scalability of Big Data platforms. The system uses an execution framework that is tailored for relational processing, thus addressing some of the performance challenges of running SQL on top of platforms such as MapReduce and Spark. These include excessive materialization of intermediate results, lack of a global cost based optimization, unnecessary sorting, lack of index support, no statistics, no support for DML and ACID, and excessive communication caused by the rigid communication patterns enforced by these platforms.

关键词： SQL Big Data analytics distributed query processing relational databases

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An Autonomic in-Network query processing for Urban Sensor Networks

An Autonomic in-Network Query Processing for Urban Sensor Ne...

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IEEE Symposium on Computers and Communication

作者： Marcos A. Carrero Rone I. da Silva Aldri L. dos Santos Carmem S. Hara DINF-Universidade Federal do Parana - UFPR-Parana DTECH - Universidade Federal de Sao Joao del-Rei - UFSJ - Minas Gerais

ISBN: (纸本)9781467371957

The sensing of urban environments usually takes into account the deployment of a large number of devices to measure their environmental attributes, such as temperature, pressure, humidity, luminosity and pollution. In such applications, nearby sensors usually produce similar readings due to their spatial and temporal correlation. In the era of big data, management of collected data requires autonomous and scalable Wireless Sensor Network (WSN) structures. In this paper, we propose an in-network data storage model, called AQPM, that provides efficient processing of both spatial and value-based queries. AQPM is autonomous and scalable. That is, it does not rely on any central entity for neither managing data storage on sensor devices nor for processing queries. Scalability is achieved by grouping sensors with similar readings into clusters, while efficient query processing relies on the concept of repositories. Repositories are sensors that store readings of a set of clusters, and are the only ones that have to be contacted for answering queries. AQPM has been implemented on NS2 simulator and experimental results show that it is more effective than existing approaches.

关键词： distributed query processing relational query processing query analysis reading Scalability Sensors Data Storage Wireless sensor networks WAREHOUSE Sensor networks Data Collection Methods

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querying distributed Spatial Datasets with Unknown Regions

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2014年第10期26卷 2534-2547页

作者： Zhu, Qijun Lee, Dik Lun Lee, Wang-Chien Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China Penn State Univ Dept Comp Sci & Engn University Pk PA 16802 USA

This paper studies the problem of querying Bounded Spatial Datasets (BSDs). A BSD contains i) objects with known locations, and ii) unknown regions, each of which bounds an unknown number of objects, within a coverage area. We consider applications where each BSD is hosted on a server or site connected to a communication network and the BSDs overlap in their coverage areas. The challenge is to query the distributed BSDs to retrieve all objects and to minimize the unknown regions which may contain objects satisfying the query, while minimizing the data transmission volume and number of interactions between the query client and the sites. We develop query processing algorithms for two important types of spatial queries, namely, range and k-nearest-neighbor (kNN) queries. We develop the site-based approach and the area-based approach for efficiently processing range and kNN queries on distributed BSDs. They aim to process only a subset of the sites to obtain the full answer for a query. Thus, optimal site selection and the corresponding site querying methods are important problems studied in this paper. In the area-based approach, we prove an optimal division and derive a practical heuristic to partition a query and select the best processing site for each partition, hence achieving even better efficiency than the site-based approach. Simulation results based on three real spatial datasets show that our proposed approaches significantly outperform the baseline that uses a centralized approach in terms of data transmission volume and the number of interactions between the query client and the distributed sites.

关键词： distributed query processing spatial databases incomplete datasets data integration range queries kNN queries cost-efficient query processing

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Supporting quantified queries in distributed databases

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND distributed SYSTEMS 2014年第5期29卷 421-459页

作者： Badia, Antonio Dobbs, Michael Univ Louisville Comp Engn & Comp Sci Dept Louisville KY 40292 USA Amazon Seattle WA 98109 USA

We show that some relational queries, which we call quantified queries are not well supported in distributed environments. We give a formal definition of quantified queries, propose a language in which to express said queries and provide a procedure to compute answers in this new language in the context of distributed databases. The proposed language is made up of high-level, declarative operators (called generalised quantifiers), and therefore it can be used in combination with several distributed frameworks. Our approach is designed to be as general as possible;it assumes horizontally partitioned relations, but nothing else, so no data placement or replication is used. We present an implementation and algorithms for the new language, propose some basic optimisations and give experimental results which show that the new approach is indeed quite efficient and scales well.

关键词： distributed query processing relational query processing generalised quantifiers

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Pronto: A Software-Defined Networking based System for Performance Management of Analytical Queries on distributed Data Stores

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PROCEEDINGS OF THE VLDB ENDOWMENT 2014年第13期7卷 1661-1664页

作者： Xiong, Pengcheng Hacigumus, Hakan NEC Labs Amer Irving TX 75063 USA

Nowadays data analytics applications are accessing more and more data from distributed data stores, creating large amount of data traffic on the network. Therefore, distributed analytic queries are prone to suffer from bad performance in terms of query execution time when they encounter a network resource contention, which is quite common in a shared network. Typical distributed query optimizers do not have a way to solve this problem because historically they have been treating the network underneath as a black box: they are unable to monitor it, let alone to control it. However, we are entering a new era of software-defined networking (SDN), which provides visibility into and control of the network's state for the applications including distributed database systems. In this demonstration, we present a system, called Pronto that leverages the SDN capabilities for a distributed query processor to achieve performance improvement and differentiation for analytical queries. The system is the real implementation of our recently developed methods on commercial SDN products. The demonstration shows the shortcomings of a distributed query optimizer, which treats the underlying network as a black box, and the advantages of the SDN-based approach by allowing the users to selectively explore various relevant and interesting settings in a distributed query processing environment.

关键词： Information management Application programs distributed database systems query languages query processing Software defined networking Analytical queries distributed data stores distributed query processing Performance improvements Performance management query execution time Software defined networking (SDN) Underlying networks

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Partout: A distributed Engine for Efficient RDF processing 23

Partout: A Distributed Engine for Efficient RDF Processing

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23rd International Conference on World Wide Web (WWW)

作者： Galarraga, Luis Hose, Katja Schenkel, Ralf Telecom ParisTech Paris France Aalborg Univ Aalborg Denmark Univ Passau Passau Germany

ISBN: (纸本)9781450327459

The increasing interest in Semantic Web technologies has led not only to a rapid growth of semantic data on the Web but also to an increasing number of backend applications relying on efficient query processing. Confronted with such a trend, existing centralized state-of-the-art systems for storing RDF and processing SPARQL queries are no longer sufficient. In this paper, we introduce PARTOUT, a distributed engine for fast RDF processing in a cluster of machines. We propose an effective approach for fragmenting RDF data sets based on a query log and allocating the fragments to hosts in a cluster of machines. Furthermore, PARTOUT's query optimizer produces efficient query execution plans for ad-hoc SPARQL queries.

关键词： Semantic Web distributed Systems RDF Data Partitioning distributed query processing

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Graph Search for Cloud Network Management

Graph Search for Cloud Network Management

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14th IEEE/IFIP Network Operations and Management Symposium (NOMS)

作者： Uddin, Misbah Stadler, Rolf Miyazawa, Masanori Hayashi, Michiaki KTH Royal Inst Technol ACCESS Linnaeus Ctr Stockholm Sweden KDDI R&D Labs Inc Fujimino City Saitama Japan

ISBN: (纸本)9781479909131

A large part of operational and configuration information in networks and clouds has graph structure, e.g., virtual network topologies, IP flows, communication links of distributed cloud applications. The final objective of this work is to develop a scalable management system that allows real-time management applications, such as network analytics and anomaly detection applications, to search for graph-structured operational information. The paper contains a step towards this objective. The key challenge is to devise an efficient and scalable search process on data that is volatile and distributed across the network infrastructure. Approaches that are currently pursued for distributed graph databases are not applicable in this context. This paper presents design options and possible solutions for engineering a scalable graph search system that supports management applications. It includes a simple information model based on property graphs and a search query language based on keyword search. The architecture of the system centers around a distributed search plane that performs query processing using a network of search nodes. Finally, the paper outlines the design of a search node, which contains a local database that maintains graph partitions.

关键词： graph search network search distributed query processing distributed management in-network management

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distributed query processing in flash-based sensor networks

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中国高等学校学术文摘·计算机科学 2008年第3期2卷 248-256页

作者： Jianliang XU Xueyan TANG Wang-Chien LEE Department of Computer Science and Engineering Hong Kong Baptist UniversityHong KongChina School of Computer Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Computer Science and Engineering Pennsylvania State UniversityPhiladelphiaPA 16802USA

Wireless sensor networks are used in a large array of applications to capture,collect,and analyze physical environmental *** existing sensor systems instruct sensor nodes to report their measurements to central repositories outside the network,which is expensive in energy *** technological advances in flash memory have given rise to the development of storagecentric sensor networks,where sensor nodes are equipped with high-capacity flash memory storage such that sensor data can b.e stored and managed inside the network to reduce expensive *** novel architecture calls for new data management techniques to fully exploit distributed in-network data *** paper describes some of our research on distributed query processing in such flash-based sensor *** particular interests are the issues that arise in the design of storage management and indexing structures combining sensor system workload and read/write/erase characteristics of flash memory.

关键词： distributed query processing wireless sensor network flash storage indexing

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Federating queries in SPARQL 1.1: Syntax, semantics and evaluation

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JOURNAL OF WEB SEMANTICS 2013年第1期18卷 1-17页

作者： Buil-Aranda, Carlos Arenas, Marcelo Corcho, Oscar Polleres, Axel UPM Fac Informat Ontol Engn Grp Madrid Spain PUC Dept Comp Sci Santiago Chile Siemens AG Osterreich A-1210 Vienna Austria

Given the sustained growth that we are experiencing in the number of SPARQL endpoints available, the need to be able to send federated SPARQL queries across these has also grown. To address this use case, the W3C SPARQL working group is defining a federation extension for SPARQL 1.1 which allows for combining graph patterns that can be evaluated over several endpoints within a single query. In this paper, we describe the syntax of that extension and formalize its semantics. Additionally, we describe how a query evaluation system can be implemented for that federation extension, describing some static optimization techniques and reusing a query engine used for data-intensive science, so as to deal with large amounts of intermediate and final results. Finally we carry out a series of experiments that show that our optimizations speed up the federated query evaluation process. (c) 2012 Elsevier B.V. All rights reserved.

关键词： SPARQL 1.1 distributed data management distributed query processing RDF

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