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9th IEEE/ACM International Symposium on Cluster Computing and the Grid

作者： Hentschel, Marco Li, Maozhen Ponraj, Mahesh Qi, Man Brunel Univ Sch Engn & Design Uxbridge UB8 3PH Middx England Canterbury Christ Church Univ Dept Comp Canterbury CT1 1QU Kent England

ISBN: (纸本)9781424439355

P2Pnetworks facilitate people belonging to a community to share resources of interest. However, discovering resources in a large scale P2P network poses a number of challenges. Although distributed Hash Table (DHT) structured P2P networks have shown enhanced scalability in routing messages, they only support key based exact matches. This paper presents DIndex, a distributed indexing component that can be used in P2P networks in support Of range queries. DIndex introduces the concept of search dimensions for partitioning a search space, and it organizes peer nodes in a three-layered structure. Experimental results show that, for aP2P network with N number of peers, the average number of hops per message is less than log(N).

关键词： P2P networks resource discovery distributed indexing DHT search dimension

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A framework of distributed indexing and data dissemination in large scale wireless sensor networks

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OPTIMIZATION LETTERS 2010年第3期4卷 335-345页

作者： Wu, Yiwei Gao, Xiaofeng Li, Yingshu Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Univ Texas Dallas Dept Comp Sci Dallas TX 75230 USA

Many data dissemination techniques have been proposed for wireless sensor networks (WSNs) to facilitate data dissemination and query processing. However, these techniques may not work well in a large scale sensor network where a huge amount of sensing data is generated. In this paper, we propose an integrated distributed connected dominating set based indexing (CBI) data dissemination scheme to support scalable handling of large amount of sensing data in large scale WSNs. Our CBI can minimize the use of limited network and computational resources while providing timely responses to queries. Moreover, our data dissemination framework ensures scalability and load balance as well as adaptivity in the presence of dynamic changes. Analysis and simulations are conducted to evaluate the performance of our CBI scheme. The results show that the CBI scheme outperforms the external storage-based scheme, local storage-based scheme and the data-centric storage-based scheme in overall performance.

关键词： distributed indexing Data dissemination Connected dominating sets Wireless sensor networks

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Scalable distributed indexing and query processing over Linked Data

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JOURNAL OF WEB SEMANTICS 2012年 10卷 3-32页

作者： Karnstedt, Marcel Sattler, Kai-Uwe Hauswirth, Manfred Natl Univ Ireland DERI Galway Ireland Ilmenau Univ Technol Fac Comp Sci & Automat Ilmenau Germany

Linked Data is becoming the core part of modern Web applications and thus efficient access to structured information expressed in RDF gains paramount importance. A number of efficient local RDF stores exist already, while distributed indexing and distributed query processing over Linked Data with similar efficiency and data management features as known from traditional database and data integration systems are only starting to develop. distributed approaches will necessarily co-exist with centralized schemes, as data will be owned by different stakeholders who may not want to provide their complete data sets to a central place. Additionally, central/integrated storage may be prohibited for organizational or legal reasons in certain areas. To support decentralized schemes, only a few attempts in this direction exist so far, but they are limited in terms of capabilities and the degree of distribution vs. efficiency, query expressivity, and scalability. To remedy this situation, the approach and proof-of-concept prototype presented in this paper provides a solution for these open challenges. As we argue for widely distributed systems as a possible answer to scalability issues, we first identify and discuss the main challenges and based on this analysis, we propose an approach for efficient and scalable query processing over distributed Linked Data sources, taking into account the latest advances in database technology. Our system is based on a layered architecture that makes use of the advantages of decentralized indexing and query processing approaches, which have been researched and matured over the last decade. Our approach is based on a logical algebra for queries over RDF data and a related physical query algebra to enable optimization, both on the logical and physical layers in query processing. The introduced operators and strategies for processing complex query plans make excessive use of parallelism and other optimization paradigms of distributed query process

关键词： Linked Data Query processing distributed storage distributed indexing Structured queries Decentralized data management

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Standards in the CHIC-Pilot distributed indexing architecture

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COMPUTER NETWORKS AND ISDN SYSTEMS 1998年第16-18期30卷 1571-1578页

作者： Valkenburg, P Beckett, D Hamilton, M Wilkinson, S TERENA Secretariat NL-1017 AW Amsterdam Netherlands

The CHIC-Pilot project is a TERENA coordinated effort investigating the feasibility of setting up a large-scale distributed indexing service for searching Web-based research information. It brings together existing search services throughout Europe, integrating them into a coherent architecture, This paper describes both formal and de-facto standards used for various components in the CHIC-Pilot architecture, that support functionality ranging from a uniform search interface and query routing to robot-based gathering of resource descriptions and the handling of metadata. We also discuss techniques for interfacing components of search services using different standards and protocols to provide a unified service to the user. (C) 1998 Elsevier Science B.V. All rights reserved.

关键词： distributed indexing whois++ centroids SOIF Dublin Core

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In-Memory distributed indexing for Large-Scale Media Data Retrieval 19

In-Memory Distributed Indexing for Large-Scale Media Data Re...

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19th IEEE International Symposium on Multimedia (ISM)

作者： Ma, Yinmiao Liu, Danlu Scott, Grant Uhlmann, Jeffrey Shyu, Chi-Ren Univ Missouri Elect Engn & Comp Sci Dept Columbia MO 65211 USA Univ Missouri Inst Informat Columbia MO 65211 USA Asia Univ Bioinformat & Biomed Engn Taichung Taiwan

ISBN: (纸本)9781538629376

Data retrieval serves a critical role in the development of multimedia applications. However, due to the exponential growth of multimedia data, high-speed and efficient indexing is becoming more and more difficult than ever. In this paper, we propose a novel approach to speed up the retrieval process by adopting a distributed computing paradigm through the Apache Spark framework Utilizing search trees in a Big Data ecosystem leads to fast and cost-effective media database retrievals by caching indexing structures into memory and aggregating ranked results with flexibilities for users to specify the importance of search cues. We conducted computational experiments on large-scaled vector files for remote sensing image database and synthesized pollen image database to demonstrate the effectiveness and scalability of our system with reasonably high accuracy.

关键词： In-Memory Computing Big Data distributed indexing Image Database Retrieval

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Analyzing design choices for distributed multidimensional indexing

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JOURNAL OF SUPERCOMPUTING 2012年第3期59卷 1552-1576页

作者： Nam, Beomseok Sussman, Alan Ulsan Natl Inst Sci & Technol Sch Elect & Comp Engn Ulsan 689798 South Korea Univ Maryland Dept Comp Sci College Pk MD 20742 USA

Scientific datasets are often stored on distributed archival storage systems, because geographically distributed sensor devices store the datasets in their local machines and also because the size of scientific datasets demands large amount of disk space. Multidimensional indexing techniques have been shown to greatly improve range query performance into large scientific datasets. In this paper, we discuss several ways of distributing a multidimensional index in order to speed up access to large distributed scientific datasets. This paper compares the designs, challenges, and problems for distributed multidimensional indexing schemes, and provides a comprehensive performance study of distributed indexing to provide guidelines to choose a distributed multidimensional index for a specific data analysis application.

关键词： Multidimensional indexing distributed indexing Decentralized indexing Data intensive computing

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Towards Cloud distributed Image indexing by Sparse Hashing 19

Towards Cloud Distributed Image Indexing by Sparse Hashing

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ACM International Conference on Multimedia Retrieval (ICMR)

作者： Mourao, Andre Magalhaes, Joao Univ Nova Lisboa NOVA LINCS Lisbon Portugal

ISBN: (纸本)9781450367653

Distributing multimedia indexes to multiple nodes enables search over very large datasets (i.e., over one billion images and videos), but comes with a set of challenges: how to distribute documents and queries efectively across nodes to support concurrent querying? and how to deal with the increased potential for lack of response from nodes (e.g., node fail-stops or dropping of network packages)? An index where partitions are based on the distribution of feature vectors in the original space can improve redundancy and increase eiciency: nearest neighbors are only present on a small, set number of partitions, reducing the number of nodes to inspect for each query. This paper describes how sparse hashes can help ind this balance and create better distribution policies for high-dimensional feature vectors. Inspired by existing literature on distributed text and media indexes, our proposal distributes and balances documents and queries to a subset of the nodes, according to their orthogonal similarities. We performed exhaustive benchmarks of our approach on a commercial cloud service. Experiments on a one billion vector dataset show that our approach has a low partitioning overhead (3 to 5 ms per query), achieves balanced document and query distribution (the variation in document and query distribution across nodes is smaller than 1% and 10%, respectively), handles concurrent queries efectively and degrades gracefully with node failures (less than 2% of precision loss per node down).

关键词： distributed indexing redundant partitioning load balancing

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A distributed In-network indexing Mechanism for the Internet of Things 3

A Distributed In-network Indexing Mechanism for the Internet...

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IEEE 3rd World Forum on Internet of Things (WF-IoT)

作者： Fathy, Yasmin Barnaghi, Payam Enshaeifar, Shirin Tafazolli, Rahim Univ Surrey Elect Engn Dept ICS Guildford GU2 7XH Surrey England

ISBN: (纸本)9781509041305

The current Web and data indexing and search mechanisms are mainly tailored to process text-based data and are limited in addressing the intrinsic characteristics of distributed, large-scale and dynamic Internet of Things (IoT) data networks. The IoT demands novel indexing solutions for large-scale data to create an ecosystem of system;however, IoT data are often numerical, multi-modal and heterogeneous. We propose a distributed and adaptable mechanism that allows indexing and discovery of real-world data in IoT networks. Comparing to the state-of-the-art approaches, our model does not require any prior knowledge about the data or their distributions. We address the problem of distributed, efficient indexing and discovery for voluminous IoT data by applying an unsupervised machine learning algorithm. The proposed solution aggregates and distributes the indexes in hierarchical networks. We have evaluated our distributed solution on a large-scale dataset, and the results show that our proposed indexing scheme is able to efficiently index and enable discovery of the IoT data with 71% to 92% better response time than a centralised approach.

关键词： Internet of Things (IoT) Wireless Sensor Networks (WSN) distributed indexing real-world data

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A distributed in-network indexing mechanism for the Internet of Things

A distributed in-network indexing mechanism for the Internet...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Yasmin Fathy Payam Barnaghi Shirin Enshaeifar Rahim Tafazolli Electronic Engineering Department University of Surrey Guildford Surrey United Kingdom

ISBN: (纸本)9781509041312

The current Web and data indexing and search mechanisms are mainly tailored to process text-based data and are limited in addressing the intrinsic characteristics of distributed, large-scale and dynamic Internet of Things (IoT) data networks. The IoT demands novel indexing solutions for large-scale data to create an ecosystem of system; however, IoT data are often numerical, multi-modal and heterogeneous. We propose a distributed and adaptable mechanism that allows indexing and discovery of real-world data in IoT networks. Comparing to the state-of-the-art approaches, our model does not require any prior knowledge about the data or their distributions. We address the problem of distributed, efficient indexing and discovery for voluminous IoT data by applying an unsupervised machine learning algorithm. The proposed solution aggregates and distributes the indexes in hierarchical networks. We have evaluated our distributed solution on a large-scale dataset, and the results show that our proposed indexing scheme is able to efficiently index and enable discovery of the IoT data with 71% to 92% better response time than a centralised approach.

关键词： real-world data Internet of Things (IoT) Wireless Sensor Networks (WSN) distributed indexing

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distributed high dimensional indexing for k-NN search

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JOURNAL OF SUPERCOMPUTING 2012年第3期62卷 1362-1384页

作者： Choi, Hyun-Hwa Lee, Mi-Young Lee, Kyu-Chul Chungnam Natl Univ Taejon South Korea Elect & Telecommun Res Inst Taejon 305606 South Korea

Although conventional index structures provide various nearest-neighbor search algorithms for high-dimensional data, there are additional requirements to increase search performances, as well as to support index scalability for large-scale datasets. To support these requirements, we propose a distributed high-dimensional index structure based on cluster systems, called a distributed Vector Approximation-tree (DVA-tree), which is a two-level structure consisting of a hybrid spill-tree and Vector Approximation files (VA-files). We also describe the algorithms used for constructing the DVA-tree over multiple machines and performing distributed k-nearest neighbors (NN) searches. To evaluate performances of the DVA-tree, we conduct an experimental study using both real and synthetic datasets. The results show that our proposed method has significant performance advantages over existing index structures on different kinds of dataset.

关键词： High-dimensional indexing distributed indexing Approximate k-NN query Cluster system

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