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检索条件"主题词=distributed databases"

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Big Data Analysis Using Apache Hadoop

Big Data Analysis Using Apache Hadoop

引用

International Conference on IT Convergence and Security (ICITCS)

作者： Shankar Ganesh Manikandan Siddarth Ravi Department of Information Technology Dhanalakshmi College of Engineering Tambaram Chennai India

We live in on-demand, on-command Digital universe with data prolifering by Institutions, Individuals and Machines at a very high rate. This data is categories as "Big Data" due to its sheer Volume, Variety and Velocity. Most of this data is unstructured, quasi structured or semi structured and it is heterogeneous in nature. The volume and the heterogeneity of data with the speed it is generated, makes it difficult for the present computing infrastructure to manage Big Data. Traditional data management, warehousing and analysis systems fall short of tools to analyze this data. Due to its specific nature of Big Data, it is stored in distributed file system architectures. Hadoop and HDFS by Apache is widely used for storing and managing Big Data. Analyzing Big Data is a challenging task as it involves large distributed file systems which should be fault tolerant, flexible and scalable. Map Reduce is widely been used for the efficient analysis of Big Data. Traditional DBMS techniques like Joins and Indexing and other techniques like graph search is used for classification and clustering of Big Data. These techniques are being adopted to be used in Map Reduce. In this paper we suggest various methods for catering to the problems in hand through Map Reduce framework over Hadoop distributed File System (HDFS). Map Reduce is a Minimization technique which makes use of file indexing with mapping, sorting, shuffling and finally reducing. Map Reduce techniques have been studied in this paper which is implemented for Big Data analysis using HDFS.

关键词： Big data Program processors Computer architecture File systems Fault tolerance Fault tolerant systems distributed databases

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SQMD: Architecture for Scalable, distributed Database System Built on Virtual Private Servers

SQMD: Architecture for Scalable, Distributed Database System...

引用

IEEE International Conference on e-Science and Grid Computing

作者： Kangseok Kim Marlon E. Pierce Rajarshi Guha Community Grids Laboratory Indiana University Bloomington IN USA School of Informatics Indiana University Bloomington IN USA

Many scientific fields routinely generate huge datasets. In many cases, these datasets are not static but rapidly grow in size. Handling these types of datasets, as well as allowing sophisticated queries necessitates scalable distributed database systems, in which scientists are efficiently able to search the datasets. In this paper we present the architecture, implementation and performance analysis of a scalable, distributed database system built on software based virtualization environments. The system architecture makes use of a software partitioning of the database based on data clustering, SQMD (single query multiple database) mechanism, a Web service interface, and virtualization software technologies. The system allows uniform access to concurrently distributed databases, using the SQMD mechanism based on the publish/subscribe paradigm. We highlight the scalability of our architecture by applying it to a database of 17 million chemical structures. In addition to simple identifier based retrieval, we will present performance results for shape similarity queries, which is extremely, time intensive with traditional architectures.

关键词： Database systems Computer architecture Performance analysis Software performance Software systems Service oriented architecture Web services Chemical technology distributed databases Scalability

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distributed Uncertain Data Mining for Frequent Patterns Satisfying Anti-monotonic Constraints

Distributed Uncertain Data Mining for Frequent Patterns Sati...

引用

International Conference on Advanced Information Networking and Applications Workshops (AINAW)

作者： Carson Kai-Sang Leung Richard Kyle MacKinnon Fan Jiang Department of Computer Science University of Manitoba Winnipeg MB Canada

High volumes of uncertain data can be generated in distributed environments in many real-life biological, medical and life science applications. As an important data mining task, frequent pattern mining helps discover frequently co-occurring items, objects, or events from these distributed databases. However, users may be interested in only some small portions of all the frequent patterns that can be mined from these databases. In this paper, we propose an intelligent computing system that (i) allows users to express their interests via the use of user-specified constraints and (ii)effectively exploits anti-monotonic properties of user-specified constraints and efficiently discovers frequent patterns satisfying these constraints from the distributed databases containing uncertain data.

关键词： Data mining distributed databases Runtime Sensors Computer aided manufacturing Computer science

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Co-Located vs distributed vs Semi-distributed MIMO: Measurement-Based Evaluation

Co-Located vs Distributed vs Semi-Distributed MIMO: Measurem...

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Asilomar Conference on Signals, Systems & Computers

作者： Thomas Choi Peng Luo Akshay Ramesh Andreas F. Molisch University of Southern California

With the growing interest in cell-free massive multiple-input multiple-output (MIMO) systems, the benefits of single-antenna access points (APs) versus multi-antenna APs must be analyzed in order to optimize deployment. In this paper, we compare various antenna system topologies based on achievable downlink spectral efficiency, using both measured and synthetic channel data in an indoor environment. We assume multi-user scenarios, analyzing both conjugate beamforming (or maximum-ratio transmission (MRT)) and zero-forcing (ZF) precoding methods. The results show that the semi-distributed multi-antenna APs can reduce the number of APs, and still achieve the comparable achievable rates as the fully-distributed single-antenna APs with the same total number of antennas.

关键词： Antenna measurements Computers Spectral efficiency Precoding distributed databases Massive MIMO Downlink

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A Service to Support Pragmatic Interoperability in IoT Ecosystems

A Service to Support Pragmatic Interoperability in IoT Ecosy...

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International Conference on Computer Supported Cooperative Work in Design

作者： Matheus H.S. Muniz Jose Maria N. David Regina Braga Fernanda Campos Victor Ströele Federal University of Juiz de Fora MG Brazil

The Internet of Things (IoT) is a technological paradigm that aims to connect millions of networked devices to provide more complex functionality. However, the heterogeneity of application/device communication standards precludes support for interoperability, which impacts developer collaboration. There are many works that propose solutions to support syntactic and semantic interoperability in IoT context. This paper aims to propose a service capable of supporting pragmatic IoT interoperability with the goal of enriching developer collaboration. This being possible through the use of inferences and similarity calculations about information provided by the developers.

关键词： Performance evaluation Ecosystems Semantics Collaboration distributed databases Data visualization Syntactics

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The GEO-Cloud Experiment: Global Earth Observation System Computed in Cloud

The GEO-Cloud Experiment: Global Earth Observation System Co...

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International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS)

作者： Jonathan Becedas Elecnor Deimos Satellite Systems Francia 9 Pol. Industrial La Nava

Earth Observation (EO) is considered a key element in the European Research Roadmap and an opportunity market for the next years. However, this field presents some critical challenges to cover the current demand of services: i) there is massive and large-sized data from Earth Observation recordings, ii) On demand storage, processing and distribution of geo-information generated with the recorded data are required. Conventional infrastructures have the risks of over/under size the infrastructure when big data is used, they are not flexible to cover sudden changes in the demand of services and the access to the information presents large latencies. These aspects limit the use of EO technology for real time use. The use of cloud computing technology can overcome the previously defined limitations. The GEO-Cloud experiment emerged to find viable solutions to provide highly demanding EO services by using future internet technologies. It is a close to reality experiment, part of the FP7 Fed4FIRE project. GEO-Cloud consists of the design, implementation and testing in cloud a complete EO system, from the acquisition of geo-data with a constellation of satellites to its on demand distribution to end users with remote access. This paper presents the GEO-Cloud experiment design, architecture and foreseen research activity.

关键词： Satellites Cloud computing Earth Europe Real-time systems distributed databases Monitoring

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REAPS: Quasi-active Fault Tolerance for Big Data Publish-Subscribe Systems

REAPS: Quasi-active Fault Tolerance for Big Data Publish-Sub...

引用

IEEE International Conference on Big Data

作者： Hang Nguyen MYS Uddin Nalini Venkatasubramanian UC Irvine USA UM Kansas City USA

ISBN: (纸本)9781665445993

In this paper, we address the challenges in supporting reliability and scalability in societal-scale notification systems that aim to reach large populations with customized alerts. We explore fault tolerance (FT) techniques in the context of Big Data Publish-Subscribe systems (BDPS), a scalable hierarchical architecture, that meshes big-data platforms (to store and operate on large volumes of data) with a distributed pub/sub broker network (to manage and communicate with a large number of end subscribers). The role of brokers in this architecture is critical since they serve to mediate interactions between subscribers and the backend big data system. We propose the REAPS (REliable Active Publish Subscribe) framework that can handle different classes of broker failures including randomized failures and geographically-correlated failures (as in a natural disaster). REAPS implements a low overhead fault tolerance service using a primary-backup approach; key features include the ability to exploit subscription similarity among brokers and techniques for quasi-active state replication to support fast recovery and delivery guarantees of notification services. We implement REAPS and conduct measurement studies on a prototype BDPS platform using real world usecases. We further evaluate REAPS under various failure scenarios to explore the scalability and performance of our proposed FT mechanisms via simulation studies.

关键词： Fault tolerance Scalability Conferences Fault tolerant systems Sociology Prototypes distributed databases

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Secure Coded distributed Computing

Secure Coded Distributed Computing

引用

IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Shanuja Sasi Onur Günlü Information Theory and Security Laboratory (ITSL) Linköping University Sweden

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

We consider two critical aspects of security in the distributed computing (DC) model: secure data shuffling and secure coded computing. It is imperative that any external entity overhearing the transmissions does not gain any information about the intermediate values (IVs) exchanged during the shuffling phase of the DC model. Our approach ensures IV confidentiality during data shuffling. Moreover, each node in the system must be able to recover the IVs necessary for computing its output functions but must also remain oblivious to the IVs associated with output functions not assigned to it. We design secure DC methods and establish achievable limits on the tradeoffs between the communication and computation loads to contribute to the advancement of secure data processing in distributed systems.

关键词： Wireless communication Computational modeling Design methodology Conferences distributed databases Signal processing Data processing Data models Security

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Sociometric Methods for Relevancy Analysis of Long Tail Science Data

Sociometric Methods for Relevancy Analysis of Long Tail Scie...

引用

IEEE International Conference on Social Computing (SocialCom)

作者： Arcot Rajasekar Sharlini Sankaran Howard Lander Tom Carsey Jonathan Crabtree Hye-Chung Kum Merce Crosas Gary King Justin Zhan The University of North Carolina Chapel Hill Chapel Hill North Carolina USA Harvard University Cambridge Massachusetts USA Texas A & M University College Station Texas USA North Carolina A & T State University Greensboro North Carolina USA

ISBN: (纸本)9781479915194

As the push towards electronic storage, publication, curation, and discoverability of research data collected in multiple research domains has grown, so too have the massive numbers of small to medium datasets that are highly distributed and not easily discoverable - a region of data that is sometimes referred to as the long tail of science. The rapidly increasing, sheer volume of these long tail data present one aspect of the Big Data problem: how does one more easily access, discover, use, and reuse long tail data to lead to new multidisciplinary collaborative research and scientific advancement? In this paper, we describe Data Bridge, a new e-science collaboration environment that will realize the potential of long tail data by implementing algorithms and tools to more easily enable data discoverability and reuse. Data Bridge will define different types of semantic bridges that link diverse datasets by applying a set of sociometric network analysis (SNA) and relevance algorithms. We will measure relevancy by examining different ways datasets can be related to each other: data to data, user to data, and method to data connections. Through analysis of metadata and ontology, by pattern analysis and feature extraction, through usage tools and models, and via human connections, Data Bridge will create an environment for long tail data that is greater than the sum of its parts. In the project's initial phase, we will test and validate the new tools with real-world data contained in the Data verse Network, the largest social science data repository. In this short paper, we discuss the background and vision for the Data Bridge project, and present an introduction to the proposed SNA algorithms and analytical tools that are relevant for discoverability of long tail science data.

关键词： distributed databases Collaboration Educational institutions Data models Communities Algorithm design and analysis Electrical resistance measurement

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A Contemporary Modular Design For Computing Science Instructional Laboratories: The Simon Fraser University Experience

A Contemporary Modular Design For Computing Science Instruct...

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EdCompCon Proceedings

作者： N. Cercone L. Hafer C. Murchison Department of Computing Science Simon Fraser University Burnaby BC Canada

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