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Optimizing Database Load and Extract for Big Data Era

Optimizing Database Load and Extract for Big Data Era

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19th international conference on Database Systems for Advanced applications (DASFAA)

作者： Sridhar, K. T. Sakkeer, M. A. XtremeData Technol Bangalore Karnataka India

ISBN: (纸本)9783319058139;9783319058122

With growing and pervasive interest in Big Data, SQL relational databases need to compete with data management by Hadoop, NoSQL and NoDB. Database research has mainly focused on result generation by query processing. But SQL databases require data in-place before queries may be processed. The process of DB loading has been a bottleneck leading to external ETL/ELT techniques for loading large data sets. This paper focuses on DB engine level techniques for optimizing both data loads and extracts in an MPP, shared-nothing SQL database, dbX, available on in-house commodity hardware and cloud systems. The agile, data loading of dbX exploits parallelism at multiple levels to achieve TBs of data load per hour making it suitable for cloud and continuous actionable knowledge applications. Implementation techniques at DB engine level, extensions to load/extract syntax and performance results are presented. Load optimization techniques help to speed up data extract to flat files and CTAS type SQL queries too. We show linear scale up with cluster scale out for load/extract in public cloud and commodity hardware systems without recourse to database tuning or use of expensive database appliances.

关键词： parallel DBMS bulk load/extract cloud big data dbX

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Bandwidth Modeling in Large distributed Systems for Big Data applications

Bandwidth Modeling in Large Distributed Systems for Big Data...

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IEEE international conference on parallel and distributed Computing, applications and Technologies (PDCAT)

作者： Bahman Javadi Boyu Zhang Michela Taufer School of Computing Engineering and Mathematics University of Western Sydney Australia Dept. of Computer and Information Sciences University of Delaware USA

The emergence of Big Data applications provides new challenges in data management such as processing and movement of masses of data. Volunteer computing has proven itself as a distributed paradigm that can fully support Big Data generation. This paradigm uses a large number of heterogeneous and unreliable Internet-connected hosts to provide Peta-scale computing power for scientific projects. With the increase in data size and number of devices that can potentially join a volunteer computing project, the host bandwidth can become a main hindrance to the analysis of the data generated by these projects, especially if the analysis is a concurrent approach based on either in-situ or in-transit processing. In this paper, we propose a bandwidth model for volunteer computing projects based on the real trace data taken from the Docking@Home project with more than 280,000 hosts over a 5-year period. We validate the proposed statistical model using model-based and simulation-based techniques. Our modeling provides us with valuable insights on the concurrent integration of data generation with in-situ and in-transit analysis in the volunteer computing paradigm.

关键词： Bandwidth Computational modeling Servers Computer applications distributed processing Data models Predictive models

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Robust and skew-resistant parallel joins in shared-nothing systems 14

Robust and skew-resistant parallel joins in shared-nothing s...

引用

23rd ACM international conference on Information and Knowledge Management, CIKM 2014

作者： Cheng, Long Kotoulas, Spyros Ward, Tomas E. Theodoropoulos, Georgios National University of Ireland Maynooth Ireland IBM Research Ireland Technische Universität Dresden Germany Durham University United Kingdom

ISBN: (纸本)9781450325981

The performance of joins in parallel database management systems is critical for data intensive operations such as querying. Since data skew is common in many applications, poorly engineered join operations result in load imbalance and performance bottlenecks. State-of-the-art methods designed to handle this problem offer significant improvements over naive implementations. However, performance could be further improved by removing the dependency on global skew knowledge and broadcasting. In this paper, we propose PRPQ (partial redistribution & partial query), an efficient and robust join algorithm for processing large-scale joins over distributed systems. We present the detailed implementation and a quantitative evaluation of our method. The experimental results demonstrate that the proposed PRPQ algorithm is indeed robust and scalable under a wide range of skew conditions. Specifically, compared to the state-of-art PRPD method, we achieve 16% - 167% performance improvement and 24% - 54% less network communication under different join workloads. Copyright 2014 ACM.

关键词： Query processing

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Continuous Pattern Detection over Billion-Edge Graph Using distributed Framework

Continuous Pattern Detection over Billion-Edge Graph Using D...

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IEEE 30th international conference on Data Engineering (ICDE)

作者： Gao, Jun Zhou, Chang Zhou, Jiashuai Yu, Jeffrey Xu Peking Univ Key Lab High Confidence Software Technol Minist Educ Beijing Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Hong Kong Peoples R China Key Lab High Confidence Software Technol CUHK Sub Minist Educ Beijing Peoples R China

ISBN: (纸本)9781479934805

Continuous pattern detection plays an important role in monitoring-related applications. The large size and dynamic update of graphs, along with the massive search space, pose huge challenges in developing an efficient continuous pattern detection system. In this paper, we leverage a distributed graph processing framework to approximately detect a given pattern over a large dynamic graph. We aim to improve the scalability and precision, and reduce the response time and message cost in the detection. We convert a given query pattern into a Single-Sink DAG (Directed Acyclic Graph), and propose an evaluation plan with message transitions on the DAG, which is shorten by SSD plan, to detect the pattern in a large dynamic graph. SSD plan can guide the data graph exploration via messages, and the messages will converge at data sink vertices, which then detect existences of the query pattern. We also conduct join operations over partial vertices during the graph exploration to improve the precision of pattern detection. In addition, we show that SSD plan can support the continuous query over dynamic graphs with slight extensions. We further design various sink vertex selection strategies and neighborhood based transition rule attachment to lower the evaluation cost. The experiments on billion-edge real-life graphs using Giraph, an open source implementation of Pregel, illustrate the efficiency and effectiveness of our method.

关键词： Biomedical monitoring Drives Image edge detection Monitoring parallel processing Pattern matching Transforms

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Towards a High Speed Video Cloud Based on Batch processing Integrated with Fast processing

Towards a High Speed Video Cloud Based on Batch processing I...

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international conference on Identification, Information and Knowledge in the Internet of Things

作者： Zhang, Weishan Xu, Liang Duan, Pengcheng Gong, Wenjuan Liu, Xin Lu, Qinghua China Univ Petr Dept Software Engn 66 Changjiang West Rd Qingdao 266580 Peoples R China Natl ICT Australia NICTA Software Syst Res Grp Canberra ACT Australia

ISBN: (纸本)9781479980031

With the rise of video surveillance applications for analyzing real-time and batching video data in large scales, traditional video processing systems are being challenged due to real-time, intelligence and fault-tolerance demand for networked high resolution and large-scale video processing. These challenges can be further exacerbated by the existing predicaments of multi-platform, multi-format, multi-codec on video itself. The emergence of cloud computing and big data techniques makes it possible to manage complicated intelligent processing for large-scale video data. This paper proposes a general cloud-based video platform that can provide a robust solution to intelligent analytic and storage for video data, which is called ViCiBaF architecture (Video Cloud integrated with Batch processing and Fast processing) architecture. This ViCiBaF architecture is elaborated through an implementation that can effectively handle massive surveillance video data, where real-time analysis, batch processing, distributed storage and cloud services are seamlessly integrated to meet the requirements of intelligent analysis, real time, fault tolerance and massive storage for massive video data. The evaluations show that the proposed approach is efficient.

关键词： genetic algorithm pervasive cloud task migration

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Performance Analysis of Middleware distributed and clustered Systems (PAMS) concept in Mobile communication devices using Android operating system 3

Performance Analysis of Middleware distributed and clustered...

引用

international conference parallel distributed Grid Computing

作者： Singh, Vinay Aggarwal, Alok JPIET Dept CSE Meerut Uttar Pradesh India

ISBN: (纸本)9781479976836

Recent advances in computational power, memory, display etc. have propelled mobile and hand held devices as a platform to display and execute a variety of applications. Popular platform vendors like Nokia S60, iPhone, Samsung and Android offer extensive middleware support which helps in developers' port and in packaging of different application modules in a suitable format. These devices have several features like processing power, memory, energy, which exhibit the ability of these devices for performing complex tasks. Nowadays multi core processor architecture like Quad-Core Processor up to 3.0 GHz and Java enabled Android operating systems mobile devices are coming into the market which have potential for support and enhancements for an enterprise. In a complex and critical real time production support enterprise environment, a middleware production support analyst is primarily responsible to access, analyze the performance and control the middleware production application servers in a faster and precise manner. Production support analyst may or may not be available to Unix Production servers at all times due to many reasons. In this work a novel approach, Performance Analysis of Middleware distributed and clustered Systems (PAMS), is proposed using Android application by which a production support analyst can still support all critical real time web based application servers while he is away from the Unix based Middleware application server and can ensure that the same is available and accessible to their customers. An Android based application is proposed which will communicate to higher production layers installed in secure network of an organization and gather the data for performance monitoring and analysis. With the proposed concept production support analyst can easily login and open the console of middleware application servers using VPN (Virtual Private Network) and take immediate necessary actions so that business flow should not be impacted and enterpri

关键词： SSLV3 Key Exchange Algorithm TLSV1 Heap space Cluster-weight JMS active connections Android APIs WeblogicAPIs VPN Firewall

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Self-splitting of workload in parallel computation

Self-splitting of workload in parallel computation

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11th international conference on the Integration of Artificial Intelligence (AI) and Operations Research (OR) techniques in Constraint Programming, CPAIOR 2014

作者： Fischetti, Matteo Monaci, Michele Salvagnin, Domenico DEI University of Padova Via Gradenigo 6/A 35131 Padova Italy

ISBN: (纸本)9783319070452

parallel computation requires splitting a job among a set of processing units called workers. The computation is generally performed by a set of one or more master workers that split the workload into chunks and distribute them to a set of slave workers. In this setting, communication among workers can be problematic and/or time consuming. Tree search algorithms are particularly suited for being applied in a parallel fashion, as different nodes can be processed by different workers in parallel. In this paper we propose a simple mechanism to convert a sequential tree-search code into a parallel one. In the new paradigm, called SelfSplit, each worker is able to autonomously determine, without any communication with the other workers, the job parts it has to process. Computational results are reported, showing that SelfSplit can achieve an almost linear speedup for hard Constraint Programming applications, even when 64 workers are considered. © 2014 Springer international Publishing.

关键词： Constraint theory

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Fast heuristics for near-optimal task allocation in data stream processing over clusters 14

Fast heuristics for near-optimal task allocation in data str...

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23rd ACM international conference on Information and Knowledge Management, CIKM 2014

作者： Chatzistergiou, Andreas Viglas, Stratis D. School of Informatics University of Edinburgh United Kingdom

ISBN: (纸本)9781450325981

We study provisioning and job reconfiguration techniques for adapting to execution environment changes when processing data streams on cluster-based deployments. By monitoring the performance of an executing job, we identify computation and communication bottlenecks. In such cases we reconfigure the job by reallocating its tasks to minimize the communication cost. Our work targets dataintensive applications where the inter-node transfer latency is significant. We aim to minimize the transfer latency while keeping the nodes below some computational load threshold. We propose a scalable centralized scheme that employs fast allocation heuristics. Our techniques are based on a general group-based job representation that is commonly found in many distributed data stream processing frameworks. Using this representation we devise lineartime task allocation algorithms that improve existing quadratic-time solutions in practical cases. We have implemented and evaluated our proposals using both synthetic and real-world scenarios. Our results show that our algorithms: (a) exhibit significant allocation throughput while producing near-optimal allocations, and (b) significantly improve existing task-level approaches. Copyright 2014 ACM.

关键词： Optimization

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The simulation and implementation of Small-Signal stability auxiliary decision-making calculation base on parallel calculating method

The simulation and implementation of Small-Signal stability ...

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2014 international conference on Simulation and Modeling Methodologies, Technologies and applications, SMTA 2014

作者： Liu, Nana Deng, Junbo Chen, Jilin Qiu, Weijiang Yu, Zhihong Electric Power Research Institute of China Beijing China Beijing Sifang Automation Co. LTD Beijing China

ISBN: (纸本)9781845649821

The power analysis system based on parallel simulation platform uses the state estimation of integrated data which are the power system current operating conditions, with distributed architecture and parallel processing, computes multiple faults tasks at the same time and splits single task, so the calculation speed is remarkably improved. It realizes the transformation of small signal stability calculation and analysis off-line to online. The auxiliary decision accords the online real time data, through the analysis of small disturbance stability, acquires system weak damping modes, and then based on the adjustment of generators output, increases the damping of low frequency oscillation system. It also effectively removes the potential risk of power grid, and provides reliable technical guarantee for the safe operation of large power grid. © 2014 WIT Press.

关键词： Decision making

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MR-TRIAGE: Scalable Multi-Criteria Clustering for Big Data Security Intelligence applications 2

MR-TRIAGE: Scalable Multi-Criteria Clustering for Big Data S...

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IEEE international conference on Big Data

作者： Shen, Yun Thonnard, Olivier Symantec Res Labs Dublin Ireland Symantec Res Labs Sophia Antipolis France

ISBN: (纸本)9781479956661

Security companies have recently realised that mining massive amounts of security data can help generate actionable intelligence and improve their understanding of Internet attacks. In particular, attack attribution and situational understanding are considered critical aspects to effectively deal with emerging, increasingly sophisticated Internet attacks. This requires highly scalable analysis tools to help analysts classify, correlate and prioritise security events, depending on their likely impact and threat level. However, this security data mining process typically involves a considerable amount of features interacting in a nonobvious way, which makes it inherently complex. To deal with this challenge, we introduce MR-TRIAGE, a set of distributed algorithms built on MapReduce that can perform scalable multicriteria data clustering on large security data sets and identify complex relationships hidden in massive datasets. The MRTRIAGE workflow is made of a scalable data summarisation, followed by scalable graph clustering algorithms in which we integrate multi-criteria evaluation techniques. Theoretical computational complexity of the proposed parallel algorithms are discussed and analysed. The experimental results demonstrate that the algorithms can scale well and efficiently process large security datasets on commodity hardware. Our approach can effectively cluster any type of security events (e. g., spam emails, spear-phishing attacks, etc) that are sharing at least some commonalities among a number of predefined features.

关键词： Clustering algorithms

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