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9th IEEE International Conference on Cloud Computing (CLOUD)

作者： Hochreiner, Christoph Vogler, Michael Schulte, Stefan Dustdar, Schahram TU Wien Distributed Syst Grp Vienna Austria

ISBN: (纸本)9781509026197

Emerging trends like Big data and the Internet of Things pose new challenges to established data stream processing engines. Especially, with the advent of the Internet of Things, the data that has to be processed can become very large. Since companies usually aim for cost efficiency, engines need to support resource elasticity to minimize the operational cost while maintaining real-time processing capabilities. In the work at hand, we propose and realize the distributed Platform for Elastic Stream processing ( PESP). An extensive evaluation demonstrates the practical feasibility and efficiency of the system design. The evaluation shows that PESP is able to reduce cost by 20% with minimal effects on the Quality of Service in comparison to an over-provisioning baseline. Compared to an under-provisioning baseline, PESP allows a Quality of Service improvement of 72%.

关键词： Stream processing distributed data processing Hybrid clouds

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A distributed component framework for integrated network and systems management

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Information Management and Computer Security 1999年第5期7卷 254-260页

作者： Knahl, M. Hofmann, H.D. Phippen, A. Network Research Group Sch. Electron. Commun. Elec. Eng. University of Plymouth Plymouth United Kingdom Dept. of Mathematics and Computing Cork Institute of Technology Ireland Network Research Group School of Computing University of Plymouth Plymouth United Kingdom

It is proposed that future work should move on from existing network and system management methodologies to consider enhancing the management methodology for ATM and other networking technologies to meet existing and future requirements. This paper outlines an area where significant potential for further research exists and proposes a component-based management architecture. The discussion indentifies the technological limitations and architectural drawbacks of current solutions and proposes the extension of existing services and an enhanced management framework to overcome the current restrictions.

关键词： Automated teller machines Computer networks Computer software distributed data processing Systems management

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Global measures of information service quality: A cross-national study

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DECISION SCIENCES 1995年第5期26卷 569-588页

作者： Kettinger, WJ Lee, CC Lee, S SALISBURY STATE UNIV FRANKLIN P PERDUE SCH BUSINESS INFORMAT & DECIS SSALISBURYMD 21801 YONSEI UNIV SCH BUSINESSDEPT INFORMAT SYSTWONJUSOUTH KOREA

Maintaining consistently high quality information services (IS) is a powerful means of increasing the overall efficiency and effectiveness of a global enterprise. This study introduces a global Information Services Function (ISF) Quality Framework that outlines the importance of both behavioral and procedural dimensions in planning, implementing and evaluating global IS quality. Based on this framework, the study investigates the cross-national psychometric properties of a behavioral measure of service quality in the IS context. Using a cross-national survey of IS customers from Korea, Hong Kong, the United States and the Netherlands, perceived ISF service quality was measured using a service quality measure (SERVQUAL) to determine cultural affects. Based on confirmatory factor analysis, support was found for four of the original five SERVQUAL quality dimensions in the U.S.A. and the Netherlands. However, the same four-dimensional measurement model did not fit the Hong Kong and Korean samples. Further factor analysis showed that the Hong Kong and Korean samples shared a somewhat similar factor structure that differs from the shared U.S.A. and Netherlands structure. These findings support previous research that has found an ''Asian factor'' with differing definitions of IS Service quality. These findings suggest that the feasibility of standardized global ISF measurement depends heavily on the relative magnitude of cultural effects. Rather than merely applying the U.S.A. ISF/SERVQUAL measure, a localized version of the instrument may need to be developed that captures the unique nature of ISF service perceptions in internationally based subsidiaries or companies.

关键词： and Total Quality Management Cultural Efforts distributed data processing Global Information Systems

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Meta-dataflows: Efficient Exploratory dataflow Jobs 18

Meta-Dataflows: Efficient Exploratory Dataflow Jobs

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44th ACM SIGMOD International Conference on Management of data

作者： Fernandez, Raul Castro Culhane, William Watcharapichat, Pijika Weidlich, Matthias Morales, Victoria Lopez Pietzuch, Peter MIT Cambridge MA 02139 USA Imperial Coll London London England Humboldt Univ Berlin Germany

ISBN: (纸本)9781450317436

distributed dataflow systems such as Apache Spark and Apache Flink are used to derive new insights from large datasets. While they efficiently execute concrete data processing workflows, expressed as dataflow graphs, they lack generic support for exploratory workflows: if a user is uncertain about the correct processing pipeline, e.g. in terms of data cleaning strategy or choice of model parameters, they must repeatedly submit modified jobs to the system. This, however, misses out on optimisation opportunities for exploratory workflows, both in terms of scheduling and memory allocation. We describe meta-dataflows (MDFs), a new model to effectively express exploratory workflows and efficiently execute them on compute clusters. With MDFs, users specify a family of dataflows using two primitives: (a) an explore operator automatically considers choices in a dataflow;and (b) a choose operator assesses the result quality of explored dataflow branches and selects a subset of the results. We propose optimisations to execute MDFs: a system can (i) avoid redundant computation when exploring branches by reusing intermediate results and discarding results from underperforming branches;and (ii) consider future data access patterns in the MDF when allocating cluster memory. Our evaluation shows that MDFs improve the runtime of exploratory workflows by up to 90% compared to sequential execution.

关键词： distributed data processing parameter space exploration exploratory workflows parallel data processing dataflow

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Enabling Semantics within Industry 4.0 8th

Enabling Semantics within Industry 4.0

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8th International Conference on Industrial Applications of Holonic and Multi-Agent Systems (HoloMAS)

作者： Jirkovsky, Vaclav Obitko, Marek Czech Tech Univ Czech Inst Robot Informat & Cybernet Zikova 4 Prague 16636 Czech Republic Rockwell Automat R&D Ctr Argentinska 1610-4 Prague 17000 Czech Republic

ISBN: (纸本)9783319646350;9783319646343

Manufacturing faces increasing requirements from customers which causes the need of exploiting emerging technologies and trends for preserving competitive advantages. The apriori announced fourth industrial revolution (also known as Industry 4.0) is represented mainly by an employment of Internet technologies into industry. The essential requirement is the proper understanding of given CPS (one of the key component of Industry 4.0) data models together with a utilization of knowledge coming from various systems across a factory as well as an external data sources. The suitable solution for data integration problem is an employment of Semantic Web Technologies and the model description in ontologies. However, one of the obstacles to the wider use of the Semantic Web technologies including the use in the industrial automation domain is mainly insufficient performance of available triplestores. Thus, on so called Semantic Big data Historian use case we are proposing the usage of state of the art distributed data storage. We discuss the approach to data storing and describe our proposed hybrid data model which is suitable for representing time series (sensor measurements) with added semantics. Our results demonstrate a possible way to allow higher performance distributed analysis of data from industrial domain.

关键词： Industry 4.0 Ontology Triplestore Big data distributed data processing Historian

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Over Load Detection and Admission Control Policy in DRTDBS

Over Load Detection and Admission Control Policy in DRTDBS

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International Conference on Advances in Information Technology and Mobile Communication (AIM 2011)

作者： Nuparam Shanker, Udai FGIET Comp Sci & Engn Dept Racbareli 229001 UP India MMMEC Dept Comp Sci & Engn Gorakhpur 273010 Uttar Pradesh India

ISBN: (纸本)9783642205729

Today's Real Time System (RTS) is characterized by managing large volume of distributed data making real time distributed data processing a reality [1, 2]. The demand for real time data services is increasing in many large scale distributed real time applications. The transaction work load in DRTDBS may not be balanced and the transaction access pattern may be time varying and skewed. Hence, computation workload on large scale distributed system can lead to large number of transaction deadline misses [3, 4]. Hence, efficient database management algorithm and protocol for accessing and maintaining data are required to satisfy timing constraints of transaction supported applications. In this paper, an algorithm has been proposed for admission control in DRTDBS consisting of local controller and global load balancer working at each site, which decide whether to admit or reject the newly arrived transaction. The simulation results show that the new algorithm successfully balances the workload in DRTDBS [5, 6].

关键词： Admission control local controller global load balancer distributed data processing computation workload

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Big data Analysis Using Hadoop Cluster 5

Big Data Analysis Using Hadoop Cluster

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5th IEEE International Conference on Computational Intelligence and Computing Research (IEEE ICCIC)

作者： Saldhi, Ankita Goel, Abhinav Yadav, Dipesh Saldhi, Ankur Saksena, Dhruv Indu, S. Ctr Dev Telemat Mandi Rd Delhi 110030 India Aardee Solut Delhi 110059 India Designo Interior Delhi 110085 India Jamia Millia Islamia Dept Comp Engn Delhi 110025 India Carnegie Mellon Univ Pittsburgh PA 15213 USA Delhi Technol Univ Elect & Commun Engn Dept Delhi 110042 India

ISBN: (纸本)9781479939756

Industries keep a check on all statistics of their business and process this data using various data mining techniques to measure profit trends, revenue, growing markets and interesting opportunities to invest. These statistical records keep on increasing and increase very fast. Unfortunately, as the data grows it becomes a tedious task to process such a large data set and extract meaningful information. Also if the data generated is in various formats, its processing possesses new challenges. Owing to its size, big data is stored in Hadoop distributed File System (HDFS). In this standard architecture, all the dataNodes function parallel but functioning of a single data Node is still in sequential fashion. This paper proposes to execute tasks assigned to a single data Node in parallel instead of executing them sequentially. We propose to use a bunch of streaming multi-processors (SMs) for each single data Node. An SM can have various processors and memory and all SMs run in parallel and independently. We process big data which may be coming from different sources in different formats to run parallelly on a Hadoop cluster, use the proposed technique and yield desired results efficiently. We have applied proposed methodology to the raw data of an industrial firm, for doing intelligent business, with a final objective of finding profit generated for the firm and its trends throughout a year. We have done analysis over a yearlong data as trends generally repeat after a year.

关键词： Big data Hadoop distributed data processing data mining Mappers Reducers

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Generic and Concurrent Computation of Belief Combination Rules 1

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5th International Conference on data Management Technologies and Applications (data)

作者： Dambreville, Frederic Ensta Bretagne CNRS UMR 6285 DGA MILab STICC 2 Rue Francois Verny Brest France

ISBN: (数字)9783319629117

ISBN: (纸本)9783319629117;9783319629100

As a form of random set, belief functions come with specific semantic and combination rule able to perform the representation and the fusion of uncertain and imprecise informations. The development of new combination rules able to manage conflict between data now offers a variety of tools for robust combination of piece of data from a database. The computation of multiple combinations from many querying cases in a database make necessary the development of efficient approach for concurrent belief computation. The approach should be generic in order to handle a variety of fusion rules. We present a generic implementation based on a map-reduce paradigm. An enhancement of this implementation is then proposed by means of a Markovian decomposition of the rule definition. At last, comparative results are presented for these implementations within the frameworks Apache Spark and Apache Flink.

关键词： Map-reduce distributed data processing Belief functions Combination rules Statistics

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Apache Tez: A Unifying Framework for Modeling and Building data processing Applications 15

Apache Tez: A Unifying Framework for Modeling and Building D...

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ACM SIGMOD International Conference on Management of data

作者： Saha, Bikas Shah, Hitesh Seth, Siddharth Vijayaraghavan, Gopal Murthy, Arun Curino, Carlo Hortonworks Santa Clara CA 95054 USA Microsoft Redmond WA USA

ISBN: (纸本)9781450327589

The broad success of Hadoop has led to a fast-evolving and diverse ecosystem of application engines that are building upon the YARN resource management layer. The open-source implementation of MapReduce is being slowly replaced by a collection of engines dedicated to specific verticals. This has led to growing fragmentation and repeated efforts-with each new vertical engine re-implementing fundamental features (e.g. fault-tolerance, security, stragglers mitigation, etc.) from scratch. In this paper, we introduce Apache Tez, an open-source framework designed to build data-flow driven processing runtimes. Tez provides a scaffolding and library components that can be used to quickly build scalable and efficient data-flow centric engines. Central to our design is fostering component re-use, without hindering customizability of the performance-critical data plane. This is in fact the key differentiator with respect to the previous generation of systems (e.g. Dryad, MapReduce) and even emerging ones (e.g. Spark), that provided an d mandated a fixed data plane implementation. Furthermore, Tez provides native support to build runtime optimizations, such as dynamic partition pruning for Hive. Tez is deployed at Yahoo!, Microsoft Azure, Linkedln and numerous Hortonworks customer sites, and a growing number of engines are being integrated with it. This confirms our intuition that most of the popular vertical engines can leverage a core set of building blocks. We complement qualitative accounts of real-world adoption with quantitative experimental evidence that Tez-based implementations of Hive, Pig, Spark, and Cascading on YARN outperform their original YARN implementation on popular benchmarks (TPC-DS, TPC-H) and production workloads.

关键词： apache hadoop distributed data processing open source big data

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Security and data integrity for lans and wans

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Information Management & Computer Security 1995年第4期3卷 15-19页

作者： Fitzgerald, Kevin J. KPMG Information Solutions Melbourne Australia

The typical characteristic of today's LAN and WAN environments is one of mixture. Old systems are mixed with a wide variety of new systems. Establishing effective network security in multi-platform, multi-vendor, enterprise-wide IT environments is the greatest challenge yet to businesses which depend on such systems and to the information security industry itself. „Security and data integrity” in this article have been taken to mean the coverage of information availability, confidentiality and integrity. © 1995 MCB UP Ltd.

关键词： Access control Computer networks distributed data processing Downsizing Reliability Security

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