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The significance of information systems research on emerging technologies: Seven information technologies that promise to improve managerial effectiveness

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DECISION SCIENCES 1997年第4期28卷 775-792页

作者： Kendall, KE Rutgers State Univ Sch Business Camden NJ 08102 USA

Emerging information technologies are those excitingly novel advances that decision makers are just beginning to notice. These emerging products or processes may have been invented some rime ago, but are only now beginning to reveal practical applications. This paper examines this collection of seven studies exploring emerging technologies, classifying them so the context of their results can be related to other emerging technologies. Once the research is classified and the contribution and managerial significance of the research is identified, it becomes apparent that certain barriers exist, holding back the technology from wider use and appreciation. Common obstacles include resistance to, or difficulty with, using the technology;uncertainty concerning the value of the technology;and the complexities involved with implementation. This paper concludes with recommendations for future research aimed at breaking down the barriers in order to move from the technological emergence phase to the technological sublime phase in which decision makers and other end users understand the technology, realize acid appreciate its value, and put the technology to its best use.

关键词： Decision Support Systems distributed data processing End-User Computing Executive Information Systems (EIS) Group Decision Support Systems (GDSS) Human/Computer Interaction Hypermedia/Hypertext Management Information Systems Social Issues Team Building and Technology Management.

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The Design of Web-based Warehouse Management System 11

The Design of Web-based Warehouse Management System

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11th World Congress on Intelligent Control and Automation

作者： Yang, Zizhuo Wang, Jun Su, Qianmin Zhong, Bocheng Shanghai Univ Engn Sci Coll Elect & Elect Engn Shanghai 201620 Peoples R China

ISBN: (纸本)9781479958252

To solve the problem caused by the simple and static way that the traditional enterprise warehouse management system used, which is unable to ensure the problem of efficient utilization of various enterprise's resources. This paper applies these technical methods such as Web distributed data processing, Optical Network for Rapid Communication, Real-time data Sharing of Relational database, B/S Multi-system Compatibility, establishing Web management application systems which are suit for needs of modern warehouse management. The testing results indicate that the system can process various tasks involved in the inventory management more efficiently, stably and duly, making the enterprise's resources utilized more sufficiently. The system can solve the problem such as low efficiency, information inaccuracy and high operating costs caused in the current enterprise's inventory management.

关键词： distributed data processing Optical Network Warehouse Management

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Trade-Offs in Large-Scale distributed Tuplewise Estimation And Learning

Trade-Offs in Large-Scale Distributed Tuplewise Estimation A...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in databases (ECML PKDD)

作者： Vogel, Robin Bellet, Aurelien Clemencon, Stephan Jelassi, Ons Papa, Guillaume Telecom Paris LTCI Inst Polytech Paris Paris France IDEMIA Paris France INRIA Paris France

ISBN: (纸本)9783030461478;9783030461461

The development of cluster computing frameworks has allowed practitioners to scale out various statistical estimation and machine learning algorithms with minimal programming effort. This is especially true for machine learning problems whose objective function is nicely separable across individual data points, such as classification and regression. In contrast, statistical learning tasks involving pairs (or more generally tuples) of data points-such as metric learning, clustering or ranking-do not lend themselves as easily to data-parallelism and in-memory computing. In this paper, we investigate how to balance between statistical performance and computational efficiency in such distributed tuplewise statistical problems. We first propose a simple strategy based on occasionally repartitioning data across workers between parallel computation stages, where the number of repartitioning steps rules the trade-off between accuracy and runtime. We then present some theoretical results highlighting the benefits brought by the proposed method in terms of variance reduction, and extend our results to design distributed stochastic gradient descent algorithms for tuplewise empirical risk minimization. Our results are supported by numerical experiments in pairwise statistical estimation and learning on synthetic and real-world datasets.

关键词： distributed machine learning distributed data processing U-Statistics Stochastic gradient descent AUC optimization

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Fast deployment of an energy-efficient heterogeneous wireless network for disaster management 2

Fast deployment of an energy-efficient heterogeneous wireles...

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2nd International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

作者： Chalouf, Mohamed Aymen Peres, Martin Krief, Francine Univ Rennes 1 IRISA Lannion France Univ Bordeaux LaBRI Talence France

ISBN: (纸本)9781479999231

After a disaster, much of infrastructure is no longer usable, including telecommunications. The hours following a disaster are crucial. Thus, we must find a solution to allow rescuers to communicate with the survivors or at least to locate them. This telecommunication solution must also enable communication between rescuers to facilitate their coordination for greater speed and efficiency. In this paper, we propose an energy-efficient heterogeneous wireless network to help a rescue team to organise itself in a disaster relief scenario for saving lives. This solution can be deployed rapidly and sustainably thanks to the dynamic spectrum and energy management.

关键词： Disaster management Power management Cognitive Radio Signalling protocol Efficient spectrum sharing distributed data processing Run-time decision engine

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Medical Big data Statistical Management System Based on Decision Tree Algorithm 6

Medical Big Data Statistical Management System Based on Deci...

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6th IEEE Advanced Information Technology, Electronic and Automation Control Conference (IEEE IAEAC)

作者： Miao, Yuhang Yu, Hang Song, Jiajia Qiqihar Med Univ Sch Basic Med Qiqihar 161006 Heilongjiang Peoples R China Qiqihar Med Univ Affiliated Hosp 2 Qiqihar 161006 Heilongjiang Peoples R China Qiqihar Med Univ Sch Marxism Qiqihar 161006 Heilongjiang Peoples R China

ISBN: (数字)9781665458641

ISBN: (纸本)9781665458641

In some hospitals, it is often seen that because there are more patients and fewer doctors, the attending doctor often rushes between the patient's wards to query the patient's physiological data, or the nurse is responsible for recording the patient's physiological data and reporting it to the doctor. The work is very labor-intensive, and the data seen by the doctor is not real-time data, so the judgment of the patient's condition is not real-time and effective. To this end, this paper develops a medical big data statistical management system, which collects, counts, and stores patients' medical information in the system's management database for centralized management and facilitates query. For the storage of medical big data(BD), this paper uses a distributed cache framework to store medical data information, and implements distributed data processing through the C4.5 algorithm of the decision tree(DT) algorithm. The experimental results of large-scale(LC) medical data processing based on the C4.5 algorithm are also The data processing efficiency of the algorithm is illustrated, which provides great convenience for medical data management.

关键词： Decision Tree Algorithm C4.5 Algorithm Medical Big data distributed data processing

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distributed Algorithms to Find Similar Time Series

Distributed Algorithms to Find Similar Time Series

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in databases (ECML PKDD)

作者： Levchenko, Oleksandr A. Kolev, Boyan Yagoubi, Djamel-Edine Shasha, Dennis Palpanas, Themis Valduriez, Patrick Akbarinia, Reza Masseglia, Florent Univ Montpellier INRIA Montpellier France Univ Montpellier LIRMM Montpellier France NYU Dept Comp Sci New York NY USA Univ Paris Paris France

ISBN: (纸本)9783030461331;9783030461324

As sensors improve in both bandwidth and quantity over time, the need for high performance sensor fusion increases. This requires both better (quasi-linear time if possible) algorithms and parallelism. This demonstration uses financial and seismic data to show how two state-of-the-art algorithms construct indexes and answer similarity queries using Spark. Demo visitors will be able to choose query time series, see how each algorithm approximates nearest neighbors and compare times in a parallel environment.

关键词： Time series Indexing Similarity search distributed data processing Spark

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When to Use a distributed dataflow Engine: Evaluating the Performance of Apache Flink 13

When to Use a Distributed Dataflow Engine: Evaluating the Pe...

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Conference on UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld

作者： Verbitskiy, Ilya Thamsen, Lauritz Kao, Odej Tech Univ Berlin Berlin Germany

ISBN: (纸本)9781509027712

With the increasing amount of available data, distributed data processing systems like Apache Flink and Apache Spark have emerged that allow to analyze large-scale datasets. However, such engines introduce significant computational overhead compared to non-distributed implementations. Therefore, the question arises when using a distributed processing approach is actually beneficial. This paper helps to answer this question with an evaluation of the performance of the distributed data processing framework Apache Flink. In particular, we compare Apache Flink executed on up to 50 cluster nodes to single-threaded implementations executed on a typical laptop for three different benchmarks: TPC-H Query 10, Connected Components, and Gradient Descent. The evaluation shows that the performance of Apache Flink is highly problem dependent and varies from early outperformance in case of TPC-H Query 10 to slower runtimes in case of Connected Components. The reported results give hints for which problems, input sizes, and cluster resources using a distributed data processing system like Apache Flink or Apache Spark is sensible.

关键词： Apache Flink COST distributed data processing Parallel dataflows Performance evaluation

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distributed boosting algorithm for classification of text documents

Distributed boosting algorithm for classification of text do...

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IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Sarnovsky, Martin Vronc, Michal Tech Univ Kosice Fac Elect Engn & Informat Dept Cybernet & Artificial Intelligence Kosice 04200 Slovakia

ISBN: (纸本)9781479934423

Presented paper focuses on the area of analysis and classification of textual documents. We present the classification of documents based on boosting method applied on the decision tree algorithm. Main objective of the paper is to present the implementation of distributed boosting algorithm based on Map Reduce paradigm. We have used the GridGain framework as a platform for distributed data processing and have tested the implemented solution on two different dataset within our testing environment.

关键词： Classification of algorithms distributed data processing Boost TEST ENVIRONMENT text documents Decision Trees Taxonomy Distribute MAPPING(MATHEMATICAL) Area Analysis

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Compliant Geo-distributed Query processing 21

Compliant Geo-distributed Query Processing

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ACM SIGMOD International Conference on Management of data (SIGMOD)

作者： Beedkar, Kaustubh Quiane-Ruiz, Jorge-Arnulfo Markl, Volker TU Berlin Berlin Germany TU Berlin DFKI Berlin Germany

ISBN: (纸本)9781450383431

In this paper, we address the problem of compliant geo-distributed query processing. In particular, we focus on dataflow policies that impose restrictions on movement of data across geographical or institutional borders. Traditional ways to distributed query processing do not consider such restrictions and therefore in geo-distributed environments may lead to non-compliant query execution plans. For example, an execution plan for a query over data sources from Europe, North America, and Asia, which may otherwise be optimal, may not comply with dataflow policies as a result of shipping some restricted (intermediate) data. We pose this problem of compliance in the setting of geo-distributed query processing. We propose a compliance-based query optimizer that takes into account dataflow policies, which are declaratively specified using our policy expressions, to generate compliant geo-distributed execution plans. Our experimental study using a geo-distributed adaptation of the TPC-H benchmark data indicates that our optimization techniques are effective in generating efficient compliant plans and incur low overhead on top of traditional query optimizers.

关键词： dataflow policies declarative policy specification compliance distributed data processing query optimization data constraints

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A Hadoop Performance Model for Multi-Rack Clusters

A Hadoop Performance Model for Multi-Rack Clusters

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5th International Conference on Computer Science and Information Technology (CSIT)

作者： Han, Jungkyu Ishii, Masakuni Makino, Hiroyuki NTT Corp Software Innovat Ctr Distributed Comp Technol Project Tokyo Japan

ISBN: (纸本)9781467358255

Hadoop becomes de facto standard framework for big data analysis due to its scalability. Despite of the importance of Hadoop's scalability, there are a few works have been made on the scalability in multi-rack clusters. In multi-rack clusters of real world, network topology becomes a major scalability bottleneck due to the limited network switch capacity. It is a waste of resources to add servers to a Hadoop cluster in such situation. Therefore, it is helpful for users to save cost by efficiently measuring the network influence to Hadoop before they add a new server to their clusters. In this paper, we describe a Hadoop performance model for the multi-rack clusters. We modeled network influence on Hadoop and achieved about 95% accuracy to the real measurement. Furthermore, we predicted Hadoop scalability in large clusters with our model and show Hadoop scales enough even in multi-rack clusters.

关键词： Hadoop Map-Reduce Performance Modeling distributed data processing

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