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7th International Conference on Computational Science (ICCS 2007)

作者： Viswanathan, M. Whangbo, T. K. Lee, K. J. Yang, Y. K. Kyungwon Univ Coll Software Kyonggi Do South Korea

ISBN: (纸本)9783540725879

Recent years have shown impressive growth in the development of ubiquitous healthcare (u-Healthcare) systems which aim for the next generation in e-Health services and associated research. Such systems are primarily being designed to provide emergency and preventive healthcare to citizens anywhere/anytime using wired and wireless mobile technologies. data mining is an indispensable aspect of such systems and represents the process of analyzing databases to extract hidden knowledge and relationships. This paper introduces and studies the development framework of a prototype ubiquitous healthcare system initiated by the South Korean government. Preliminary results with a distributed data mining system are presented in the context of a larger future integration with the ubiquitous healthcare framework.

关键词： distributed data mining ubiquitous healthcare decision supporting system healthcare data data classification

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A Reliable and Intelligent Protocol for distributed data mining Architecture in Sporadic Wireless Sensor Network

A Reliable and Intelligent Protocol for Distributed Data Min...

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International Symposium on Mathematical Sciences and Computing Research (iSMSC)

作者： Wagan, Asif Ali Jung, Low Tang Univ Teknol PETRONAS Dept Comp & Informat Sci Tronoh 31750 Perak Malaysia

ISBN: (纸本)9781479978946

distributed data mining (DDM) approaches are considered most suitable for managing large datasets. DDM can reduce the communication cost, computational cost, and storage cost. However a key issue for DDM is accuracy of knowledge discovery. To mitigate the accuracy issue, it is necessary to provide reliable network. In practice, it is assumed that the network is available all the time but in reality, it may not available most of the time. In this study, we considered a sporadic wireless sensor network (SWSN), SWSN is a wireless network where most of the time a complete path is not available from a source to a destination, or such a path is highly unreliable. It is also possible in this type of network a communication path is available for a very short time and it may breaks when try to send data over it and limited storage capacity. The objective of this study is to overcome the above mentioned issues by providing a reliable and intelligent protocols for DDM architecture in a SWSN.

关键词： sporadic network distributed data mining reliable protocol intelligent protocol sporadic wireless sensor network

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HealthSCOPE: An Interactive distributed data mining Framework for Scalable Prediction of Healthcare Costs 14

HealthSCOPE: An Interactive Distributed Data Mining Framewor...

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14th IEEE International Conference on data mining (IEEE ICDM)

作者： Marquardt, James Newman, Stacey Hattarki, Deepa Srinivasan, Rajagopalan Sushmita, Shanu Ram, Prabhu Prasad, Viren Hazel, David Ramesh, Archana De Cock, Martine Teredesai, Ankur Univ Washington Tacoma Ctr Data Sci Inst Technol 1900 Commerce St Tacoma WA 98402 USA Edifecs 2600 116th Ave Ne 200 Bellevue WA 98004 USA Univ Ghent Dept Appl Math Comp Sci & Stat Krijgslaan 281 S9 B-9000 Ghent Belgium

ISBN: (纸本)9781479942749

In this demonstration proposal we describe HealthSCOPE (Healthcare Scalable COst Prediction Engine), a framework for exploring historical and present day healthcare costs as well as for predicting future costs. HealthSCOPE can be used by individuals to estimate their healthcare costs in the coming year. In addition, HealthSCOPE supports a population based view for actuaries and insurers who want to estimate the future costs of a population based on historical claims data, a typical scenario for accountable care organizations (ACOs). Using our interactive data mining framework, users can view claims (sample files will be provided), use HealthSCOPE to predict costs for the upcoming year, interactively select from a set of possible medical conditions, understand the factors that contribute to the cost, and compare costs against historical averages. The back-end system contains cloud based prediction services hosted on the Microsoft Azure infrastructure that allow the easy deployment of models encoded in Predictive Model Markup Language (PMML) and trained using either Spark MLLib or various non-distributed environments.

关键词： Healthcare cost prediction insurance claims data distributed data mining Spark Microsoft Azure PMML

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Research on distributed data mining Tool Used in Control System

Research on Distributed Data Mining Tool Used in Control Sys...

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International Conference on Advanced Computer Control

作者： Ma, Yuekun Li, Zhigang Yu, Shuh Chen, Lei Hebei Polytech Univ Coll Comp & Automat Control Tangshan 063009 Peoples R China

ISBN: (纸本)9780769535166

In this paper, A distributed data mining tool that suitable for distributed and isomeric database environment of intelligent control system by borrowing ideas from architecture of grid is introduced. It can also perform incremental data mining based on history mining result. This tool makes full use of Metadata of each kind of resources. The resources associated in the data mining are all packed into form of web set-vice, which is give a description by respective attribute document of the XML format. SOAP takes charge of the communication between the separated parts of the whole data mining-process.

关键词： distributed data mining control rule grid architecture web service metadata

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International Workshop on Federated Learning for distributed data mining 23

International Workshop on Federated Learning for Distributed...

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29th ACM SIGKDD Conference on Knowledge Discovery and data mining (KDD)

作者： Hong, Junyuan Zhu, Zhuangdi Lyu, Lingjuan Zhou, Yang Boddeti, Vishnu Naresh Zhou, Jiayu Michigan State Univ E Lansing MI 48824 USA Microsoft Seattle WA USA Sony AI Tokyo Japan Auburn Univ Auburn AL USA

ISBN: (纸本)9798400701030

The past decade has witnessed wide applications of machine learning to various domains for decision-making, including crime detection, urban planning, drug discovery, and health monitoring, which benefited from surging data resources. As data collection in real-world applications is often done in different locations, being able to mine and discover knowledge from distributed data sources is an essential requirement for building powerful predictive models. However, directly uploading all data sources to an untrustworthy centralized data server for learning will lead to risks of privacy leakage. Federated Learning (FL) emerges as a decentralized learning framework that aggregates knowledge from distributed data without centralizing them, hence mitigating privacy risks. By hosting this workshop, we aim to attract a broad spectrum of audiences, including researchers and practitioners from academia and industry interested in the latest advances in FL. As an effort to advance the fundamental development of FL in data mining, this workshop will encourage ideas exchange on the trustworthiness, scalability, robustness, and broad applications of FL.

关键词： federated learning distributed data mining trustworthiness applications

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distributed data mining in the grid environment

Distributed data mining in the grid environment

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2011 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS 2011)

作者： Wang tingzhong Academy of information technology Luoyang Normal University luoyang,China Fan ganglong Academy of information technology Luoyang Normal University luoyang,China

In many scientific and business areas, it is often necessary to analyze large data sets, maintained over geographically distributed sites, which makes DDM a major research issue on today’ data mining *** a latest member in distributed computing technology family, the grid can play a significant role in providing an effective computational support for knowledge discovery *** mining algorithm and knowledge discovery processes are both compute and data intensive;therefore the grid can offers a computing and data management infrastructure for supporting decentralized and parallel data *** paper describes the grid framework and discusses how grid computing can be used to support the distributed data ***-based data mining uses grids as decentralized platforms where to execute data mining tasks and knowledge discovery algorithms and applications .Finally the paper outlines the promising future for developing grid-based distributed data mining.

关键词： distributed data mining knowledge grid

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distributed data mining for Multiple Sourced Heterogeneous datasets: A Survey

Distributed Data Mining for Multiple Sourced Heterogeneous D...

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2018 3rd International Conference on Computational Modeling, Simulation and Applied Mathematics (CMSAM 2018)

作者： Xing-ying LI Shan-zi LI Yi-xuan WU Ai-jia HE Xiao-ya HUANG Xin ZHAO Beijing Normal University

In the information age of the 21st century, a large amount of information is collected and applied. However, due to the heterogeneity of system environment for data storage and computing, how to mine these distributed data sources has become a valuable research topic that attracted more and more attention. In this paper, we firstly presented the problem scenario and main challenges confronting with the problem of distributed data mining on multiple sourced heterogeneous data sets. Then, we surveyed research works related to the problem and elicited their main features on different technology domains to show current distributed solutions for different data mining algorithm categories. Finally, we reviewed in detail the research works and discussed the challenges remained in the distributed data mining problem for multiple sourced heterogeneous data sets.

关键词： Multiple sourced heterogeneous data sets distributed data mining distributed algorithms

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The Weka4WS framework for distributed data mining in service-oriented Grids

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2008年第16期20卷 1933-1951页

作者： Talia, Domenico Trunfio, Paolo Verta, Oreste Univ Calabria DEIS I-87036 Arcavacata Di Rende CS Italy

The service-oriented architecture paradigm can he exploited for the implementation of data and knowledge based I applications in distributed environments. The Web services resource framework (WSRF) has recently emerged as the standard for the implementation of Grid services and applications. WSRF can be exploited for developing high-level services for distributed data mining applications. This paper describes Weka4WS, a framework that extends the widely used open source Weka toolkit to support distributed data mining on WSRF-enabled Grids. Weka4WS adopts the WSRF technology for running remote data mining algorithms and managing distributed computations. The Weka4WS user interface supports the execution of both local and remote data mining tasks. On every computing node, a WSRF-compliant Web service is used to expose all the data mining algorithms provided by the Weka library. The paper describes the design and implementation of Weka4WS using the WSRF libraries and services provided by Globus Toolkit 4. A performance analysis of Weka4WS for executing distributed data mining tasks in different network scenarios is presented. Copyright (C) 2008 John Wiley & Sons, Ltd.

关键词： Grid computing distributed data mining Web services resource framework

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Improving diagnostic accuracy using agent-based distributed data mining system

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INFORMATICS FOR HEALTH & SOCIAL CARE 2013年第3期38卷 182-195页

作者： Sridhar, S. Anna Univ Dept Informat Sci & Technol Chennai 600025 Tamil Nadu India

The use of data mining techniques to improve the diagnostic system accuracy is investigated in this paper. The data mining algorithms aim to discover patterns and extract useful knowledge from facts recorded in databases. Generally, the expert systems are constructed for automating diagnostic procedures. The learning component uses the data mining algorithms to extract the expert system rules from the database automatically. Learning algorithms can assist the clinicians in extracting knowledge automatically. As the number and variety of data sources is dramatically increasing, another way to acquire knowledge from databases is to apply various data mining algorithms that extract knowledge from data. As data sets are inherently distributed, the distributed system uses agents to transport the trained classifiers and uses meta learning to combine the knowledge. Commonsense reasoning is also used in association with distributed data mining to obtain better results. Combining human expert knowledge and data mining knowledge improves the performance of the diagnostic system. This work suggests a framework of combining the human knowledge and knowledge gained by better data mining algorithms on a renal and gallstone data set.

关键词： Expert systems distributed data mining knowledge integration

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Study on distributed data mining Model in Wireless Sensor Networks

Study on Distributed Data Mining Model in Wireless Sensor Ne...

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2010 International Conference on Intelligent Computing and Integrated Systems

作者： Hong Yuehua,Xu Shuang,Wu Huajian Yulin Normal University YuLin,P.R.China

Aiming at the severe energy and computing resource constraints of Wireless Sensor Network(WSN),based on rough set theory and ART2 network,a distributed data mining model for WSN is *** model poses a three-layer MLP for data aggregation in the clustered sensor network. And the input layer neuron and the first layer neuron are located in every cluster member,while the second layer neuron and the output layer neuron are located in every cluster head. The features of the training samples were extracted to build up the decision table;the rough set theory was applied to reduce the decision ***,the reduced decision attributes were used to construct ART2 neural network classification data. Constructed data mining algorithm can be integrated in each sensor network *** results prove data dimension is reduced and data redundancy is eliminated after the raw-data is processed by data mining algorithm,and the communication traffic is decreased and the life of WSN is extended.

关键词： wireless sensor network distributed data mining rough set theory ART2 neural network

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