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A Novel Algorithm for distributed data stream Using Big data Classification Model

INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND WEB ENGINEERING 2020年第4期15卷 1-17页

作者： Qiu, Yongxiao Du, Guanghui Chai, Song Hebei Finance Univ Baoding Peoples R China China Mobile Grp Hebei Co Ltd Baoding Peoples R China

In order to solve the problem of real-time detection of power grid equipment anomalies, this paper proposes a data flow classification model based on distributed processing. In order to realize distributed processing of power grid data flow, a local node mining method and a global mining mode based on uneven data flow classification are designed. A data stream classification model based on distributed processing is constructed, then the corresponding data sequence is selected and formatted abstractly, and the local node mining method and global mining mode under this model are designed. In the local node miner, the block-to-block mining strategy is implemented by acquiring the current data blocks. At the same time, the expression and real-time maintenance of local mining patterns are completed by combining the clustering algorithm, thus improving the transmission rate of information between each node and ensuring the timeliness of the overall classification algorithm.

关键词： data Cleaning distributed data stream Local Node Mining Transmission Rate

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The Research on distributed data stream Mining based on Mobile Agent

The Research on Distributed Data Stream Mining based on Mobi...

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International Conference on Power Electronics and Engineering Application (PEEA)

作者： Yue Fuqiang Xichang Coll Sch Automot & Elect Engn Xichang Peoples R China

ISBN: (纸本)9781627485791

Mobile Agent technology applied to the problem of mining data streams which, Mobile Agent application intelligence, distributed data stream mining to solve the noise present in the data processing is difficult to identify, classify slow, inefficient mining problems is proposed based on Mobile Agent distributed data stream mining model, the model design and data base displacement detection, data stream classification algorithms in a distributed environment to better address the problem of mining data streams. Mobile Agent based system for distributed data stream mining has a greater theoretical and practical significance. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [name organizer]

关键词： distributed data stream Mining Mobile Agent

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The Research on distributed data stream Mining based on Mobile Agent

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Procedia Engineering 2011年 23卷 103-108页

作者： Yue Fuqiang school of automotive and electronic engineering Xichang College Xichang China

关键词： distributed data stream Mining Mobile Agent

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The Research on distributed data stream Mining based on Mobile Agent

The Research on Distributed Data Stream Mining based on Mobi...

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2011 International Conference on Power Electronics and Engineering Application

作者： Yue Fuqiang school of automotive and electronic engineering Xichang College

关键词： distributed data stream Mining Mobile Agent

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A Micro-Cluster based Ensemble Approach for Classifying distributed data streams

A Micro-Cluster based Ensemble Approach for Classifying Dist...

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23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Mao, Guojun Yang, Yi Beijing Univ Technol Sch Informat Technol Beijing 100086 Peoples R China Beijing Univ Technol Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9780769545967

Mining distributed data streams is a focus of much research in recent years, and it has brought many challenging problems. One of these problems is learning and maintaining the global patterns from multiple data streams in distributed environments. In this paper, we discuss micro-cluster based classification problems in distributed data streams, and propose an approach for mining data streams in the distributed environments with both labeled and unlabeled data. For each local site, a local micro-cluster based ensemble is used and its updating algorithms are designed. Making use of the time-based sliding window techniques, the local models in a fixed time-span are transferred to a central site after being generated in all local sites, and then the global patterns related to this time-span can be mined at the central site. In our approach, the global patterns are micro-cluster based rather than typical classifiers such decision trees, and we can get an expected classification accuracy when a higher mining performance is assured. The experiments demonstrate that our approach is effective and efficient to classify multiple data streams in distributed environments.

关键词： distributed data stream micro-cluster ensemble classifiing model combination

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A Solution for Mining Big data Based on distributed data streams and Its Classifying Algorithms 2nd

A Solution for Mining Big Data Based on Distributed Data Str...

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2nd International Conference on data Mining and Big data (DMBD)

作者： Mao, Guojun Qiao, Jiewei Cent Univ Finance & Econ Informat Sch Beijing Peoples R China

ISBN: (纸本)9783319618456;9783319618449

With the advent of the era of big data, a require to discover valuable knowledge from big data is being one of focuses. However, big data is a term which has been described with the features of alarming velocity, super volume and various data structures, and so how to express big data to effectively mine is becoming a key problem Aiming at requires of big data analyses, this paper will construct the concept of DDS (distributed data stream), and build the mining model and some key algorithms. The experiments show integrating these algorithms under our model can get higher mining accuracies to distributed data streams.

关键词： Big data data mining distributed data stream Micro-cluster

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Communication-efficient processing of multiple continuous aggregate queries

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INFORMATION SCIENCES 2014年 284卷 1-17页

作者： Jeon, Joo Hyuk Lee, Ki Yong Kim, Myoung Ho Korea Adv Inst Sci & Technol Taejon 305701 South Korea Sookmyung Womens Univ Seoul 140742 South Korea

Recently, great attention has been paid to distributed stream processing systems (DSPSs), where a number of distributed source nodes generate data streams and queries are continuously executed over those streams. An emerging challenge in large-scale DSPSs is to efficiently process multiple continuous aggregate queries. To process a continuous aggregate query, each source node periodically sends partial aggregates computed over its local stream to the sink node, where the final results are computed. Since sending partial aggregates for each query separately can lead to significant communication overhead, lowering the overall performance and scalability, multiple aggregate queries must be processed collectively, rather than separately. In this paper, we propose an efficient method for collectively processing multiple continuous aggregate queries, which minimizes the total communication costs from source nodes to the sink node. The proposed method finds the smallest set of partial aggregates that each source node needs to send to the sink node to correctly answer all the queries, and thus minimizes the number of message transmissions. Furthermore, since the proposed method operates on-the-fly, it can efficiently handle registration or deregistration of queries at any time. Through theoretical analysis and experiments, we show that the proposed method outperforms the existing ones in terms of communication costs, (C) 2014 Elsevier Inc. All rights reserved.

关键词： Aggregate query Multi-query optimization distributed data stream Sliding window

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The research on data Flow Technology in Computer Network Security Monitoring

The research on Data Flow Technology in Computer Network Sec...

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IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA)

作者： Yin XiaoHong Nanchang Inst Sci & Technol Nanchang 330108 Jiangxi Peoples R China

ISBN: (纸本)9781479969890

With the rapid development of computer technology and application of Internet is becoming more and more widely, the Internet plays a more and more important role in people's life. At the same time, all kinds of network security events emerge in endlessly, seriously threaten the application and development of the Internet. With the purpose of safety, network monitoring, have more and more important significance in the maintenance of normal efficiently network run, key facilities, information system security, etc.,. How to realize effective network transmission and efficient online analysis to a huge number of distributed network security monitoring data so as to provide further support for a variety of applications become a major challenge in the field of network security and data processing.

关键词： Network Security Monitoring distributed data stream Cost Efficient Processing Multi-query Optimization stream Cube

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Research on Cost-Efficient Processing of Continuous Extreme Queires

Research on Cost-Efficient Processing of Continuous Extreme ...

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3rd International Symposium on Intelligent Information Technology Application

作者： Kong, Xuehui Wang, Shuyang Li, Yuehua Li, Tian NE Forestry Univ Coll Mech & Elect Engn Harbin 150040 Peoples R China

ISBN: (纸本)9780769538594

We address the problem of cost-efficient processing of continuous extreme queires (MAX or MIN) over distributed sliding window streams, and propose several methods for communication reduction and resource sharing among queries. Firstly, we develop an effective pruning technique to minimize the number of elements to be kept. It can be shown that on average only O(logN) key points need to be stored for exact answer of extreme query, where N is the number of points contained in the sliding window. Then we consider the distributed environment, where remote nodes delay the data transmission as late as possible, and adopt the pruning strategy to filter local stream tuples, which is quite efficient in communication reduction. Analytical analysis and experimental evidences show the efficiency of proposed approach both on storage/communication reduction and efficiency improvement.

关键词： distributed data stream continuous extreme value queries data pruning communication reduction resource sharing

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