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Statistical strategies for the analysis of massive data sets

BIOMETRICAL JOURNAL 2020年第2期62卷 270-281页

作者： Hwang, Hon Ryan, Louise Univ Technol Sydney Sch Math & Phys Sci Fac Sci 123 Broadway Sydney NSW 2007 Australia Harvard T Chan Sch Publ Hlth Dept Biostat Boston MA USA Univ Melbourne Australian Res Council Ctr Excellence Math & Stat Melbourne Vic Australia

The advent of the big data age has changed the landscape for statisticians. Public and private organizations alike these days are interested in capturing and analyzing complex customer data in order to improve their service and drive efficiency gains. However, the large volume of data involved often means that standard statistical methods fail and new ways of thinking are needed. Although great gains can be obtained through the use of more advanced computing environments or through developing sophisticated new statistical algorithms that handle data in a more efficient way, there are also many simpler things that can be done to handle large data sets in an efficient and intuitive manner. These include the use of distributed analysis methodologies, clever subsampling, data coarsening, and clever data reductions that exploit concepts such as sufficiency. These kinds of strategies represent exciting opportunities for statisticians to remain front and center in the data science world.

关键词： distributed data divide and recombine Hadoop sufficiency

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Parallelization of the self-organized maps algorithm for federated learning on distributed sources

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JOURNAL OF SUPERCOMPUTING 2021年第6期77卷 6197-6213页

作者： Kholod, Ivan Rukavitsyn, Andrey Paznikov, Alexey Gorlatch, Sergei St Petersburg Electrotech Univ LETI St Petersburg Russia Univ Munster Munster Germany

This paper describes a formally based approach for parallelizing the Kohonen algorithm used for the federated learning process in a special kind of neural networks-Self-Organizing Maps. Our approach enables executing the parallel algorithm version on the distributed data sources, taking into account the kind of data distribution on the nodes. Compared to the traditional approaches, we distinguish two kinds of data distributions-horizontal and vertical: for both, our suggested approach avoids gathering data in a single storage, but rather moves computations nearer to the data source nodes. This reduces the execution time of the algorithm, the network traffic, and the risk of an unauthorized access to the data during their transmission. Our experimental evaluation demonstrates the advantages of the approach.

关键词： Self-Organizing Maps (SOM) Neural networks distributed data Federated learning Kohonen algorithm

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distributed maintenance of multiple product views

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COMPUTER-AIDED DESIGN 2000年第7期32卷 421-431页

作者： Hoffmann, CM Joan-Arinyo, R Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Univ Politecn Catalunya Dept Llenguatges & Sist Informat Catalunya Spain

We present three mechanisms for maintaining consistent product views in a distributed product information database. The mechanisms are used when one of the views makes a change to the product model and the other views... 详细信息

关键词： computer aided design master model distributed data product view features constraints neutral representations

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An Efficient Federated Multiview Fuzzy C-Means Clustering Method

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第4期32卷 1886-1899页

作者： Hu, Xingchen Qin, Jindong Shen, Yinghua Pedrycz, Witold Liu, Xinwang Liu, Jiyuan Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China Wuhan Univ Technol Sch Management Wuhan 430070 Peoples R China Chongqing Univ Sch Econ & Business Adm Chongqing 400044 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada Polish Acad Sci Syst Res Inst PL-00901 Warsaw Poland Istinye Univ Fac Engn & Nat Sci Dept Comp Engn TR-34396 Sariyer Istanbul Turkiye

Multiview clustering has been received considerable attention due to the widespread collection of multiview data from diverse domains and sources. However, storing multiview data across multiple devices in many real scenarios poses significant challenges for efficient data analysis. Federated learning framework enables collaborative machine learning on distributed devices while preserving privacy constraints. Even though there have been intensive algorithms on multiview fuzzy clustering, federated multiview fuzzy clustering has not been adequately investigated so far. In this study, we first develop the federated learning mode into multiview fuzzy clustering and realize the federated optimization procedure, called federated multiview fuzzy C-means clustering. Then, we design an original strategy of consensus prototype learning during federated multiview fuzzy clustering. It is termed as federated multiview fuzzy C-means consensus prototypes clustering (FedMVFPC). We also further develop the federated alternative optimization algorithm with proven convergence. This study also introduces the notion of clustering prototype communication within the federated learning framework, and integrates the clustering prototypes of different views into a unified optimization formulation. The experimental studies on various benchmark datasets demonstrate that the proposed FedMVFPC method improves the federated clustering performance and efficiency. It achieves comparable or better clustering performance against the existing state-of-the-art multiview clustering algorithms.

关键词： Prototypes Fuzzy logic Clustering algorithms Clustering methods Optimization Federated learning distributed databases Fuzzy clustering federated learning multiview clustering distributed data prototype learning

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Rough sets in distributed decision information systems

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KNOWLEDGE-BASED SYSTEMS 2016年第Feb.15期94卷 13-22页

作者： Hu, Jun Pedrycz, Witold Wang, Guoyin Wang, Kai Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

In "traditional" rough set methodologies, data are assumed to be stored in a single data repository. However, this assumption is not always true in many real-world problems, where data may be distributed across multiple locations, which is especially pertinent with the development of the Internet. To cope with this phenomenon, in this paper we extend the methodology of rough sets to distributed decision information systems. We first present a definition of rough sets in distributed decision information systems. Then we study the reducibility of distributed decision information systems at two different levels of granularity. The conditions for a decision information table or an attribute in distributed decision information systems to be reducible are presented, and an approach to compute reducts of a distributed decision information system is developed. The experimental results show that the proposed approach can be used to simplify distributed decision information systems, while retain their classification abilities. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Rough sets Knowledge reduction distributed data distributed decision information systems

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Trajectory Pattern Mining for Urban Computing in the Cloud

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2017年第2期28卷 586-599页

作者： Altomare, Albino Cesario, Eugenio Comito, Carmela Marozzo, Fabrizio Talia, Domenico Inst High Performance Comp & Networks ICAR CNR Via P Bucci7-11 C I-87036 Arcavacata Di Rende CS Italy Univ Calabria DIMES Dept I-87036 Arcavacata Di Rende CS Italy

The increasing pervasiveness of mobile devices along with the use of technologies like GPS, Wifi networks, RFID, and sensors, allows for the collections of large amounts of movement data. This amount of data can be analyzed to extract descriptive and predictive models that can be properly exploited to improve urban life. From a technological viewpoint, Cloud computing can play an essential role by helping city administrators to quickly acquire new capabilities and reducing initial capital costs by means of a comprehensive pay-as-you-go solution. This paper presents a workflow-based parallel approach for discovering patterns and rules from trajectory data, in a Cloud-based framework. Experimental evaluation has been carried out on both real-world and synthetic trajectory data, up to one million of trajectories. The results show that, due to the high complexity and large volumes of data involved in the application scenario, the trajectory pattern mining process takes advantage from the scalable execution environment offered by a Cloud architecture in terms of both execution time, speed-up and scale-up.

关键词： Trajectory mining urban computing cloud computing distributed data

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Construction of Japanese Virtual Observatory (JVO)

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FUJITSU SCIENTIFIC & TECHNICAL JOURNAL 2004年第2期40卷 217-223页

作者： Ishihara, Y Mizumoto, Y Ohishi, M Kawarai, K Fujitsu Ltd Tokyo Japan Natl Astron Observ Japan Mitaka Tokyo Japan

Large amounts of high-quality astronomical data are obtained from the Subaru Telescope and other observatories located throughout the world, and this data is stored in worldwide locations. It is now more important than ever to have an observational database that enables astronomers,to search for and collect valuable astronomical data from this vast collection and perform research through statistical analysis, The National Astronomical Observatory of Japan (NAOJ) has therefore started construction of a system called the Japanese Virtual Observatory (JVO) that will connect these observational databases via high-speed networks so they can be used collectively as a single virtual data archive. Fujitsu has been collaborating with the NAOJ to develop JVO prototype systems. To build the JVO, a virtual integrated environment for the distributed data will be required. To achieve this, three issues must be considered: 1) construction of a virtual data storage system, 2) efficient use of the vast amount of distributed data, and 3) the fragileness of the distributed environment. This paper describes and evaluates the JVO prototype constructed using data Grid technology to ascertain the feasibility of a virtual integrated environment for the distributed data.

关键词： .locations databases observational astronomers distributed data Construction of Japanese National Astronomical Observatory Virtual Observatory JVO astronomical data Subaru single virtual data archive JVO prototype virtual integrated Grid technology

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Performance analysis of HMM- and ANN-based spectrum vacancy predictor behaviour for cognitive radios

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INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING 2012年第4期11卷 206-213页

作者： Karia, Deepak C. Lande, B. K. Daruwala, R. D. Univ Mumbai SPIT Dept Elect & Telecommun Bombay 400058 Maharashtra India Univ Mumbai SAKEC Dept Elect Bombay 400088 Maharashtra India Univ Mumbai VJTI Dept Elect Bombay 400019 Maharashtra India

Cognitive Radios (CRs) are devices, which should be cognizant about the Spectrum Holes upon which the idea of a CR resides, which relates to the sensing and channel management function of the device. CR, therefore, must employ channel prediction techniques so as to decide the usage of channel and also prevents interference with the primary users (or incumbent users). In this paper, we use HMM to predict the channel usage patterns and to determine the channel occupancy states. We make use of BWA to train the parameters of the HMM model, Viterbi algorithm to estimate the hidden state of the channel and BWFA to predict the next state of the sequence. In addition to this, we compare the performance of the HMM-based model with that of a neural network-based predictor, which employs a time-delay feed forward network and which uses back propagation algorithm for training.

关键词： CRs cognitive radios channel prediction HMM hidden Markov models Baum-Welch algorithm Viterbi algorithm real-time data distributed data neural network back-propagation algorithm

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Querying sensor data for environmental monitoring

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INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2011年第3期10卷 132-142页

作者： Toscani, Daniele Giordani, Ilaria Cislaghi, Mauro Quarenghi, Luigi Consorzio Milano Ric I-20125 Milan Italy Univ Milano Bicocca Dept Comp Sci Syst & Commun I-20126 Milan Italy

data sources are increasing in number and distribution on the territory: sensor networks and individual devices are not any more atomic devices, but they are transforming data providers and consumers, paving the way to new personalised services. In this context, the paper presents a new software infrastructure for the integration of heterogeneous data (web services and sensors' API for streaming data, databases and files for historical data) and any type of data analysis and inference algorithms (e.g. forecast, statistical analysis, data cleaning, anomaly detection and optimisation). This infrastructure, named SUSHI (Supporting Unified access for Streaming and Historical data), provides explicit representation of domain objects, queries over heterogeneous data sources, online access to different sources, inclusion of processing components such as forecasting and simulation modules. The paper describes the SUSHI concept and architecture, a case study in environmental management and possible future developments for new services.

关键词： environmental monitoring querying data integration data analysis forecasting sensor networks environmental monitoring databases web services distributed data streaming data

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dataSpace: A data Web for the exploratory analysis and mining of data

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COMPUTING IN SCIENCE & ENGINEERING 2002年第4期4卷 44-51页

作者： Grossman, R Mazzucco, M Univ Illinois Lab Adv Comp Natl Ctr Data Min Chicago IL 60607 USA

A data web can simplify the processes of cleaning, transforming, and exploring data in a data mining application. dataSpace is one such mechanism that lets scientists view, retrieve, and apply simple transformations t... 详细信息

关键词： data mining distributed processing natural sciences computing dataanalysis Internet software agents dataSpace data web data exploring distributed data remotedata analysis scientific data analysis agent based systems data mining

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