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Service-oriented middleware for distributed data mining on the grid

JOURNAL OF PARALLEL AND distributed COMPUTING 2008年第1期68卷 3-15页

作者： Congiusta, Antonio Talia, Domenico Trunfio, Paolo Univ Calabria DEIS I-87036 Arcavacata Di Rende Italy Exeura Srl I-87036 Arcavacata Di Rende Italy

Distribution of data and computation allows for solving larger problems and executing applications that are distributed in nature. The grid is a distributed computing infrastructure that enables coordinated resource sharing within dynamic organizations consisting of individuals, institutions, and resources. The grid extends the distributed and parallel computing paradigms allowing for resource negotiation and dynamical allocation, heterogeneity, open protocols, and services. Grid environments can be used both for compute-intensive tasks and data intensive applications by exploiting their resources, services, and data access mechanisms. data mining algorithms and knowledge discovery processes are both compute and data intensive, therefore the grid can offer a computing and data management infrastructure for supporting decentralized and parallel data analysis. This paper discusses how grid computing can be used to support distributed data mining. Research activities in grid-based data mining and some challenges in this area are presented along with some promising future directions for developing grid-based distributed data mining. (c) 2007 Elsevier Inc. All rights reserved.

关键词： distributed data mining grid computing grid services WSRF

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A differentially private distributed data mining scheme with high efficiency for edge computing

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2021年第1期10卷 1-12页

作者： Sun, Xianwen Xu, Ruzhi Wu, Longfei Guan, Zhitao North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China Fayetteville State Univ Dept Math & Comp Sci Fayetteville NC 28301 USA

A wide range of data mining applications benefit from the low latency offered by edge computing. However, edge computing suffers from limited computing resources, which inhibits the applications of the computationally expensive data mining methods. In the edge-cloud environment, usually, the participants turn to collaboratively train machine-learning models that yield more accurate prediction results. However, data owners may not be willing to sharing the own data for the privacy concerns. To handle such disparate goals, we focus on tree-based distributed data mining scheme with differential privacy, which is computationally friendly. The basic idea of our approach is based on a distributed ensemble strategy. Each participant builds an elegant decision model based on their own data, which has a good tradeoff between the computation and the accuracy of the data distribution, and shares it with other participants after being injected with the elaborate noise. Then the useful knowledge transferred from the decision models is acquired by other participants in an adaptive ensemble strategy. Both the theoretical analysis and the experiments show that our scheme provides an efficient data mining manner that can achieve a good prediction accuracy while providing rigorous privacy guarantee over the distributed data.

关键词： distributed data mining Differential privacy AdaBoost Edge computing

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A Windowing strategy for distributed data mining optimized through GPUs

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PATTERN RECOGNITION LETTERS 2017年 93卷 23-30页

作者： Limon, Xavier Guerra-Hernández, Alejandro Cruz-Ramírez, Nicandro Acosta-Mesa, Hector-Gabriel Grimaldo, Francisco Univ Veracruzana Ctr Invest Inteligencia Artificial Sebastian Camacho 5 Xalapa 91000 Veracruz Mexico Univ Valencia Dept Informat Avigunda Univ S-N E-46100 Burjassot Spain

This paper introduces an optimized Windowing based strategy for inducing decision trees in distributed data mining scenarios. Windowing consists in selecting a sample of the available training examples (the window) to induce a decision tree with an usual algorithm, e.g., J48;finding instances not covered by this tree (counter examples) in the remaining training examples, adding them to the window to induce a new tree;and repeating until a termination criterion is met. In this way, the number of training examples required to induce the tree is reduced considerably, while maintaining the expected accuracy levels;which is paid in terms of time performance. Our proposed enhancements solve this by searching for counter examples on GPUs and further reducing their number in the window. The resulting strategy is implemented in JaCa-DDM, our agents & artifacts tool for distributed data mining, keeping the benefits of Windowing, while distributing the process and being faster than the traditional centralized approach, even performing similarly to Bagging and Random Forests in some cases. Experiments in data mining tasks are addressed, including a case study on pixel-based segmentation for the detection of precancerous cervical lesions on medical images. (C) 2016 Elsevier B.V. All rights reserved.

关键词： distributed data mining Decision trees Multi-Agent Systems Windowing GPU computation

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Privacy preserving sub-feature selection in distributed data mining

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APPLIED SOFT COMPUTING 2015年 36卷 552-569页

作者： Bhuyan, Hemanta Kumar Kamila, Narendra Kumar Mahavir Inst Engn & Technol Dept Comp Sci & Engn Bhubaneswar Orissa India CV Raman Coll Engn Dept Comp Sci & Engn Bhubaneswar Orissa India

This paper addresses the selection of sub-feature from each feature using fuzzy methodologies maintaining the privacy during collection of data from participating parties in distributed environment. Based on fuzzy random variables conditional expectation is used in which two fuzzy sets are generated using Borel set that helps to determine sub-feature within certain interval. The privacy and selection of sub-feature leading to a distinguished class is the main objective of this research work. These two problems are directly related to data mining problems of classification and characterization of feature. In many cases traditional techniques are not suitable for complex databases. However our methodology provides better way for selection of sub-features under different situations. The proposed model and techniques both presents extensive theoretical analysis and experimental results. The experiments show the effectiveness and performance based on real world data set. (C) 2015 Published by Elsevier B.V.

关键词： Privacy distributed data mining Feature selection Fuzzy probability Fuzzy random variable

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A communication efficient and scalable distributed data mining for the astronomical data

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ASTRONOMY AND COMPUTING 2016年第0期16卷 166-173页

作者： Govada, A. Sahay, S. K. BITS Pilani Dept Comp Sci & Informat Syst KK Birla Goa Campus Sancoale 403726 Goa India

In 2020, similar to 60PB of archived data will be accessible to the astronomers. But to analyze such a paramount data will be a challenging task. This is basically due to the computational model used to download the data from complex geographically distributed archives to a central site and then analyzing it in the local systems. Because the data has to be downloaded to the central site, the network BW limitation will be a hindrance for the scientific discoveries. Also analyzing this PB-scale on local machines in a centralized manner is challenging. In this, virtual observatory is a step towards this problem, however, it does not provide the data mining model (Zhang et al., 2004). Adding the distributed data mining layer to the VO can be the solution in which the knowledge can be downloaded by the astronomers instead the raw data and thereafter astronomers can either reconstruct the data back from the downloaded knowledge or use the knowledge directly for further analysis. Therefore, in this paper, we present distributed Load Balancing Principal Component Analysis for optimally distributing the computation among the available nodes to minimize the transmission cost and downloading cost for the end user. The experimental analysis is done with Fundamental Plane (FP) data, Gadotti data and complex Mfeat data. In terms of transmission cost, our approach performs better than Qi et al. and Yue et al. The analysis shows that with the complex Mfeat data similar to 90% downloading cost can be reduced for the end user with the negligible loss in accuracy. (C) 2016 Elsevier B.V. All rights reserved.

关键词： distributed data mining Astronomical data Principal component analysis Load balancing

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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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A Security-enabled Grid System for MINDS distributed data mining

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JOURNAL OF GRID COMPUTING 2014年第3期12卷 521-542页

作者： Kim, Seonho Kim, Jinoh Weissman, Jon B. Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA Texas A&M Univ Dept Comp Sci & Informat Syst Commerce TX 75428 USA

Interest has been increasing in mining information from a huge amount of data for various purposes such as business processes, scientific discoveries, and security. MINDS (Minnesota INtrusion Detection System) is a tool utilizing data mining techniques to identify both known and unknown network intrusions. While performing well with a high degree of accuracy in detecting intrusions, MINDS is intrinsically limited due to its centralized design. At the same time, there are increasing needs for coordinating multiple distributed sites to find correlations of suspicious events and for sharing distributed anomaly data in different administrative domains since network intrusions become more sophisticated and harder to identify by a single site analysis. In this work, we consider a distributed model of MINDS to eliminate the limitations that the current MINDS suffers from due to its centralized nature. We utilize Grid technologies to achieve the distribution of services. In addition, we develop security frameworks for authentication and access control since it is important to preserve privacy and sensitivity of data regardless of distribution. To verify the feasibility of our approach, we also present our experimental results conducted in a local testbed with four nodes and in a wide-area setting in PlanetLab with 20 nodes geographically dispersed.

关键词： distributed data mining Security-enabled Grid Access Control MINDS

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Modeling and implementing distributed data mining strategies in JaCa-DDM

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KNOWLEDGE AND INFORMATION SYSTEMS 2019年第1期60卷 99-143页

作者： Limon, Xavier Guerra-Hernandez, Alejandro Cruz-Ramirez, Nicandro Grimaldo, Francisco Univ Veracruzana Ctr Invest Inteligencia Artificial Sebastian Camacho 5 Mexico City 91000 DF Mexico Univ Valencia Dept Informat Avinguda Univ S-N E-46100 Valencia Spain

This work introduces JaCa-DDM, a novel distributed data mining system founded on the agents and artifacts paradigm, conceived to design, implement, deploy, and evaluate learning strategies. Jason rational agents conform to such strategies to cope with distributed computing environments, where CArtAgO artifacts encapsulate learning algorithms, data sources, evaluation tools, and other services implemented in Weka for data mining tasks. The set of strategies presented in this paper aims at encouraging the use of JaCa-DDM to develop new ones, suited to different needs. For this, our system provides tools to evaluate the resulting models in terms of accuracy, number of instances employed to learn, time of convergence, and volume of communications. Although the emphasis in decision trees, JaCa-DDM can be easily extended by adopting new artifacts, e.g., for meta-learning. The main contributions of the paper are as follows: (i) From the multi-agent systems perspective, our approach illustrates how to exploit the so-called "agentification" of Weka for the sake of code reusability, while preserving the benefits of reasoning at the Belief-Desire-Intention level with Jason;(ii) from the data mining perspective, JaCa-DDM is promoted as an extensible tool to define and test distributed strategies;and (iii) a set of strategies including centralizing, meta-learning and Windowing-based approaches, is carefully analyzed to provide comparisons among them.

关键词： Multi-agent systems distributed data mining Agent mining Agents and artifacts paradigm BDI agents

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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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distributed data mining in grid computing environment

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AGRO FOOD INDUSTRY HI-TECH 2017年第1期28卷 2719-2723页

作者： Xue, Huifang Xian Int Univ Xian Peoples R China

With the development of cloud storage and the rise of big data, the amount of data information is growing exponentially. The diversification of the storage mode has contributed to the generation of distributed data mining. Besides, under the grid computing environment, distributed data mining is based on the analysis, classification and mining of a large number of data. In this paper, based on the grid computing environment, the distributed data mining technology was used, and the data of distributed storage was analyzed. A set of data mining computing platform with the functions of the distribution, the isomerization, the on-line elasticity expansion and the analysis data was established. A new way of thinking for the construction of distributed data mining system was proposed.

关键词： Grid distributed data mining web service cloud storage

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