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An Agent-Based Computational Framework for distributed data analysis

COMPUTER 2020年第3期53卷 16-25页

作者： Fukuda, Munehiro Gordon, Collin Mert, Utku Sell, Matthew Univ Washington Div Comp & Software Syst Bothell WA 98011 USA JBT FoodTech Chicago IL USA Apple Cupertino CA USA Fluke Elect Everett WA USA

Major software tools for distributed-data analysis, such as MapReduce and Spark, examine data items on separate disks by streaming them into memory. We contrast data streaming against an agent-based approach that move... 详细信息

关键词： data analysis Multi Agent Systems Parallel Processing data Streaming Spark Map Reduce Software Tools distributed data analysis Agent Based Computational Framework Agent Based Approach Contrast data data Items distributed databases data analysis Instruction Sets Big data Mobile Agents Task analysis Software Tools

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Mining privacy-preserving association rules using transaction hewer allocator and facile hash algorithm in multi-cloud environments

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METHODSX 2025年 14卷 103317页

作者： Dhinakaran, D. Gopalakrishnan, S. Selvaraj, D. Girija, M. S. Prabaharan, G. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Comp Sci & Engn Chennai India Madanapalle Inst Technol & Sci Dept Comp Sci & Engn Data Sci Madanapalle Andhra Prades India Panimalar Engn Coll Dept Elect & Commun Engn Chennai India RMK Engn Coll Dept Comp Sci & Design Chennai India

In this era of data-driven decision-making, it is important to securely and efficiently extract knowledge from distributed datasets. However, in outsourced data for tasks like frequent itemset mining, privacy is an important issue. The difficulty is to secure sensitive data while delivering the insights of the data. First, this paper proposes a new multi-cloud approach to preserve privacy, which includes two main components, named the Transaction Hewer and Allocator module and the Facile Hash Algorithm (FHA), in extracting the frequent itemset. All these components work together to protect the privacy of the data, wherever it is, during the transmission phase or the computation phase, even if it is raw data or processed data, on the different distributed cloud platforms. The complexities involved in the mining of frequent itemsets led us to introduce the Apriori with Tid Reduction (ATid) algorithm considering scalability and computational operational improvements to the mining process due to the Tid Reduction concept. We conduct performance evaluation on several datasets and show that our proposed framework achieves higher performance than existing methods, and encryption and decryption processes reduce the computational time by up to 25 % compared to the best alternative. It also exhibits approximately 15 % reduction in communication costs and displays scalability with the growing number of transactions, compared to the state-of-the-art evaluation metrics that indicate improved communication overhead. center dot Introduces a multi-cloud privacy framework with Facile Hash Algorithm and Transaction Hewer and Allocator. center dot Enhances scalability using ATid algorithm with Tid Reduction.

关键词： Association rule mining distributed data analysis data encryption data privacy Frequent itemset mining Horizontal partitioning

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A Fuzzy Logic Recommendation System to Support the Design of Cloud-Edge data analysis in Cyber-Physical Systems

IEEE OPEN JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY

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IEEE OPEN JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY 2022年 3卷 174-187页

作者： Queiroz, Jonas Leitao, Paulo Oliveira, Eugenio Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot P-5300253 Braganca Portugal Univ Porto Fac Engn Artificial Intelligence & Comp Sci Lab P-4200465 Porto Portugal

The ongoing 4th industrial revolution is characterized by the digitization of industrial environments, mainly based on the use of Internet of Things, Cloud Computing and Artificial Intelligence (AI). Regarding AI, although data analysis has shown to be a key enabler of industrial Cyber-Physical Systems (CPS) in the development of smart machines and products, the traditional Cloud-centric solutions are not suitable to attend the data and time-sensitive requirements. Complementary to Cloud, Edge Computing has been adopted to enable the data processing capabilities at or close to the physical components. However, defining which data analysis tasks should be deployed on Cloud and Edge computational layers is not straightforward. This work proposes a framework to guide engineers during the design phase to determine the best way to distribute the data analysis capabilities among computational layers, contributing for a lesser ad-hoc design of distributed data analysis in industrial CPS. Besides defining the guidelines to identify the data analysis requirements, the core contribution relies on a Fuzzy Logic recommendation system for suggesting the most suitable layer to deploy a given data analysis task. The proposed approach is validated in a smart machine testbed that requires the implementation of different data analysis tasks for its operation.

关键词： data analysis Cloud computing Task analysis Industrial electronics Fuzzy logic Edge computing Cyber-physical systems Cyber-physical system design distributed data analysis fuzzy recommendation system

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distributed data Compression for Edge Devices 17th

Distributed Data Compression for Edge Devices

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17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI) / 6th 5G-PINE Workshop / AI-BIO Workshop / DAAI Workshop / DARE Workshop / EEAI Workshop / 10th MHDW Workshop

作者： Van Vaerenbergh, Kevin Tourwe, Tom EluciDATALab Sirris Blvd A Reyerslaan 80 B-1030 Brussels Belgium

ISBN: (纸本)9783030791599;9783030791575;9783030791568

In this paper, we elaborate on the issue of reliable storage and efficient communication of large quantities of data in the absence of continuous connectivity. We illustrate how advanced machine learning techniques can run locally at the edge, in the context of data compression related to special-purpose vehicles. Two different data compression techniques are compared by calculating general compression metrics, e.g., compression rate and root mean-squared error, while also validating the results using an event detection algorithm. These techniques exploit real-world usage data captured in the field using the I-HUMS platform provided by our industrial partner ILIAS solutions Inc.

关键词： IoT distributed data analysis Time series compression

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distributed AI for Special-Purpose Vehicles 15th

Distributed AI for Special-Purpose Vehicles

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39th International Conference on Computer Safety, Reliability and Security (SAFECOMP)

作者： Van Vaerenbergh, Kevin Cabral, Henrique Dagnely, Pierre Tourwe, Tom EluciDATALab Sirris Blvd A Reyerslaan 80 B-1030 Brussels Belgium

ISBN: (纸本)9783030555825;9783030555832

In this paper, we elaborate on two issues that are crucial to consider when exploiting data across a fleet of industrial assets deployed in the field: 1) reliable storage and efficient communication of large quantities of data in the absence of continuous connectivity, and 2) the traditional centralized data analytics model which is challenged by the inherently distributed context when considering a fleet of distributed assets. We illustrate how advanced machine learning techniques can run locally at the edge, in the context of two industry-relevant use cases related to special-purpose vehicles: data compression and vehicle overload detection. These techniques exploit real-world usage data captured in the field using the I-HUMS platform provided by our industrial partner ILIAS solutions Inc.

关键词： IoT distributed data analysis Time series data compression

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Declarative Big data analysis for High-Energy Physics: TOTEM Use Case 25th

Declarative Big Data Analysis for High-Energy Physics: TOTEM...

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25th International Conference on Parallel and distributed Computing (Euro-Par)

作者： Avati, Valentina Blaszkiewicz, Milosz Bocchi, Enrico Canali, Luca Castro, Diogo Cervantes, Javier Grzanka, Leszek Guiraud, Enrico Kaspar, Jan Kothuri, Prasanth Lamanna, Massimo Malawski, Maciej Mnich, Aleksandra Moscicki, Jakub Murali, Shravan Piparo, Danilo Tejedor, Enric AGH Univ Sci & Technol Krakow Poland CERN CH-1211 Geneva 23 Switzerland

ISBN: (纸本)9783030294007;9783030293994

The High-Energy Physics community faces new data processing challenges caused by the expected growth of data resulting from the upgrade of LHC accelerator. These challenges drive the demand for exploring new approaches for data analysis. In this paper, we present a new declarative programming model extending the popular ROOT data analysis framework, and its distributed processing capability based on Apache Spark. The developed framework enables high-level operations on the data, known from other big data toolkits, while preserving compatibility with existing HEP data files and software. In our experiments with a real analysis of TOTEM experiment data, we evaluate the scalability of this approach and its prospects for interactive processing of such large data sets. Moreover, we show that the analysis code developed with the new model is portable between a production cluster at CERN and an external cluster hosted in the Helix Nebula Science Cloud thanks to the bundle of services of Science Box.

关键词： High-Energy Physics distributed data analysis Apache Spark Scalability

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distributed data Anonymization 17

Distributed Data Anonymization

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IEEE 17th Int Conf on Dependable, Autonom and Secure Comp / IEEE 17th Int Conf on Pervas Intelligence and Comp / IEEE 5th Int Conf on Cloud and Big data Comp / IEEE 4th Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)

作者： SheikhAlishahi, Mina Martinelli, Fabio Eindhoven Univ Technol Eindhoven Netherlands Consiglio Nazl Ric IIT CNR Ist Informat & Telemat Pisa Italy

ISBN: (纸本)9781728130248

data generalization is a widely-used privacy technique, in which an accurate value of sensitive information is replaced with a more general representation. The problem of data generalization becomes challenging when data is distributed among several agents, who are interested in releasing their table of data to shape a data mining algorithm on the whole of their data. The main issue originates from the fact that when each agent generalizes her own dataset locally, the released tables of data suffer from non-homogeneity. To sole the issue, all agents can generalize their data to the widest range of generalization. However, this approach causes utility loss. To optimally address this problem, in this study we present a framework that serves as a tool for data owners to generalize their data homogeneously before being published. The effectiveness of the proposed mechanism is validated through an experimental analysis on a benchmark dataset.

关键词： distributed data analysis privacy secure computation generalization

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Integration of end-user Cloud storage for CMS analysis

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年第Part3期78卷 1079-1082页

作者： Riahi, Hassen Aimar, Alberto Ayllon, Alejandro Alvarez Balcas, Justas Ciangottini, Diego Hernandez, Jose M. Keeble, Oliver Magini, Nicolo Manzi, Andrea Mascetti, Luca Mascheroni, Marco Tanasijczuk, Andres Jorge Vaandering, Eric Wayne European Org Nucl Res CH-1211 Geneva 23 Switzerland CALTECH Pasadena CA 91125 USA Univ Perugia Via Alessandro Pascoli I-06123 Perugia Italy INFN Perugia Via Alessandro Pascoli I-06123 Perugia Italy CIEMAT E-28040 Madrid Spain Fermi Natl Lab Batavia IL 60510 USA Univ Calif San Diego La Jolla CA 92093 USA

End-user Cloud storage is increasing rapidly in popularity in research communities thanks to the collaboration capabilities it offers, namely synchronisation and sharing. CERN IT has implemented a model of such storage named, CERNBox, integrated with the CERN AuthN and AuthZ services. To exploit the use of the end-user Cloud storage for the distributed data analysis activity, the CMS experiment has started the integration of CERNBox as a Grid resource. This will allow CMS users to make use of their own storage in the Cloud for their analysis activities as well as to benefit from synchronisation and sharing capabilities to achieve results faster and more effectively. It will provide an integration model of Cloud storages in the Grid, which is implemented and commissioned over the world's largest computing Grid infrastructure, Worldwide LHC Computing Grid (WLCG). In this paper, we present the integration strategy and infrastructure changes needed in order to transparently integrate end-user Cloud storage with the CMS distributed computing model. We describe the new challenges faced in data management between Grid and Cloud and how they were addressed, along with details of the support for Cloud storage recently introduced into the WLCG data movement middleware, FTS3. The commissioning experience of CERNBox for the distributed data analysis activity is also presented. (C) 2017 Published by Elsevier B.V.

关键词： data transfer protocols Cloud storage distributed data management distributed data analysis

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Agent-Based data analysis Towards the Dynamic Adaptation of Industrial Automation Processes 7th

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7th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS)

作者： Queiroz, Jonas Leitao, Paulo Univ Porto Fac Engn P-4200465 Oporto Portugal Univ Porto LIACC Artificial Intelligence & Comp Sci Lab P-4169007 Oporto Portugal Polytech Inst Braganca Campus Sta Apolonia P-5300253 Braganca Portugal

ISBN: (纸本)9783319311654;9783319311647

Industrial complex systems demand the dynamic adaptation and optimization of their operation to cope with operational and business changes. In order to address such requirements and challenges, cyber-physical systems promotes the development of intelligent production units and products. The realization of such concepts requires, amongst others, advanced data analysis approaches, capable to take advantage of increased availability of data, in order to overcome the inherent dynamics of industrial environments, by providing more modular, adaptable and responsiveness systems. In this context, this work introduces an agent-based data analysis approach to support the supervisory and control levels of industrial processes. It proposes to endow agents with data analysis capabilities and cooperation strategies, enabling them to perform distributed data analysis and dynamically improve their analysis capabilities, based on the aggregation of shared knowledge. Some experiments have been performed in the context of an electric micro grid to validate this approach.

关键词： Multi-agent system distributed data analysis Adaptive system

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Agents' MonitoringApproach for Big data 6

Agents' MonitoringApproach for Big Data

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Sixth International Conference on Developments in eSystems Engineering (DeSE)

作者： Randles, Martin Lamb, David Attwood, Andrew LJMU Sch Comp & Math Sci Liverpool Merseyside England LJMU Sch Engn Liverpool Merseyside England

ISBN: (纸本)9781479952649

data sources are becoming more prevalent as digital devices in the form of sensor networks, capture and record vast amounts of individual and environmental data. Much of this data is to some extent redundant, as it is captured or held at many locations, or is of a low priority level, so can safely be ignored. The distributed nature of such data, however, means that it is impossible to identify redundant or useless information without full scale analysis. The velocity of data arrival, the volume of data and the heterogeneous nature (variety) of the data makes this task unfeasible for any real time analysis, which is becoming more desirable in real world situations. Thus this paper is looking to utilize a multi-agent system or federation of agents to analyse data in a distributed manner. The method of setting up such an agent team is proposed so as to engender a cohesive team ethos endowing the agent federation with the power of a single agent's goal. It is then shown that this leads to a specific network topology to emerge within the agent team. Furthermore such a topology allows an acquaintance monitoring algorithm to be applied. This is shown to actively attenuate and prioritize data by suggesting only those sensor nodes that are likely to be in possession of useful and non-redundant data, using only data local to each agent team member. The results are gained, in this first instance, by a simulation

关键词： Multi-agent systems Teamwork Sensor networks distributed data analysis Acquaintance monitoring

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