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international Conference on Machine Learning, Big Data and business Intelligence (MLBDBI)

作者： Xin Lu Yu Wang Jiao Yuan Xun Wang Kun Fu Ke Yang School of software University of Electronic Science and Technology of China Chengdu China China Railway Eryuan Engineering Group Co. Ltd Chengdu China

ISBN: (数字)9781728196381

ISBN: (纸本)9781728196398

The existing parallel DBSCAN (density based spatial clustering of applications with noise) algorithm needs to determine the parameter settings manually, and the datasets will be repeatedly accessed in the process of data partitioning and data merging, which reduces the efficiency of the algorithm excuting. Therefore, this paper proposes a parallel adaptive DBSCAN algorithm based on k-dimensional tree partition. It divides the dataset into several balanced data partitions by using k-dimensional tree, and carries out parallel computing in spark distributed computing framework, thus increasing the concurrent processing ability of the algorithm program and improving the I/O access speed. In addition, the improved adaptive DBSCAN parameter method is applied to each data partition for clustering analysis to obtain local clusters, which solves the random problem of manual setting parameters in the clustering process, and ensures the data quality of clustering mining. At the same time of creating local clusters, this algorithm also puts the mapping relationship between data points and adjacent points into the HashMap data structure of the master node, and uses it to merge local clusters into whole clusters, which can reduce the time cost of data merging. The experimental results show that the proposed algorithm can save about 18% running time compared with RDD-DBSCAN algorithm without reducing the clustering quality. With the increase of the number of cluster nodes, the running efficiency of the algorithm can be further improved, so it is suitable for processing massive data clustering analysis.

关键词： Machine learning algorithms Merging Clustering algorithms Data structures Partitioning algorithms Sparks Data mining

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A study on application method for automation solution using blockchain dapp platform 1

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19th international Conference on Parallel and distributed computing, applications and Technologies, PDCAT 2018

作者： Kim, Seong-Kyu Kwon, Hyun-Taek Kim, Young-Kun Park, Yong-Pil Keum, Dae-Won Kim, Ung-Mo School of Electronic and Electrical Computer Engineering Sungkyunkwan University Seoul Korea Republic of Geobluelab Seoul Korea Republic of Electronic and Electrical Computer Engineering Sungkyunkwan University Seoul Korea Republic of

ISBN: (数字)9789811359071

ISBN: (纸本)9789811359064

The GOB Chain is a universal Blockchain platform designed to play the role of a perfect Blockchain hub for every Blockchain. First, it connects every Blockchain in order to facilitate the exchange of data between Blockchains based on W3C standard meta data, thus guaranteeing inter-operability and use, as well as searches of transaction information. In addition, it links the Blockchain technology to various existing legacy systems so that it can be immediately applied to the business in service. It allows the GOB chain to apply the Blockchain technology to existing businesses at the sites of the Fourth Industrial Revolution, thus enabling new productivity and competitiveness and improving profitability to a remarkable extent. The GOB Chain cooperates with all Blockchain technologies, companies, and researchers around the world to overcome the limitations of the existing Blockchain technology, and implements the Blockchain-platform ecology chain and the eco system, making our lives more convenient while revolutionizing every industry. In addition, it automates all block-chain systems and suggests a system that anyone can easily provide, operate, and maintain block-chain services. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Smart contract

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Personalized Commodity Recommendations of Retail business using User Feature based Collaborative Filtering 16

Personalized Commodity Recommendations of Retail Business us...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Wang, Feiran Wen, Yiping Guo, Tianhang Chen, Jinjun Cao, Buqing Hunan Univ Sci & Technol Key Lab Knowledge Proc & Networked Manufacture Xiangtan Peoples R China Swinburne Univ Technol Swinburne Data Sci Res Inst Hawthorn Vic Australia

ISBN: (纸本)9781728111414

Collaborative filtering is an extensively adopted approach for commodity recommendation. This paper proposes a user feature based collaborative filtering algorithm named UFCF for personalized commodity recommendations of retail business. It adopts matrix factorization and user features that are extracted from users' behaviors to improve the accuracy of recommendation result and alleviate the impact of sparse data. Experiments with real datasets from a supermarket marketing group demonstrate the effectiveness of the algorithm.

关键词： recommendation collaborative filtering user feature

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Manufacturing Industry Supply Chain Management Based on the Ethereum Blockchain

Manufacturing Industry Supply Chain Management Based on the ...

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IEEE international symposium on Parallel and distributed Processing with applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Zhijun Xu Yichen Liu Jun Zhang Zhaoxiong Song Jun Li Jihua Zhou School of Computer Science Beijing University of Posts and Telecommunications Beijing China College of Computer Science Inner Mongolia University Hohhot China Chinese Academy of Sciences Institute of Computing Technology Beijing China Bubi Technologies Beijing China Chongqing Jinmei Communication Chongqing China

ISBN: (纸本)9781728152103

Nowadays the composition and structure of the manufacturing industry supply chain have become increasingly complex. The loss and not-in-time transmission of the supply chain information have aggravated the bullwhip effect. At the same time, due to the lack of reliable information storage, the difficulties of traceability and accountability have also caused supply chain management to fall into the bottleneck. The blockchain has the characteristics of supporting distributed networking, information synchronization among nodes, digital encryption, traceable information and block content that cannot be tampered, which is suitable for use in supply chain, and can provide solution for it. In this paper, a design scheme of integrated platform for information service provided by participants in supply chain and based on Ethereum blockchain is proposed. By using Ethereum smart contracts, the common business involved in the supply chain is realized using blockchain technology, and the key information of supply chain production and circulation is stored on the blockchain to ensure the information cannot be tampered. At the same time, a reputation evaluating method based on smart contracts is used to evaluate the reputation of enterprises in supply chain, which can provide reference for supplier selection among enterprises.

关键词： cryptocurrencies distributed databases manufacturing industries production engineering computing supply chain management supply chains supply chain Manufacturing industry Supply chain management production engineering computing distributed databases Companies Reputation Information Services design scheme Data Storage

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Booter blacklist generation based on content characteristics 14th

Booter blacklist generation based on content characteristics

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14th EAI international Conference on Collaborative computing: Networking, applications and Worksharing, CollaborateCom 2018

作者： Zhang, Wang Bai, Xu Chen, Chanjuan Chen, Zhaolin Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China China National Machinery Industry Corporation Beijing China Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9783030129804

distributed Denial of Service (DDoS) attacks-as-a-service, known as Booter or Stresser, is convenient and low-priced for ordinary people to launch DDoS attacks. It makes DDoS attacks even more rampant. However, until now there is not much research on Booter and little acquaintance with their backend infrastructure, customers, business, etc. In this paper, we present a new method which focuses on the content (text) characteristics on Booters websites and selects more discriminative features between Booter and non-Booter to identify Booters more effectively in the Internet. The experimental results show that the classification accuracy of distinguishing Booter and non-Booter websites is 98.74%. In addition, our method is compared with several representative methods and the results show that the proposed method outperforms the classical methods in 66% of the classification cases on three datasets: Booter websites, 20-Newsgroups and WebKB. © 2019, ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Denial-of-service attack

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Optimal mapping of applications on data centers in sensor-cloud environment 5th

Optimal mapping of applications on data centers in sensor-cl...

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5th international Doctoral symposium on Applied Computation and Security Systems, ACSS 2018

作者： Sen, Biplab Kanti Khatua, Sunirmal Das, Rajib K. Department of Computer Science and Engineering University of Calcutta KolkataWest Bengal India

ISBN: (纸本)9789811332494

In sensor-cloud environment, sensing-as-a-service (Sen-aaS) is an emerging service paradigm that allows on-demand provisioning of sensor resources as a service in a pay-per-use model. For each application, a disjoint set of virtual sensors (VS) are consolidated in a collaborative wireless sensor network (WSN) platform distributed across the globe. The virtual sensor network (VSN) of an application, formed using VSs, may span across multiple WSNs and the base station for each of these WSNs are placed in a host on a nearest cloud data center (DC). Here, sensor cloud plays the key role to conglomerate the data from various VSs, store them in different hosts, and transmit the same to end user application as a service (Sen-aaS). In this work, we address the problem of mapping applications on the hosts that conglomerate data from various VSs and transmit it to the end user as a constraint optimization problem. The main motivation is to minimize the maximum data migration time of all applications on sensor cloud while satisfying the host’s load-balancing constraint. We have proposed an algorithm which can solve the problem optimally under certain conditions. For the general case, if the load-balancing constraint is somewhat relaxed, the maximum delay obtained by our algorithm is optimal. When the load-balancing constraint is to be strictly satisfied, the cost of our solution is slightly more than the optimal provided by an Integer Linear Program. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Wireless sensor networks

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Context-aware multi-layered ontology for composite situation model in pervasive computing

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Ingenierie des Systemes d'Information 2020年第5期25卷 543-558页

作者： Lakehal, Abderrahim Alti, Adel Roose, Philippe LRSD Laboratory Computer Science Department Sciences Faculty Ferhat Abbas University SETIF-1 Sétif19000 Algeria Department of Management Information Systems College of Business & Economics Qassim University P.O. Box 6633 Buraidah51452 Saudi Arabia LIUPPA University of PAU Anglet64000 France

With the rapid advancement of technologies and analysis tools in the smart systems, enabling real-time context monitoring of user's living conditions and quality services delivery is increasing. Current studies in this area are focused on developing mobile applications with specific services, based on toolkit that allow developers to obtain context information from sensors. However, there exists a notable lack of ontology able to represent all the necessary context information starting from distributed users, and constantly changing environment. The modeling of user’s domains to represent diverse mobile and IoT devices, and finalizing with the description of user’s composite situations in smart-*(health, home, cities, car, office, etc.) domains. Considering interoperability, reusability, and flexibility, a new context composite situation ontology for smart systems is proposed with better representation of heterogeneous context. The ontology enables to sense, reason, and infer composite situations in various smart domains, prioritizes critical situations and facilitates the delivery of smart mobile service. Proposed ontology is formalized and validated on different smart environments with different user’s situations. Several experiments were carried out with a real-life motivating scenario. Experimental results showed that the proposed approach has reduced queries times and improved flexibility. © 2020 international Information and engineering Technology Association. All rights reserved.

关键词： Ontology

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A Co-location-based Approach for business Site Selection Using Ontologies 16

A Co-location-based Approach for Business Site Selection Usi...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Bao, Xuguang Wang, Lizhen Yunnan Univ Sch Informat Sci & Engn Dept Comp Sci & Engn Kunming 650091 Yunnan Peoples R China

ISBN: (纸本)9781728111414

Site selection is one of the most crucial and important decisions made by any business. Such a decision depends on various factors of sites, including socioeconomic, geography, ecology, as well as specific requirements of businesses. The existing approaches for site selection (commonly used by economists) are manual, subjective and not scalable. This paper presents a data-driven situation-specific site selection approach (called D3SA for short) for users who are eager to create a business (hotel, hot-pot restaurant, etc.). Given a business and some related parameters, it recommends suggested sites that can satisfy users' requirements. Unlike existing works, we consider using prevalent co-location patterns discovered from spatial data sets to select the sites. Meanwhile, a novel co-location generation algorithm is proposed to discover co-location patterns efficiently. Furthermore, ontologies are used in order to classify the whole spatial data according to the semantic of businesses to assist to find the "best" co-location pattern. The experimental results evaluated based on both a real POI (Points of Interests) dataset in Beijing and extended synthetic data sets show the effectiveness and efficiency of D3SA.

关键词： site selection spatial data mining co-location pattern mining ontology

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Design and Implementation of WeChat Robot Based on Machine Learning 17

Design and Implementation of WeChat Robot Based on Machine L...

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第十七届分布式计算及其应用国际学术研讨会

作者： Yafei Zhu Lingyun Chen Hong Zhou Wanli Feng Quanyin Zhu Faculty of Computer & Software Engineering Huaiyin Institute of Technology

ISBN: (纸本)9781538674468;9781538674451

In order to build an efficient WeChat use-assisted management system and improve the use efficiency, a novel type of application robot based on machine learning is proposed. The proposed system integrates information acquisition module, through machine learning autonomous learning-group-rule module. The simulation management of WeChat group module, and the user image content analysis module respectively. After the user introduces the management system, the proposed system automatically analyzes the group management rules, and conducts trial operations, constantly corrects the learned rules, and finally simulates the group management behavior, and helps the user to identify the picture content according to the user's own choice. Experiments demonstrate that the application robot has achieved high efficiency and the results demonstrate that the application of the proposed system can catch the needs of the user's daily use.

关键词： Machine Learning Image Recognition WeChat Robot System

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Temporal-variation-aware profit-maximized and delay-bounded task scheduling in green data center 12th

Temporal-variation-aware profit-maximized and delay-bounded ...

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12th international Conference on Internet and distributed computing Systems, IDCS 2019

作者： Yuan, Haitao Bi, Jing Zhou, Meng Chu School of Software Engineering Beijing Jiaotong University Beijing100044 China School of Software Engineering in Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

ISBN: (纸本)9783030349134

An increasing number of enterprises deploy their business applications in green data centers (GDCs) to address irregular and drastic natures in task arrival of global users. GDCs aim to schedule tasks in the most cost-effective way, and achieve the profit maximization by increasing green energy usage and reducing brown one. However, prices of power grid, revenue, solar and wind energy vary dynamically within tasks’ delay constraints, and this brings a high challenge to maximize the profit of GDCs such that their delay constraints are strictly met. Different from existing studies, a Temporal-variation-aware Profit-maximized Task Scheduling (TPTS) algorithm is proposed to consider dynamic differences, and intelligently schedule all tasks to GDCs within their delay constraints. In each interval, TPTS solves a constrained profit maximization problem by a novel Simulated-annealing-based Chaotic Particle swarm optimization (SCP). Compared to several state-of-the-art scheduling algorithms, TPTS significantly increases throughput and profit while strictly meeting tasks’ delay constraints. © Springer Nature Switzerland AG 2019.

关键词： Profitability

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