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Selected papers from Smart Data 2018 & Big Data Service...
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Selected papers from Smart Data 2018 & Big Data Service 2018

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (30 November 2018) | Viewed by 11965

Special Issue Editors

Prof. Wookey Lee

E-Mail Website
Guest Editor
Professor & Director of VOICE AI research institute, Inha University, Incheon, Korea
Interests: VOICE sensor; Deep Learning; Patent, Information Retrieval
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Carson K. Leung

E-Mail Website
Guest Editor
Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
Interests: AI; data mining and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Big Data has become a core technology with which to provide innovative solutions in many fields such as healthcare, manufacturing, social life, etc. The goal of the 2018 IEEE International Conference on Smart Data (SmartData-2018) is to provide a forum for scientists, engineers, and researchers to discuss and exchange novel ideas, results, experiences, and work-in-process on all aspects of Smart Data.

Topics of interest include, but are not limited to, the following:

Track 1: Data Science and Its Foundations

  • Foundational Theories of Data Science
  • Theoretical Models in Big Data
  • Foundational Algorithms and Methods for Big Data
  • Interdisciplinary Theories and Models for Smart Data
  • Data Classification and Taxonomy
  • Data Metrics and Metrology

Track 2: Big Data Infrastructure and Systems

  • Programming Models/Environments for Cluster/Cloud Computing
  • High Performance/Throughtput Platforms for Big Data Computing
  • Parallel Computing for Big Data
  • Open Source Big Data Systems (including Hadoop, Spark, Flink, and Storm)
  • System Architecture and Infrastructure of Big Data
  • New Programming Models for Big Data beyond Hadoop/MapReduce
  • Big Data Appliance
  • Big Data Ecosystems

Track 3: Big Data Storage and Management

  • Big Data Collection, Transformation, and Transmission
  • Big Data Integration and Cleaning
  • Uncertainty and Incompleteness Handling in Big Data/Smart Data
  • Quality Management of Big Data/Smart Data
  • Big Data Storage Models
  • Query and Indexing Technologies
  • Distributed File Systems
  • Distributed Database Systems
  • Large-Scale Graph/Document Databases
  • NewSQL/NoSQL for Big Data

Track 4: Big Data Processing and Analytics

  • Smart Data Search, Mining, and Drilling from Big Data
  • Semantic Integration and Fusion of Multi-Source Heterogeneous Big Data
  • In-Memory/Streaming/Graph-Based Computing for Big Data/Smart Data
  • Brain-Inspired/Nature-Inspired Computing for Big Data/Smart Data
  • Distributred Representation Learning of Smart Data
  • Machine Learning/Deep Learning for Big Data/Smart Data
  • Applications of Conventional Theories (e.g., Fuzzy Set, Rough Set, and Soft Set) in Big Data
  • New Models, Algorithms, and Methods for Big/Smart Data Processing and Analytics
  • Exploratory Data Analysis
  • Visualization Analytics for Big Data
  • Big Data-Based Prediction Methods
  • Big Data-Aided Decision-Marking

Track 5: Big/Smart Data Applications

  • Big/Smart Data Applications in Science, Internet, Finance, Telecommunictions, Business, Medicine, Healthcare, Government, Transportation, Industry, and Manufacturing
  • Big/Smart Data Applications in Government and Public Sectors
  • Big/Smart Data Applications in Enterprises
  • Security, Privacy and Trust in Big Data
  • Big Data Opening and Sharing
  • Big Data Exchange and Trading
  • Data as a Service (DaaS)
  • Standards for Big/Smart Data
  • Case Studies of Big/Smart Data Applications
  • Practices and Experiences of Big Data Project Deployments
  • Ethic Issues about Big Data Applications

Prof. Wookey Lee
Prof. Carson Kai-Sang Leung
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

15 pages, 573 KiB  
Article
NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach
by Bingyang Huang, Chaokun Wang and Binbin Wang
Sensors 2019, 19(2), 260; https://doi.org/10.3390/s19020260 - 10 Jan 2019
Cited by 22 | Viewed by 3717
Abstract
With the enrichment of the entity information in the real world, many networks with attributed nodes are proposed and studied widely. Community detection in these attributed networks is an essential task that aims to find groups where the intra-nodes are much more densely [...] Read more.
With the enrichment of the entity information in the real world, many networks with attributed nodes are proposed and studied widely. Community detection in these attributed networks is an essential task that aims to find groups where the intra-nodes are much more densely connected than the inter-nodes. However, many existing community detection methods in attributed networks do not distinguish overlapping communities from non-overlapping communities when designing algorithms. In this paper, we propose a novel and accurate algorithm called Node-similarity-based Multi-Label Propagation Algorithm (NMLPA) for detecting overlapping communities in attributed networks. NMLPA first calculates the similarity between nodes and then propagates multiple labels based on the network structure and the node similarity. Moreover, NMLPA uses a pruning strategy to keep the number of labels per node within a suitable range. Extensive experiments conducted on both synthetic and real-world networks show that our new method significantly outperforms state-of-the-art methods. Full article
(This article belongs to the Special Issue Selected papers from Smart Data 2018 & Big Data Service 2018)
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18 pages, 7491 KiB  
Article
Improvement of Kafka Streaming Using Partition and Multi-Threading in Big Data Environment
by Bunrong Leang, Sokchomrern Ean, Ga-Ae Ryu and Kwan-Hee Yoo
Sensors 2019, 19(1), 134; https://doi.org/10.3390/s19010134 - 02 Jan 2019
Cited by 13 | Viewed by 7703
Abstract
The large amount of programmable logic controller (PLC) sensing data has rapidly increased in the manufacturing environment. Therefore, a large data store is necessary for Big Data platforms. In this paper, we propose a Hadoop ecosystem for the support of many features in [...] Read more.
The large amount of programmable logic controller (PLC) sensing data has rapidly increased in the manufacturing environment. Therefore, a large data store is necessary for Big Data platforms. In this paper, we propose a Hadoop ecosystem for the support of many features in the manufacturing industry. In this ecosystem, Apache Hadoop and HBase are used as Big Data storage and handle large scale data. In addition, Apache Kafka is used as a data streaming pipeline which contains many configurations and properties that are used to make a better-designed environment and a reliable system, such as Kafka offset and partition, which is used for program scaling purposes. Moreover, Apache Spark closely works with Kafka consumers to create a real-time processing and analysis of the data. Meanwhile, data security is applied in the data transmission phase between the Kafka producers and consumers. Public-key cryptography is performed as a security method which contains public and private keys. Additionally, the public-key is located in the Kafka producer, and the private-key is stored in the Kafka consumer. The integration of these above technologies will enhance the performance and accuracy of data storing, processing, and securing in the manufacturing environment. Full article
(This article belongs to the Special Issue Selected papers from Smart Data 2018 & Big Data Service 2018)
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