BigDaM: Efficient Big Data Management and Interoperability Middleware for Seaports as Critical Infrastructures

被引:2
|
作者
Nikolakopoulos, Anastasios [1 ]
Segui, Matilde Julian [2 ]
Pellicer, Andreu Belsa [2 ]
Kefalogiannis, Michalis [3 ]
Gizelis, Christos-Antonios [3 ]
Marinakis, Achilleas [3 ]
Nestorakis, Konstantinos [3 ]
Varvarigou, Theodora [1 ]
机构
[1] Natl Tech Univ Athens, Sch Elect & Comp Engn, Athens 15773, Greece
[2] Univ Politecn Valencia, Dept Commun, Valencia 46022, Spain
[3] IT Innovat Ctr OTE Grp, Maroussi 15124, Greece
基金
欧盟地平线“2020”;
关键词
marketplaces; interoperability; critical infrastructure; smart data model; data virtualization; big data analysis; big data management;
D O I
10.3390/computers12110218
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Over the last few years, the European Union (EU) has placed significant emphasis on the interoperability of critical infrastructures (CIs). One of the main CI transportation infrastructures are ports. The control systems managing such infrastructures are constantly evolving and handle diverse sets of people, data, and processes. Additionally, interdependencies among different infrastructures can lead to discrepancies in data models that propagate and intensify across interconnected systems. This article introduces "BigDaM", a Big Data Management framework for critical infrastructures. It is a cutting-edge data model that adheres to the latest technological standards and aims to consolidate APIs and services within highly complex CI infrastructures. Our approach takes a bottom-up perspective, treating each service interconnection as an autonomous entity that must align with the proposed common vocabulary and data model. By injecting strict guidelines into the service/component development's lifecycle, we explicitly promote interoperability among the services within critical infrastructure ecosystems. This approach facilitates the exchange and reuse of data from a shared repository among developers, small and medium-sized enterprises (SMEs), and large vendors. Business challenges have also been taken into account, in order to link the generated data assets of CIs with the business world. The complete framework has been tested in the main EU ports, part of the transportation sector of CIs. Performance evaluation and the aforementioned testing is also being analyzed, highlighting the capabilities of the proposed approach.
引用
收藏
页数:34
相关论文
共 50 条
  • [31] SparkDQ: Efficient generic big data quality management on distributed data-parallel computation
    Gu, Rong
    Qi, Yang
    Wu, Tongyu
    Wang, Zhaokang
    Xu, Xiaolong
    Yuan, Chunfeng
    Huang, Yihua
    JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 2021, 156 (156) : 132 - 147
  • [32] A note on energy efficient data, services and memory management in Big Data Information Systems Preface
    Kolodziej, Joanna
    Burczynski, Tadeusz
    Zomaya, Albert Y.
    INFORMATION SCIENCES, 2015, 319 : 69 - 70
  • [33] Energy Efficient Big Data Infrastructure Management in Geo-Federated Cloud Data Centers
    Subbiah, Sankari
    Varalakshmi, Perumal
    Prarthana, R.
    Devi, Renuka C.
    SECOND INTERNATIONAL SYMPOSIUM ON COMPUTER VISION AND THE INTERNET (VISIONNET'15), 2015, 58 : 151 - 157
  • [34] Distributed computing and big data techniques for efficient fault detection and data management in wireless networks
    Kiran, Ajmeera
    Renjith, P. N.
    Gupta, Sapna
    Ambala, Srinivas
    Raju, Preethi Sambandam
    Sriramsetti, Drakshayani
    OPTICAL AND QUANTUM ELECTRONICS, 2023, 55 (13)
  • [35] Efficient Provenance Management via Clustering and Hybrid Storage in Big Data Environments
    Hu, Die
    Feng, Dan
    Xie, Yulai
    Xu, Gongming
    Gu, Xinrui
    Long, Darrell
    IEEE TRANSACTIONS ON BIG DATA, 2020, 6 (04) : 792 - 803
  • [36] Big Data enabled Intelligent Immune System for energy efficient manufacturing management
    Wang, S.
    Liang, Y. C.
    Li, W. D.
    Cai, X. T.
    JOURNAL OF CLEANER PRODUCTION, 2018, 195 : 507 - 520
  • [37] Efficient Medical Big Data Management With Keyword-Searchable Encryption in Healthchain
    Li, Chaoyang
    Dong, Mianxiong
    Li, Jian
    Xu, Gang
    Chen, Xiu-Bo
    Liu, Wen
    Ota, Kaoru
    IEEE SYSTEMS JOURNAL, 2022, 16 (04): : 5521 - 5532
  • [38] Efficient IoT Data Management for Geological Disasters Based on Big Data-Turbocharged Data Lake Architecture
    Huang, Xiaohui
    Fan, Junqing
    Deng, Ze
    Yan, Jining
    Li, Jiabao
    Wang, Lizhe
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2021, 10 (11)
  • [39] Efficient Resource Management System Based on 4Vs of Big Data Streams
    Kaur, Navroop
    Sood, Sandeep K.
    BIG DATA RESEARCH, 2017, 9 : 98 - 106
  • [40] H2RDF+ : An Efficient Data Management System for Big RDF Graphs
    Papailiou, Nikolaos
    Tsoumakos, Dimitrios
    Konstantinou, Ioannis
    Karras, Panagiotis
    Koziris, Nectarios
    SIGMOD'14: PROCEEDINGS OF THE 2014 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, 2014, : 909 - 912