Time series big data: a survey on data stream frameworks, analysis and algorithms

被引:14
|
作者
Almeida, Ana [1 ,2 ]
Bras, Susana [2 ,3 ]
Sargento, Susana [1 ,2 ]
Pinto, Filipe Cabral [1 ,4 ]
机构
[1] Inst Telecomunicacoes, Aveiro, Portugal
[2] Univ Aveiro, Dept Eletron Telecomunicacoes & Informat, Aveiro, Portugal
[3] Univ Aveiro, IEETA, DETI, LASI, Aveiro, Portugal
[4] Altice Labs, Aveiro, Portugal
关键词
Big data; Time series; Stream processing engines; Forecasting; Anomaly detection; Machine learning; ANOMALY DETECTION; NETWORK;
D O I
10.1186/s40537-023-00760-1
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Big data has a substantial role nowadays, and its importance has significantly increased over the last decade. Big data's biggest advantages are providing knowledge, supporting the decision-making process, and improving the use of resources, services, and infrastructures. The potential of big data increases when we apply it in real-time by providing real-time analysis, predictions, and forecasts, among many other applications. Our goal with this article is to provide a viewpoint on how to build a system capable of processing big data in real-time, performing analysis, and applying algorithms. A system should be designed to handle vast amounts of data and provide valuable knowledge through analysis and algorithms. This article explores the current approaches and how they can be used for the real-time operations and predictions.
引用
收藏
页数:32
相关论文
共 50 条
  • [11] A Survey of Distributed Data Stream Processing Frameworks
    Isah, Haruna
    Abughofa, Tariq
    Mahfuz, Sazia
    Ajerla, Dharmitha
    Zulkernine, Farhana
    Khan, Shahzad
    IEEE ACCESS, 2019, 7 : 154300 - 154316
  • [12] SURVEY OF DATA PARTITIONING ALGORITHMS FOR BIG DATA STORES
    Phansalkar, Shraddha
    Ahirrao, Swati
    2016 FOURTH INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED AND GRID COMPUTING (PDGC), 2016, : 163 - 168
  • [13] A survey on data‐efficient algorithms in big data era
    Amina Adadi
    Journal of Big Data, 8
  • [14] Systematic Mapping for Big Data Stream Processing Frameworks
    Alayyoub, Mohammed
    Yazici, Ali
    Karakaya, Ziya
    2016 ELEVENTH INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION MANAGEMENT (ICDIM 2016), 2016, : 31 - 36
  • [15] A Survey on Big Data Security Algorithms
    Prasanna, K. Lakshmi
    Reddy, M. Thrilok
    Prakash, S. Shiva
    HELIX, 2018, 8 (02): : 3290 - 3293
  • [16] Improving Multiple Time Series Forecasting with Data Stream Mining Algorithms
    Mochinski, Marcos Alberto
    Barddal, Jean Paul
    Enembreck, Fabricio
    2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2020, : 1060 - 1067
  • [17] Survey on Big Data Analysis Algorithms for Network Security Measurement
    Chen, Hanlu
    Fu, Yulong
    Yan, Zheng
    NETWORK AND SYSTEM SECURITY, 2017, 10394 : 128 - 142
  • [18] A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis
    Fahad, Adil
    Alshatri, Najlaa
    Tari, Zahir
    Alamri, Abdullah
    Khalil, Ibrahim
    Zomaya, Albert Y.
    Foufou, Sebti
    Bouras, Abdelaziz
    IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING, 2014, 2 (03) : 267 - 279
  • [19] Distance variable improvement of time-series big data stream evaluation
    Wibisono, Ari
    Mursanto, Petrus
    Adibah, Jihan
    Bayu, Wendy D. W. T.
    Rizki, May Iffah
    Hasani, Lintang Matahari
    Ahli, Valian Fil
    JOURNAL OF BIG DATA, 2020, 7 (01)
  • [20] Distance variable improvement of time-series big data stream evaluation
    Ari Wibisono
    Petrus Mursanto
    Jihan Adibah
    Wendy D. W. T. Bayu
    May Iffah Rizki
    Lintang Matahari Hasani
    Valian Fil Ahli
    Journal of Big Data, 7