Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Cover of the book

2020 | OriginalPaper | Chapter

A Survey of Real-Time Big Data Processing Algorithms

Authors : Devesh Kumar Lal, Ugrasen Suman

Published in: Reliability and Risk Assessment in Engineering

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Data collection and processing in real time is one of the most challenging domains for big data. The sustainable proliferation of unbounded streaming data has become arduous for data collection, data pre-process, data optimization, etc. Real-time streaming for data collection can effectively be performed by windowing mechanism. In this communication, we have discussed various windowing mechanisms such as sliding window, tumbling window, landmark window, index-based window, adaptive size tumbling window, and partitioned-based window. The reliability measure, which depends upon selection of appropriate windowing mechanism, has also been discussed. These window-based algorithms have been compared on the basis of CPU utilization, memory consumption, time efficiency, and operation compatibility. In this paper, we have surveyed various aggregation algorithms such as reactive aggregator, flatFAT, flatFIT, B-Int, DABA, and two stacks aggregator and compared them based on time complexity. Remarkably, a hybrid window mechanism has been introduced in this study which can handle the most recent data stream and variable rate of data stream by sliding window and tumbling window, respectively.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
next chapter The Internet of Renewable Energy: Big Data-Driven Smart Grid Management with the Reliability and Security Analysis
Literature
1.
Gibbonsand BP, Tirthapura S (2002) Distributed streams algorithms for sliding windows. In: Proceedings of the fourteenth annual ACM symposium on parallel algorithms and architectures. ACM
2.
Rivetti N, Busnel Y, Mostefaoui A (2015) Efficiently summarizing data streams over sliding windows. In: 2015 IEEE 14th international symposium on network computing and applications (NCA). IEEE
3.
Mousavi H, Zaniolo C (2013) Fast computation of approximate biased histograms on sliding windows over data streams. In: Proceedings of the 25th international conference on scientific and statistical database management. ACM
4.
Badiozamany S, Orsborn K, Risch T (2016) Framework for real-time clustering over sliding windows. In: Proceedings of the 28th international conference on scientific and statistical database management. ACM
5.
Wei Z, Liu X, Li F, Shang S, Du X, Wen JR (2016) Matrix sketching over sliding windows. In: Proceedings of the 2016 international conference on management of data. ACM
6.
Wu F, Wu Q, Zhong Y, Jin X (2009) Mining frequent patterns in data stream over sliding windows. In: 2009 international conference on computational intelligence and software engineering, 2009, CiSE. IEEE, New York
7.
Zaharia M, Das T, Li H, Hunter T, Shenker S, Stoica I (2013) Discretized streams: fault-tolerant streaming computation at scale. In: Proceedings of the twenty-fourth ACM symposium on operating systems principles. ACM
8.
Epasto A, Lattanzi S, Vassilvitskii S, Zadimoghaddam M (2017) Submodular optimization over sliding windows. In: Proceedings of the 26th international conference on world wide web international world wide web conferences steering committee
9.
Zhang L, Zhanhuai L, Yiqiang Z, Min Y, Yang Z (2007) A priority random sampling algorithm for time-based sliding windows over weighted streaming data. In: Proceedings of the 2007 ACM symposium on applied computing. ACM
10.
Braverman V, Ostrovsky R, Zaniolo C (2009) Optimal sampling from sliding windows. In: Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems ACM
11.
Balazinska M, Hwang JH, Shah MA (2009) Fault-tolerance and high availability in data stream management systems. In: Encyclopedia of database systems. Springer US, 1109–1115
12.
Liberty E (2013) Simple and deterministic matrix sketching. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM
13.
Patroumpas K, Sellis T (2009) Window update patterns in stream operators. In: East European conference on advances in databases and information systems. Springer, Berlin
14.
Bhatotia P, Acar UA, Junqueira FP, Rodrigues R (2014) Slider: incremental sliding window analytics. In: Proceedings of the 15th international middleware conference. ACM
15.
Badiozamany S (2016) Real-time data stream clustering over sliding windows. Diss. Acta Univ Ups
16.
Zhang L, Lin J, Karim R (2017) Sliding window-based fault detection from high-dimensional data streams. IEEE Trans Syst Man Cybernet Syst 47(2):289–303
17.
Golab L (2004) Querying sliding windows over online data streams. In: International conference on extending database technology. Springer, Berlin
18.
Patroumpas K, Sellis T (2006) Window specification over data streams. In: Current trends in database technology–EDBT, pp 445–464
19.
Balkesen C, Tatbul N (2011) Scalable data partitioning techniques for parallel sliding window processing over data streams. In: International workshop on data management for sensor networks (DMSN)
20.
Marcu OC, Tudoran R, Nicolae B, Costan A, Antoniu G, Hernandez MSP (2017) Exploring shared state in key-value store for window-based multi-pattern streaming analytics. In: Proceedings of the 17th IEEE/ACM international symposium on cluster, cloud and grid computing. IEEE Press
21.
Chen H, Wang Y, Wang Y, Ma X (2016) GDSW: a general framework for distributed sliding window over data streams. In: IEEE 22nd international conference on parallel and distributed systems (ICPADS). IEEE
22.
Tangwongsan K, Hirzel M, Schneider S (2017) Low-latency sliding-window aggregation in worst-case constant time. In: Proceedings of the 11th ACM international conference on distributed and event-based systems. ACM
23.
Hirzel M, Schneider S, Tangwongsan K (2017) Sliding-window aggregation algorithms: tutorial. In: Proceedings of the 11th ACM international conference on distributed and event-based systems. ACM
24.
Tangwongsan K et al (2015) General incremental sliding-window aggregation. In: Proceedings of the VLDB endowment vol 8(7), pp 702–713
25.
Shein AU, Chrysanthis PK, Labrinidis A (2017) FlatFIT: accelerated incremental sliding-window aggregation for real-time analytics. In: Proceedings of the 29th international conference on scientific and statistical database management. ACM
26.
Arasu A, Widom J (2004) Resource sharing in continuous sliding-window aggregates. In: Proceedings of the thirtieth international conference on very large data bases, vol 30. VLDB Endowment
27.
Cormode G, Yi K (2011) Brief announcement: tracking distributed aggregates over time-based sliding windows. PODC 11
Metadata
Title
A Survey of Real-Time Big Data Processing Algorithms
Authors
Devesh Kumar Lal
Ugrasen Suman
Copyright Year
2020
Publisher
Springer Singapore
Book
Reliability and Risk Assessment in Engineering

Print ISBN: 978-981-15-3745-5

Electronic ISBN: 978-981-15-3746-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-3746-2_1