Advertisement

Big Data Analytics

  • Bhagya Nathali Silva
  • Muhammad Diyan
  • Kijun HanEmail author
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

During the last few decades, with the emergence of smart things and technological advancements of embedded devices, Big Data (BD) and Big Data Analytics (BDA) have been extensively popularized in both industrial and academic domains. The initial portion of the chapter aims to deliver a generic insight toward BD and BDA. In later sections, details that are more specific to BD and BDA are discussed. In fact, BD notion is characterized by its distinctive features such as large amounts of data, high-speed data generation, and wide variety among data type and sources. Consideration on these characteristics assists in determining potential data processing techniques. Hence, this chapter further elaborates on key BD analytical scenarios. Moreover, application of BD, BD analytical tools, and data types of BD are described, in order to enlighten the readers about this broad subject domain. Finally, the chapter concludes by identifying potential opportunities as well as challenges faced by BD and BDA.

List of Abbreviations

IoT

Internet of things

BD

Big Data

BDA

Big Data analytics

ML

Machine learning

DL

Deep learning

MR

MapReduce

TB

Terabyte

PB

Petabyte

EB

Exabyte

ZB

Zettabyte

YB

Yottabyte

RDBMS

Relational database management system

XML

Extensible Markup Language

WSN

Wireless sensor networks

CPS

Cyber physical systems

MM

Multimedia

AI

Artificial intelligence

DM

Data mining

OLAP

Online analytical processing

BPM

Business performance management

NLP

Natural language processing

NER

Named-entity recognition

DBMS

Database management systems

URL

Uniform resource locator

NoSQL

Not only SQL

HDFS

Hadoop Distributed File System

References

  1. Aggarwal CC, Wang H (2011) Text mining in social networks. In: Social network data analytics. Springer, pp 353–378Google Scholar
  2. Baah GK, Gray A, Harrold MJ (2006) On-line anomaly detection of deployed software: a statistical machine learning approach. In: Proceedings of the 3rd international workshop on Software quality assurance, 2006. ACM, pp 70–77Google Scholar
  3. Beaver D, Kumar S, Li HC, Sobel J, Vajgel P (2010) Finding a needle in haystack: Facebook’s photo storage. In: OSDI, 2010, pp 1–8Google Scholar
  4. Cafarella MJ, Halevy A, Khoussainova N (2009) Data integration for the relational web. Proceed VLDB Endowment 2:1090–1101CrossRefGoogle Scholar
  5. Chen CP, Zhang C-Y (2014) Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf Sci 275:314–347CrossRefGoogle Scholar
  6. Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Netw Appl 19:171–209CrossRefGoogle Scholar
  7. Cho J, Garcia-Molina H (2002) Parallel crawlers. In: Proceedings of the 11th international conference on World Wide Web, 2002. ACM, pp 124–135Google Scholar
  8. Clegg B (2009) Before the Big Bang: the prehistory of our universe. St. Martin’s PressGoogle Scholar
  9. Gandomi A, Haider M (2015) Beyond the hype: Big data concepts, methods, and analytics. Int J Inf Manage 35:137–144CrossRefGoogle Scholar
  10. Gantz J, Reinsel D (2011) Extracting value from chaos. IDC iView 1142:1–12Google Scholar
  11. Hilbert M (2016) Big data for development: a review of promises and challenges. Dev Policy Rev 34:135–174CrossRefGoogle Scholar
  12. Hinton GE (2007) Learning multiple layers of representation. Trends Cogn Sci 11:428–434CrossRefGoogle Scholar
  13. Hu H, Wen Y, Chua T-S, Li X (2014) Toward scalable systems for big data analytics: a technology tutorial. IEEE Access 2:652–687CrossRefGoogle Scholar
  14. Khan M, Silva BN, Han K (2016) Internet of things based energy aware smart home control system. IEEE Access 4:7556–7566CrossRefGoogle Scholar
  15. Khan M, Silva BN, Jung C, Han K (2017) A context-aware smart home control system based on ZigBee sensor network. KSII Trans Internet Inf Syst 11:1057–1069Google Scholar
  16. Khan M, Silva BN, Han K (2018) Efficiently processing big data in real-time employing deep learning algorithms. In: Deep learning innovations and their convergence with big data. IGI Global, pp 61–78Google Scholar
  17. Labrinidis A, Jagadish HV (2012) Challenges and opportunities with big data. Proceed VLDB Endowment 5:2032–2033CrossRefGoogle Scholar
  18. Laney D (2001) 3D data management: controlling data volume, velocity and variety. META Group Res Note 6:1Google Scholar
  19. Lenzerini M (2002) Data integration: a theoretical perspective. In: Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, 2002. ACM, pp 233–246Google Scholar
  20. Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers AH (2011) Big data: the next frontier for innovation, competition, and productivityGoogle Scholar
  21. Moeng M, Melhem R (2010) Applying statistical machine learning to multicore voltage and frequency scaling. In: Proceedings of the 7th ACM international conference on computing frontiers, 2010. ACM, pp 277–286Google Scholar
  22. Pal SK, Talwar V, Mitra P (2002) Web mining in soft computing framework: relevance, state of the art and future directions. IEEE Trans Neural Netw 13:1163–1177CrossRefGoogle Scholar
  23. Sagiroglu S, Sinanc D (2013) Big data: a review. In: 2013 international conference on collaboration technologies and systems (CTS). IEEE, pp 42–47Google Scholar
  24. Shi J, Wan J, Yan H, Suo H (2011) A survey of cyber-physical systems. In: 2011 international conference on wireless communications and signal processing (WCSP). IEEE, pp 1–6Google Scholar
  25. Silva BN, Khan M, Han K (2017a) Integration of Big Data analytics embedded smart city architecture with RESTful web of things for efficient service provision and energy management. Fut Gener Comput SystGoogle Scholar
  26. Silva BN, Khan M, Han K (2017b) Internet of things: a comprehensive review of enabling technologies, architecture, and challenges. IETE Tech Rev 1–16Google Scholar
  27. Silva BN, Khan M, Han K (2018a) Towards sustainable smart cities: a review of trends, architectures, components, and open challenges in smart cities. Sustain Cities Soc 38:697–713CrossRefGoogle Scholar
  28. Silva BN et al (2018b) Urban planning and smart city decision management empowered by real-time data processing using big data analytics. Sensors 18.  https://doi.org/10.3390/s18092994CrossRefGoogle Scholar
  29. Tatbul N (2010) Streaming data integration: challenges and opportunitiesGoogle Scholar
  30. Watts DJ (2004) Six degrees: the science of a connected age. WW Norton & CompanyGoogle Scholar
  31. Wu X et al (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14:1–37CrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Bhagya Nathali Silva
    • 1
  • Muhammad Diyan
    • 1
  • Kijun Han
    • 1
    Email author
  1. 1.School of Computer Science and EngineeringKyungpook National UniversityDaeguKorea

Personalised recommendations