Skip to main content
SpringerLink
Log in

Storage Optimization Using File Compression Techniques for Big Data

  • Conference paper
  • First Online:
Intelligent Data Engineering and Analytics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1177))

Abstract

The world is surrounded by technology. There are lots of devices everywhere around us. It is impossible to imagine our lives without technology, as we have got dependent on it for most of our work. One of the primary functions for which we use technology or computers especially is to store and transfer data from a host system or network to another one having similar credentials. The restriction in the capacity of computers means that there’s restriction on amount of data which can be stored or has to transport. So, in order to tackle this problem, computer scientists came up with data compression algorithms. A file compression system’s objective is to build an efficient software which can help to reduce the size of user files to smaller bytes so that it can easily be transferred over a slower Internet connection and it takes less space on the disk. Data compression or the diminishing of rate of bit includes encoding data utilizing less number of bits as compared to the first portrayal. Compression can be of two writes lossless and lossy. The first one decreases bits by recognizing and disposing of measurable excesses, and due to this reason, no data is lost or every info is retained. The latter type lessens record estimate by expelling pointless or less vital data. This paper proposed a file compression system for big data as system utility software, and the users would also be able to use it on the desktop and lossless compression takes place in this work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Shanmugasundaram, S., Lourdusamy, R.: A comparative study of text compression algorithms. Int. J. Wisdom Based Comput. 1(3), 68–76 (2011)

    Google Scholar 

  2. Horspool, R.N., Cormack, G.V.: Constructing word-based text compression algorithms. In: Data Compression Conference, pp. 62–71 (1992)

    Google Scholar 

  3. Sangwan, N.: Text encryption with Huffman compression. Int. J. Comput. Appl. 54(6) (2012)

    Google Scholar 

  4. Kodituwakku, S.R., Amarasinghe, U.S.: Comparison of lossless data compression algorithms for text data. Indian J. Comput. Sci. Eng. 1(4), 416–425 (2010)

    Google Scholar 

  5. Basu, S., Kannayaram, G., Ramasubbareddy, S., Venkatasubbaiah, C.: Improved genetic algorithm for monitoring of virtual machines in cloud environment. In: Smart Intelligent Computing and Applications, pp. 319–326. Springer, Singapore (2019)

    Google Scholar 

  6. Somula, R., Sasikala, R.: Round robin with load degree: an algorithm for optimal cloudlet discovery in mobile cloud computing. Scalable Comput. Pract. Exp. 19(1), 39–52 (2018)

    Google Scholar 

  7. Somula, R., Anilkumar, C., Venkatesh, B., Karrothu, A., Kumar, C.P., Sasikala, R.: Cloudlet services for healthcare applications in mobile cloud computing. In: Proceedings of the 2nd International Conference on Data Engineering and Communication Technology, pp. 535–543. Springer, Singapore (2019)

    Google Scholar 

  8. Somula, R.S., Sasikala, R.: A survey on mobile cloud computing: mobile computing + cloud computing (MCC = MC + CC). Scalable Comput. Pract. Exp. 19(4), 309–337 (2018)

    Article  Google Scholar 

  9. Somula, R., Sasikala, R.: A load and distance aware cloudlet selection strategy in multi-cloudlet environment. Int. J. Grid High Perform. Comput. (IJGHPC) 11(2), 85–102 (2019)

    Article  Google Scholar 

  10. Somula, R., Sasikala, R.: A honey bee inspired cloudlet selection for resource allocation. In: Smart Intelligent Computing and Applications, pp. 335–343. Springer, Singapore (2019)

    Google Scholar 

  11. Nalluri, S., Ramasubbareddy, S., Kannayaram, G.: Weather prediction using clustering strategies in machine learning. J. Comput. Theor. Nanosci. 16(5–6), 1977–1981 (2019)

    Article  Google Scholar 

  12. Sahoo, K.S., Tiwary, M., Mishra, P., Reddy, S.R.S., Balusamy, B., Gandomi, A.H.: Improving end-users utility in software-defined wide area network systems. IEEE Trans. Netw. Serv. Manag. (2019)

    Google Scholar 

  13. Sahoo, K.S., Tiwary, M., Sahoo, B., Mishra, B.K., RamaSubbaReddy, S., Luhach, A.K.: RTSM: response time optimisation during switch migration in software-defined wide area network. IET Wirel. Sens. Syst. (2019)

    Google Scholar 

  14. Somula, R., Kumar, K.D., Aravindharamanan, S., Govinda, K.: Twitter sentiment analysis based on US presidential election 2016. In: Smart Intelligent Computing and Applications, pp. 363–373. Springer, Singapore (2020)

    Google Scholar 

  15. Sai, K.B.K., Subbareddy, S.R., Luhach, A.K.: IOT based air quality monitoring system using MQ135 and MQ7 with machine learning analysis. Scalable Comput. Pract. Exp. 20(4), 599–606 (2019)

    Article  Google Scholar 

  16. Somula, R., Narayana, Y., Nalluri, S., Chunduru, A., Sree, K.V.: POUPR: properly utilizing user-provided recourses for energy saving in mobile cloud computing. In: Proceedings of the 2nd International Conference on Data Engineering and Communication Technology, pp. 585–595. Springer, Singapore (2019)

    Google Scholar 

  17. Vaishali, R., Sasikala, R., Ramasubbareddy, S., Remya, S., Nalluri, S.: Genetic algorithm based feature selection and MOE fuzzy classification algorithm on Pima Indians Diabetes dataset. In: 2017 International Conference on Computing Networking and Informatics (ICCNI), pp. 1–5. IEEE (2017)

    Google Scholar 

  18. Somula, R., Sasikala, R.: A research review on energy consumption of different frameworks in mobile cloud computing. In: Innovations in Computer Science and Engineering, pp. 129–142. Springer, Singapore (2019)

    Google Scholar 

  19. Rao, N.P., Kannayaram, G., Ramasubbareddy, S., Swetha, E., Srinivas, A.S.: Software fault management using scheduling algorithms. J. Comput. Theor. Nanosci. 16(5–6), 2124–2127 (2019)

    Article  Google Scholar 

  20. Pramod Reddy, A., Ramasubbareddy, S., Kannayaram, G.: Parallel processed multi-lingual optical character recognition application. J. Comput. Theor. Nanosci. 16(5–6), 2091–2095 (2019)

    Article  Google Scholar 

  21. Kumar, I.P., Sambangi, S., Somukoa, R., Nalluri, S., Govinda, K.: Server security in cloud computing using block-chaining technique. In: Data Engineering and Communication Technology, pp. 913–920. Springer, Singapore (2020)

    Google Scholar 

  22. Kumar, I.P., Gopal, V.H., Ramasubbareddy, S., Nalluri, S., Govinda, K.: Dominant color palette extraction by K-means clustering algorithm and reconstruction of image. In: Data Engineering and Communication Technology, pp. 921–929. Springer, Singapore (2020)

    Google Scholar 

  23. Nalluri, S., Saraswathi, R.V., Ramasubbareddy, S., Govinda, K., Swetha, E.: Chronic heart disease prediction using data mining techniques. In: Data Engineering and Communication Technology, pp. 903–912. Springer, Singapore (2020)

    Google Scholar 

  24. Krishna, A.V., Ramasubbareddy, S., Govinda, K.: Task scheduling based on hybrid algorithm for cloud computing. In: International Conference on Intelligent Computing and Smart Communication 2019, pp. 415–421. Springer, Singapore (2020)

    Google Scholar 

  25. Srinivas, T.A.S., Ramasubbareddy, S., Govinda, K., Manivannan, S.S.: Web image authentication using embedding invisible watermarking. In: International Conference on Intelligent Computing and Smart Communication 2019, pp. 207–218. Springer, Singapore (2020)

    Google Scholar 

  26. Krishna, A.V., Ramasubbareddy, S., Govinda, K.: A unified platform for crisis mapping using web enabled crowdsourcing powered by knowledge management. In: International Conference on Intelligent Computing and Smart Communication 2019, pp. 195–205. Springer, Singapore (2020)

    Google Scholar 

  27. Saraswathi, R.V., Nalluri, S., Ramasubbareddy, S., Govinda, K., Swetha, E.: Brilliant corp yield prediction utilizing internet of things. In: Data Engineering and Communication Technology, pp. 893–902. Springer, Singapore (2020)

    Google Scholar 

  28. Kalyani, D., Ramasubbareddy, S., Govinda, K., Kumar, V.: Location-based proactive handoff mechanism in mobile ad hoc network. In: International Conference on Intelligent Computing and Smart Communication 2019, pp. 85–94. Springer, Singapore (2020)

    Google Scholar 

  29. Bhukya, K.A., Ramasubbareddy, S., Govinda, K., Srinivas, T.A.S.: Adaptive mechanism for smart street lighting system. In: Smart Intelligent Computing and Applications, pp. 69–76. Springer, Singapore (2020)

    Google Scholar 

  30. Srinivas, T.A.S., Somula, R., Govinda, K.: Privacy and security in Aadhaar. In: Smart Intelligent Computing and Applications, pp. 405–410. Springer, Singapore (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. SCOPE School, VIT University, Vellore, Tamil Nadu, India

    T. Aditya Sai Srinivas, K. Govinda & C. S. Pavan Kumar

  2. Information Technology, VNRVJIET, Hyderabad, Telangana, India

    Somula Ramasubbareddy

Authors
  1. T. Aditya Sai Srinivas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Somula Ramasubbareddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. Govinda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. C. S. Pavan Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. S. Pavan Kumar .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute Industrial Technology, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  2. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

  3. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  4. School of Management, National Institute of Technology Karnataka, Surathkal, Karnataka, India

    Ritanjali Majhi

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Aditya Sai Srinivas, T., Ramasubbareddy, S., Govinda, K., Pavan Kumar, C.S. (2021). Storage Optimization Using File Compression Techniques for Big Data. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_38

Download citation

Publish with us

Policies and ethics

Search

Navigation