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Data Summarization Techniques for Big Data—A Survey

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Abstract

In current digital era according to (as far) massive progress and development of internet and online world technologies such as big and powerful data servers we face huge volume of information and data day by day from many different resources and services which was not available to human kind just a few decades ago. This data comes from available different online resources and services that are established to serve customers. Services and resources like Sensor Networks, Cloud Storages, Social Networks and etc., produce big volume of data and also need to manage and reuse that data or some analytical aspects of the data. Although this massive volume of data can be really useful for people and corporates it could be problematic as well. Therefore big volume of data or big data has its own deficiencies as well. They need big storage/s and this volume makes operations such as analytical operations, process operations, retrieval operations real difficult and hugely time consuming. One resolution to overcome these difficult problems is to have big data summarized so they would need less storage and extremely shorter time to get processed and retrieved. The summarized data will be then in “compact format” and still informative version of the entire data. Data summarization techniques aim then to produce a “good” quality of summaries. Therefore, they would hugely benefit everyone from ordinary users to researches and corporate world, as it can provide an efficient tool to deal with large data such as news (for new summarization).

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Authors and Affiliations

  1. School of Computer Science and IT, RMIT University, Melbourne, Australia

    Z. R. Hesabi, Z. Tari, A. Fahad & I. Khalil

  2. School of Information Technology, Deakin University, Melbourne, Australia

    A. Goscinski

  3. IBM Research Laboratory, Melbourne, Australia

    C. Queiroz

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  1. Z. R. Hesabi
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  2. Z. Tari
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  3. A. Goscinski
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  4. A. Fahad
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  5. I. Khalil
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  6. C. Queiroz
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Correspondence to Z. R. Hesabi .

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  1. Department of Electrical and Computer Engineering, North Dakota State University, Fargo, North Dakota, USA

    Samee U. Khan

  2. School of Information Technologies, The University of Sydney, Sydney, New South Wales, Australia

    Albert Y. Zomaya

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Hesabi, Z., Tari, Z., Goscinski, A., Fahad, A., Khalil, I., Queiroz, C. (2015). Data Summarization Techniques for Big Data—A Survey. In: Khan, S., Zomaya, A. (eds) Handbook on Data Centers. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2092-1_38

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