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Big Data Search and Mining

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Big Data

Part of the book series: Studies in Big Data ((SBD,volume 11))

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Abstract

Most enterprises are generating data at an unprecedented way. On the other hand, traditional consumers are transforming into digital consumers due to high adoption of social media and networks by individuals. Since transactions on these sites are huge and increasing rapidly, social networks have become the new target for several business applications. Big Data mining deals with tap** large amount of data that is complex with a wide variety of data types and provides actionable insights at the right time. The search and mining applications over Big Data resulted in the development of a new kind of technologies, platforms, and frameworks. This chapter introduces the notion of search and data mining in the Big Data context and technologies supporting Big Data. We also present some data mining techniques that deal with scalability and heterogeneity of large data. We further discuss clustering social networks using topology discovery and also address the problem of evaluating and managing text-based sentiments from social network media. Further, this chapter accentuates some of the open source tools for Big Data mining.

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Author information

Authors and Affiliations

  1. Infosys Labs, Infosys Limited, Hyderabad, India

    P. Radha Krishna

Authors
  1. P. Radha Krishna
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Correspondence to P. Radha Krishna .

Editor information

Editors and Affiliations

  1. School of Computer and Information Scien, University of Hyderabad, Hyderabad, Andhra Pradesh, India

    Hrushikesha Mohanty

  2. School of Computer Engineering, KIIT University, Bhubaneshwar, Odisha, India

    Prachet Bhuyan

  3. Teradata India Private Limited, Hyderabad, Andhra Pradesh, India

    Deepak Chenthati

Exercises

Exercises

  1. 1.

    Define (a) Big Data search and (b) Big Data mining

  2. 2.

    What type of intermediate data does MapReduce store? Where does MapReduce store them?

  3. 3.

    Write Mapper and Reducer functions for k-means clustering algorithm.

  4. 4.

    Give the algorithm to find the page ranking.

  5. 5.

    List ideas to improve/tune (existing) text processing and mining approaches to support big data scale.

  6. 6.

    (a) Explain the significance of social networks and their role in the context of Big Data.

    (b) List challenges of Big Data in supporting social network analytics and discuss approaches to handle them with justification.

  1. 7.

    How the centrality measures and structure of the social networks are useful in analyzing social networks.

  2. 8.

    What is community detection? Discuss various community detection approaches.

  3. 9.

    Explain social network clustering algorithm (using topology discovery) that allows overlap clusters.

  4. 10.

    Explain the concepts of active learning and concept drift. How these concepts are useful for big data search and mining.

  5. 11.

    What is sentiment mining? Describe an approach for extracting sentiments from a given text with examples.

  6. 12.

    Develop a suitable architecture for supporting real-time sentiment mining and discuss their components.

  7. 13.

    List open source tools and their characteristics to perform Big Data analytics.

  8. 14.

    Discuss various alternative mechanisms to MapReduce along with their merits

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Radha Krishna, P. (2015). Big Data Search and Mining. In: Mohanty, H., Bhuyan, P., Chenthati, D. (eds) Big Data. Studies in Big Data, vol 11. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2494-5_4

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  • DOI: https://doi.org/10.1007/978-81-322-2494-5_4

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2493-8

  • Online ISBN: 978-81-322-2494-5

  • eBook Packages: EngineeringEngineering (R0)

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