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A survey on indexing techniques for big data: taxonomy and performance evaluation

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Abstract

The explosive growth in volume, velocity, and diversity of data produced by mobile devices and cloud applications has contributed to the abundance of data or ‘big data.’ Available solutions for efficient data storage and management cannot fulfill the needs of such heterogeneous data where the amount of data is continuously increasing. For efficient retrieval and management, existing indexing solutions become inefficient with the rapidly growing index size and seek time and an optimized index scheme is required for big data. Regarding real-world applications, the indexing issue with big data in cloud computing is widespread in healthcare, enterprises, scientific experiments, and social networks. To date, diverse soft computing, machine learning, and other techniques in terms of artificial intelligence have been utilized to satisfy the indexing requirements, yet in the literature, there is no reported state-of-the-art survey investigating the performance and consequences of techniques for solving indexing in big data issues as they enter cloud computing. The objective of this paper is to investigate and examine the existing indexing techniques for big data. Taxonomy of indexing techniques is developed to provide insight to enable researchers understand and select a technique as a basis to design an indexing mechanism with reduced time and space consumption for BD-MCC. In this study, 48 indexing techniques have been studied and compared based on 60 articles related to the topic. The indexing techniques’ performance is analyzed based on their characteristics and big data indexing requirements. The main contribution of this study is taxonomy of categorized indexing techniques based on their method. The categories are non-artificial intelligence, artificial intelligence, and collaborative artificial intelligence indexing methods. In addition, the significance of different procedures and performance is analyzed, besides limitations of each technique. In conclusion, several key future research topics with potential to accelerate the progress and deployment of artificial intelligence-based cooperative indexing in BD-MCC are elaborated on.

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Acknowledgments

The authors would like to thank the University of Malaya for grant “Big Data and Mobile Cloud For Collaborative Experiments”, Project Number: P012C-13AFR and Malaysian Ministry of Higher Education under the University of Malaya High Impact Research Grant “Mobile Cloud Computing: Device and Connectivity”, Project Number: M.C/625/1/HIR/MOE/FCSIT/03.

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  1. Department of Computer System and Information Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Abdullah Gani, Aisha Siddiqa, Shahaboddin Shamshirband & Fariza Hanum

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  1. Abdullah Gani
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Correspondence to Shahaboddin Shamshirband.

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Gani, A., Siddiqa, A., Shamshirband, S. et al. A survey on indexing techniques for big data: taxonomy and performance evaluation. Knowl Inf Syst 46, 241–284 (2016). https://doi.org/10.1007/s10115-015-0830-y

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