Author
Listed:
- Zineddine Kouahla
(Labstic Laboratory, Department of Computer Science, Guelma University, Guelma 24000, Algeria)
- Ala-Eddine Benrazek
(Labstic Laboratory, Department of Computer Science, Guelma University, Guelma 24000, Algeria)
- Mohamed Amine Ferrag
(Labstic Laboratory, Department of Computer Science, Guelma University, Guelma 24000, Algeria)
- Brahim Farou
(Labstic Laboratory, Department of Computer Science, Guelma University, Guelma 24000, Algeria)
- Hamid Seridi
(Labstic Laboratory, Department of Computer Science, Guelma University, Guelma 24000, Algeria)
- Muhammet Kurulay
(Department of Mathematics Engineering, University of Yildiz Technical, Istanbul 34349, Turkey)
- Adeel Anjum
(Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China)
- Alia Asheralieva
(Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China)
Abstract
The past decade has been characterized by the growing volumes of data due to the widespread use of the Internet of Things (IoT) applications, which introduced many challenges for efficient data storage and management. Thus, the efficient indexing and searching of large data collections is a very topical and urgent issue. Such solutions can provide users with valuable information about IoT data. However, efficient retrieval and management of such information in terms of index size and search time require optimization of indexing schemes which is rather difficult to implement. The purpose of this paper is to examine and review existing indexing techniques for large-scale data. A taxonomy of indexing techniques is proposed to enable researchers to understand and select the techniques that will serve as a basis for designing a new indexing scheme. The real-world applications of the existing indexing techniques in different areas, such as health, business, scientific experiments, and social networks, are presented. Open problems and research challenges, e.g., privacy and large-scale data mining, are also discussed.
Suggested Citation
Zineddine Kouahla & Ala-Eddine Benrazek & Mohamed Amine Ferrag & Brahim Farou & Hamid Seridi & Muhammet Kurulay & Adeel Anjum & Alia Asheralieva, 2021.
"A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues,"
Future Internet, MDPI, vol. 14(1), pages 1-44, December.
Handle:
RePEc:gam:jftint:v:14:y:2021:i:1:p:19-:d:715620
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