A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues

Zineddine Kouahla,Adeel Anjum,Muhammet Kurulay,Alia Asheralieva,Brahim Farou,Hamid Seridi,Ala-Eddine Benrazek,Mohamed Amine Ferrag

doi:10.3390/fi14010019

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.

Highlights

Academic Editor: Xiumin WangWidespread utilization of Internet of Things (IoT) systems and applications has resulted in the massive data expansion promising greater benefits for businesses and individuals, and introducing significant challenges for big data analytics
We first present our proposed taxonomy of existing indexing techniques in the literature We provide a detailed description of the majority of the indexing technique under the proposed taxonomy
We present the reasons of the emergence of Big IoT Data and why indexing techniques are required

Summary

Introduction

Widespread utilization of Internet of Things (IoT) systems and applications has resulted in the massive data expansion promising greater benefits for businesses and individuals, and introducing significant challenges for big data analytics. Such an expansion plays an important role in the dynamics of large data. Large data can be classified according to their volume, variety and velocity (“3V’s” for short) These categories were first introduced by Gartner, Inc. to highlight some elements of the challenges associated with large volume data [1,2,3,4,5].

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