Challenges and Recommendations in Big Data Indexing Strategies

Abstract

Index structures are one of the main strategies for effective data access applied for indexing data. According to expansion of data, traditional indexing strategies on big data meets several challenges that lead to weak performance; they haven't abilities to handle the rapid increase of data in terms of accurate retrieval results and processing time. So, it is necessary to substitute traditional index with another efficient index structure called learned index. Learned index goes to use machine learning models to tackle such issues and achieve more enhancements of processing time and accurate results. In this research, the authors discuss different indexing strategies on big data both traditional and learned indexes, demonstrate the main features of them, perform comparison in terms of its performance, and present big data indexing challenges and solutions. Consequently, the research suggests replacing traditional indexes by dynamic index models, which lead to less processing time and more accurate results taking into consideration specification of hardware used.