Application-Oriented Succinct Data Structures for Big Data

• Published in: The review of socionetwork strategies Vol. 13; no. 2; pp. 227 - 236
• Main Author: Shibuya, Tetsuo
• Format: Journal Article
• Language: English
• Published: Tokyo Springer Japan 01-10-2019; Springer Nature B.V
• Subjects: Big Data; Bioinformatics; Business and Management; Cryptography; Data management; Data structures; Information Systems Applications (incl.Internet); IT in Business; Object oriented programming; Simulation and Modeling; Succinct data structures; Survey; de Bruijn graph; Work space reduction; Oblivious RAM; Compressed communication; Sublinear paradigm
• ISSN: 2523-3173; 1867-3236
• DOI: 10.1007/s12626-019-00045-1

A data structure is called succinct if its asymptotical space requirement matches the original data size. The development of succinct data structures is an important factor to deal with the explosively increasing big data. Moreover, wider variations of big data have been produced in various fields recently and there is a substantial need for the development of more application-specific succinct data structures. In this study, we review the recently proposed application-oriented succinct data structures motivated by big data applications in three different fields: privacy-preserving computation in cryptography, genome assembly in bioinformatics, and work space reduction for compressed communications.