Unit LeaderYasuo Tabei
Postdoctoral ResearcherTakaaki Nishimoto
Postdoctoral ResearcherShunsuke Kanda
Visiting ScientistHideo Bannai
Visiting ScientistYoshitaka Yamamoto
Technical Staff IDaisuke Kimura
Massive datasets, so called big data, are ubiquitous in research and industry. Data mining researchers/practitioners face the problem of processing and analyzing such huge datasets for knowledge discoveries in various fields. However, coping with big data is a challenge because of its huge computational cost. One important approach for solving this bottleneck in big data era is to (i) build indexes from datasets as a preprocessing by using space-efficient data structures and (ii) process datasets on the indexes. Succinct data structure (SDS) is a space-efficient representation for data structures while supporting fast data operations on the representation. Recently, various types of SDSs have been proposed for compactly representing and indexing strings, trees, graphs, set of integers and so on. We research on basics of SDSs and their applications to artificial intelligence and knowledge discovery for scalable information processing.