Succinct Information Processing Unit (https://aip.riken.jp/labs/generic_tech/succinct_inf_process/) at RIKEN AIP
Speaker 1 (Approx. 25 mins) : Yasuo Tabei (Unit Leader)
Title : Introduction of succinct information processing unit
Abstract : This talk introduces an overview of recent research activities in our unit. The goal of our unit is to develop compressed data structures for enhancing a scalability and usability of machine learning and data mining methods.
Speaker 2 (Approx. 25 mins) : Takaaki Nishimoto
Title : LZRR: LZ77 parging with right reference
Abstract : Lossless data compression has been widely studied in computer science.
One of the most widely used lossless data compressions is Lempel-Ziv(LZ) 77 parsing, which achieves a high compression ratio.
Bidirectional parsing is a lossless data compression and computes a sequence of phrases copied from another substring on either the left or the right position in an input string.
Gagie et al.(LATIN 2018) recently showed that a large gap exists between the number of smallest bidirectional phrases of a given string and that of LZ77 phrases.
In addition, finding the smallest bidirectional parse of a given text is NP-complete.
Several variants of bidirectional parsing have been proposed thus far, but no prior work for bidirectional parsing has achieved high compression that is smaller than that of LZ77 phrasing for any string.
In this paper, we present the first practical bidirectional parsing named LZ77 parsing with right reference (LZRR), in which the number of LZRR phrases is theoretically guaranteed to be smaller than the number of LZ77 phrases.
Experimental results using benchmark strings show the number of LZRR phrases is approximately six percent smaller than that of LZ77 phrases.
Speaker 3 (Approx. 25 mins) : Shunsuke Kanda
Title : Dynamic similarity search on integer sketches
Abstract : Similarity-preserving hashing is a core technique for fast similarity searches, and it randomly maps data points in a metric space to strings of discrete symbols (i.e., sketches) in the Hamming space. While traditional hashing techniques produce binary sketches, recent ones produce integer sketches for preserving various similarity measures. However, most similarity search methods are designed for binary sketches and inefficient for integer sketches. Moreover, most methods are either inapplicable or inefficient for dynamic datasets, although modern real-world datasets are updated over time. We propose dynamic filter trie (DyFT), a dynamic similarity search method for both binary and integer sketches. An extensive experimental analysis using large real-world datasets shows that DyFT performs superiorly with respect to scalability, time performance, and memory efficiency. For example, on a huge dataset of 216 million data points, DyFT performs a similarity search 6,000 times faster than a state-of-the-art method while reducing to one-thirteenth in memory.