Fast Similarity Search for Structural Data using Structural Digests

2015 Fiscal Year Final Research Report

• Project/Area Number: 24500111
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Media informatics/Database
• Research Institution: The University of Electro-Communications
• Principal Investigator: Koga Hisashi 電気通信大学, 情報システム学研究科, 准教授 (40361836)
• Project Period (FY): 2012-04-01 – 2016-03-31
• Keywords: グラフ間類似度 / 類似検索 / ハッシュ / 構造ベース画像処理

Outline of Final Research Achievements

This project studied the similarity search for structural data such as graphs. Because structural data are complex, the overhead to compute the similarity between two data tends to be enormous. Therefore, we generate a small data structure called "digest" for an individual graph and measure the similarity between two data in a short time by comparing their digests. Here, the search accuracy depends on how to summarize the structural information of a graph onto its digest. By embedding the similarity information regarding substructures into the digests, we succeeded in realizing the similarity search with a high retrieval accuracy.

We also studied the structure-based image processing which is a promising application of similarity search for structural data.

Free Research Field

類似検索