LDC: Enabling Search By Partial Distance In A Hyper-Dimensional Space

Recent advances in research fields like multimedia and bioinformatics have brought about a new generation of hyper-dimensional databases which can contain hundreds or even thousands of dimensions. Such hyperdimensional databases pose significant problems to existing high-dimensional indexing techniques which have been developed for indexing databases with (commonly) less than a hundred dimensions. To support efficient querying and retrieval on hyper-dimensional databases, we propose a methodology called Local Digital Coding (LDC) which can support k-nearest neighbors (KNN) queries on hyper-dimensional databases and yet co-exist with ubiquitous indices, such as B+ -trees. LDC extracts a simple bitmap representation called Digital Code(DC) for each point in the database. Pruning during KNN search is performed by dynamically selecting only a subset of the bits from the DC based on which subsequent comparisons are performed. In doing so, expensive operations involved in computing L-norm d...

• Type: Conference
• Year: 2004
• Where: ICDE
• Authors: Nick Koudas, Beng Chin Ooi, Heng Tao Shen, Anthony K. H. Tung
• Keywords: Database, Hyper-dimensional Databases, ICDE 2004, Indexing Databases, Such Hyperdimensional Databases