• Journal of KIISE:Databases
• /
• v.35 no.4
• /
• pp.297-306
• /
• 2008

MJoin is proposed as an algorithm to join multiple data streams efficiently, whose characteristics are unpredictably changed. It extends a symmetric hash join to handle multiple data streams. Whenever a tuple arrives from a remote stream source, MJoin checks whether all of hash tables have matching tuples. However, when a join involves many data streams with low join selectivity, the performance of this checking process is significantly influenced by the checking order of hash tables. In this paper, we propose a BiHT-Join algorithm which extends MJoin to conduct this checking in a constant time regardless of a join order. BiHT-Join maintains a bit-vector which represents the existence of tuples in streams and decides a successful/unsuccessful join through comparing a bit-vector. Based on the bit-vector comparison, BiHT-Join can conduct a hash join only for successful joining tuples based on this decision. Our experimental results show that the proposed BiHT-Join provides better performance than MJoin in the processing of multiple streams.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.12
• /
• pp.2061-2068
• /
• 2016

A Bloom filter is a space-efficient data structure popularly applied in many network algorithms. This paper proposes a vectored Bloom filter to provide a high-speed Internet protocol (IP) address lookup. While each hash index for a Bloom filter indicates one bit, which is used to identify the membership of the input, each index of the proposed vectored Bloom filter indicates a vector which is used to represent the membership and the output port for the input. Hence the proposed Bloom filter can complete the IP address lookup without accessing an off-chip hash table for most cases. Simulation results show that with a reasonable sized Bloom filter that can be stored using an on-chip memory, an IP address lookup can be performed with less than 0.0003 off-chip accesses on average in our proposed architecture.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.7
• /
• pp.2599-2613
• /
• 2015

Multi-index hashing (MIH) is the state-of-the-art method for indexing binary codes, as it di-vides long codes into substrings and builds multiple hash tables. However, MIH is based on the dataset codes uniform distribution assumption, and will lose efficiency in dealing with non-uniformly distributed codes. Besides, there are lots of results sharing the same Hamming distance to a query, which makes the distance measure ambiguous. In this paper, we propose a data-oriented multi-index hashing method (DOMIH). We first compute the covariance ma-trix of bits and learn adaptive projection vector for each binary substring. Instead of using substrings as direct indices into hash tables, we project them with corresponding projection vectors to generate new indices. With adaptive projection, the indices in each hash table are near uniformly distributed. Then with covariance matrix, we propose a ranking method for the binary codes. By assigning different bit-level weights to different bits, the returned bina-ry codes are ranked at a finer-grained binary code level. Experiments conducted on reference large scale datasets show that compared to MIH the time performance of DOMIH can be improved by 36.9%-87.4%, and the search accuracy can be improved by 22.2%. To pinpoint the potential of DOMIH, we further use near-duplicate image retrieval as examples to show the applications and the good performance of our method.