Multidimensional Latent Semantic Query Using
Hilbert Space Filling Curve in Peer-to-Peer Network
Fu Xianghua, Feng Boqin
(School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an
710049, China)
Abstract: A new method called multidimensional latent semantic query (MLSQ) was
presented in order to provide efficient multi-keyword query and search mechanism in
structured P2P (peer-to-peer) systems. With Hilbert space filling curve (HSFC) and latent
semantic index (LSI) , MLSQ maps similar data objects in high dimension semantic space
into an adjacent range in one dimensional value space, and considers every data object
sequence number in one dimensional space as a key. MLSQ distributes the keys to adjacent
nodes of structured P2P systems. By embedding the process of searching data points of HSFC
into the nodes of structured P2P networks and using the data search mechanism of networks,
MLSQ can facilitate to search the data objects in accordance with query requirements. The
theoretical analysis and experimental results show that MLSQ can improve the query
accuracy and reduce the number of communication messages, so MLSQ is better than the
existing multidimensional query based on keywords matching in structured P2P networks.
Keywords: structured peer-to-peer system; space fillcing curve; multidimensional latent
semantic; sema-
ntic query