STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
LATIN '00 Proceedings of the 4th Latin American Symposium on Theoretical Informatics
Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Bidirectional expansion for keyword search on graph databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Efficient query processing in geographic web search engines
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
BLINKS: ranked keyword searches on graphs
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Discover: keyword search in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Voronoi-based K nearest neighbor search for spatial network databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Scalable network distance browsing in spatial databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Keyword proximity search in complex data graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Querying Communities in Relational Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Instance optimal query processing in spatial networks
The VLDB Journal — The International Journal on Very Large Data Bases
A sketch-based distance oracle for web-scale graphs
Proceedings of the third ACM international conference on Web search and data mining
Query Processing Using Distance Oracles for Spatial Networks
IEEE Transactions on Knowledge and Data Engineering
Nearest keyword search in XML documents
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Keyword search in graphs: finding r-cliques
Proceedings of the VLDB Endowment
Distance oracles for vertex-labeled graphs
ICALP'11 Proceedings of the 38th international conference on Automata, languages and programming - Volume Part II
Text vs. space: efficient geo-search query processing
Proceedings of the 20th ACM international conference on Information and knowledge management
Multi-approximate-keyword routing in GIS data
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Partitioned multi-indexing: bringing order to social search
Proceedings of the 21st international conference on World Wide Web
Keyword-aware optimal route search
Proceedings of the VLDB Endowment
Hi-index | 0.00 |
It is quite common for networks emerging nowadays to have labels or textual contents on the nodes. On such networks, we study the problem of top-k nearest keyword (k-NK) search. In a network G modeled as an undirected graph, each node is attached with zero or more keywords, and each edge is assigned with a weight measuring its length. Given a query node q in G and a keyword λ, a k-NK query seeks k nodes which contain λ and are nearest to q. k-NK is not only useful as a stand-alone query but also as a building block for tackling complex graph pattern matching problems. The key to an accurate k-NK result is a precise shortest distance estimation in a graph. Based on the latest distance oracle technique, we build a shortest path tree for a distance oracle and use the tree distance as a more accurate estimation. With such representation, the original k-NK query on a graph can be reduced to answering the query on a set of trees and then assembling the results obtained from the trees. We propose two efficient algorithms to report the exact k-NK result on a tree. One is query time optimized for a scenario when a small number of result nodes are of interest to users. The other handles k-NK queries for an arbitrarily large k efficiently. In obtaining a k-NK result on a graph from that on trees, a global storage technique is proposed to further reduce the index size and the query time. Extensive experimental results conform with our theoretical findings, and demonstrate the effectiveness and efficiency of our k-NK algorithms on large real graphs.