Min-max heaps and generalized priority queues
Communications of the ACM
Efficient management of transitive relationships in large data and knowledge bases
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Retrieving and organizing web pages by “information unit”
Proceedings of the 10th international conference on World Wide Web
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Reachability and distance queries via 2-hop labels
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Holistic twig joins: optimal XML pattern matching
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Introduction to Algorithms
Beyond Steiner's Problem: A VLSI Oriented Generalization
WG '89 Proceedings of the 15th International Workshop on Graph-Theoretic Concepts in Computer Science
XRANK: ranked keyword search over XML documents
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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
Dual Labeling: Answering Graph Reachability Queries in Constant Time
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
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
Efficiently Querying Large XML Data Repositories: A Survey
IEEE Transactions on Knowledge and Data Engineering
XSEarch: a semantic search engine for XML
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Sum-max monotonic ranked joins for evaluating top-k twig queries on weighted data graphs
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
On-line exact shortest distance query processing
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Distance-join: pattern match query in a large graph database
Proceedings of the VLDB Endowment
Effective pruning for XML structural match queries
Data & Knowledge Engineering
On-line preferential nearest neighbor browsing in large attributed graphs
DASFAA'10 Proceedings of the 15th international conference on Database systems for advanced applications
R2DF framework for ranked path queries over weighted RDF graphs
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Answering pattern match queries in large graph databases via graph embedding
The VLDB Journal — The International Journal on Very Large Data Bases
The exact distance to destination in undirected world
The VLDB Journal — The International Journal on Very Large Data Bases
Diversified top-k graph pattern matching
Proceedings of the VLDB Endowment
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Querying large-scale graph-structured data with twig patterns is attracting growing interest. Generally, a twig pattern could have an extremely large, potentially exponential, number of matches in a graph. Retrieving and returning to the user this many answers may both incur high computational overhead and overwhelm the user. In this paper we propose two efficient algorithms, DP-B and DP-P, for retrieving top-ranked twig-pattern matches from large graphs. Our first algorithm, DP-B, is able to retrieve exact top-ranked answer matches from potentially exponentially many matches in time and space linear in the size of our data inputs even in the worst case. Further, beyond the linear-cost result of DP-B, our second algorithm, DP-P, could take far less than linear time and space cost in practice. To the best of our knowledge, our algorithms are the first to have these performance properties. Our experimental results demonstrate the high performance of both algorithms on large datasets. We also analyze and compare the performance trade-off between DP-B and DP-P from the theoretical and practical viewpoints.