Directed hypergraphs and applications
Discrete Applied Mathematics - Special issue: combinatorial structures and algorithms
Multidimensional Database Technology
Computer
Semantic Integration and Querying of Heterogeneous Data Sources Using a Hypergraph Data Model
BNCOD 19 Proceedings of the 19th British National Conference on Databases: Advances in Databases
A Framework for the Classification and Description of Multidimensional Data Models
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
The VLDB Journal — The International Journal on Very Large Data Bases
Multidimensional Data Modeling for Complex Data
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Active learning and compilation of higher order schema integration queries
Active learning and compilation of higher order schema integration queries
Querying through a user interface
Data & Knowledge Engineering
Survey of graph database models
ACM Computing Surveys (CSUR)
UQBE: uncertain query by example for web service mashup
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Semantic queries in databases: problems and challenges
Proceedings of the 18th ACM conference on Information and knowledge management
HyperGraphDB: a generalized graph database
WAIM'10 Proceedings of the 2010 international conference on Web-age information management
Bridging the Gap: Query by Semantic Example
IEEE Transactions on Multimedia
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A formal query approach called Hypergraph Query-by-Example (HQBE) is presented that can be applied in for large-scale data analysis, deliberating the user from the knowledge on specific query language details or even the database structure. The query construction of HQBE makes use of the hypergraph-based multidimensional artifacts and relations between them as the unifying data representation. Since the construction of a query is supported by a semantic discovery process and a traversal of relationship structures and data elements, the query hypergraph patterns could support the flexible on-demand analysis for obtaining the desired results.