An amateur's introduction to recursive query processing strategies
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
PODS '87 Proceedings of the sixth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Performance evaluation of data intensive logic programs
Foundations of deductive databases and logic programming
Argument reduction by factoring
VLDB '89 Proceedings of the 15th international conference on Very large data bases
A graph-oriented object model for database end-user interfaces
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
On the expected size of recursive Datalog queries
PODS '91 Proceedings of the tenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Architecture and applications of the Hy+ visualization system
IBM Systems Journal
Mysql Reference Manual
The VLDB Journal — The International Journal on Very Large Data Bases - Prototypes of deductive database systems
What You Always Wanted to Know About Datalog (And Never Dared to Ask)
IEEE Transactions on Knowledge and Data Engineering
Hyperlog: A Graph-Based System for Database Browsing, Querying, and Update
IEEE Transactions on Knowledge and Data Engineering
Rule Ordering in Bottom-Up Fixpoint Evaluation of Logic Programs
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
GLASS: A Graphical Query Language for Semi-Structured Data
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
VIQING: Visual Interactive QueryING
VL '98 Proceedings of the IEEE Symposium on Visual Languages
Implementation of a Constraint-Based Visualization System
VL '00 Proceedings of the 2000 IEEE International Symposium on Visual Languages (VL'00)
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We have constructed a graph database system where a query can be expressed intuitively as a diagram. The query result is also visualized as a diagram based on the intrinsic relationship among the returned data. In this database system, CORAL plays the role of a query execution engine to evaluate queries and deduce results. In order to understand the effectiveness of CORAL optimization techniques on visual query processing.We present and analyze the performance and scalability of CORAL's query rewriting strategies, which include Supplementary Magic Templates, Magic Templates, Context Factoring, Naïve Backtracking, and Without Rewriting method. Our research surprisingly shows that the Without Rewriting method takes the minimum total time to process the benchmark queries. Furthermore, CORAL's default optimization method Supplementary Magic Templates is not uniformly the best choice for every query. The “optimization” of visual queries is beneficial if one could select the right optimization approach for each query.