Designing Data-Intensive Web Applications
Designing Data-Intensive Web Applications
Specification and verification of data-driven web services
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A verifier for interactive, data-driven web applications
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Specification and design of workflow-driven hypertexts
Journal of Web Engineering
Specification and verification of data-driven Web applications
Journal of Computer and System Sciences
A language-driven approach for the design of interactive applications
Interacting with Computers
Extending the equivalent transformation framework to model dynamic interactive systems
WSEAS Transactions on Computers
Automatic verification of database-driven systems: a new frontier
Proceedings of the 12th International Conference on Database Theory
Automatic verification of data-centric business processes
Proceedings of the 12th International Conference on Database Theory
Proceedings of the IEEE/ACM international conference on Automated software engineering
Specification and verification of multi-user data-driven web applications
WS-FM'09 Proceedings of the 6th international conference on Web services and formal methods
Automated driver generation for analysis of web applications
FASE'11/ETAPS'11 Proceedings of the 14th international conference on Fundamental approaches to software engineering: part of the joint European conferences on theory and practice of software
Bounded verification of Ruby on Rails data models
Proceedings of the 2011 International Symposium on Software Testing and Analysis
Automatic verification of data-centric business processes
BPM'11 Proceedings of the 9th international conference on Business process management
Verification of database-driven systems via amalgamation
Proceedings of the 32nd symposium on Principles of database systems
Foundations of data-aware process analysis: a database theory perspective
Proceedings of the 32nd symposium on Principles of database systems
PROPOLIS: provisioned analysis of data-centric processes
Proceedings of the VLDB Endowment
Hi-index | 0.00 |
When comparing alternative query execution plans (QEPs), a cost-based query optimizer in a relational database management system needs to estimate the selectivity of conjunctive predicates. To avoid inaccurate independence assumptions, modern optimizers try to exploit multivariate statistics (MVS) that provide knowledge about joint frequencies in a table of a relation. Because the complete joint distribution is almost always too large to store, optimizers are given only partial knowledge about this distribution. As a result, there exist multiple, non-equivalent ways to estimate the selectivity of a conjunctive predicate. To consistently combine the partial knowledge during the estimation process, existing optimizers employ cumbersome ad hoc heuristics. These methods unjustifiably ignore valuable information, and the optimizer tends to favor QEPs for which the least information is available. This bias problem yields poor QEP quality and performance. We demonstrate MAXENT, a novel approach based on the maximum entropy principle, prototyped in IBM DB2 LUW. We illustrate MAXENT's ability to consistently estimate the selectivity of conjunctive predicates on a per-table basis. In contrast to the DB2 optimizer's current ad hoc methods, we show how MAXENT exploits all available information about the joint column distribution and thus avoids the bias problem. For some complex queries against a real-world database, we show that MAXENT improves selectivity estimates by orders of magnitude relative to the current DB2 optimizer, and also show how these improved estimate influence plan choices as well as query execution times.