Quickly generating billion-record synthetic databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Self-tuning histograms: building histograms without looking at data
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
STHoles: a multidimensional workload-aware histogram
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Materialized view and index selection tool for Microsoft SQL server 2000
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Benchmark Handbook: For Database and Transaction Processing Systems
Benchmark Handbook: For Database and Transaction Processing Systems
Dynamic multidimensional histograms
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
ToXgene: a template-based data generator for XML
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Benchmarking Database Systems A Systematic Approach
VLDB '83 Proceedings of the 9th International Conference on Very Large Data Bases
Selectivity Estimation Without the Attribute Value Independence Assumption
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Automating Statistics Management for Query Optimizers
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Dynamic Histograms: Capturing Evolving Data Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
DB2 Advisor: An Optimizer Smart Enough to Recommend its own Indexes
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Conditional selectivity for statistics on query expressions
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Simple and realistic data generation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Generating Queries with Cardinality Constraints for DBMS Testing
IEEE Transactions on Knowledge and Data Engineering
QAGen: generating query-aware test databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
A parallel general-purpose synthetic data generator
ACM SIGMOD Record
Generating targeted queries for database testing
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Multi-RQP: generating test databases for the functional testing of OLTP applications
Proceedings of the 1st international workshop on Testing database systems
SVTe: a tool to validate database schemas giving explanations
Proceedings of the 1st international workshop on Testing database systems
Generating XML structure using examples and constraints
Proceedings of the VLDB Endowment
Automation of broad sanity test generation
Programming and Computing Software
Query-Aware Test Generation Using a Relational Constraint Solver
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
A framework for testing DBMS features
The VLDB Journal — The International Journal on Very Large Data Bases
Constraint-based test database generation for SQL queries
Proceedings of the 5th Workshop on Automation of Software Test
Automated SQL query generation for systematic testing of database engines
Proceedings of the IEEE/ACM international conference on Automated software engineering
Constrained anonymization of production data: a constraint satisfaction problem approach
SDM'10 Proceedings of the 7th VLDB conference on Secure data management
A data generator for cloud-scale benchmarking
TPCTC'10 Proceedings of the Second TPC technology conference on Performance evaluation, measurement and characterization of complex systems
Parallel data generation for performance analysis of large, complex RDBMS
Proceedings of the Fourth International Workshop on Testing Database Systems
Data generation using declarative constraints
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
SemGen: towards a semantic data generator for benchmarking duplicate detectors
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Efficient update data generation for DBMS benchmarks
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
A tool for generating synthetic authorship records for evaluating author name disambiguation methods
Information Sciences: an International Journal
Scalable test data generation from multidimensional models
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Rapid development of data generators using meta generators in PDGF
Proceedings of the Sixth International Workshop on Testing Database Systems
Reversing statistics for scalable test databases generation
Proceedings of the Sixth International Workshop on Testing Database Systems
Generation of test databases using sampling methods
Proceedings of the 2013 International Symposium on Software Testing and Analysis
CAiSE'13 Proceedings of the 25th international conference on Advanced Information Systems Engineering
UpSizeR: Synthetically scaling an empirical relational database
Information Systems
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Evaluation and applicability of many database techniques, ranging from access methods, histograms, and optimization strategies to data normalization and mining, crucially depend on their ability to cope with varying data distributions in a robust way. However, comprehensive real data is often hard to come by, and there is no flexible data generation framework capable of modelling varying rich data distributions. This has led individual researchers to develop their own ad-hoc data generators for specific tasks. As a consequence, the resulting data distributions and query workloads are often hard to reproduce, analyze, and modify, thus preventing their wider usage. In this paper we present a flexible, easy to use, and scalable framework for database generation. We then discuss how to map several proposed synthetic distributions to our framework and report preliminary results.