Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
Implications of certain assumptions in database performance evauation
ACM Transactions on Database Systems (TODS)
Scientific data management in the coming decade
ACM SIGMOD Record
Scaling games to epic proportions
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Data base system performance prediction using an analytical model (invited paper)
VLDB '81 Proceedings of the seventh international conference on Very Large Data Bases - Volume 7
A performance evaluation of data base machine architectures (invited paper)
VLDB '81 Proceedings of the seventh international conference on Very Large Data Bases - Volume 7
A flexible, large-scale, distributed agent based epidemic model
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Proceedings of the 23rd international conference on Supercomputing
Indemics: an interactive data intensive framework for high performance epidemic simulation
Proceedings of the 24th ACM International Conference on Supercomputing
Behavioral simulations in MapReduce
Proceedings of the VLDB Endowment
Indemics: An interactive high-performance computing framework for data-intensive epidemic modeling
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on simulation in complex service systems
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Realistic agent-based epidemic simulations usually involve a large scale social network containing individual details. The co-evolution of epidemic dynamics and human behavior requires the simulation systems to compute complex real-world interventions. Calls from public health policy makers for executing such simulation studies during a pandemic typically have tight deadlines. It is highly desirable to implement new interventions in existing high-performance epidemic simulations, with minimum development effort and limited performance degradation. Indemics is a database supported high-performance epidemic simulation framework, which enables complex intervention studies to be designed and executed within a short time. Unlike earlier approaches that implement new interventions inside the simulation engine, Indemics utilizes DBMS and reduces implementation effort from weeks to days. In this paper, we propose a methodology for modeling and predicting performance of Indemics-supported intervention studies. We demonstrate our methodology with experimental results.