SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
Applications of inductive logic programming
Communications of the ACM
PYTHIA: a knowledge-based system to select scientific algorithms
ACM Transactions on Mathematical Software (TOMS)
Communications of the ACM
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Inductive logic programming and knowledge discovery in databases
Advances in knowledge discovery and data mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
PELLPACK: a problem-solving environment for PDE-based applications on multicomputer platforms
ACM Transactions on Mathematical Software (TOMS)
A knowledge discovery methodology for the performance evaluation of scientific software
Neural, Parallel & Scientific Computations
Neuro-Fuzzy Support for Problem-Solving Environments: A Step Toward Automated Solution of PDEs
IEEE Computational Science & Engineering
POEMS: End-to-End Performance Design of Large Parallel Adaptive Computational Systems
IEEE Transactions on Software Engineering
Machine Learning
Mining the Performance of Complex Systems
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
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Often scientists need to locate appropriate software for their problems and then select from among many alternatives. We have previously proposed an approach for dealing with this task by processing performance data of the targeted software. This approach has been tested using a customized implementation referred to as PYTHIA. This experience made us realize the complexity of the algorithmic discovery of knowledge from performance data and of the management of these data together with the discovered knowledge. To address this issue, we created PYTHIA-II -- a modular framework and system which combines a general knowledge discovery in databases (KDD) methodology and recommender system technologies to provide advice about scientific software/hardware artifacts. The functionality and effectiveness of the system is demonstrated for two existing performance studies using sets of software for solving partial differential equations. From the end-user perspective, PYTHIA-II allows users to specify the problem to be solved and their computational objectives. In turn, PYTHIA-II (i) selects the software available for the user's problem, (ii) suggests parameter values, and (iii) assesses the recommendation provided. PYTHIA-II provides all the necessary facilities to set up database schemas for testing suites and associated performance data in order to test sets of software. Moreover, it allows easy interfacing of alternative data mining and recommendation facilities. PYTHIA-II is an open-ended system implemented on public domain software and has been used for performance evaluation in several different problem domains.