Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A case history analysis of software error cause-effect relationships
IEEE Transactions on Software Engineering
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
The game of life: a CLEAN programming tutorial and case study
ACM SIGPLAN Notices
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Decompositions and functional dependencies in relations
ACM Transactions on Database Systems (TODS)
Data mining: concepts and techniques
Data mining: concepts and techniques
Seeing is believing: the importance of visualization in manufacturing simulation
Proceedings of the 32nd conference on Winter simulation
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Racing Committees for Large Datasets
DS '02 Proceedings of the 5th International Conference on Discovery Science
XmdvTool: integrating multiple methods for visualizing multivariate data
VIS '94 Proceedings of the conference on Visualization '94
MatML: XML for information exchange with materials property data
Proceedings of the 4th international workshop on Data mining standards, services and platforms
Comparing mathematical and heuristic approaches for scientific data analysis
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Hi-index | 0.02 |
Experimental data in many domains serves as a basis for predicting useful trends. If the data and analysis are available over the Web this promotes E-Business by connecting clientele worldwide. This paper describes such a predictive tool "QuenchMiner™" in the domain "Materials Science". Data mining, more specifically the "Apriori Algorithm", is used to derive association rules that represent relationships between input conditions and results of domain experiments. This enables the tool to answer questions such as "Given cooling medium and agitation during material heat treatment, predict cooling rate". This allows users to perform case studies on the Web and use their results to optimize the involved processes, thus increasing customer satisfaction. Another interesting aspect is predicting material microstructure during heat treatment. Microstructure controls material properties such as hardness. Hence its prediction helps in making decisions about materials selection. Microstructure prediction has similarities to an artificial intelligence process called "Game-of-Life". Some challenges in our work are incorporating domain expert judgement while mining association rules, simulating microstructure evolution under different conditions, and dealing with uncertainty. These challenges and associated research issues are outlined here. To the best of our knowledge, this is the first tool performing Web-based predictive analysis in Materials Science.