Building an EDA Assistant: A Progress Report

Robert St. Amant, Paul R. Cohen
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, PMLR R1:501-512, 1997.

Abstract

Since 1993 we have been working on a system to help people with exploratory data analysis (EDA). AIDE, an Assistant for Intelligent Data Exploration, is a knowledge-based planning system that incrementally explores a dataset, guided by user directives and its own evaluation of indications in the data. Its plan library contains strategies for generating and interpreting indications in data, selecting techniques to build appropriate descriptions of data, carrying out relevant procedures, and combining individual results into a coherent larger picture. The system is mixed-initiative, autonomously pursuing high- and low-level goals while still allowing the user to inform or override its decisions. Elsewhere we have described AIDE’s operations and primitive data structures [22], its planning representation [23], its user interface [25, 24], and the system as a whole [21]. This progress report discusses a recent evaluation we conducted with AIDE and explains why we believe that this line of research is important to AI and statistics researchers. We will begin with a very brief overview of the system. The bulk of the paper describes the evaluation, our analysis of the results, and the lessons we learned through the experience of building and evaluating AIDE. We end with a discussion of the generality of our results and the potential for future work.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR1-amant97a, title = {Building an EDA Assistant: A Progress Report}, author = {Amant, Robert St. and Cohen, Paul R.}, booktitle = {Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics}, pages = {501--512}, year = {1997}, editor = {Madigan, David and Smyth, Padhraic}, volume = {R1}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r1/amant97a/amant97a.pdf}, url = {https://proceedings.mlr.press/r1/amant97a.html}, abstract = {Since 1993 we have been working on a system to help people with exploratory data analysis (EDA). AIDE, an Assistant for Intelligent Data Exploration, is a knowledge-based planning system that incrementally explores a dataset, guided by user directives and its own evaluation of indications in the data. Its plan library contains strategies for generating and interpreting indications in data, selecting techniques to build appropriate descriptions of data, carrying out relevant procedures, and combining individual results into a coherent larger picture. The system is mixed-initiative, autonomously pursuing high- and low-level goals while still allowing the user to inform or override its decisions. Elsewhere we have described AIDE’s operations and primitive data structures [22], its planning representation [23], its user interface [25, 24], and the system as a whole [21]. This progress report discusses a recent evaluation we conducted with AIDE and explains why we believe that this line of research is important to AI and statistics researchers. We will begin with a very brief overview of the system. The bulk of the paper describes the evaluation, our analysis of the results, and the lessons we learned through the experience of building and evaluating AIDE. We end with a discussion of the generality of our results and the potential for future work.}, note = {Reissued by PMLR on 30 March 2021.} }
Endnote
%0 Conference Paper %T Building an EDA Assistant: A Progress Report %A Robert St. Amant %A Paul R. Cohen %B Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1997 %E David Madigan %E Padhraic Smyth %F pmlr-vR1-amant97a %I PMLR %P 501--512 %U https://proceedings.mlr.press/r1/amant97a.html %V R1 %X Since 1993 we have been working on a system to help people with exploratory data analysis (EDA). AIDE, an Assistant for Intelligent Data Exploration, is a knowledge-based planning system that incrementally explores a dataset, guided by user directives and its own evaluation of indications in the data. Its plan library contains strategies for generating and interpreting indications in data, selecting techniques to build appropriate descriptions of data, carrying out relevant procedures, and combining individual results into a coherent larger picture. The system is mixed-initiative, autonomously pursuing high- and low-level goals while still allowing the user to inform or override its decisions. Elsewhere we have described AIDE’s operations and primitive data structures [22], its planning representation [23], its user interface [25, 24], and the system as a whole [21]. This progress report discusses a recent evaluation we conducted with AIDE and explains why we believe that this line of research is important to AI and statistics researchers. We will begin with a very brief overview of the system. The bulk of the paper describes the evaluation, our analysis of the results, and the lessons we learned through the experience of building and evaluating AIDE. We end with a discussion of the generality of our results and the potential for future work. %Z Reissued by PMLR on 30 March 2021.
APA
Amant, R.S. & Cohen, P.R.. (1997). Building an EDA Assistant: A Progress Report. Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R1:501-512 Available from https://proceedings.mlr.press/r1/amant97a.html. Reissued by PMLR on 30 March 2021.

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