A repository with results on data mining experiments
The Experiment Databases tool makes empirical results from AI data mining experiments more accessible and reusable. The site hosts a query engine in SQL format, retrieving results from mining experiments recorded into their repository. These can then be easily studied, and compared toother similar experiments - something that could otherwise take long hours of sieving through results from articles and research reports.
At a high level, the relational data model consists of an experiment, a learner , a data set, a machine where the test is run, and results on a model and evaluation method. There is also scope for storing parameters of each learner, and ensemble experiments.
Queries are qritten using standard SQL language over the documented data model, and useful syntax like desc "table" is also available.
Check out some examples of queries here.
The Experiment Databases tool makes empirical results from AI data mining experiments more accessible and reusable. The site hosts a query engine in SQL format, retrieving results from mining experiments recorded into their repository. These can then be easily studied, and compared toother similar experiments - something that could otherwise take long hours of sieving through results from articles and research reports.
At a high level, the relational data model consists of an experiment, a learner , a data set, a machine where the test is run, and results on a model and evaluation method. There is also scope for storing parameters of each learner, and ensemble experiments.
Queries are qritten using standard SQL language over the documented data model, and useful syntax like desc "table" is also available.
Check out some examples of queries here.
Hi,
ReplyDeleteThanks for writing about our experiment repository.
We did some updates recently, so to make your query work you have to try something like:
select e.type, l.name, l.package, v.evalue
from experiment e, learner l, learner_inst li, data_inst di, dataset d, evaluation v, eval_metric m
where e.liid=li.liid and li.lid=l.lid and e.diid=di.diid and di.did=d.did and d.name='wine' and e.eid=v.eid and v.mid=m.mid and m.name='predictive_accuracy'
We are also working on a graphical interface for queries, I'll keep you posted :).
Cheers,
Joaquin