Abstract | ||
---|---|---|
Many sorts of structured data are commonly stored in a multi-relational
format of interrelated tables. Under this relational model, exploratory data
analysis can be done by using relational queries. As an example, in the
Internet Movie Database (IMDb) a query can be used to check whether the average
rank of action movies is higher than the average rank of drama movies.
We consider the problem of assessing whether the results returned by such a
query are statistically significant or just a random artifact of the structure
in the data. Our approach is based on randomizing the tables occurring in the
queries and repeating the original query on the randomized tables. It turns out
that there is no unique way of randomizing in multi-relational data. We propose
several randomization techniques, study their properties, and show how to find
out which queries or hypotheses about our data result in statistically
significant information. We give results on real and generated data and show
how the significance of some queries vary between different randomizations. |
Year | Venue | Keywords |
---|---|---|
2009 | Clinical Orthopaedics and Related Research | relational model,artificial intelligent,structured data,exploratory data analysis,statistical significance,relational data |
DocType | Volume | Citations |
Journal | abs/0906.5 | 0 |
PageRank | References | Authors |
0.34 | 7 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Markus Ojala | 1 | 103 | 6.03 |
Gemma C. Garriga | 2 | 144 | 10.25 |
Aristides Gionis | 3 | 6808 | 386.81 |
Heikki Mannila | 4 | 6595 | 1495.69 |