Smart Drill-Down: A New Data Exploration Operator

By Manas R. Joglekar, Hector Garcia-Molina, Aditya G. Parameswaran. 2014

32 citations recorded by the source corpus.

We present a data exploration system equipped with smart drill-down, a novel operator for interactively exploring a relational table to discover and summarize “interesting” groups of tuples. Each such group of tuples is represented by a rule. For instance, the rule (a, b, ★, 1000) tells us that there are a thousand tuples with value a in the first column and b in the second column (and any value in the third column). Smart drill-down presents an analyst with a list of rules that together describe interesting aspects of the table. The analyst can tailor the definition of interesting, and can interactively apply smart drill-down on an existing rule to explore that part of the table. In the demonstration, conference attendees will be able to use the data exploration system equipped with smart drill-down, and will be able to contrast smart drill-down to traditional drill-down, for various interestingness measures, and resource constraints.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In Signal Processing

Citations in the corpus

Automated Machine Learning: State-of-The-Art and Open Challenges (2019, background): “Smart Drill-Down uses an approximation algorithm to ﬁnd the optimal list of interesting rules as it is a NP-Hard problem.”
Interactive Data Exploration via Machine Learning Models (2016, methodology): “DICE [15] supports exploration of data cubes using faceted search and in [12] they propose a new “drill-down” operator for exploring and summarizing groups of tuples.”