Integrating keyword search with multiple dimension tree views over a summary corpus data cube

We demonstrate a system that integrates a novel OLAP component with a keyword search engine, to support querying over sparse and ragged corpus data. The key contribution of our system is the integration of dynamically selected point sets such as search results with OLAP querying over aggregated data. During the demonstration, participants will be able to enter a keyword search; observe the returned list of result files; observe distributional features such as outliers and clusters of results in the corpus in multiple dimension views; and select and partition corpus slices in the OLAP component to narrow search results. Participants will be able to experience not just the individual querying features of our system, but the way that they work together to facilitate smooth interaction sequences that combine OLAP and keyword search querying

Integrating keyword search with multiple dimension tree views over a summary corpus data cube

We demonstrate a system that integrates a novel OLAP component with a keyword search engine, to support querying over sparse and ragged corpus data. The key contribution of our system is the integration of dynamically selected point sets such as search results with OLAP querying over aggregated data. During the demonstration, participants will be able to enter a keyword search; observe the returned list of result files; observe distributional features such as outliers and clusters of results in the corpus in multiple dimension views; and select and partition corpus slices in the OLAP component to narrow search results. Participants will be able to experience not just the individual querying features of our system, but the way that they work together to facilitate smooth interaction sequences that combine OLAP and keyword search querying