Indexing Scientific Data

To address these three problems, we introduced multi-resolution bitmap indexes, which group data into bins at multiple granularities. We achieved a query performance which is 10 times faster than traditional bitmap indexes by using bitmap indexes built at these multiple granularities. To address the issue of size, we introduced an adaptive version of multi-resolution bitmap indexes. The adaptive index adds and drops auxiliary indexes as needed for the query workload and is a fraction of the size of the data being indexed. We achieved a performance improvement of a factor of 6, compared to an ordinary multi-resolution bitmap index of the same size. We also introduced a novel algorithm to consolidate data points into regions of interest. By exploiting the special properties of compressed bitmap indexes and scientific meshes we achieved sublinear running times, with respect to the number of points in the query result, for both the index lookup and region consolidation.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Sinha, Rishi Rakesh

Contributors dc:contributor

• Winslett, Marianne

• Computer Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI3290382

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81791