Enhanced bitmap indexes for large scale data management Guadalupe Canahuate PhD Candidate Department of Computer Science Ohio State University Advances in technology have enabled the production of massive volumes of data through observations and simulations in many application domains, including scientific databases and data warehouses. These new data sets and the associated queries pose a challenge for efficient storage and data retrieval that requires novel indexing structures and algorithms. We propose a series of enhancements to existing bitmap indexes to account for the inherent characteristics of large scale datasets and to efficiently support the type of queries needed to analyze the data. In this talk, I will present the enhancements that address the large storage requirement and the poor update performance of traditional bitmap indexes.To reduce the space requirement of bitmap indexes we propose an ordering algorithm called adaptive Gray Code ordering as a hybrid between lexicographic and Gray code orderings. To improve their update performance we extend the existing run-length encoders and make insertion cost independent of the attribute cardinality. I will also describe our new encoding and query execution for non-clustered bitmap indexes that reduces the storage requirement, and improves both query execution and update time for low cardinality attributes.