Improving the performance of high-energy physics analysis through bitmap indices

Bitmap indices are popular multi-dimensional data structures for accessing read-mostly data such as data warehouse (DW) applications, decision support systems (DSS) and online analytical processing (OLAP). One of their main strengths is that they provide good performance characteristics for complex adhoc queries and an efficient combination of multiple index dimensions in one query. Considerable research work has been done in the area of finite (and low) attribute cardinalities. However, additional complexity is imposed on the design of bitmap indices for high cardinality or even non-discrete attributes, where different optimisation techniques than the ones proposed so far have to be applied. We discuss the design and implementation of bitmap indices for high-energy physics (HEP) analysis, where the potential search space consists of hundreds of independent dimensions. A single HEP query typically covers 10 to 100 dimensions out of the whole search space. In this context we evaluated two different bitmap encoding techniques, namely equality encoding and range encoding. For both methods the number of bit slices (or bitmap vectors) per attribute is a central optimisation parameter. We present some (first) results for choosing the optimal number of bit slices for multi-dimensional indices with attributes of different value distribution and query selectivity. We believe that this discussion is not only applicable to HEP but also to DW, DSS and CLAP type problems in general. (15 refs).