SQL Server 2012 introduced two innovations targeted for data warehousing workloads: column store indexes and batch (vectorized) processing mode. Together they greatly improve performance of typical data warehouse queries, routinely by 10X and in some cases by a 100X or more. The main limitations of the initial version are addressed in the upcoming release. Column store indexes are updatable and can be used as the base storage for a table. The repertoire of batch mode operators has been expanded, existing operators have been improved, and query optimization has been enhanced. This paper gives an overview of SQL Server's column stores and batch processing, in particular the enhancements introduced in the upcoming release.

The SQL Server 11 release (code named Denali) introduces a new data warehouse query acceleration feature based on a new index type called a column store index. The new index type combined with new query operators processing batches of rows greatly improves data warehouse query performance: in some cases by hundreds of times and routinely a tenfold speedup for a broad range of decision support queries. Column store indexes are fully integrated with the rest of the system, including query processing and optimization. This paper gives an overview of the design and implementation of column store indexes including enhancements to query processing and query optimization to take full advantage of the new indexes. The resulting performance improvements are illustrated by a number of example queries.

We seek to leverage knowledge about information organization in a domain to effectively and efficiently meet targeted information needs of expert users. The semantic components model represents document content in a manner that is complementary to full text and keyword indexing. Semantic component instances are segments of text about a particular aspect of the main topic of the document and may not correspond to structural elements in the document. This paper describes the semantic components model and presents experimental evidence from a large interactive searching study showing that semantic components, used to supplement full text and keyword indexing and to extend the query language, enhanced the retrieval of domain-specific documents in response to realistic queries posed by real users.

Do improvements in system performance demonstrated by batch evaluations confer the same benefit for real users? We carried out experiments designed to investigate this question. After identifying a weighting scheme that gave maximum improvement over the baseline in a non-interactive evaluation, we used it with real users searching on an instance recall task. Our results showed the weighting scheme giving beneficial results in batch studies did not do so with real users. Further analysis did identify other factors predictive of instance recall, including number of documents saved by the user, document recall, and number of documents seen by the user.