RNA splicing is an essential step in gene expression, and is often variable, giving rise to multiple alternatively spliced mRNA and protein isoforms from a single gene locus. The design of effective databases to support experimental and computational investigations of alternative splicing (AS) is a significant challenge. In an effort to integrate accurate exon and splice site annotation with current knowledge about splicing regulatory elements and predicted AS events, and to link information about the splicing of orthologous genes in different species, we have developed the Hollywood system. This database was built upon genomic annotation of splicing patterns of known genes derived from spliced alignment of complementary DNAs (cDNAs) and expressed sequence tags, and links features such as splice site sequence and strength, exonic splicing enhancers and silencers, conserved and non-conserved patterns of splicing, and cDNA library information for inferred alternative exons. Hollywood was implemented as a relational database and currently contains comprehensive information for human and mouse. It is accompanied by a web query tool that allows searches for sets of exons with specific splicing characteristics or splicing regulatory element composition, or gives a graphical or sequence-level summary of splicing patterns for a specific gene. A streamlined graphical representation of gene splicing patterns is provided, and these patterns can alternatively be layered onto existing information in the UCSC Genome Browser. The database is accessible at .

We describe an automatic database design tool that exploits correlations between attributes when recommending materialized views (MVs) and indexes. Although there is a substantial body of related work exploring how to select an appropriate set of MVs and indexes for a given workload, none of this work has explored the effect of correlated attributes (e.g., attributes encoding related geographic information) on designs. Our tool identifies a set of MVs and secondary indexes such that correlations between the clustered attributes of the MVs and the secondary indexes are enhanced, which can dramatically improve query performance. It uses a form of Integer Linear Programming (ILP) called ILP Feedback to pick the best set of MVs and indexes for given database size constraints. We compare our tool with a state-of-the-art commercial database designer on two workloads, APB-1 and SSB (Star Schema Benchmark---similar to TPC-H). Our results show that a correlation-aware database designer can improve query performance up to 6 times within the same space budget when compared to a commercial database designer.

In relational query processing, there are generally two choices for access paths when performing a predicate lookup for which no clustered index is available. One option is to use an unclustered index. Another is to perform a complete sequential scan of the table. Many analytical workloads do not benefit from the availability of unclustered indexes; the cost of random disk I/O becomes prohibitive for all but the most selective queries.It has been observed that a secondary index on an unclustered attribute can perform well under certain conditions if the unclustered attribute is correlated with a clustered index attribute [4]. The clustered index will co-locate values and the correlation will localize access through the unclustered attribute to a subset of the pages. In this paper, we show that in a real application (SDSS) and widely used benchmark (TPC-H), there exist many cases of attribute correlation that can be exploited to accelerate queries. We also discuss a tool that can automatically suggest useful pairs of correlated attributes. It does so using an analytical cost model that we developed, which is novel in its awareness of the effects of clustering and correlation.Furthermore, we propose a data structure called a Correlation Map (CM) that expresses the mapping between the correlated attributes, acting much like a secondary index. The paper also discusses how bucketing on the domains of both attributes in the correlated attribute pair can dramatically reduce the size of the CM to be potentially orders of magnitude smaller than that of a secondary B+Tree index. This reduction in size allows us to create a large number of CMs that improve performance for a wide range of queries. The small size also reduces maintenance costs as we demonstrate experimentally.

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