Large scientific databases often consist of heterogeneous components that differ in the number of attributes and the number of tuples. In this paper, we show that increased performance is achieved when a complex schema is used to represent different components of a data repository. The driving application used in this study is a pharmaceutical drug repository. The types of access patterns associated with this repository consist of the retrieval of a selected set of attribute values for a consecutive or a random set of tuples. These consecutive and random access patterns, which are typical of a drug discovery experiment, are used to measure the data retrieval throughput for different schemas.

An explosion in the amount of data generated through chemical and biological experimentation has been observed in recent years. This rapid proliferation of vast amounts of data has led to a set of cheminformatics and bioinformatics applications that manipulate dynamic, heterogeneous, and massive data. An example of such applications in the pharmaceutical industry is the computational process involved in the early discovery of lead drug candidates for a given target disease. This computational process includes repeated sequential and random accesses to a drug candidate database. Using the above pharmaceutical application, an experimental study was conducted in this paper that shows that for optimal performance, the degree of parallelism exploited in the application should be adjusted according to the drug candidate database instance size and the machine size. Additionally, different degrees of parallelism should be used depending on whether the access to the drug candidate database is random or sequential.