An approach for automatic data virtualization

Analysis of large and/or geographically distributed scientific datasets is emerging as a key component of grid computing. One challenge in this area is that scientific datasets are typically stored as binary or character flat-files, which makes specification of processing much harder. In view of this, there has been recent interest in data virtualization, and data services to support such virtualization. This paper presents an approach for automatically creating data services to support data virtualization. Specifically, we show how a relational table like data abstraction can be supported for complex multidimensional scientific datasets that are resident on a cluster. We have designed and implemented a tool that processes SQL queries (with select and where statements) on multi-dimensional datasets. We have designed a meta-data description language that is used for specifying the data layout. From such description, our tool automatically generates efficient data subsetting and access functions. We have extensively evaluated our system. The key observations from our experiments are as follows. First, our tool can correctly and efficiently handle a variety of different data layouts. Second, our system scales well as the number of nodes or the amount of data is scaled. Third, the performance of the automatically generated code for indexing and extracting functions is quite comparable to the performance of hand-written codes.