Agma Traina and Caetano Traina (University of São Paulo)
The evolution of the Relational Database Management Systems must include not only resources to handle big data, but also complex data (such as images, audios, videos, graphs, multidimensional data, long texts, time series, genetic sequences, etc.), where order-based comparisons are not appropriate, and identity-based comparisons are meaningless. Comparing complex data by similarity stirrers much more meaning from data. However, current RDBMSs do not yet have adequate resources to express and execute similarity comparisons. In this lecture, we will present works being developed at the University of Sao Paulo (USP) Databases and Images Group (GBdI) to include similarity queries comprehensively across all the core components of RDBMS, including the interpreter, logical optimizer, physical optimizer and executor. It will be shown how search operators based on Relational Algebra and their respective implementation are being modeled on efficient physical access operators, based on indexing techniques developed specifically for complex data, as well as the development of selectivity and cost prediction techniques to enable optimizers to have a high hit rate.
– Caetano Traina Jr is Professor with the Computer Science Department of the University of São Paulo at São Carlos, Brazil. His research interests include access methods for complex data, data mining, similarity searching, big data and multimedia databases.
– Agma Traina is a Professor with the Computer Science Department of the University of São Paulo at São Carlos, Brazil. Her research interests include image databases, image mining, indexing methods for complex data, multimedia retrieval by content, and visual analytics.