We propose a database language, based on simple extensions of the relational data model and SQL, and show that it is Turing-complete and supportive of sequences and data streams. In fact, we obtain a simple characterization of the blocking/non-blocking behavior of queries and operators on streams: we show that non-blocking operators can be characterized algebraically by the monotonic behavior o...

Substring matching is fundamental to data mining methods for sequential data. It involves checking the existence of a short subsequence within a longer sequence, ensuring no gaps within a match. Whilst a large amount of existing work has focused on substring matching and mining techniques for certain sequences, there are only a few results for uncertain sequences. Uncertain sequences provide po...

The study of databases began with the design of efficient storage and data sharing techniques for large amount of data. This paper concerns the processing of imprecision and indiscernibility in relational databases using vague rough technique leading to vague rough relational database model. We utilize the notion of indiscernibility and possibility from rough set theory coupled with the idea of...

Relational database systems are a successful platform to manage large amounts of data, but do not cope well with uncertainty. However, the amount of uncertain data is growing at an unprecedented rate from both traditional sources (e.g. integrating enterprise data) and from next generation sources (e.g. information extraction). This trend has prompted the database community to investigate a prom...

Many real world systems and applications require information management components that provide support for managing imprecise data. There have thus been several proposals for extending relational database systems in order to represent as well as query such imprecise data. Little work, however, has been done in modeling uncertainty at the conceptual schema level and in developing design methodo...