We present a data model for tracking mobile objects and reporting the result of continuous queries. The model relies on a discrete view of the spatio-temporal space, where the 2D space and the time axis are respectively partitioned in a finite set of user-defined areas and in constantsize intervals. We define a query language to retrieve objects that match mobility patterns describing a sequence of moves and discuss evaluation techniques to maintain incrementally the result of queries. 1

This paper develops a database query language called Transducer Datalog motivated by the needs of a new and emerging class of database applications. In these applications, such as text databases and genome databases, the storage and manipulation of long character sequences is a crucial feature. The issues involved in managing this kind of data are not addressed by traditional database systems, either in theory or in practice. To address these issues, we recently introduced a new machine model called a generalized sequence transducer. These generalized transducers extend ordinary transducers by allowing them to invoke other transducers as "subroutines." This paper establishes the computational properties of Transducer Datalog, a query language based on this new machine model. In the process, we develop a hierarchy of time-complexity classes based on the Ackermann function. The lower levels of this hierarchy correspond to well-known complexity classes, such as polynomial time...

We introduce parameterized pattern queries as a new paradigm to extend traditional pattern expressions over sequence databases. A parameterized pattern is essentially a string made of constant symbols or variables where variables can be matched against any symbol of the input string. Parameterized patterns allow a concise and expressive definition of regular expressions that would be very complex to describe without variables. They can also be used to express additional constraints, to “relax” pattern expressions by allowing more freedom, and finally to cluster patterns in order to minimize the number of symbols’ comparisons.

While much recent research has focussed on extending databases beyond the traditional relational model, relatively little has been done to develop database tools for querying data organized in (multidimensional) arrays. The scientific computing community has made little use of available database technology. Instead, multidimensional scientific data is typically stored in local files conforming to various data exchange formats and queried via specialized access libraries tied in to general purpose programming languages. To allow such data to be queried using known database techniques, we design and implement a query language for multidimensional arrays. Our main design decision is to treat arrays as functions from index sets to values rather than as collection types. This leads to clean syntax and semantics as well as simple but powerful optimization rules. We present a calculus for arrays that extends standard calculi for complex objects. We derive a higher-level comprehension style ...