constraint databases

This paper presents a new system called TVICS that converts traffic video data into vehicular motion information in spatio-temporal databases. The TVICS system interpolates the vehicular trajectory data (time, location and velocity), which are extracted from video, and integrates them with spatial road information for the storage of dynamic transportation environments. TVICS can avoid the storage issues caused by traffic videos with their associated large data size. Moreover, users can manage and operate multiform and multidimensional traffic data in a spatio-temporal transportation environment. Experimental results show that TVICS has a high accuracy in transportation applications.

Extension of the relational database model to represent complex data has been a focus of much research in recent years. At the same time, an alternative extension of the relational database model has proposed using constraint databases that finitely describe infinite relations. This paper attempts to combine these two divergent approaches. In particular a query language called Datalog with set order constraints, or Datalog ⊂P(Z) , is proposed. This language can express many natural problems with sets, including reasoning about inheritance hierarchies. Datalog ⊂P(Z) queries over set constraint databases are shown to be evaluable bottom-up in closed form and to have DEXPTIME-complete data complexity.

We present for the first-order theory of atomic Boolean algebras of sets with linear cardinality constraints a quantifier elimination algorithm. In the case of atomic Boolean algebras of sets, this is a new generalization of Boole’s well-known variable elimination method for conjunctions of Boolean equality constraints. We also explain the connection of this new logical result with the evaluation of relational calculus queries on constraint databases that contain Boolean linear cardinality constraints.

This paper describes the implementation of a constraint database system with integer and set of integers data types. The system called DISCO allows Datalog queries and input databases with both integer gap-order [30] and set order constraints [31]. The DISCO query language can easily express many complex problems involving sets. The paper also presents efficient running times for several sample queries.

MLPQ/GIS [4,6] is a constraint database [5] system like CCUBE [1] and DEDALE [3] but with a special emphases on spatio-temporal data. Features include data entry tools (first four icons in Fig. 1), icon-based queries such as Intersection, Union, Area, Buffer, Max and Min, which optimize linear objective functions, and for Datalog queries. For example, in Fig. 1 we loaded and displayed a constraint database that represents the midwest United States and loaded two contraint relations describing the movements of two persons. The query icon opened a dialog box into which we entered the query which finds (t, i) pairs such that the two people are in the same state i at the same time t. MLPQ/GIS can animate [2] spatio-temporal objects that are linear constraint relations over and t.
Users can also display in discrete color zones (isometric maps) any spatially distributed variable z that is a linear function and For example, Fig. 2 shows the mean annual air temperature Nebraska. Animation and isometric map display can be combined.

We propose a representation of spatio-temporal objects with continuous and cyclic or acyclic periodic movements. We also describe an extended relational algebra query language for databases with such objects. We show that the new spatio-temporal databases are closed under the extended relational algebra queries, and each fixed relational algebra query can be evaluated in PTIME in the size of the input database.

We provide a generalization of Datalog based on generalizing databases by adding integer order constraints to relational tuples. For Datalog queries with integer (gap)-order constraints (denoted as Datalog<), we show that there is a closed-form evaluation. We also show that the tuple recognition problem can be done in PTIME in the size of the generalized database, assuming that the size of the constants in the query is logarithmic in the size of the database. Note that the absence of negation in critical, Datalog¬ queries with integer order constraints can express any Turing-computable function.

The use of constraints within logic programming and within database languages promises interesting new applications. We show that Datalog queries with simple integer order constraints can be evaluated bottom-up in closed form over generalized databases.

Data classification is usually based on measurements recorded at the same time. This paper considers temporal data classification where the input is a temporal database that describes measurements over a period of time in history while the predicted class is expected to occur in the future. We describe a new temporal classification method that improves the accuracy of standard classification methods. The benefits of the method are tested on weather forecasting using the meteorological database from the Texas Commission on Environmental Quality and on influenza using the Google Flu Trends database.