An analytic solution to the alibi query in the space–time prisms model for moving object data

Moving objects produce trajectories, which are stored in databases by means of finite samples of time-stamped locations. When speed limitations in these sample points are also known, space–time prisms (also called beads) (Pfoser and Jensen 1999, Egenhofer 2003, Miller 2005) can be used to model the uncertainty about an object's location in between sample points. In this setting, a query of particular interest that has been studied in the literature of geographic information systems (GIS) is the alibi query. This boolean query asks whether two moving objects could have physically met. This adds up to deciding whether the chains of space–time prisms (also called necklaces of beads) of these objects intersect. This problem can be reduced to deciding whether two space–time prisms intersect. The alibi query can be seen as a constraint database query. In the constraint database model, spatial and spatiotemporal data are stored by boolean combinations of polynomial equalities and inequalities over the real numbers. The relational calculus augmented with polynomial constraints is the standard first-order query language for constraint databases and the alibi query can be expressed in it. The evaluation of the alibi query in the constraint database model relies on the elimination of a block of three exªistential quantifiers. Implementations of general purpose elimination algorithms, such as those provided by QEPCAD, Redlog, and Mathematica, are, for practical purposes, too slow in answering the alibi query for two specific space–time prisms. These software packages completely fail to answer the alibi query in the parametric case (i.e., when it is formulated in terms of parameters representing the sample points and speed constraints). The main contribution of this article is an analytical solution to the parametric alibi query, which can be used to answer the alibi query on two specific space–time prisms in constant time (a matter of milliseconds in our implementation). It solves the alibi query for chains of space–time prisms in time proportional to the sum of the lengths of the chains. To back this claim up, we implemented our method in Mathematica alongside the traditional quantifier elimination method. The solutions we propose are based on the geometric argumentation and they illustrate the fact that some practical problems require creative solutions, where at least in theory, existing systems could provide a solution.

[1]  Gabriel M. Kuper,et al.  Constraint Databases , 2010, Springer Berlin Heidelberg.

[2]  Floris Geerts,et al.  Constraint Query Languages , 2008, Encyclopedia of GIS.

[3]  T. Hägerstrand What about people in Regional Science? , 1970 .

[4]  Ralf Hartmut Güting,et al.  Moving Objects Databases (The Morgan Kaufmann Series in Data Management Systems) (The Morgan Kaufmann Series in Data Management Systems) , 2005 .

[5]  Floris Geerts,et al.  Moving Objects and Their Equations of Motion , 2004, CDB.

[6]  James Renegar,et al.  On the Computational Complexity and Geometry of the First-Order Theory of the Reals, Part III: Quantifier Elimination , 1992, J. Symb. Comput..

[7]  Stéphane Grumbach,et al.  Constraint Databases , 1999, JFPLC.

[8]  Torsten Hägerstraand WHAT ABOUT PEOPLE IN REGIONAL SCIENCE , 1970 .

[9]  Jan Paredaens,et al.  Towards a theory of spatial database queries (extended abstract) , 1994, PODS.

[10]  Dima Grigoriev,et al.  Solving Systems of Polynomial Inequalities in Subexponential Time , 1988, J. Symb. Comput..

[11]  H. Miller A MEASUREMENT THEORY FOR TIME GEOGRAPHY , 2005 .

[12]  Bart Kuijpers,et al.  Introduction to constraint databases , 2002, SGMD.

[13]  Petteri Nurmi,et al.  Moving Object Databases , 2008, Encyclopedia of GIS.

[14]  Oscar H. Ibarra,et al.  Moving Objects: Logical Relationships and Queries , 2001, SSTD.

[15]  Bart Kuijpers,et al.  Constraint Databases, Data Structures and Efficient Query Evaluation , 2004, CDB.

[16]  James Renegar,et al.  On the Computational Complexity and Geometry of the First-Order Theory of the Reals, Part I: Introduction. Preliminaries. The Geometry of Semi-Algebraic Sets. The Decision Problem for the Existential Theory of the Reals , 1992, J. Symb. Comput..

[17]  James Renegar On the computational complexity and geome-try of the first-order theory of the reals , 1992 .

[18]  A. Tarski A Decision Method for Elementary Algebra and Geometry , 2023 .

[19]  Max J. Egenhofer,et al.  Modeling Moving Objects over Multiple Granularities , 2002, Annals of Mathematics and Artificial Intelligence.

[20]  Bart Kuijpers,et al.  Trajectory databases: Data models, uncertainty and complete query languages , 2007, J. Comput. Syst. Sci..

[21]  Marie-Françoise Roy,et al.  On the combinatorial and algebraic complexity of Quanti erEliminationS , 1994 .

[22]  Dieter Pfoser,et al.  Capturing the Uncertainty of Moving-Object Representations , 1999, SSD.

[23]  Ouri Wolfson,et al.  Moving Objects Information Management: The Database Challenge , 2002, NGITS.

[24]  A Pettorossi Automata theory and formal languages , 2008 .

[25]  LeesBrian 25 Volumes of the International Journal of Geographical Information Science , 2011 .

[26]  Thanasis Hadzilacos,et al.  Advances in Spatial and Temporal Databases , 2015, Lecture Notes in Computer Science.

[27]  Joos Heintz,et al.  Sur la complexité du principe de Tarski-Seidenberg , 1989 .

[28]  J. Renegar,et al.  On the Computational Complexity and Geometry of the First-Order Theory of the Reals, Part I , 1989 .

[29]  George E. Collins,et al.  Quantifier elimination for real closed fields by cylindrical algebraic decomposition , 1975 .