Enhancing GISs for spatio-temporal reasoning
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
Spatio-temporal Annotated Constraint Logic Programming
PADL '01 Proceedings of the Third International Symposium on Practical Aspects of Declarative Languages
MuTACLP: A Language for Temporal Reasoning with Multiple Theories
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
MuTACLP: A Language for Declarative GIS Analysis
CL '00 Proceedings of the First International Conference on Computational Logic
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
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In this paper, we describe the design and implementation of PILOTS, a ProgrammIng Language for spatiO-Temporal data Streaming applications. Using PILOTS, application developers can easily program an application that handles spatio-temporal data streams by writing a high-level declarative program specification. Whereas spatio-temporal data is available with various spatial density and time frequency depending on data sources (e.g., weather forecast data can be given hourly/-daily for a vast geographic area, while GPS data can be given every second or at a higher frequency for a specific geographic location), applications often need to process data at a constant frequency. To view such heterogeneous data streams as homogeneous data streams, PILOTS specifically provides first-class support for data selection and interpolation so that applications can get data consistently regardless of the data's original spatio-temporal heterogeneity. To enable reasoning about errors in correlated spatio-temporal data streams, we introduce the notion of error signatures, patterns in output data streams that appear when input data is erroneous. These patterns are produced thanks to a mathematical model that explicitly specifies the redundancy exhibited in the input data. PILOTS applications readily produce error signatures, which can be an important tool to semi-automatically detect data error conditions and enable better decision support systems. As a motivating application, we illustrate a PILOTS program that receives as input data: the airspeed, the ground speed, and the wind speed for a flight. We then compute the error signatures exhibited by failing the airspeed data stream simulating a pitot tube icing scenario (such as the one occurring in Air France flight 447 in June 2009 ultimately killing all people onboard), and by failing the ground speed data stream simulating a GPS constellation shutdown.