Designing and mining multi-terabyte astronomy archives: the Sloan Digital Sky Survey
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
ACM SIGGRAPH Computer Graphics
Design and implementation of finite resolution crisp and fuzzy spatial objects
Data & Knowledge Engineering
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Managing uncertainty in sensor database
ACM SIGMOD Record
Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
Indexing multi-dimensional uncertain data with arbitrary probability density functions
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Supporting uncertainty in moving objects in network databases
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Topological predicates between vague spatial objects
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
VASA: An algebra for vague spatial data in databases
Information Systems
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Research in modeling and querying spatial data has primarily focused on traditional "crisp" spatial objects with exact location and spatial extent. More recent work, however, has begun to address the need for spatial data types describing spatial phenomena that cannot be modeled by objects having sharp boundaries. Other work has focused on point objects whose location is not precisely known and is typically described using a probability distribution. In this paper, we present a new technique for modeling and querying vague spatial objects. Using shapelets, an image decomposition technique developed in astronomy, as base data type, we introduce a comprehensive set of low-level operations that provide building blocks for versatile high-level operations on vague spatial objects. In addition, we describe an implementation of this data model as an extension to PostgreSQL, including an indexing technique for shapelet objects. Unlike existing techniques for modeling and querying vague or fuzzy data, our approach is optimized for localized, smoothly varying spatial objects, and as such is more suitable for many real-world datasets.