KEYWORDS: Data modeling, Data integration, Information fusion, Sensors, Data conversion, Associative arrays, Data fusion, Databases, Cameras, Error analysis
Geospatial information systems provide a unique frame of reference to bring together a large and diverse set of data from
a variety of sources. However, automating this process remains a challenge since: 1) data (particularly from sensors) is
error prone and ambiguous, 2) analysis and visualization tools typically expect clean (or exact) data, and 3) it is difficult
to describe how different data types and modalities relate to each other. In this paper we describe a data integration
approach that can help address some of these challenges. Specifically we propose a light weight ontology for an
Information Space Model (ISM). The ISM is designed to support functionality that lies between data catalogues and
domain ontologies. Similar to data catalogues, the ISM provides metadata for data discovery across multiple,
heterogeneous (often legacy) data sources e.g. maps servers, satellite images, social networks, geospatial blogs. Similar
to domain ontologies, the ISM describes the functional relationship between these systems with respect to entities
relevant to an application e.g. venues, actors and activities. We suggest a minimal set of ISM objects, and attributes for
describing data sources and sensors relevant to data integration. We present a number of statistical relational learning
techniques to represent and leverage the combination of deterministic and probabilistic dependencies found within the
ISM. We demonstrate how the ISM provides a flexible language for data integration where unknown or ambiguous
relationships can be mitigated.
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