Paper Type
Research-in-Progress Paper
Abstract
The explosion of social and sensor data available on the Web provides both challenges and opportunities for their exploitation in contemporary decision support systems. In this paper, we propose a framework for aggregating and linking heterogeneous data from various sources and transforming them to Linked Data. This allows reuse and integration of the produced data with other data resources enabling spatial business intelligence for various domain-specific applications. Our framework can be easily applied to aggregate and interlink data from various types of sources: legacy systems, citizen sensor data, sensor data, and open web data. This paper outlines a number of possible applications of the framework and discusses in detail an example use case where the proposed methodology facilitates identification of business opportunities in London City through analysis of various information facets including property pricing, population spending, sensor and social data.
Recommended Citation
Crowley, David N.; Dabrowski, Maciej; and Breslin, John G., "Decision Support using Linked, Social, and Sensor Data" (2013). AMCIS 2013 Proceedings. 9.
https://aisel.aisnet.org/amcis2013/BusinessIntelligence/RoundTablePresentations/9
Decision Support using Linked, Social, and Sensor Data
The explosion of social and sensor data available on the Web provides both challenges and opportunities for their exploitation in contemporary decision support systems. In this paper, we propose a framework for aggregating and linking heterogeneous data from various sources and transforming them to Linked Data. This allows reuse and integration of the produced data with other data resources enabling spatial business intelligence for various domain-specific applications. Our framework can be easily applied to aggregate and interlink data from various types of sources: legacy systems, citizen sensor data, sensor data, and open web data. This paper outlines a number of possible applications of the framework and discusses in detail an example use case where the proposed methodology facilitates identification of business opportunities in London City through analysis of various information facets including property pricing, population spending, sensor and social data.