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Creators/Authors contains: "Balasubramani, Booma Sowkarthiga"

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  1. ; ; ; ; ( , SIGSPATIAL '20: 28th International Conference on Advances in Geographic Information Systems)
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  2. ; ; ; ; ( , Proceedings of the 2nd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS@SIGSPATIAL)
    Cities are actively creating open data portals to enable predictive analytics of urban data. However, the large number of observable patterns that can be extracted by techniques such as Association Rule Mining (ARM) makes the task of sifting through patterns a tedious and time-consuming task. In this paper, we explore the use of domain ontologies to: (i) filter and prune rules that are specific variations of a more general concept in the ontology, and (ii) replace specific rules by a single "general" rule, with the intent to downsize the number of general rules while keeping the semantics of the larger generated set. We show how the combination of several methods reduces significantly the number of rules thus effectively allowing city administrators to use open data to understand patterns, use patterns for decision-making, and better direct limited government resources. 
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