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 as rules by techniques such as Association Rule Mining (ARM) makes the task of sifting through patterns a tedious and timeconsuming task. In this paper, we explore the use of domain ontologies to: (i) filter and prune rules that are variations of a more general concept in the ontology, and (ii) replace groups of rules by a single general rule with the intent of downsizing the number of initial rules while preserving the semantics. We show how the combination of several methods reduces significantly the number of rules thus effectively allowing city administrators to use open data to generate patterns, use them for decision making, and better direct limited government resources.

According to the Center for Disease Control (CDC), there are almost 48 million people affected by foodborne diseases in the U.S. every year, including 3,000 deaths. The most effective way of avoiding food poisoning would be its prevention. However, complete prevention is not possible, therefore Public Health departments perform routine restaurant inspections, combined with the practice of inspecting specific restaurants once a disease outbreak is identified. Following other health applications (e.g., prediction of a flu outbreak using Twitter), we use social media and a predictive analytics approach to identify the need for targeted visits by city inspectors.

To run a smart city, data is collected from disparate sources such as IoT devices, social media, private and public organizations, and government agencies. In the US, the City of Chicago has been a pioneer in the collection of data and in the development of a framework, called OpenGrid, to curate and analyze the collected data. OpenGrid is a geospatial situational awareness platform that allows policy makers, service providers, and the general public to explore city data and to perform advanced data analytics to enable planning of services, prediction of events and patterns, and identification of incidents across the city. This paper presents the instance matching module of GIVA, a Geospatial data Integration, Visualization, and Analytics platform, as applied to the integration of information related to businesses, which is spread across several datasets. In particular, we describe the integration of two datasets, Business Licenses and Food Inspections, so as to enable predictive analytics to determine which food establishments the city should inspect first. The paper describes semantic web-based instance matching mechanisms to compare the Business Names and Address fields. CCS Concepts • Information systems → Entity resolution; Ontologies; Spatialtemporal systems;

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