Many Geographic Information Systems (GIS) handle a large volume of geospatial data. Spatial joins over two or more geospatial datasets are very common operations in GIS for data analysis and decision support. However, evaluating spatial joins can be very time intensive due to the size of datasets. In this paper, we propose an interactive framework that provides faster approximate answers of spatial joins. The proposed framework utilizes two statistical methods: probabilistic join and sampling based join. The probabilistic join method provides speedup of two orders of magnitude with no correctness guarantee, while the sampling based method provides an order of magnitude improvement over the full indexing tree joins of datasets and also provides running confidence intervals. The framework allows users to trade-off speed versus bounded accuracy, hence it provides truly interactive data exploration. The two methods are evaluated empirically with real and synthetic datasets.
Several recent studies have addressed the topic of “exposome”, an approach for evaluating environmental exposures and their influence on human health conditions. Environmental exposures affecting human health range from a combination of factors like air pollution, tobacco smoke, and pollen, to climate, such as heat and humidity. With continued advances in information technology, and the worldwide deployment of mobile and wireless networks, patients’ health conditions can be continuously monitored by numerous intelligent devices. Sensors can be integrated into their mobile devices such as smart phones for continuous health assistance and disease attack prevention. However, researchers must overcome many challenges, such as data acquisition, data scales and data uncertainty, in order to evaluate environmental exposures. In this paper, we propose a framework for a generic health monitoring system for modeling and analyzing individual exposure to environmental triggers. We propose to integrate a wide range of individual exposures using wireless sensors and mobile devices for exposure assessment. This paper provides a solid framework by considering asthma as a specific case and presents challenges and opportunities in developing a data management system for continuously changing data and algorithms for evaluating environmental exposures.
Abstract. A large volume of geospatial data is available on the web through various forms of applications. However, access to these data is limited by certain types of queries due to restrictive web interfaces. A typical scenario is the existence of numerous business web sites that provide the address of their branch locations through a limited "nearest location" web interface. For example, a chain restaurant's web site such as McDonalds can be queried to find some of the closest locations of its branches to the user's home address. However, even though the site has the location data of all restaurants in, for example, the state of California, the provided web interface makes it very difficult to retrieve this data set. We conceptualize this problem as a more general problem of running spatial range queries by utilizing only k-Nearest Neighbor (k-NN) queries. Subsequently, we propose two algorithms to cover the rectangular spatial range query by minimizing the number of k-NN queries as possible. Finally, we evaluate the efficiency of our algorithms through empirical experiments.
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