In smart cities, commuters have the opportunities for smart routing that may enable selecting a route with less car accidents, or one that is more scenic, or perhaps a straight and flat route. Such smart personalization requires a data management framework that goes beyond a static road network graph. This paper introduces PreGo, a novel system developed to provide real time personalized routing. The recommended routes by PreGo are smart and personalized in the sense of being (1) adjustable to individual users preferences, (2) subjective to the trip start time, and (3) sensitive to changes of the road conditions. Extensive experimental evaluation using real and synthetic data demonstrates the efficiency of the PreGo system.

Existing routing services calculate the best route from source to destination over a road network graph. Most commercial routing services offer the best route in terms of either the shortest travel distance or the shortest travel time (with or without considering current traffic conditions). While travel distance and travel time are crucial route preferences for the commuter, other preferences are equally, or even more, important. Examples of other route preferences include fuel consumption, gas emissions, road safety, points of interest along the route, construction activities, open shops and restaurants. While some route preferences are static (e.g., travel distance and points of interests), other route preferences are dynamic and vary according to the time of the day (e.g., traffic-dependent travel time and the number of open shops/restaurants). Volunteered Geographic information (VGI) has been proposed as an approach to collect massive amounts of route information and, more specifically, the time varying parameters. This demo presents PreGo, a time-dependent multi-preference routing engine. During the demo, audience would interact with the PreGo routing engine to (1) find the optimal route w.r.t. The user's personal preferences for a given start time, (2) dynamically obtain the best start time for a trip given a set of preferences, (3) feed the system with VGI and examine their effect on the chosen route at real time, and (4) examine the correctness and efficiency of the PreGo selected routes compared to routes chosen by other commercial systems.

Congestive Heart Failure (CHF) is a serious chronic condition often leading to 50% mortality within 5 years. Improper treatment and post-discharge care of CHF patients leads to repeat frequent hospitalizations (i.e., readmissions). Accurately predicting patient's risk-of-readmission enables care-providers to plan resources, perform factor analysis, and improve patient quality of life. In this paper, we describe a supervised learning framework, Dynamic Hierarchical Classification (DHC) for patient's risk-of-readmission prediction. Learning the hierarchy of classifiers is often the most challenging component of such classification schemes. The novelty of our approach is to algorithmically generate various layers and combine them to predict overall 30-day risk-of-readmission. While the components of DHC are generic, in this work, we focus on congestive heart failure (CHF), a pressing chronic condition. Since healthcare data is diverse and rich and each source and feature-subset provides different insights into a complex problem, our DHC based prediction approach intelligently leverages each source and feature-subset to optimize different objectives (such as, Recall or AUC) for CHF risk-of-readmission. DHC's algorithmic layering capability is trained and tested over two real world datasets and is currently integrated into the clinical decision support tools at MultiCare Health System (MHS), a major provider of healthcare services in the northwestern US. It is integrated into a QlikView App (with EMR integration planned for Q2) and currently scores patients everyday, helping to mitigate readmissions and improve quality of care, leading to healthier outcomes and cost savings.

Traditional routing services aim to save driving time by recommending the shortest path, in terms of distance or time, to travel from a start location to a given destination. However, these methods are relatively static and to a certain extent rely on traffic patterns under relatively normal conditions to calculate and recommend an appropriate route. As such, they do not necessarily translate effectively during severe weather events such as tornadoes. In these scenarios, the guiding principal is not, optimize for travel time, but rather, optimize for survivability of the event, i.e., can we recommend an evacuation route to those users inside the hazardous areas. In this demo, we present a framework for routing services for evacuating and avoiding real world severe weather threats that is able to: (1) Identify the users inside the dangerous region of a severe weather event (2) Recommend an evacuation route to guide the users out to a safe destination or shelter (3) Assure the recommended route to be one of the shortest paths after excluding the risky area (4) Maintain the flow of traffic by normalizing the evacuation on the possible safe routes. During the demo, attendees will be able to use the system interactively through its graphical user interface within a number of different scenarios. They will be able to locate the severe weather events on real time basis in any area in USA and examine detailed information about each event, to issue an evacuation query from an existing dangerous area by identifying a destination location and receiving the routing direction on their mobile devices, to issue an avoidance routing query to ask for a shortest path that avoids the dangerous region, to have an inside look into the internal system components and finally, to evaluate the overall system performance.