Research Overview

Societal challenges involve a core component of connecting people efficiently, effectively, and promptly to needed resources.  Our research group is developing a framework for Social Life Networks (SLNs) -- networks of people, sensors, and actuators -- that use cybernetic principles to solve a variety of important social problems such as emergency response, traffic management, wellness, agricultural production, medical needs, and water management. The fundamental goal for SLNs is to identify people’s needs in a given situation, locate appropriate resources to meet those needs, and connect them optimally.The SLN collects and interprets data from diverse heterogeneous sources. It determines the actual status by combining the environmental context and all affected individuals’ situations, compares this to the desired state, and routes appropriate information to the right person in the right place using recommendation engines and other machine learning techniques.

EventShop

EventShop is a platform for situation modeling and recognition from diverse data streams.  This framework allows for ingestion of different data streams and converts them to a unifying space, time, and thematic (STT) representation. Using different spatio-temporal operators, disparate data streams with different themes may be combined to generate new spatio-temporal themes of interest in a given application. Pattern recognition operators can detect and recognize different situations allowing for appropriate actions to be taken.  EventShop provides a platform that is agnostic to data source, granularity, and form. The user can use any of the complex operators, and ‘plug and play’ the desired data to perform complex analyses.

EventShop has been applied to different applications including helping populations during hurricanes and floods, to enhancing care for individual patients with allergy and asthma. EventShop is an open source platform being developed further and supported by Da’Buntu {www.dabuntu.com}.