ASPEN: Seamless Declarative Programming across Sensors, Streams, and the Cloud
In the ASPEN project we have been exploring an approach that seeks to separate logical dataflow from most of the algorithmic logic -- using a declarative, SQL-like (but iterative and incremental) programming model to capture the dataflow, data transformation, and state management needed by an application, combined with small bits of procedural code to handle complex logic. Our platform provides distributed query optimization that takes runtime conditions into account, while also supporting a range of learning, prediction, and connection-finding algorithms. In this talk I will describe our basic ASPEN prototype including cluster and sensor subsystems, and provide an overview of how we address issues of query optimization, distributed query execution, and incremental recomputation.
Work done jointly with Mengmeng Liu, Svilen Mihaylov, Boon Thau Loo, and Sudipto Guha
Zachary Ives is an Associate Professor and the Markowitz Faculty Fellow at the University of Pennsylvania. His research interests include data integration and sharing, "big data", sensor networks, and data provenance and authoritativeness. He is a recipient of the NSF CAREER award, and an alumnus of the DARPA Computer Science Study Panel and Information Science and Technology advisory panel. He has also been awarded the Christian R. and Mary F. Lindback Foundation Award for Distinguished Teaching. He serves as the undergraduate curriculum chair for Penn's Singh Program in Market and Social Systems Engineering. He is a co-author of the textbook Principles of Data Integration.