Jiajing Wang, Amith Singhee, et al.
IEEE TCADIS
A number of key technological, social, and business disruptions will drive a new generation of smarter energy applications. The disruptions include the following: 1) large sensor deployments, resulting in a huge increase in data volumes and variety, 2) a move toward clean energy and intermittent renewable energy sources, and 3) a move to highly distributed energy resources. To enable resilient and efficient power delivery, with these disruptions, will require a host of new applications that analyze large amounts and varieties of data in the context of the connected grid and perform analysis, visualization, and control in real-Time with very low latency. In this paper, we present a set of capabilities that enable such applications, and a software and hardware platform that combines these capabilities to enable rapid development of a wide array of high-performance and analytics-rich applications. These capabilities include: 1) high-performance time-series ingestion, 2) a flexible data model that spans multiple contexts, 3) high-performance, in-memory analysis of time-varying, hierarchical graphs, 4) data service for co-presenting real-Time and static spatiotemporal data for real-Time web-based visualization, and 5) a seamless combination of event-based and service-oriented programming models.
Jiajing Wang, Amith Singhee, et al.
IEEE TCADIS
Amith Singhee, Emrah Acar, et al.
ISQED 2012
Wei Zhang, Xiaodong Cui, et al.
INTERSPEECH 2019
Amith Singhee, Mark Lavin, et al.
e-Energy 2015