Data and Sensemaking

Speaker:  Jibonananda Sanyal – Oak Ridge, TN, United States
Topic(s):  Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science


Humans are generating data at an unprecedented scale and making sense of this data is increasingly a challenge. This talk weaves around experiences in deriving scientific knowledge from ensemble simulations and sensor data sources at different geographic scales. The first part of the talk discusses evaluation-driven development of uncertainty visualization techniques based on an ‘’I see”, “we see”, and “they see” paradigm and the subsequent application of the approach to understanding uncertainty in operational ensembles of hydrology and weather. The second part of the talk discusses large-scale building energy simulations, not only to understand uncertainty, but also to evaluate the impact of design choices across the nation. In the process of this discovery, sampling techniques and effective ways to manage 200+ TBs of simulation data will be presented. The talk also touches upon resulting policy changes needed in anticipation of the changing climate and population patterns. Lastly, the talk discusses sensors, provenance in sensor data management, and model-predictive control in the context of current research efforts to overcome the scalability aspect of optimizing energy control at the household level to meet aggregate utility and grid level needs for a secure energy future.

About this Lecture

Number of Slides:  40
Duration:  50 - 60 minutes
Languages Available:  English
Last Updated: 

Request this Lecture

To request this particular lecture, please complete this online form.

Request a Tour

To request a tour with this speaker, please complete this online form.

All requests will be sent to ACM headquarters for review.