A fundamental challenge for many research projects is the ability to handle large quantities of heterogeneous data. Data collected from different sources and time periods can be inconsistent, or stored in different formats and data management systems. Thus, a critical step in many projects is to develop a customized query and analytical engine to translate inputs. But for each new dataset, or for each new query type or analytic task for an existing dataset, a new query interface or program must be developed, requiring significant investments of time and effort. This project will develop an automatic engine for searching large, heterogeneous data collections for weather and meteorology, particularly from instruments in the western US, in a regional network called MesoWest.This project develops an automatic query and analytical engine for large, heterogeneous spatial and temporal data. This capability allows users to automatically deploy a query and analytical engine instance over their large, heterogeneous data with spatial and temporal dimensions. The system supports a simple search-box and map-like query interface that allows numerous powerful analytical queries. Techniques to make these queries robust, relevant, and highly scalable will be developed. The project also enables users to execute queries over multiple data sources simultaneously and seamlessly. The goal of the work is to dramatically simplify the management and analysis of large spatio-temporal data at different institutions, groups, and corporations.
Master Student. Google.
PhD student. Research Interest: interactive data analytics and systems.
Jeff M. Phillips
Undergraduate student, Graduated in Spring 2013.