Additional Participants

Senior Personnel

Anthony Stefanidis

Neal Pettigrew

Michael Worboys


Susan Elston

Graduate Student

Heather Deese

Nagafakshmy Vijayasankaran

Jixiang Jiang

Raymond Emerson

Avinash Rude

Jon Devine

Undergraduate Student

Blaine Green

Parang Saraf

Alexander Gross

Karl Semich

Joshua Belanger

Technician, Programmer

Chris Frank

Project Period

October 2009-September 2010

Level of Access

Open-Access Report

Grant Number


Submission Date



Many environmental observations are collected at different space and time scales that preclude easy integration of the data and hinder broader understanding of ecosystem dynamics. Ocean Observing Systems provide a specific example of multi-sensor systems observing several variables in different space - time regimes. This project integrates diverse space-time environmental sensor streams based on the conversion of their information content to a common higher-level abstraction: a space-time event data type. The space-time event data type normalizes across the diversity of observation level data to produce a common data type for exploration and analysis. Gulf of Maine Ocean Observing System (GOMOOS) data provide the multivariate time and space-time series from which space-time events are detected and assembled. Event detection employs a combined top down-bottom up approach. The top down component specifies an event ontology while the bottom up component is based on extraction of primitive events (e.g. decreasing, increasing, local maxima and minima sequences) from time and space-time series. Exploration and analysis of the extracted events employs a graphic exploratory environment based on a graphic primitive called an event band and its composition into event band stacks and panels that support investigation of various space-time patterns.

The project contributes a new information integration approach based on the concept of an event that can be extended to many domains including socio-economic, financial, legislative, surveillance and health related information. The project will contribute new data mining strategies for event detection in time and space-time series and a set of flexible exploratory tools for examination and development of hypotheses on space-time event patterns and interactions.

Included in

Oceanography Commons