Paper No. 11
Presentation Time: 4:00 PM
IDROP: A GRAPHICAL USER INTERFACE FOR COMMUNITY REMOTE SENSING
CONWAY, Mike1, RAJASEKAR, Arcot
2 and MOORE, Reagan
2, (1)University of North Carolina at Chapel Hill, Data Intensive Cyberinfrastructure Environments Center, RENCI, 100 Europa Drive, suite 540, Chapel Hill, NC 27519, (2)University of North Carolina at Chapel Hill, Data Intensive Cyberinfrastructure Environments Center, 100 Manning Drive, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, michael_conway@unc.edu
Community Remote Sensing (CRS) is an emerging field where information is collected about the environment by the general public and integrated into collections to provide a holistic view of the environment with local details. One of the challenges of the CRS community is the problem of how to involve the public in gathering and ingesting data into an underlying CRS data infrastructure in a coherent manner such that it can seamlessly enable new science and aid decision making. Community-driven data collection is diverse (such as rainfall, temperature, humidity, water shed level, crop yields, etc.) including sensor-based point measurements, textual data capturing information in free form, photographic images and video. The data and metadata (contextual information) that need to be collected is also diverse in nature. We present an adaptive ingestion tool, called iDrop, that will help the CRS system at its most critical point - where the citizen scientists interact with the CRS system. The iDrop interface is extensible and can evolve to meet the needs of collaborating CRS communities.
iDrop is a graphical user interface developed for data ingestion into the iRODS data grid system [1]. At the simplest level, iDrop allows a user to ingest multiple types of data and metadata. The adaptive nature of the GUI comes through mapping of user actions to policies and procedures that control the data collections. Each collection has a set of ingestion policies (called expectations) defined by a set of rules. These rules in turn transform the iDrop depending upon the data being collected and the level of expertise of the user. The expectations guide the user actions, and change organically based on the input from the user and on the processing of the input by the CRS servers (e.g. as the user ingests data, depending upon the quality of the data it may ask for other supporting details or metadata, and expose more choices as needed). The intelligent and adaptive nature of iDrop makes it an ideal tool for novice users since the interface adapts as the user gains experience. Each community can define their own expectations for the synergistic evolution of both their collections and user interfaces.
[1] Rajasekar A., R. Moore, M. Wan, and W. Schroeder, Cyber Infrastructure for Community Remote Sensing, IGARSS 2010.
