An architecture supporting the collection and monitoring of data openly contributed over the World Wide Web

• Published in: Proceedings Tenth IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. WET ICE 2001 pp. 380 - 385
• Main Author: Stork, D.G.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2001
• Subjects: Algorithm design and analysis; Collaboration; Computer architecture; Data security; Humans; Machine learning; Machine learning algorithms; Monitoring; Scalability
• ISBN: 9780769512693; 0769512690
• ISSN: 1080-1383; 2375-5377
• DOI: 10.1109/ENABL.2001.953448

Open data collection over the World Wide Web-in which any Web user can contribute to large databases of informal data-presents several challenges that require novel approaches in human interface design, algorithmic machine learning and collaborative infrastructure. Foremost among these challenges is the need to ensure data integrity and quality, by automatically or semiautomatically identifying unreliable or hostile contributors and rejecting their contributed data. Other, traditional requirements include security, scalability and the need to support collaboration between separate but dated data collection projects. Further, for the open software/data model, the system must permit users to freely browse, navigate and download the contributed data. This paper presents a collaborative architecture for a set of such data collection projects that addresses these challenges and requirements. Key components of this architecture have been implemented and tested as part of the Open Mind Initiative.