CTRnet Digital Library for Disaster Information Services Seungwon Yang 1, Andrea Kavanaugh 1, Nádia P. Kozievitch 4, Lin Tzy Li 1,4,5, Venkat Srinivasan 1, Steven D. Sheetz 2, Travis Whalen 3, Donald J. Shoemaker 3, Ricardo da S. Torres 4, and Edward A. Fox 1 1 Computer Science, 2 Accounting and Information Systems, 3 Sociology, Virginia Tech, Blacksburg, VA, USA 4 Institute of Computing, University of Campinas, 5 Telecommun. Res. & Dev. Center, CPqD Foundation, Campinas, SP, Brazil CTRnet Digital Library for Disaster Information Services Seungwon Yang 1, Andrea Kavanaugh 1, Nádia P. Kozievitch 4, Lin Tzy Li 1,4,5, Venkat Srinivasan 1, Steven D. Sheetz 2, Travis Whalen 3, Donald J. Shoemaker 3, Ricardo da S. Torres 4, and Edward A. Fox 1 1 Computer Science, 2 Accounting and Information Systems, 3 Sociology, Virginia Tech, Blacksburg, VA, USA 4 Institute of Computing, University of Campinas, 5 Telecommun. Res. & Dev. Center, CPqD Foundation, Campinas, SP, Brazil Work supported by the NSF under Grant No and CAPES scholarship (BEX 1385/10-0). Findings, conclusions, or recommendations expressed are those of the authors and do not necessarily reflect NSF’s views. Inception: Extension of The 4/16 Digital Library, which collected data and provided services relating to the 4/16/2007 campus shooting at Virginia Tech Goal: Developing integrative approaches to collect, analyze and visualize under a DL environment to provide services to stakeholder groups Collaboration: Working with the Internet Archive (IA) to collect disaster-related resources and IBM for their video analysis tool Proposed CTRnet system architecture includes multiple modules to collect, analyze and visualize. We have been exploring those modules using our developed modules and existing online tools (see Table 1). WORK PROCEDURES, CONTENT, TECHNOLOGY Web Resources (Table 2) 17 disaster collections developed using Heritrix tool Hosted by the Internet Archive (IA) Accessible from Tweet Archives (Table 3) Multiple tweet archives developed using yourTwapperKeeper tool Accessible at Content-Based Image Retrieval (CBIR) technology to organize and vidualize images based on their feature similarities (Fig 2) Word clouds show 20 most frequent words from tweets (Fig 3). They are updated every 10 minutes with new data. Ontology is useful to describe collected data from diverse sources Four disaster databases converted to ontologies These ontologies then merged into a single CTR ontology Consists of a total of 185 elements Plan to include: Response Recovery Preparedness Mitigation aspect of disasters Providing services for collected data Expanding the CTR ontology using semi- automatic methodology for high quality work Develop a CTR toolkit based on current modules and new modules (e.g., modules for analyses and visualizations) INTRODUCTION WEB RESOURCE & TWEET ARCHIVES CTR ONTOLOGY VISUALIZATION OF CONTENT FUTURE WORKS Analyze Visualize Collect Table 1. Work procedures, content and technology in CTRnet Fig 3. Word clouds of Japan earthquake and Libya revolution ( Fig 2. Image ranking by the BIC descriptor. Fig 1. Top level concepts in CTR ontology Table 2. Disaster collections in IA Table 3. Tweet archives developed