The Smarter Campus project is a partnership between IBM and academia, to improve the effectiveness of schools by developing better technology, management, measurement and processes. One of the targets of the Smarter Campus project is the research project lifecycle.
The research project lifecycle in most universities is largely a labor intensive, time-consuming, and sub-optimized process. Professors face challenges finding good funding opportunities, identifying collaborators outside their field of expertise, obtaining required resources, and locating needed student assistants. Students have difficulty finding good project opportunities that match their interests, skills, and availability. Administrators have difficulty managing the process in a way that moves their entire institution toward university-wide goals.
Smarter Campus delivers social networking tools, text analytics, and optimization software in an integrated system to support the research lifecycle so that professors, students, and administrators have more time to focus on results and impact. The approach starts with crawling the web to discover and index unstructured data from research publications, grant awards, student social networking profiles and term papers. This information is then stored along with structured data, such as student transcripts, in a data warehouse. Data analytics is used to analyze the unstructured content to populate research area taxonomy from the project proposal document. Then IBM optimization technology is used to suggest assignments of resources, such as student assistants, to the research projects. A social networking capability is used to display the student and faculty profiles and areas of interest.
The solution relies on search and discovery in a variety of unstructured and semi-structured content (e.g., faculty web pages, research publications, student social network profiles, transcripts) from multiple sources on the Internet (e.g., VIVO) and behind university firewalls. We show how content analytics of qualitative and textually stated preferences in unstructured data can be used in a quantitative mathematical optimization system to advance individual and organization-wide objectives.
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