Browse Prior Art Database

Method & system for identification of input data for grant funded research projects using data ranking based on business priorities

IP.com Disclosure Number: IPCOM000202044D
Publication Date: 2010-Dec-02
Document File: 4 page(s) / 279K

Publishing Venue

The IP.com Prior Art Database

Abstract

The publication describes a system for identification of principal investigators for research projects based on availability of funding opportunities for a research organization. The system relies on unstructured content analytics and a resource allocation optimization framework to identify the individuals in a research organization (e.g. research university) who are the most suitable candidates to pursue available funding opportunities.

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 47% of the total text.

Page 01 of 4

Method & system for identification of input data for grant funded research projects using data ranking based on business priorities

Introduction

The research project lifecycle in most research organizations is largely a labor intensive, time-consuming, and sub-optimized process. Principal investigators face challenges finding good funding opportunities, identifying collaborators outside their field of expertise, obtaining required resources, and locating needed research assistants. Students have difficulty finding good research 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.

The system described in this publication 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 and behind university firewalls. 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.

By integrating social networking tools, text analytics, and optimization software the system supports the research lifecycle so that professors, students, and administrators have more time to focus on results and impact. The approach starts with crawling the internet and intranet data sources 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. 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.

Pre-operational Phase of the System Lifecycle

Before the system can be put into operation, it must be configured with a set of rules that define a principal investigator name should appear in text (e.g. using name dictionaries and/or regular expressions) and uniform resource identifiers (URI) of data repositories (http://en.wikipedia.org/wiki/Uniform

_Resource

                                         Identifier). The repositories can contain both structured and unstructured data and can be private (e.g. intranet, university internal databases) or public (e.g. research journals accessible via the web, university internet site).

System Operational Phases

Once the system is operational, it starts a periodically repeated (e.g. every 24 hours) process of crawling the repositories (step 2 on Figure 1) and retrieving those documents that may contain principal investigator names (hereinafter candidate names) (step 3). The documents are passed to a candidate names analyzer

_

1


Page 02 of 4

component (step 4) that...