SBIR Phase I: Wavelet-based Data Exploration for Auditing, Decision Support, and Corporate Performance Monitoring

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 0610739
Agency Tracking Number: 0610739
Amount: $99,906.00
Phase: Phase I
Program: SBIR
Awards Year: 2006
Solicitation Year: 2005
Solicitation Topic Code: EO
Solicitation Number: NSF 05-605
Small Business Information
12403 Chasemount Court, Bowie, MD, 20720
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Stephen Russell
 (301) 717-0337
Business Contact
 Stephen Russell
Title: Mr
Phone: (301) 717-0337
Research Institution
This Small Business Innovation Research (SBIR) Phase I project will provide a visualization and analysis capability to auditing software that will enable non-scientifically oriented users to identify and monitor patterns, trends, and exceptions faster and with greater accuracy. Existing auditing tools require auditors to randomly sample data or use unfamiliar complex statistical methods to examine data. This problem is complicated by volume of data collected by enterprise systems and regulatory complexity such as the FAR and the Sarbanes-Oxley Act. Wavelets provide a method for exploring greater volumes of data at multiple levels of resolution while highlighting exceptions, trends, and variances. Applying wavelet methods to budgetary and financial control environments, presents two significant challenges: creating a methodology that is inherently scalable and timely; and secondly, enabling auditors, decision-makers, and support systems to visualize and act on the knowledge derived from the raw data. The objective of this Phase 1 research is to demonstrate the feasibility of utilizing wavelets as the basis for an enhanced auditing and financial control methodology. The broader impact of this SBIR initiative will be to improve the decision quality and timeliness of not only financial auditing and management decision-makers, but also of the systems that implement and monitor business processes. The result of this research effort will improve the economic productivity of several sectors and introduce new research opportunities in decision support and data engineering to both academia and industry.

* Information listed above is at the time of submission. *

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