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BIO-INFORMATICS
MyGeneBank
Start date:
Partner:
Working staff:
Summer 2001
Arizona Cancer Center (AZCC), Dr. Gene Gerner
Faculty – Dr. Olivia Sheng, UA-MIS and Dr. David Mount, UA-Biology and AZCC
Students – Dr. Hua Su, Sherry Sun, Hui Liu and Yakov Kimolov, UA-MIS
Research on the genetic, diet and environmental variability as prognostic or predictive factors for cancer
risks is important for cancer prediction, cancer gene profiling, and drug and therapy discovery in the
Arizona Cancer Center and other research organizations in both public and private sectors. One of the
main challenges facing the research is how to integrate, share and analyze the diverse and dispersed biomedical information including gene expression and changes, microarray, tumor count, medication,
chemotherapeutic agents, drug inhibitors, genetic background, and clinical trial and history data from
basic science research and clinical practices. The goal of establishing MyGeneBank is to provide
researchers, clinicians and the general public easy access, flexible querying, statistical and data mining
analysis, and data visualization capabilities through a personalized portal and integrated data warehouses
and computational resources. We expect to produce innovations in knowledge management technologies
and services for cancer and other bio-medical applications.
ENTERPRISE SYSTEMS
Proof-of-Enterprise Technology for University Financial and Budgeting Services
Start date:
Partner:
Working staff:
May 2000
University of Arizona (UA)’s Financial Services Office (FSO) and Budget Office
Dick Roberts and Charlie
Faculty – Dr. Olivia Sheng and Dr. Bill Neumann, UA-MIS
Classified Staff – Ravi Bhat, UA’s Budget Office
Students – Jack Mackeral, Wei Gao, Holley Huang, Norman Jetta, Neerja Sewak,
Ruthmar, Keddar Thanker, Vinh Gu Vu, Megui, Hemant Arora,
This is a proof-of- concept project to explore the viability and the deployment strategies for
implementing Oracle Financials in the University of Arizona’s Financial Services Office and Budget
Office. The project focuses on process and gap analysis, Oracle Financials configuration and mapping
strategies, data warehousing and analysis technologies, process and change management issues.
MEDICAL INFORMATICS
Knowledge Management for Adaptive and Personalized Pain Diagnosis Decision
Start date:
Partner:
Working staff:
January 2001
ProActive Therapies, CEO and President, Mark Pirtle
Faculty – Dr. Olivia Sheng, UA-MIS and Dr. Paul Hu, UU-AIS
Students – Lin Lin, Li Ji, and Allis Kuo
Graudated members – Lee Dunlap, Richard Farmers, Elvina Hendrata and Dan
McDonald, UA-MIS Master Science
This research addresses the need to maintain domain expert knowledge using automated learning
methods in the context of pain diagnosis decision support. We will introduce the design and
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implementation of an adaptive pain diagnosis decision support system that can suggest likely diagnoses
and learn about how new diagnosis rules based on patients’ answers to pain related questions. This
system has an interactive, web-based GUI that is easy to operate and understand. It also has a web-based
rule base administration interface that enables the therapists to update the knowledge base. Further
more, we introduce the use of data mining techniques to change or personalize to diagnostic rules.
STORAGE AND CONTENT MANAGEMENT
Net Shark – A Caching Based Geographically Distributed Network
Start date:
Partner:
Working staff:
June 2001
IBM Sillicon Valley Lab, San Jose, Dr. Bala Iyer
IBM Storage Systems Divison, Tucson, Dr. Tarek Makansi
Faculty – Dr. Olivia Sheng, UA-MIS
Students – Xiao Fang and Wei Gao, UA-MIS
The demand for network storage has been increasing at an exponential rate owing to the widespread use
of the Internet and the shortage of local storage space. Popularity of the network storage systems entails
significant increase in the amount of data stored, the number of concurrent users, and the size and
number of files transferred between the systems and their clients. Performance of these systems needs to
scale accordingly. This geographically distributed network storage system named NetShark.
PERSONALIZATION
Link Selection for Personalized Web Portal Design
Start date:
Partner:
Working staff
Spring 2001
UA’s Center for Computing, Information and Telecommunication (CCIT)
Faculty – Dr. Olivia Sheng, UA-MIS
Students – Xiao Fang, UA-MIS
Personalization doesn’t mean to be unique in every aspect. Personalized web portals do share something
in common. For example, every personalized university web portal might have a phone book link. This
research coins the “link selection” problem for web portal design that selects common links shared by
personalized web portals. We will propose efficient web log mining algorithms to extract these links,
and present performance testing results.
COMPUTER SECURITY
Data Mining Techniques for Anomaly Detection of Computer Security
Start date:
Partner:
Working staff:
October 2001
UA-CCIT and U.S. Army (invited)
Faculty – Olivia Sheng, UA-MIS
Students – Wanshiou Yang, National Sun Yat Sen University, Taiwan
Charlie Chi, UA- Computer Science
With the tremendous growth of computers and computing devices, information system security has
become an issue of serious global concern. Developing effective methods for preventing and detecting
intrusions, therefore, will be essential for assuring system security. In addition to many prevention
techniques, intrusion detection system is often used as another wall to protect computer systems.
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Building an effective intrusion detection system, however, is an enormous knowledge engineering task.
Experts first analyze and categorize attack scenarios and system vulnerabilities, and hand-code the
corresponding rules and patterns of detection. Because of manual and ad hoc nature of the development
process, current intrusion detection systems have limited extensibility and adaptability. In this research,
therefore, we take a data-centric point of view, and consider intrusion detection as a data analysis
process. The central theme of our approach is to apply data mining methods to the extensively gathered
audit data to discover user’s unusual, deviated patterns. By integrating these patterns, our research aims
to develop a more automated approach for improving the detecting ability of intrusion detection system.
OTHER PROJECTS
EPROCUREMENT – DR. ZENG
KNOWLEDGE MANAGEMENT
KNOWLEDGE REFRESHING – XIAO FANG
AMRY KNOWLEDGE MANAGEMENT - OLIVIA
MEDICAL INFORMATICS
PACS AND INCREMENTAL DATA MINING – LIN LIN AND OLIVIA
OTHER PROJECTS FROM 451/551 PROJECTS – ASK XIAO FANG TO MIGRATE THE OLD
AND NEW 451/551 WEBSITES
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