Optimization of PostgreSQL for Physician Shift Scheduling Workflow
Presented by:
Shahram Ghandeharizadeh received his Ph.D. degree in Computer Science from the University of Wisconsin, Madison, in 1990. Since then, he has been on the faculty at the University of Southern California. In 1992, Dr. Ghandeharizadeh received the National Science Foundation Young Investigator's Award for his research on the physical design of parallel database systems. In 1995, he received an award from the School of Engineering at USC in recognition of his research activities. He was a recipient of the ACM Software System Award 2008 for his contributions to Gamma, a scalable database management system. His research interests include design, implementation and evaluation of novel architectures for high performance data intensive applications, multimedia based social networking systems, parallel database systems, and active databases. He has served on the organizing committees of numerous conferences; most recently as the technical program chair of FAB 2018 and general chair of the upcoming VLDB 2019. His activities are supported by several grants from the National Science Foundation, Oracle, Microsoft, BMC Software, and Hewlett-Packard. He is the director of the database laboratory at USC. He was a member of ACM SIGMOD executive committee and the Editor-in-Chief of ACM SIGMOD DiSC.
No video of the event yet, sorry!
This talk presents the use of PostgreSQL for physician shift scheduling workflow in support of hospital operations, physician coordination, professional time-off, and related analytics. We report on the different classes of SQL queries and DML commands (insert, delete, update) that constitute this application workload. We present a wide range of techniques to enhance response time and throughput of this workload. These include changes to the physical organization of tables, stored procedures, use of index structures and materialized views, and caching techniques.
- Date:
- Duration:
- 20 min
- Room:
- Conference:
- PostgresConf US 2018
- Language:
- English
- Track:
- Use Cases
- Difficulty:
- Medium