Adaptive progress indicator for long running SQL queries

Authors:
Mario Milicevic;Krunoslav Zubrinic;Ivona Zakarija
Affiliations:
Department of Electrical Engineering and Information Technology, University of Dubrovnik, Dubrovnik, Croatia;Department of Electrical Engineering and Information Technology, University of Dubrovnik, Dubrovnik, Croatia;Department of Electrical Engineering and Information Technology, University of Dubrovnik, Dubrovnik, Croatia
Venue:
ACS'08 Proceedings of the 8th conference on Applied computer scince
Year:
2008

Citing 8
Cited 0

The importance of percent-done progress indicators for computer-human interfaces

CHI '85 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Usability Engineering

Usability Engineering
Toward a progress indicator for database queries

SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Estimating progress of execution for SQL queries

SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Increasing the Accuracy and Coverage of SQL Progress Indicators

ICDE '05 Proceedings of the 21st International Conference on Data Engineering
When can we trust progress estimators for SQL queries?

Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Multi-query SQL progress indicators

EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Percent-done progress indicators are a technique for graphically showing how much of a long task has been completed. In the database environment such information is especially important during the long-running query execution. The proposed method constructs (during the learning phase) adaptive progress indicator model analyzing influence of averaged system state (represented with attributes describing CPU, memory and disk subsystem activity) on the query response time. Experimental evaluation shows that adaptive progress indicators can enhance users' experience and productivity.