On the propagation of errors in the size of join results
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Balancing histogram optimality and practicality for query result size estimation
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Selectivity and cost estimation for joins based on random sampling
Journal of Computer and System Sciences
ACM Computing Surveys (CSUR)
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficient mid-query re-optimization of sub-optimal query execution plans
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Join synopses for approximate query answering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Ripple joins for online aggregation
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Large-Sample and Deterministic Confidence Intervals for Online Aggregation
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
Robust query processing through progressive optimization
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
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
Self-tuning database systems: a decade of progress
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Stop-and-restart style execution for long running decision support queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Dynamic workload management for very large data warehouses: juggling feathers and bowling balls
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Supporting time-constrained SQL queries in oracle
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Managing operational business intelligence workloads
ACM SIGOPS Operating Systems Review
Adaptive progress indicator for long running SQL queries
ACS'08 Proceedings of the 8th conference on Applied computer scince
The design of a query monitoring system
ACM Transactions on Database Systems (TODS)
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Adaptive join processing in pipelined plans
Proceedings of the 13th International Conference on Extending Database Technology
ParaTimer: a progress indicator for MapReduce DAGs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Performance prediction for concurrent database workloads
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
A statistical approach towards robust progress estimation
Proceedings of the VLDB Endowment
Multi-query SQL progress indicators
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Managing dynamic mixed workloads for operational business intelligence
DNIS'10 Proceedings of the 6th international conference on Databases in Networked Information Systems
Halt or continue: estimating progress of queries in the cloud
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
PREDIcT: towards predicting the runtime of large scale iterative analytics
Proceedings of the VLDB Endowment
Workload management: a technology perspective with respect to self-* characteristics
Artificial Intelligence Review
Hi-index | 0.00 |
The problem of estimating progress for long-running queries has recently been introduced. We analyze the characteristics of the progress estimation problem, from the perspective of providing robust, worst-case guarantees. Our first result is that in the worst case, no progress estimation algorithm can yield anything even moderately better than the trivial guarantee that identifies the progress as lying between 0% and 100%. In such cases, we introduce an estimator that can optimally bound the error. However, we show that in many "good" scenarios, it is possible to design effective progress estimators with small error bounds. We then demonstrate empirically that these "good" scenarios are common in practice and discuss possible ways of combining the estimators.