C4.5: programs for machine learning
C4.5: programs for machine learning
AutoAdmin “what-if” index analysis utility
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An adaptation of Relief for attribute estimation in regression
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Automated Selection of Materialized Views and Indexes in SQL Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Database tuning advisor for microsoft SQL server 2005: demo
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Interpreting the data: Parallel analysis with Sawzall
Scientific Programming - Dynamic Grids and Worldwide Computing
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Automatic SQL tuning in oracle 10g
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Self-tuning database systems: a decade of progress
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Pig latin: a not-so-foreign language for data processing
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
SCOPE: easy and efficient parallel processing of massive data sets
Proceedings of the VLDB Endowment
Fa: A System for Automating Failure Diagnosis
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Online System Problem Detection by Mining Patterns of Console Logs
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Tuning database configuration parameters with iTuned
Proceedings of the VLDB Endowment
Hadoop: The Definitive Guide
FlumeJava: easy, efficient data-parallel pipelines
PLDI '10 Proceedings of the 2010 ACM SIGPLAN conference on Programming language design and implementation
Towards automatic optimization of MapReduce programs
Proceedings of the 1st ACM symposium on Cloud computing
ParaTimer: a progress indicator for MapReduce DAGs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
iTuned: a tool for configuring and visualizing database parameters
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
A case for machine learning to optimize multicore performance
HotPar'09 Proceedings of the First USENIX conference on Hot topics in parallelism
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Runtime measurements in the cloud: observing, analyzing, and reducing variance
Proceedings of the VLDB Endowment
The performance of MapReduce: an in-depth study
Proceedings of the VLDB Endowment
Hadoop++: making a yellow elephant run like a cheetah (without it even noticing)
Proceedings of the VLDB Endowment
Xplus: a SQL-tuning-aware query optimizer
Proceedings of the VLDB Endowment
Automatic optimization for MapReduce programs
Proceedings of the VLDB Endowment
Observing SQL queries in their natural habitat
ACM Transactions on Database Systems (TODS)
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Hadoop's adolescence: an analysis of Hadoop usage in scientific workloads
Proceedings of the VLDB Endowment
Scorpion: explaining away outliers in aggregate queries
Proceedings of the VLDB Endowment
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While users today have access to many tools that assist in performing large scale data analysis tasks, understanding the performance characteristics of their parallel computations, such as MapReduce jobs, remains difficult. We present PerfXplain, a system that enables users to ask questions about the relative performances (i.e., runtimes) of pairs of MapReduce jobs. PerfXplain provides a new query language for articulating performance queries and an algorithm for generating explanations from a log of past MapReduce job executions. We formally define the notion of an explanation together with three metrics, relevance, precision, and generality, that measure explanation quality. We present the explanation-generation algorithm based on techniques related to decision-tree building. We evaluate the approach on a log of past executions on Amazon EC2, and show that our approach can generate quality explanations, outperforming two naïve explanation-generation methods.