physical.bib

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@comment{{Command line: /usr/bin/bib2bib -ob physical.bib -c 'class : "physical"' lib.bib}}

@inproceedings{abha04,
  title = {Automated Statistics Collection in {DB2}{UDB}},
  author = {Ashraf Aboulnaga and Peter J. Haas and Mokhtar Kandil and Sam Lightstone and Guy M. Lohman and Volker Markl and Ivan Popivanov and Vijayshankar Raman},
  booktitle = {International Conference on Very Large Data Bases (VLDB '04)},
  year = {2004},
  month = aug,
  pages = {1146-1157},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/abha04.pdf},
  url2 = {http://www.cs.uwaterloo.ca/~ashraf/pubs/vldb04autostats.pdf}
}

@inproceedings{agch03,
  title = {Automating Layout of Relational Databases},
  author = {Sanjay Agrawal and Surajit Chaudhuri and Abhinandan Das and Vivek Narasayya},
  booktitle = {International Conference on Data Engineering (ICDE'03)},
  year = {2003},
  pages = {607-618},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/agch03.pdf}
}

@inproceedings{agch04,
  title = {Database Tuning Advisor for {Microsoft} {SQL} Server},
  author = {Sanjay Agrawal and Surajit Chaudhuri and Lubor Koll\'{o}r and Arunprasad P. Marathe and Vivek R. Narasayya and Manoj Syamala},
  booktitle = {International Conference on Very Large Data Bases (VLDB '04)},
  year = {2004},
  pages = {1110-1121},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/agch04.pdf}
}

@inproceedings{agch01,
  title = {Materialized View and Index Selection Tool for {Microsoft} {SQL} {Server} 2000},
  author = {Sanjay Agrawal and Surajit Chaudhuri and Vivek R. Narasayya},
  booktitle = {Proc. ACM SIGMOD International Conference on Management of Data},
  year = {2001},
  pages = {608},
  annote = {This is a one-page description of a SIGMOD demo.},
  class = {physical}
}

@inproceedings{agch00,
  title = {Automated Selection of Materialized Views and Indexes in {SQL} Databases},
  author = {Sanjay Agrawal and Surajit Chaudhuri and Vivek R. Narasayya},
  booktitle = {Proc. International Conference on Very Large Data Bases},
  year = {2000},
  pages = {496-505},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/chaudvldb00.pdf}
}

@inproceedings{agch06,
  title = {Automatic physical design tuning: workload as a sequence},
  author = {Sanjay Agrawal and Eric Chu and Vivek Narasayya},
  booktitle = {Proc. ACM SIGMOD International Conference on Management of Data (SIGMOD'06)},
  year = {2006},
  pages = {683-694},
  annote = {Considers a version of the database physical design problem in which the input is a sequence of queries and updates. The goal is to recommend a target physical design for each query or update in the sequence, taking into account both the effect of the physical design on the cost of executing the query or update and the cost of changing the physical design.},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/agch06.pdf}
}

@inproceedings{brch07,
  title = {An Online Approach to Physical Design Tuning},
  author = {Nicolas Bruno and Surajit Chaudhuri},
  booktitle = {Proc. International Conference on Data Engineering (ICDE'07)},
  year = {2007},
  month = apr,
  pages = {826-835},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/brch07.pdf}
}

@inproceedings{brch06,
  title = {To Tune or not to Tune? A Lightweight Physical Design Alerter},
  author = {Nicolas Bruno and Surajit Chaudhuri},
  booktitle = {Proc. International Conference on Very Large Data Bases (VLDB'06)},
  year = {2006},
  pages = {499-510},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/brch06.pdf},
  url2 = {http://www.vldb.org/conf/2006/p499-bruno.pdf}
}

@inproceedings{brch06b,
  title = {Physical Design Refinement: The Merge-Reduce Approach},
  author = {Nicolas Bruno and Surajit Chaudhuri},
  booktitle = {Proc. International Conference on Extending Database Technology (EDBT'06)},
  year = {2006},
  number = {3896},
  pages = {386-404},
  publisher = {Springer-Verlag},
  series = {Lecture Notes in Computer Science},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/brch06b.pdf}
}

@inproceedings{brch05,
  title = {Automatic Physical Database Tuning: A Relaxation-based Approach},
  author = {Nicolas Bruno and Surajit Chaudhuri},
  booktitle = {Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data (SIGMOD'05)},
  year = {2005},
  annote = {Assumes that the optimizer requests indexes that it thinks might be useful for a particular query. These requested indexes form the initial configuration, which is then modified to meet a space constraint.},
  class = {physical}
}

@article{chna01,
  title = {Automating Statistics Management for Query Optimizers},
  author = {Surajit Chaudhuri and Vivek Narasayya},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2001},
  number = {1},
  pages = {7-20},
  volume = {13},
  annote = {Hardcopy on file. This is the journal version of \cite{chna00}. A variety of heuristic techniques for choosing minimal sets of heuristics in such a way that the quality of plans produced by the optimizer is not reduced.},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/chna01.pdf}
}

@inproceedings{chna00,
  title = {Automating Statistics Management for Query Optimizers},
  author = {Surajit Chaudhuri and Vivek Narasayya},
  booktitle = {16th International Conference on Data Engineering},
  year = {2000},
  pages = {339-348},
  annote = {The journal version of this paper is \cite{chna01}.},
  class = {physical}
}

@inproceedings{chna98,
  title = {AutoAdmin 'What-if' Index Analysis Utility},
  author = {Surajit Chaudhuri and Vivek R. Narasayya},
  booktitle = {Proc. ACM SIGMOD International Conference on Management of Data},
  year = {1998},
  pages = {367-378},
  annote = {How to implement hypothetic database configurations, so that workload costs can be estimated under those configurations. Configuration includes hypothetic indexes and statistics that allow the optimizer to decide whether such an index should be used. Proposes that sampling be used to collect the statistics. Allows specification of scale factors so configurations with larger/smaller databases can be simulated. Presents an analysis interface that supports workload analysis and configuration analysis for current and hypothetical configurations.},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/chna98.pdf}
}

@inproceedings{chna97,
  title = {An Efficient Cost-Driven Index Selection Tool for {Microsoft SQL Server}},
  author = {Surajit Chaudhuri and Vivek R. Narasayya},
  booktitle = {Proc. International Conference on Very Large Data Bases},
  year = {1997},
  pages = {146-155},
  annote = {Assumes that an upper bound is given on the number of indexes. Workload is specified as a set of SQL DML statements, including insert, delete and update. Search space includes both single and multi-attribute indexes. Index configurations are evaluated by the DBMS optimizer, and several techniques are used to reduce the number of configurations for which optimizer evaluation is required. To generate a set of candidate indexes, this method determines an optimal index configuration independently for each query in the workload. The initial candidate set is then taken as the union of the indexes in the single-query optimal configurations. A hybrid exhaustive/greedy approach is used to control search. To find a k-index configuration, first find the optimal m-index configuration (m <= k) using exhaustive search, then add k-m indexes greedily. Multi-column indexes are handled by first finding an good configuration with single-column indexes, then generating and adding a set of candidate two-column indexes, and then rerunning the optimizer on the new candidate set. This is repeated to handle indexes with more than two columns.},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/chna97.pdf}
}

@article{come78,
  title = {The Difficulty of Optimum Index Selection},
  author = {Douglas Comer},
  journal = {ACM Transactions on Database Systems},
  year = {1978},
  number = {4},
  pages = {440-445},
  volume = {3},
  class = {physical}
}

@inproceedings{coba05,
  title = {Goals and Benchmarks for Autonomic Configuration Recommenders},
  author = {Mariano P. Consens and Denilson Barbosa and Adrian M. Teisanu and Laurent Mignet},
  booktitle = {Proc. ACM SIGMOD International Conference on Management of Data (SIGMOD'05)},
  year = {2005},
  annote = {Uses random queries that are generated from templates. Templates constrain generated queries to ensure that they are reasonable and that they can benefit from indexing.},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/coba05.pdf}
}

@article{fisc88,
  title = {Physical Database Design for Relational Databases},
  author = {Sheldon J. Finkelstein and Mario Schkolnick and Paolo Tiberio},
  journal = {ACM Transactions on Database Systems},
  year = {1988},
  number = {1},
  pages = {91-128},
  volume = {13},
  class = {physical}
}

@inproceedings{leki00,
  title = {Towards self-tuning data placement in parallel database systems},
  author = {Mong Li Lee and Masaru Kitsuregawa and Beng Chin Ooi and Kian-Lee Tan and Anirban Mondal},
  booktitle = {Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data},
  year = {2000},
  pages = {225-236},
  annote = {Adaptive declustering in shared-nothing systems using a two-level, tree-structured index.},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/leki00.pdf}
}

@inproceedings{razh02,
  title = {Automating Physical Database Design in a Parallel Database},
  author = {Jun Rao and Chun Zhang and Guy M. Lohman and Nimrod Megiddo},
  booktitle = {Proc. ACM SIGMOD International Conference on Management of Data},
  year = {2002},
  pages = {558-569},
  annote = {Automatic hash-based relation partitioning in shared nothing systems.},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/p558-rao.pdf}
}

@inproceedings{vazu00,
  title = {{DB2 Advisor}: An Optimizer Smart Enough to Recommend Its Own Indexes.},
  author = {Gary Valentin and Michael Zuliani and Daniel C. Zilio and Guy M. Lohman and Alan Skelley},
  booktitle = {16th International Conference on Data Engineering},
  year = {2000},
  pages = {101-110},
  annote = {General recommendation method is to add virtual indexes to the schema, optimize the query, and check whether any virtual statistics are used in the optimal plan. Statistics for virtual indexes are inferred from existing column statistics. To recommend indexes for a workload, recommend for each query in the workload in sequence and then greedily select a subset of the recommended indexes.},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/vazu00.pdf}
}

@inproceedings{zizu04,
  title = {Recommending Materialized Views and Indexes with {IBM} {DB2} Design Advisor},
  author = {Daniel C. Zilio and Calisto Zuzarte and Sam Lightstone and Wenbin Ma and Guy M. Lohman and Roberta Cochrane and Hamid Pirahesh and Latha S. Colby and Jarek Gryz and Eric Alton and Dongming Liang and Gary Valentin},
  booktitle = {IEEE International Conference on Autonomic Computing (ICAC'04)},
  year = {2004},
  pages = {180-188},
  annote = {General approach is to generate candidate MVs and indexes based on the workload, and then filter to meet a space constraint. Multiquery optimization is used when generating candidate MVs.},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/zizu04.pdf}
}

@proceedings{logh94,
  title = {Bulletin of the IEEE Technical Committee on Data Engineering},
  year = {1994},
  editor = {David Lomet and Shahram Ghandeharizadeh},
  month = sep,
  volume = {17(3)},
  annote = {Special Issue on Data Placement for Parallelism},
  class = {physical},
  url = {http://www.db.uwaterloo.ca/~kmsalem/lib/lib/94SEP-CD.pdf},
  url2 = {ftp://ftp.research.microsoft.com/pub/debull/94SEP-CD.pdf}
}

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