Curio: A Novel Solution for Efficient Storage and Indexing in Data Warehouses.

Anindya Datta, Krithi Ramamritham, Helen M. Thomas: Curio: A Novel Solution for Efficient Storage and Indexing in Data Warehouses. VLDB 1999: 730-733

@inproceedings{DBLP:conf/vldb/DattaRT99,
  author    = {Anindya Datta and
               Krithi Ramamritham and
               Helen M. Thomas},
  editor    = {Malcolm P. Atkinson and
               Maria E. Orlowska and
               Patrick Valduriez and
               Stanley B. Zdonik and
               Michael L. Brodie},
  title     = {Curio: A Novel Solution for Efficient Storage and Indexing in
               Data Warehouses},
  booktitle = {VLDB'99, Proceedings of 25th International Conference on Very
               Large Data Bases, September 7-10, 1999, Edinburgh, Scotland,
               UK},
  publisher = {Morgan Kaufmann},
  year      = {1999},
  isbn      = {1-55860-615-7},
  pages     = {730-733},
  ee        = {db/conf/vldb/DattaRT99.html},
  crossref  = {DBLP:conf/vldb/99},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Data warehousing and On-Line Analytical Processing (OLAP) are becoming critical components of decision support as advances in technology are improving the ability to manage and retrieve large volumes of data. Data warehousing refers to ``a collection of decision support technologies aimed at enabling the knowledge worker (executive, manager, analyst) to make better and faster decisions'' [1]. OLAP refers to the technique of performing complex analysis over the information stored in a data warehouse. It is often used by management analysts and decision makers in a variety of functional areas such as sales and marketing planning. Typically, OLAP queries look for specific trends and anomalies in the base information by aggregating, ranging, filtering and grouping data in many different ways [8]. Efficient query processing is a critical requirement for OLAP because the underlying data warehouse is very large, queries are often quite complex, and decision support applications typically require interactive response times.

Copyright © 1999 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

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Printed Edition

Malcolm P. Atkinson, Maria E. Orlowska, Patrick Valduriez, Stanley B. Zdonik, Michael L. Brodie (Eds.): VLDB'99, Proceedings of 25th International Conference on Very Large Data Bases, September 7-10, 1999, Edinburgh, Scotland, UK. Morgan Kaufmann 1999, ISBN 1-55860-615-7
Contents

References

[1]: Surajit Chaudhuri, Umeshwar Dayal: An Overview of Data Warehousing and OLAP Technology. SIGMOD Record 26(1): 65-74(1997)
[2]: ...
[3]: Venky Harinarayan, Anand Rajaraman, Jeffrey D. Ullman: Implementing Data Cubes Efficiently. SIGMOD Conference 1996: 205-216
[4]: ...
[5]: Ralph Kimball: The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses. John Wiley 1996, ISBN 0-471-15337-0
[6]: ...
[7]: Patrick E. O'Neil: Model 204 Architecture and Performance. HPTS 1987: 40-59
[8]: Patrick E. O'Neil, Dallan Quass: Improved Query Performance with Variant Indexes. SIGMOD Conference 1997: 38-49
[9]: ...
[10]: ...
[11]: ...