{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:15:30Z","timestamp":1779174930083,"version":"3.51.4"},"reference-count":22,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>In recent years, the data warehouse industry has witnessed decreased use of indexing but increased use of compression and clustering of data facilitating efficient data access and data pruning in the query processing area. A classic example of data pruning is the partition pruning, which is used when table data is range or list partitioned. But lately, techniques have been developed to prune data at a lower granularity than a table partition or sub-partition. A good example is the use of data pruning structure called zone map. A zone map prunes zones of data from a table on which it is defined. Data pruning via zone map is very effective when the table data is clustered by the filtering columns.<\/jats:p>\n          <jats:p>The database industry has offered support to cluster data in tables by its local columns, and to define zone maps on clustering columns of such tables. This has helped improve the performance of queries that contain filter predicates on local columns. However, queries in data warehouses are typically based on star\/snowflake schema with filter predicates usually on columns of the dimension tables joined to a fact table. Given this, the performance of data warehouse queries can be significantly improved if the fact table data is clustered by columns of dimension tables together with zone maps that maintain min\/max value ranges of these clustering columns over zones of fact table data. In recognition of this opportunity of significantly improving the performance of data warehouse queries, Oracle 12c release 1 has introduced the support for dimension based clustering of fact tables together with data pruning of the fact tables via dimension based zone maps.<\/jats:p>","DOI":"10.14778\/3137765.3137769","type":"journal-article","created":{"date-parts":[[2017,9,7]],"date-time":"2017-09-07T13:35:53Z","timestamp":1504791353000},"page":"1622-1633","source":"Crossref","is-referenced-by-count":15,"title":["Dimensions based data clustering and zone maps"],"prefix":"10.14778","volume":"10","author":[{"given":"Mohamed","family":"Ziauddin","sequence":"first","affiliation":[{"name":"Oracle Corporation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"Witkowski","sequence":"additional","affiliation":[{"name":"Oracle Corporation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"You Jung","family":"Kim","sequence":"additional","affiliation":[{"name":"Oracle Corporation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dmitry","family":"Potapov","sequence":"additional","affiliation":[{"name":"Oracle Corporation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Janaki","family":"Lahorani","sequence":"additional","affiliation":[{"name":"Oracle Corporation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Murali","family":"Krishna","sequence":"additional","affiliation":[{"name":"Oracle Corporation"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling","author":"Kimball R.","year":"2002","unstructured":"Kimball , R. , Ross , M. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling ( 2 nd edition), John Wiley & Sons, Inc. , New York , 2002 . Kimball, R., Ross, M. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling (2nd edition), John Wiley & Sons, Inc., New York, 2002.","edition":"2"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/564691.564754"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/362686.362692"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687553.1687563"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/322234.322238"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the VLDB Endowment","author":"Schneider D. A.","year":"1990","unstructured":"Schneider , D. A. , DeWitt , D. J. Tradeoffs in processing complex join queries via hashing in multiprocessor database machines . Proceedings of the VLDB Endowment , 1990 . Schneider, D. A., DeWitt, D. J. Tradeoffs in processing complex join queries via hashing in multiprocessor database machines. Proceedings of the VLDB Endowment, 1990."},{"key":"e_1_2_1_7_1","volume-title":"Sep","author":"Oracle Partitioning","year":"2014","unstructured":"Oracle Partitioning with Oracle Database 12c. Oracle White Paper , Sep 2014 . http:\/\/www.oracle.com\/technetwork\/database\/options\/partitioning\/partitioning-wp-12c-1896137.pdf Oracle Partitioning with Oracle Database 12c. Oracle White Paper, Sep 2014. http:\/\/www.oracle.com\/technetwork\/database\/options\/partitioning\/partitioning-wp-12c-1896137.pdf"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/582353.582376"},{"key":"e_1_2_1_9_1","volume-title":"June","author":"Technical Overview","year":"2012","unstructured":"A Technical Overview of Oracle Exadata Database Machine and Exadata Storage Server. Oracle White Paper , June 2012 . http:\/\/www.oracle.com\/technetwork\/database\/exadata\/exadata-technical-whitepaper-134575.pdf A Technical Overview of Oracle Exadata Database Machine and Exadata Storage Server. Oracle White Paper, June 2012. http:\/\/www.oracle.com\/technetwork\/database\/exadata\/exadata-technical-whitepaper-134575.pdf"},{"key":"e_1_2_1_10_1","unstructured":"IBM Netezza Data Warehouse Appliance. http:\/\/www-01.ibm.com\/software\/data\/netezza\/  IBM Netezza Data Warehouse Appliance. http:\/\/www-01.ibm.com\/software\/data\/netezza\/"},{"key":"e_1_2_1_11_1","volume-title":"Small Materialized Aggregates: A Light Weight Index Structure for Data Warehousing. Proceedings of the VLDB Endowment","author":"Guido MoerKotte","year":"1998","unstructured":"Guido MoerKotte , Small Materialized Aggregates: A Light Weight Index Structure for Data Warehousing. Proceedings of the VLDB Endowment 1998 . Guido MoerKotte, Small Materialized Aggregates: A Light Weight Index Structure for Data Warehousing. Proceedings of the VLDB Endowment 1998."},{"key":"e_1_2_1_12_1","volume-title":"Scans Meet Storage Indexes. Oracle Magazine, May\/June 2011","author":"Smart","year":"2011","unstructured":"Smart Scans Meet Storage Indexes. Oracle Magazine, May\/June 2011 . http:\/\/www.oracle.com\/technetwork\/issue-archive\/ 2011 \/11-may\/o31exadata-354069.html Smart Scans Meet Storage Indexes. Oracle Magazine, May\/June 2011. http:\/\/www.oracle.com\/technetwork\/issue-archive\/2011\/11-may\/o31exadata-354069.html"},{"key":"e_1_2_1_13_1","volume-title":"July","author":"Oracle Database In-Memory","year":"2015","unstructured":"Oracle Database In-Memory . Oracle White Paper , July 2015 . http:\/\/www.oracle.com\/technetwork\/database\/in-memory\/overview\/twp-oracle-database-in-memory-2245633.html Oracle Database In-Memory. Oracle White Paper, July 2015. http:\/\/www.oracle.com\/technetwork\/database\/in-memory\/overview\/twp-oracle-database-in-memory-2245633.html"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.14778\/2824032.2824061"},{"key":"e_1_2_1_15_1","unstructured":"O'Neil P. O'Neil B. Chen X. Star Schema Benchmark. http:\/\/www.cs.umb.edu\/~poneil\/StarSchemaB.PDF  O'Neil P. O'Neil B. Chen X. Star Schema Benchmark. http:\/\/www.cs.umb.edu\/~poneil\/StarSchemaB.PDF"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1376616.1376712"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2007.367892"},{"key":"e_1_2_1_18_1","volume-title":"Proceedings of the VLDB Endowment","author":"Bello R. G.","year":"1998","unstructured":"Bello , R. G. , Materialized Views in Oracle , Proceedings of the VLDB Endowment , 1998 . Bello, R. G., et al. Materialized Views in Oracle, Proceedings of the VLDB Endowment, 1998."},{"key":"e_1_2_1_19_1","unstructured":"Warren H. S. Hacker's Delight Addison-Wesley. 2002.   Warren H. S. Hacker's Delight Addison-Wesley. 2002."},{"key":"e_1_2_1_20_1","volume-title":"Proceedings of the VLDB Endowment","author":"Stonebraker M.","year":"2005","unstructured":"Stonebraker , M. , Store : A Column-oriented DBMS , Proceedings of the VLDB Endowment , 2005 . Stonebraker, M., et al. C-Store: A Column-oriented DBMS, Proceedings of the VLDB Endowment, 2005."},{"key":"e_1_2_1_21_1","unstructured":"Amazon Redshift. Database Developer Guide (API Version 2012-12-01). Choosing Sort Keys. http:\/\/docs.aws.amazon.com\/redshift\/latest\/dg\/t_Sorting_data.html  Amazon Redshift. Database Developer Guide (API Version 2012-12-01). Choosing Sort Keys. http:\/\/docs.aws.amazon.com\/redshift\/latest\/dg\/t_Sorting_data.html"},{"key":"e_1_2_1_22_1","volume-title":"November","author":"Hybrid Columnar Compression","year":"2012","unstructured":"Hybrid Columnar Compression (HCC) on Exadata. Oracle White Paper , November 2012 . http:\/\/www.oracle.com\/technetwork\/database\/exadata\/ehcc-twp-131254.pdf Hybrid Columnar Compression (HCC) on Exadata. Oracle White Paper, November 2012. http:\/\/www.oracle.com\/technetwork\/database\/exadata\/ehcc-twp-131254.pdf"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3137765.3137769","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:06:54Z","timestamp":1672222014000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3137765.3137769"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":22,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2017,8]]}},"alternative-id":["10.14778\/3137765.3137769"],"URL":"https:\/\/doi.org\/10.14778\/3137765.3137769","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2017,8]]}}}