Data Compression in Scientific and Statistical Databases
IEEE Transactions on Software Engineering
Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
ACM SIGMOD Record
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
OLAP, relational, and multidimensional database systems
ACM SIGMOD Record
Cubetree: organization of and bulk incremental updates on the data cube
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
An array-based algorithm for simultaneous multidimensional aggregates
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data warehousing with Oracle: an administrator's handbook
Data warehousing with Oracle: an administrator's handbook
An alternative storage organization for ROLAP aggregate views based on cubetrees
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
TBSAM: An Access Method for Efficient Processing of Statistical Queries
IEEE Transactions on Knowledge and Data Engineering
On the Data Model and Access Method of Summary Data Management
IEEE Transactions on Knowledge and Data Engineering
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Batched Interpolation Searching on Databases
Proceedings of the Third International Conference on Data Engineering
Aggregation Algorithms for Very Large Compressed Data Warehouses
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
A New Compression Method with Fast Searching on Large Databases
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Statistical Databases: Characteristics, Problems, and some Solutions
VLDB '82 Proceedings of the 8th International Conference on Very Large Data Bases
Querying Multiple Features of Groups in Relational Databases
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Attribute value reordering for efficient hybrid OLAP
DOLAP '03 Proceedings of the 6th ACM international workshop on Data warehousing and OLAP
InfiniteDB: a pc-cluster based parallel massive database management system
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
An efficient implementation for MOLAP basic data structure and its evaluation
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Speeding up queries in column stores: a case for compression
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Gradual data aggregation in multi-granular fact tables on resource-constrained systems
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
Using a time granularity table for gradual granular data aggregation
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Concept hierarchy_based cube aggregation for ETL process in matriculation warehouse
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
EaCRS: an extendible array based compression scheme for high dimensional data
Proceedings of the Second Symposium on Information and Communication Technology
Attribute value reordering for efficient hybrid OLAP
Information Sciences: an International Journal
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Aggregation and cube are important operations for online analytical processing (OLAP). Many efficient algorithms to compute aggregation and cube for relational OLAP have been developed. Some work has been done on efficiently computing cube for multidimensional data warehouses that store data sets in multidimensional arrays rather than in tables. However, to our knowledge, there is nothing to date in the literature describing aggregation algorithms on compressed data warehouses for multidimensional OLAP. This paper presents a set of aggregation algorithms on compressed data warehouses for multidimensional OLAP. These algorithms operate directly on compressed data sets, which are compressed by the mapping-complete compression methods, without the need to first decompress them. The algorithms have different performance behaviors as a function of the data set parameters, sizes of outputs and main memory availability. The algorithms are described and the I/O and CPU cost functions are presented in this paper. A decision procedure to select the most efficient algorithm for a given aggregation request is also proposed. The analysis and experimental results show that the algorithms have better performance on sparse data than the previous aggregation algorithms.