File structures: an analytic approach
File structures: an analytic approach
The design and analysis of spatial data structures
The design and analysis of spatial data structures
The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Linear clustering of objects with multiple attributes
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
The hB-tree: a multiattribute indexing method with good guaranteed performance
ACM Transactions on Database Systems (TODS)
Research problems in data warehousing
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Improved query performance with variant indexes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Bitmap index design and evaluation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
A class of data structures for associative searching
PODS '84 Proceedings of the 3rd ACM SIGACT-SIGMOD symposium on Principles of database systems
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Indexing Techniques for Queries on Nested Objects
IEEE Transactions on Knowledge and Data Engineering
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Encoded Bitmap Indexing for Data Warehouses
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Selection of Views to Materialize in a Data Warehouse
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Back to the Future: Dynamic Hierarchical Clustering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Small Materialized Aggregates: A Light Weight Index Structure for Data Warehousing
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Materialized View Selection for Multidimensional Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Computing Iceberg Queries Efficiently
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Storage Estimation for Multidimensional Aggregates in the Presence of Hierarchies
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
The Universal B-Tree for Multidimensional Indexing: general Concepts
WWCA '97 Proceedings of the International Conference on Worldwide Computing and Its Applications
Join algorithm costs revisited
The VLDB Journal — The International Journal on Very Large Data Bases
Finding Your Way through Multidimensional Data Models
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
Heuristic optimization of OLAP queries in multidimensionally hierarchically clustered databases
Proceedings of the 4th ACM international workshop on Data warehousing and OLAP
Integrating the UB-Tree into a Database System Kernel
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Managing and analyzing massive data sets with data cubes
Handbook of massive data sets
SISYPHUS: the implementation of a chunk-based storage manager for OLAP data cubes
Data & Knowledge Engineering - Special issue: Advances in OLAP
Processing OLAP queries in hierarchically clustered databases
Data & Knowledge Engineering - Special issue: Advances in OLAP
Exploiting hierarchical clustering in evaluating multidimensional aggregation queries
DOLAP '03 Proceedings of the 6th ACM international workshop on Data warehousing and OLAP
Enhancing OLAP functionality using self-organizing neural networks
Neural, Parallel & Scientific Computations - Special issue: Computing intelligence in management
Physical Database Design: the database professional's guide to exploiting indexes, views, storage, and more
Processing star queries on hierarchically-clustered fact tables
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Star join revisited: Performance internals for cluster architectures
Data & Knowledge Engineering
Automated design of multidimensional clustering tables for relational databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Hierarchical clustering for OLAP: the CUBE File approach
The VLDB Journal — The International Journal on Very Large Data Bases
Improved search strategies and extensions to k-medoids-based clustering algorithms
International Journal of Business Intelligence and Data Mining
Design of the ERATOSTHENES OLAP server
PCI'01 Proceedings of the 8th Panhellenic conference on Informatics
LinearDB: a relational approach to make data warehouse scale like MapReduce
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Ad hoc star join query processing in cluster architectures
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Efficient evaluation of partially-dimensional range queries using adaptive r*-tree
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Ameliorating memory contention of OLAP operators on GPU processors
DaMoN '12 Proceedings of the Eighth International Workshop on Data Management on New Hardware
Integrating Star and Snowflake Schemas in Data Warehouses
International Journal of Data Warehousing and Mining
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Data-warehousing applications cope with enormous data sets in the range of Gigabytes and Terabytes. Queries usually either select a very small set of this data or perform aggregations on a fairly large data set. Materialized views storing pre-computed aggregates are used to efficiently process queries with aggregations. This approach increases resource requirements in disk space and slows down updates because of the view maintenance problem. Multidimensional hierarchical clustering (MHC) of OLAP data overcomes these problems while offering more flexibility for aggregation paths. Clustering is introduced as a way to speed up aggregation queries without additional storage cost for materialization. Performance and storage cost of our access method are investigated and compared to current query processing scenarios. In addition performance measurements on real world data for a typical star schema are presented.