Making data structures persistent
Journal of Computer and System Sciences - 18th Annual ACM Symposium on Theory of Computing (STOC), May 28-30, 1986
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficient computation of temporal aggregates with range predicates
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Progressive approximate aggregate queries with a multi-resolution tree structure
SIGMOD '01 Proceedings of the 2001 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
Designing Access Methods for Bitemporal Databases
IEEE Transactions on Knowledge and Data Engineering
Partially persistent data structures of bounded degree with constant update time
Nordic Journal of Computing
Improving the Query Performance of High-Dimensional Index Structures by Bulk-Load Operations
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Hierarchical Prefix Cubes for Range-Sum Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries
Proceedings of the 27th International Conference on Very Large Data Bases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
An asymptotically optimal multiversion B-tree
The VLDB Journal — The International Journal on Very Large Data Bases
pCube: Update-Efficient Online Aggregation with Progressive Feedback and Error Bounds
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Parallel Algorithms for Computing Temporal Aggregates
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Scalable Algorithms for Large Temporal Aggregation
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
A new method for functional arrays
Journal of Functional Programming
Temporal and spatio-temporal aggregations over data streams using multiple time granularities
Information Systems - Special issue: Best papers from EDBT 2002
Representing spatiality in a conceptual multidimensional model
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Range Aggregate Processing in Spatial Databases
IEEE Transactions on Knowledge and Data Engineering
Specification-based data reduction in dimensional data warehouses
Information Systems
Efficient temporal counting with bounded error
The VLDB Journal — The International Journal on Very Large Data Bases
Queries on dates: fast yet not blind
Proceedings of the 14th International Conference on Extending Database Technology
Update propagation in a streaming warehouse
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Exploiting temporal correlation in temporal data warehouses
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
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Data warehouses support the analysis of historical data. This often involves aggregation over a period of time. Furthermore, data is typically incorporated in the warehouse in the increasing order of a time attribute, e.g., date of sale or time of a temperature measurement. In this paper we propose a framework to take advantage of this append only nature of updates due to a time attribute. The framework allows us to integrate large amounts of new data into the warehouse and generate historical summaries efficiently. Query and update costs are virtually independent from the extent of the data set in the time dimension, making our framework an attractive aggregation approach for append-only data streams. A specific instantiation of the general approach is developed for MOLAP data cubes, involving a new data structure for append-only arrays with pre-aggregated values. Our framework is applicable to point data and data with extent, e.g., hyper-rectangles.