Electronic filter design handbook (third ed.)
Electronic filter design handbook (third ed.)
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
An array-based algorithm for simultaneous multidimensional aggregates
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient computation of Iceberg cubes with complex measures
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
On computing correlated aggregates over continual data streams
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Space-efficient online computation of quantile summaries
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
Continuous queries over data streams
ACM SIGMOD Record
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Scaling up Dynamic Time Warping to Massive Dataset
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Identifying Representative Trends in Massive Time Series Data Sets Using Sketches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Mining Multi-Dimensional Constrained Gradients in Data Cubes
Proceedings of the 27th International Conference on Very Large Data Bases
Intelligent Rollups in Multidimensional OLAP Data
Proceedings of the 27th International Conference on Very Large Data Bases
VLDB '95 Proceedings of the 21th 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
Using Datacube Aggregates for Approximate Querying and Deviation Detection
IEEE Transactions on Knowledge and Data Engineering
Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams
Distributed and Parallel Databases
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
An efficient exception mining algorithm in multi-dimensional data cube
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
GreenGPS: a participatory sensing fuel-efficient maps application
Proceedings of the 8th international conference on Mobile systems, applications, and services
Multidimensional cyclic graph approach: Representing a data cube without common sub-graphs
Information Sciences: an International Journal
A neural-based approach for extending OLAP to prediction
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
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As OLAP engines are widely used to support multidimensional data analysis, it is desirable to support in data cubes advanced statistical measures, such as regression and filtering, in addition to the traditional simple measures such as count and average. Such new measures will allow users to model, smooth, and predict the trends and patterns of data. Existing algorithms for simple distributive and algebraic measures are inadequate for efficient computation of statistical measures in a multidimensional space. In this paper, we propose a fundamentally new class of measures, compressible measures, in order to support efficient computation of the statistical models. For compressible measures, we compress each cell into an auxiliary matrix with a size independent of the number of tuples. We can then compute the statistical measures for any data cell from the compressed data of the lower-level cells without accessing the raw data. Time- and space-efficient lossless aggregation formulae are derived for regression and filtering measures. Our analytical and experimental studies show that the resulting system, regression cube, substantially reduces the memory usage and the overall response time for statistical analysis of multidimensional data.