Equi-depth multidimensional histograms
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
OLAP, relational, and multidimensional database systems
ACM SIGMOD Record
Data cube approximation and histograms via wavelets
Proceedings of the seventh international conference on Information and knowledge management
Selectivity estimation in spatial databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Approximating multi-dimensional aggregate range queries over real attributes
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
STHoles: a multidimensional workload-aware histogram
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Selectivity Estimation Without the Attribute Value Independence Assumption
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Improving similarity measures of histograms using smoothing projections
Pattern Recognition Letters
Condensed Cube: An Efficient Approach to Reducing Data Cube Size
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Deterministic wavelet thresholding for maximum-error metrics
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Overcoming Limitations of Approximate Query Answering in OLAP
IDEAS '05 Proceedings of the 9th International Database Engineering & Application Symposium
The cgmCUBE project: Optimizing parallel data cube generation for ROLAP
Distributed and Parallel Databases
Approximate range---sum query answering on data cubes with probabilistic guarantees
Journal of Intelligent Information Systems
Optimized stratified sampling for approximate query processing
ACM Transactions on Database Systems (TODS)
Scalable approximate query processing with the DBO engine
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Compact Hilbert Indices for Multi-Dimensional Data
CISIS '07 Proceedings of the First International Conference on Complex, Intelligent and Software Intensive Systems
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Journal of Computer and System Sciences
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
Efficiently compressing OLAP data cubes via R-tree based recursive partitions
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
FGIT'12 Proceedings of the 4th international conference on Future Generation Information Technology
Efficient tracking of moving objects using a relational database
Information Systems
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The problem of efficiently compressing massive high-dimensional data cubes still waits for efficient solutions capable of overcoming well-recognized scalability limitations of state-of-the-art histogram-based techniques, which perform well on small-in-size low-dimensional data cubes, whereas their performance in both representing the input data domain and efficiently supporting approximate query answering against the generated compressed data structure decreases dramatically when data cubes grow in dimension number and size. To overcome this relevant research challenge, in this paper we propose LCS-Hist, an innovative multidimensional histogram devising a complex methodology that combines intelligent data modeling and processing techniques in order to tame the annoying problem of compressing massive high-dimensional data cubes. With respect to similar histogram-based proposals, our technique introduces (i) a surprising consumption of the storage space available to house the compressed representation of the input data cube, and (ii) a superior scalability on high-dimensional data cubes. Finally, several experimental results performed against various classes of data cubes confirm the advantages of LCS-Hist, even in comparison with those given by state-of-the-art similar techniques.