An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Data mining: concepts and techniques
Data mining: concepts and techniques
On computing correlated aggregates over continual data streams
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
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
MAIDS: mining alarming incidents from data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A Distributed System for Answering Range Queries on Sensor Network Data
PERCOMW '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops
OLAP over uncertain and imprecise data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams
Distributed and Parallel Databases
Improving range-sum query evaluation on data cubes via polynomial approximation
Data & Knowledge Engineering
Accuracy Control in Compressed Multidimensional Data Cubes for Quality of Answer-based OLAP Tools
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
Approximate range---sum query answering on data cubes with probabilistic guarantees
Journal of Intelligent Information Systems
Sketching probabilistic data streams
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Estimating statistical aggregates on probabilistic data streams
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Finding frequent items in probabilistic data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Sliding-window top-k queries on uncertain streams
Proceedings of the VLDB Endowment
LCS-Hist: taming massive high-dimensional data cube compression
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Exponentially Decayed Aggregates on Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A Framework for Clustering Uncertain Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Intelligent Techniques for Warehousing and Mining Sensor Network Data
Intelligent Techniques for Warehousing and Mining Sensor Network Data
Enabling OLAP in mobile environments via intelligent data cube compression techniques
Journal of Intelligent Information Systems
CAMS: OLAPing Multidimensional Data Streams Efficiently
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Event-based lossy compression for effective and efficient OLAP over data streams
Data & Knowledge Engineering
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
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Multidimensional data streams are playing a leading role in next-generation Data Stream Management Systems (DSMS). This essentially because real-life data streams are inherently multidimensional, multi-level and multi-granular in nature, hence opening the door to a wide spectrum of applications ranging from environmental sensor networks to monitoring and tracking systems, and so forth. As a consequence, there is a need for innovative models and algorithms for representing and processing such streams. Moreover, supporting OLAP analysis and mining tasks is a "first-class" issue in the major context of knowledge discovery from streams, for which above-mentioned models and algorithms are baseline components. This issue becomes more problematic when uncertain and imprecise multidimensional data streams are considered. Inspired by these critical research challenges, in this paper we present a state-of-the-art technique for supporting OLAP over uncertain multidimensional data streams, and provide research perspectives for future efforts in this scientific field.