By João Pedro Costa, José Cecílio, Pedro Martins, Pedro Furtado (auth.), Alfredo Cuzzocrea, Umeshwar Dayal (eds.)
This publication constitutes the refereed complaints of the thirteenth foreign convention on info Warehousing and information Discovery, DaWak 2011 held in Toulouse, France in August/September 2011.
The 37 revised complete papers provided have been conscientiously reviewed and chosen from 119 submissions. The papers are prepared in topical sections on actual and conceptual facts warehouse types, info warehousing layout methodologies and instruments, information warehouse functionality and optimization, trend mining, matrix-based mining recommendations and move, sensor and time-series mining.
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Extra resources for Data Warehousing and Knowledge Discovery: 13th International Conference, DaWaK 2011, Toulouse, France, August 29-September 2,2011. Proceedings
Org/user/MoritzStefaner/article/4937436 5. : The Cube Data Model: a Conceptual Model and Algebra for On-line Analytical Processing in Data Warehouses. com/retrieve/pii/S0167923699000524 6. : Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total. In: ICDE, pp. 152–159 (1996) 7. : A Foundation for Multi-Dimensional Databases. In: Proceedings of the International Conference on Very Large Data Bases, pp. 106– 115. 7255&rep=rep1&type=pdf 8. : Modeling Large Scale OLAP Scenarios.
E. read only semantic. With planning new facts will be created or existing facts are manipulated. Therefore, the novel operators for planning must support this. From the list of basic planning functions shown in Section 3 not all require a separate operator. Similar to the original model, where typical OLAP operators like roll-up and The Planning OLAP Model - A Multidimensional Model 19 drill down are expressed in terms of the basic aggregation formation operator, we only need a few basic operators to keep the extended algebra simple and minimal.
Definition 3. , en ) ∈ Ca1 × · · · × Can ∧ f ∈ F ∧ ei ∈ Dimt ∧ ei = g −1 (eri , enew , eold ) ∧ ∀ei ∈ Dimt ∃eri ∈ Dimr } The fact-schema S of M O is the same as that of the original M O since only values are changed and no dimensions are added or removed. The set of facts is the same, too, because the disaggregation operator does not introduce new facts. It only maps the facts for the target dimension to new values. The set of dimensions is again taken from the original M O, but the target dimension Dimt changes in the sense that intuitively the new dimension values are calculated based on the new sum and the fractions of the given reference dimension Dimr .