By Zineb El Akkaoui, José-Norberto Mazón, Alejandro Vaisman, Esteban Zimányi (auth.), Alfredo Cuzzocrea, Umeshwar Dayal (eds.)
This booklet constitutes the refereed complaints of the 14th foreign convention on information Warehousing and data Discovery, DaWaK 2012 held in Vienna, Austria, in September 2012. The 36 revised complete papers provided have been rigorously reviewed and chosen from ninety nine submissions. The papers are prepared in topical sections on information warehouse layout methodologies, ETL methodologies and instruments, multidimensional facts processing and administration, information warehouse and OLAP extensions, information warehouse functionality and optimization, info mining and information discovery concepts, information mining and information discovery purposes, development mining, info flow mining, info warehouse confidentiality and protection, and allotted paradigms and algorithms.
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Extra resources for Data Warehousing and Knowledge Discovery: 14th International Conference, DaWaK 2012, Vienna, Austria, September 3-6, 2012. Proceedings
For role-playing dimeensions we create a view for each one and put its prim key as a foreign key in facct table. e. e. description), where this new table exports its primary key (also a surrogate key) to be a foreeign key in original table. In this context, it is importtant enhance that in SDWCASE E the position of a spatial attributes/measure can be shaared among several spatial attrributes/measures. To accomplish this, it is necessaryy to define the same name and d the same geometric type. Furthermore, for each spaatial attribute/measure that will share s its position, the attributes isNormalized and isShaared must be defined as true.
The first type has only descriptive/conventional levels, the second one has only geometric levels, and the third one has both geomeetric and conventional levels. The authors also makke a distinction between numericcal and spatial measure, where the latter is a collectionn of geometries. As we can notee, this work mixes the concepts of DW and OLAP modelling and does not support the concept of spatial attributes, consequently the position oof a spatial attribute cannot be normalized n and/or shared.
3, denoting a good correspondence between the two plans. The main diﬀerence arises in sprints 1, 7, 8, and 10. In particular, in sprint 1, the team plan aimed at anticipating critical stories, thus exceeding the sprint capacity. The strong diﬀerence in the composition of the ﬁrst sprint necessarily aﬀected the subsequent sprints. Noticeably, both plans made good use of aﬃnity relationships. In order to have a further evaluation of the optimal plan, we discussed it with the team chief after the project end.