Proceedings ArticleDOI
OLAP dimension constraints
Carlos A. Hurtado,Alberto O. Mendelzon +1 more
- pp 169-179
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TLDR
A sound and complete algorithm for solving the implication of dimension constraints, that uses heuristics based on the structure of the dimension and the constraints to speed up its execution is given.Abstract:
In multidimensional data models intended for online analytic processing (OLAP), data are viewed as points in a multidimensional space. Each dimension has structure, described by a directed graph of categories, a set of members for each category, and a child/parent relation between members. An important application of this structure is to use it to infer summarizability, that is, whether an aggregate view defined for some category can be correctly derived from a set of precomputed views defined for other categories. A dimension is called heterogeneous if two members in a given category are allowed to have ancestors in different categories. In previous work, we studied the problem of inferring summarizability in a particular class of heterogeneous dimensions. In this paper, we propose a class of integrity constraints and schemas that allow us to reason about summarizability in general heterogeneous dimensions. We introduce the notion of frozen dimensions, which are minimal homogeneous dimension instances representing the different structures that are implicitly combined in a heterogeneous dimension. Frozen dimensions provide the basis for efficiently testing implication of dimension constraints, and are useful aid to understanding heterogeneous dimensions. We give a sound and complete algorithm for solving the implication of dimension constraints, that uses heuristics based on the structure of the dimension and the constraints to speed up its execution. We study the intrinsic complexity of the implication problem, and the running time of our algorithm.read more
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References
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Proceedings ArticleDOI
Summarizability in OLAP and statistical data bases
H.-J. Lenz,Arie Shoshani +1 more
TL;DR: A framework for precisely specifying the context in which statistical objects are defined is introduced, which uses a three-step process to define normalized statistical objects.
Proceedings ArticleDOI
Multidimensional data modeling for complex data
TL;DR: The paper presents nine requirements to multidimensional data models, each of which is exemplified by a real world, clinical case study, and defines an extended multiddimensional data model, and an associated algebra, which address all nine requirements.
Proceedings ArticleDOI
Regular path queries with constraints
Serge Abiteboul,Victor Vianu +1 more
TL;DR: The evaluation of path expression queries on semi-structured data in a distributed asynchronous environment is considered and decidability and complexity results on the implication for path constraints are established.
Conceptual Data Warehouse Design
TL;DR: This paper shows how to systematically derive a conceptual warehouse schema that is even in generalized multidimensional normal form from an operational database.
Book ChapterDOI
Querying Multidimensional Databases
Luca Cabibbo,Riccardo Torlone +1 more
TL;DR: A model and a query language are introduced to establish a theoretical basis for multi-dimensional data analysis based on the notions of dimension and f-table and compared with other approaches.