The concepts abstracted from reality and represented through the dimensions in a Statistical Database (SDB) support the user to use them in query formulation and processing. Instead, all those useful properties involved in a query that cannot be obtained through dimension/s in aN SDB (for example, the concept of adjacency) can be presented in a Geographical Database (GDB). This paper presents a conceptual approach to allow the end user, working in a Geographic Database (GDB) environment to use data cube stored in a Statistical Database (SDB). In this context we need to extend the geographic data structure with some special "functional attributes": They support links between environments mentioned above through geographic dimensions always implicitly or explicitly present in SDB. Therefore, the main objective of this paper is to propose a solution to answer queries involving data stored in both environments in a transparent way to the user. Then, a query language to support the integration of multidimensional operators with geographic operators is proposed. Finally, the main characteristics of the proposed approach are illustrated through some examples.

Recently numerous proposals for modelling and querying Multidimensional Databases (MDDB) are proposed. Among the still open problems there is a rigorous classification of the different types of hierarchies. In this paper we propose and discuss some different types of hierarchies within a single dimension of a cube. These hierarchies divide in different levels of aggregation a single dimension. Depending on them, we discuss the characterization of some OLAP operators which refer to hierarchies in order to maintain the data cube consistency. Moreover, we propose a set of operators for changing the hierarchy structure. The issues discussed provides modelling flexibility during the scheme design phase and correct data analysis.

We introduce a first-order language with real polynomial arithmetic and aggregation operators (count, iterated sum and multiply), which is well suited for the definition of aggregate queries involving complex statistical functions. It offers a good trade-off between expressive power and complexity, with a tractable data complexity. Interestingly, some fundamental properties of first-order with real arithmetic are preserved in the presence of aggregates. In particular, there is an effective quantifier elimination for formulae with aggregation. We consider the problem of querying data that has already been aggregated in aggregate views, and focus on queries with an aggregation over a conjunctive query. Our main conceptual contribution is the introduction of a new equivalence relation among conjunctive queries, the isomorphism modulo a product. We prove that the equivalence of aggregate queries such as for instance averages reduces to it. Deciding if two queries are isomorphic modulo a p...

Aggregate data are obtained by applying statistical aggregations and statistical analysis functions over raw data. We believe that a major adherence of aggregate data to the process that generates them can make their modeling simpler and more effective. We propose a formal description of the aggregation process and derive from this description a data structure for the representation of aggregate data. Several new concepts are introduced, in particular the implicit attributes, and an extension of the aggregation operator, called the generalized summarization. We also show that the introduced data structure allows us to identify in a simple way the population of a statistical object.

There have been numerous proposals aimed at correcting the deficiency in existing database models in order to manipulate statistical data. The manipulation of these data, such as statistical tables that are widely used in many statistical database application areas, is examined. A functional model Mefisto, which is based on a data structure called statistical entity and on a set of operations capable of manipulating this data structure, is proposed. The characteristics that an aggregate data model has are discussed and a brief survey of the main proposals in literature is made. The operators that allow statistical entities to be manipulated both from the descriptive and from the summary data point of view are discussed, and some examples are given. Each operator can be seen as a family of operators, and each is able to automatically compute the summary values of the statistical entity obtained by its application. A brief discussion regarding the limitations of the relational model for this type of data and a comparison with other proposals are presented. The advantages of the Mefisto model over those proposals are illustrated. It is shown that it is possible to define user friendly query languages based on the Mefisto model.>

We propose a method to protect sensitive information in a two-dimensional statistical table based on the suppression of certain marginal cells.A sensitive cell set is considered unprotected if its exact value can be computed from the values of nonsensitive cells and unsuppressed marginal cells.We provide efficient algorithms to solve the following problems: deciding whether the sensitive cell sets are protected, identifying and evaluating all unprotected cell sets, suppressing the fewest marginal cells to protect all the sensitive cells.