Fundamenta Informaticae - Volume 132, issue 2

Authors: Cuzzocrea, Alfredo | Ras, Zbigniew W.

Article Type: Other

DOI: 10.3233/FI-2014-1038

Citation: Fundamenta Informaticae, vol. 132, no. 2, pp. i-iii, 2014

Authors: Iftikhar, Nadeem | Pedersen, Torben Bach

Article Type: Research Article

Abstract: The majority of today's systems increasingly require sophisticated data management as they need to store and to query large amounts of data for analysis and reporting purposes. In order to keep more “detailed” data available for longer periods, “old” data has to be reduced gradually to save space and improve query performance, especially on resource-constrained systems with limited storage and query processing capabilities. A number of data reduction solutions have been developed, however an effective solution particularly based on gradual data reduction is missing. This paper presents an effective solution for data reduction based on gradual granular data aggregation. With …the gradual granular data aggregation mechanism, older data can be made coarse-grained while keeping the newest data fine-grained. For instance, when data is 3 months old aggregate to 1 minute level from 1 second level, when data is 6 months old aggregate to 2 minutes level from 1 minute level and so on. The proposed solution introduces a time granularity based data structure, namely a relational time granularity table that enables long term storage of old data by maintaining it at different levels of granularity and effective query processing due to a reduction in data volume. In addition, the paper describes the implementation strategy derived from a farming case study using standard database technologies. Show more

Keywords: Data aggregation, multi-dimensional data aggregation, gradual granular data aggregation, multi-granular data, time granularity

DOI: 10.3233/FI-2014-1039

Citation: Fundamenta Informaticae, vol. 132, no. 2, pp. 153-176, 2014

Authors: Viswanathan, Ganesh | Schneider, Markus

Article Type: Research Article

Abstract: Data warehouses help to store and analyze large multidimensional datasets and provide enterprise decision support. With an increased availability of spatial data in recent years, several new strategies have been proposed to enable their integration into data warehouses and and perform complex OLAP analysis. Cardinal directions have turned out to be very important qualitative spatial relations due to their numerous applications in spatial wayfinding, GIS, qualitative spatial reasoning and in domains such as cognitive sciences, AI and robotics. They are frequently used as selection and restriction criteria in spatial queries. In data warehouses, cardinal directions can be used to perform …spatial OLAP and feature navigation operations. In this article, we introduce and develop the Objects Interaction Graticule (OIG) approach to query the cardinal direction relations among spatio-temporal objects in data warehouses. First, we apply a tiling strategy that determines the zones belonging to the nine cardinal directions of each spatial object at a particular time and intersects them. This leads to a collection of grids over time called the Objects Interaction Graticule (OIG). For each grid cell, the information about the spatial objects that intersect it is stored in a Objects Interaction Matrix. In the second phase, an interpretation method is applied to these matrices to determine the cardinal direction between the moving objects. The results obtained for each valid instant over the objects' lifetime describe the variation in the objects movement over time. This is integrated as a spatio-temporal OLAP operation in a novel moving objects data warehouse (MODW) that provides an extensible framework for supporting complex structured objects. Finally, we define new directional predicates that extend MDX querying and leverage OLAP between moving objects. Show more

Keywords: Cardinal directions, data warehouse design, spatio-temporal OLAP

DOI: 10.3233/FI-2014-1040

Citation: Fundamenta Informaticae, vol. 132, no. 2, pp. 177-202, 2014

Authors: Boukraa, Doulkifli | Boussaid, Omar | Bentayeb, Fadila

Article Type: Research Article

Abstract: This paper presents a multidimensional model and a language to construct cubes for the purpose of on-line analytical processing. Both the multidimensional model and the cube model are based on the concept of complex object which models complex entities of real world. The multidimensional model is presented at two layers: the class diagram layer and the package layer. Both layers are used by a projection operation that aims at extracting cubes: at the package diagram layer, the projection dynamically assigns the roles of fact and dimensions to the complex objects of the multidimensional model whereas at the class diagram layer, …it allows designing the measures. We also provide operations that optimize the construction of new cubes by using existing ones. The set of operations for cube construction are expressed by formal operators, thus forming a language. To show the feasibility of our multidimensional model and operators, we present implementation details of a real case study. Show more

Keywords: complex object, relationship, attribute hierarchy, object hierarchy, multidimensional model, complex object cube, projection, language

DOI: 10.3233/FI-2014-1041

Citation: Fundamenta Informaticae, vol. 132, no. 2, pp. 203-238, 2014

Authors: Cuzzocrea, Alfredo | Gunopulos, Dimitrios

Article Type: Research Article

Abstract: Focusing on novel database application scenarios, where data sets arise more and more in uncertain and imprecise formats, in this paper we propose a novel decomposition framework for efficiently computing and querying multidimensional OLAP data cubes over probabilistic data, which well-capture previous kind of data. Several models and algorithms supported in our proposed framework are formally presented and described in details, based on well-understood theoretical statistical/probabilistic tools, which converge to the definition of the so-called probabilistic OLAP data cubes, the most prominent result of our research. Finally, we complete our analytical contribution by introducing an innovative Probability Distribution Function (PDF)-based …approach, which makes use of well-known probabilistic estimators theory, for efficiently querying probabilistic OLAP data cubes, along with a comprehensive experimental assessment and analysis over synthetic probabilistic databases. Show more

DOI: 10.3233/FI-2014-1042

Citation: Fundamenta Informaticae, vol. 132, no. 2, pp. 239-266, 2014

Authors: Bimonte, Sandro | Kang, Myoung-Ah | Paolino, Luca | Sebillo, Monica | Zaamoune, Mehdi | Vitiello, Giuliana

Article Type: Research Article

Abstract: Integration of spatial data into multidimensional models leads to the concept of Spatial OLAP (SOLAP). Usually, SOLAP models exploit discrete spatial data. Few works integrate continuous field data into dimensions and measures. In this paper, we provide a formal multidimensional model that supports measures and dimension as continuous field data, independently of their implementation. We provide also a proposal for a logical model allowing aggregation of field measures in a feasible ROLAP architecture.

DOI: 10.3233/FI-2014-1043

Citation: Fundamenta Informaticae, vol. 132, no. 2, pp. 267-290, 2014