A UML Profile for Variety and Variability Awareness in Multidimensional Design: An application to Agricultural Robots

Variety and variability are an inherent source of information wealth in schemaless sources, and executing OLAP sessions on multidimensional data in their presence has recently become an object of research. However, all models devised so far propose a ``rigid'' view of the multidimensional content, without taking into account variety and variability. To fill this gap, in this paper we propose V-ICSOLAP, an extension of the ICSOLAP UML profile that supports extensibility and type/name variability for each multidimensional element, as well as complex data types for measures and levels. The real case study we use to motivate and illustrate our approach is that of trajectory analysis for agricultural robots. As a proof-of-concept for V-ICSOLAP, we propose an implementation that relies on the PostgreSQL multi-model DBMS and we evaluate its performances. We also provide a validation of our UML profile by ranking it against other meta-models based on a set of quality metrics

Action Stories for Counter Terrorism (extended abstract)

Due to the raised terrorist threat worldwide, there is an urgent need to research that assists security and police services to protect the public and key assets and to prevent attacks from taking place. Successful protection and prevention may require potential and known suspects to be monitored or arrested. These operations are high risk because inappropriate surveillance, interview or arrest may have damaging political, public relations and intelligence effects. In addition to better tracking information on which to base suspicions, the security and police services need to have confidence that operations will yield evidence that can demonstrate conclusively that a deceptive activity such as a terrorist attack was in the process of being planned or executed before an operation takes place

A Descriptive Framework for Temporal Data Visualizations Based on Generalized Space-Time Cubes

International audienceWe present the generalized space-time cube, a descriptive model for visualizations of temporal data. Visualizations are described as operations on the cube, which transform the cube's 3D shape into readable 2D visualizations. Operations include extracting subparts of the cube, flattening it across space or time or transforming the cubes geometry and content. We introduce a taxonomy of elementary space-time cube operations and explain how these operations can be combined and parameterized. The generalized space-time cube has two properties: (1) it is purely conceptual without the need to be implemented, and (2) it applies to all datasets that can be represented in two dimensions plus time (e.g. geo-spatial, videos, networks, multivariate data). The proper choice of space-time cube operations depends on many factors, for example, density or sparsity of a cube. Hence, we propose a characterization of structures within space-time cubes, which allows us to discuss strengths and limitations of operations. We finally review interactive systems that support multiple operations, allowing a user to customize his view on the data. With this framework, we hope to facilitate the description, criticism and comparison of temporal data visualizations, as well as encourage the exploration of new techniques and systems. This paper is an extension of Bach et al.'s (2014) work

An architecture for Olap-based enterprise-level Decision Support Systems

In this work it is considered that the strategic development of an enterprise is aimed at the improvement of the market position and financial status. Decision Support System for elaboration of development strategy of an enterprise is used. Suggested information support for algorithmic modules realization is based on OLAP technology.У статті пропонується розробляти стратегії розвитку підприємства на підставі поліпшення ринкової позиції підприємства та фінансового положення. Для розробки стратегії розвитку використовується система підтримки прийняття рішень. Пропонується використовувати OLAP-технології для інформаційної підтримки алгоритмічних модулів

A framework for multidimensional indexes on distributed and highly-available data stores

No-relational databases are nowadays a common solution when dealing with a huge data set and massive query workload. These systems have been redesigned from scratch in order to achieve scalability and availability at the cost of providing only a reduce set of low-level functionality, thus forcing the client application to implement complex logic. As a solution, our research group developed Hecuba, a set of tools and interfaces, which aims to facilitate developers with an efficient and painless interaction with non-relational technologies. This paper presents a part of Hecuba related to a particular missing feature: multidimensional indexing. Our work focuses on the design of architectures and the algorithms for providing multidimensional indexing on a distributed database without compromising scalability and availability