Graphs enriched by cubes for OLAP on bibliographic networks

Abstract

International audienceWith the recent growth of bibliographic data, many research fields work on defining new techniques for bibliographic data analysis. In this context, data of interest could be represented as heterogeneous networks, in which there are multiple object and link types that have multidimensional attributes. In order to analyze information network in multidimensional way, OLAP (Online Analytical Processing) is an important tool. OLAP is effective for analysing classical data, however, it must be adapted for networked data by considering nodes and the interactions among nodes. In order to quickly analyse information, we propose graphs enriched by cubes. Each node and edge of the considered network are described by a cube. It allows greater multidimensional analysis possibilities as a user may gain insight within both network and cubes. Our proposal also solves the slowly changing problem in OLAP analysis. To illustrate our approach, we integrate three bibliographic databases. Then we implement our approach and we show results on a real data set. We perform the experimental studies of the efficiency of our proposal

Jin–Xin relaxation solution to the spatially-varying Burgers equation in one dimension

“…A number of numerical methods are available in the literature, as computer simulations are desired for large scale computations [10][11][12]. In this paper numerical methods are used to solve the spatiallyvarying Burgers equation.…”