An OLAP cube design approach for online analytical processing of Big Data

 

• PhD Student: Othmane Latreche

Abstract: The work required at the thesis level consists in proposing solutions for the design, implementation and analysis of big data cubes. The work will be applied to the StackExchange help network which will be enriched by other data sources. The work must be carried out in three stages

• Proposing a platform allowing the representation and the visualization of metadata associated with big data. The platform must offer the possibility of updating and possibly integrating data external to the company.

 

• Proposing a solution allowing decision makers to create massive data cubes by exploiting metadata and / or company data by identifying multidimensional concepts and generate the cubes to be analysed.

• Finally, proposing analysis solutions for big data cubes by a set of generic operators that will be instantiated and adapted to the nature of the data to be analyzed.

Online analytical processing (OLAP) in natural language

 

• PhD Student: Mohammed Boughedda

Abstract: The work required at the thesis level consists in proposing solutions for online analytical access to decision-making bases, mainly modeled according to the multidimensional principle. The work will be applied to the StackExchang help network. The work must be carried out in three stages

Online Analysis and Performance Optimisation in Graph-Oriented BI Databases

 

• PhD Student: Ahmed Bouhemhem

Abstract: The goal of this thesis is to allow online analysis of graph-oriented data while addressing certain aspects related to the performance of calculation and analysis of cubes. The work required at the thesis level consists in proposing solutions for 

o Designing OLAP cubes on graph-oriented data, taking into account the specificities of this data.

o Optimizing the performance of calculation of successive cubes (bases and cuboids). Graph algorithms will be explored for this purpose.