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Dive into the research topics where Jiefu Song is active.

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Featured researches published by Jiefu Song.


data warehousing and knowledge discovery | 2014

Reducing Multidimensional Data

Faten Atigui; Franck Ravat; Jiefu Song; Gilles Zurfluh

Our aim is to elaborate a multidimensional database reduction process which will specify aggregated schema applicable over a period of time as well as retains useful data for decision support. Firstly, we describe a multidimensional database schema composed of a set of states. Each state is defined as a star schema composed of one fact and its related dimensions. Each reduced state is defined through reduction operators. Secondly, we describe our experiments and discuss their results. Evaluating our solution implies executing different requests in various contexts: unreduced single fact table, unreduced relational star schema, reduced star schema or reduced snowflake schema. We show that queries are more efficiently calculated within a reduced star schema.


model and data engineering | 2016

Unifying Warehoused Data with Linked Open Data: A Conceptual Modeling Solution

Franck Ravat; Jiefu Song

Linked Open Data (LOD) become one of the most important sources of information allowing enhancing business analyses based on warehoused data with external data. However, Data Warehouses (DWs) do not directly cooperate with LOD datasets due to the differences between data models. In this paper, we describe a conceptual multidimensional model, named Unified Cube, which is generic enough to include both warehoused data and LOD. Unified Cubes provide a comprehensive representation of useful data and, more importantly, support well-informed decisions by including multiple data sources in one analysis. To demonstrate the feasibility of our proposal, we present an implementation framework for building Unified Cubes based on DWs and LOD datasets.


international conference on digital information management | 2016

Enabling OLAP analyses on the web of data

Franck Ravat; Jiefu Song

This paper describes a business-oriented analysis environment facilitating analyses of coherent data from Data Warehouses (DWs) and Linked Open Data (LOD) datasets. Specifically, we present a multidimensional modeling solution, named Unified Cube, which provides a single, comprehensive representation of data from multiple sources. Unified Cubes include both concepts close to business terms and user-friendly graphical notations. An implementation framework is proposed to enable unified analyses of warehoused data and LOD. The feasibility of the proposed concepts is illustrated with examples based on real-world datasets.


33ème Congrès sur l'INFormatique des ORganisations et Systèmes d’Information et de Décision (INFORSID 2015) | 2015

Combining Business Intelligence with Semantic Web: Overview and Challenges

Sébastien Laborie; Franck Ravat; Jiefu Song; Olivier Teste


research challenges in information science | 2016

Designing multidimensional cubes from warehoused data and linked open data

Franck Ravat; Jiefu Song; Olivier Teste


International Journal of Data Warehousing and Mining | 2016

OLAP Analysis Operators for Multi-State Data Warehouses

Franck Ravat; Jiefu Song; Olivier Teste


International Journal of Decision Support System Technology | 2015

Facilitate Effective Decision-Making by Warehousing Reduced Data: Is It Feasible?

Faten Atigui; Franck Ravat; Jiefu Song; Olivier Teste; Gilles Zurfluh


conference on current trends in theory and practice of informatics | 2018

Managing Reduction in Multidimensional Databases

Franck Ravat; Jiefu Song; Olivier Teste


Fundamenta Informaticae | 2018

A Unified Approach to Multisource Data Analyses

Franck Ravat; Jiefu Song


Ingénierie des Systèmes d'Information | 2017

Vers un modèle unifié de données entreposées et de données ouvertes liées. Concepts et expérimentations.

Franck Ravat; Jiefu Song; Olivier Teste

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Faten Atigui

Conservatoire national des arts et métiers

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