Web warehousing plays a key role in providing the managers with up-to-date and comprehensive information about their business domain. On the other hand, since XML is now a standard de facto for the exchange of semi-structured data, integrating XML data into web warehouses is a hot topic. In this paper we propose a semi-automated methodology for designing web warehouses from XML sources modeled by XML Schemas. In the proposed methodology, design is carried out by first creating a schema graph, then navigating its arcs in order to derive a correct multidimensional representation. Differently from previous approaches in the literature, particular relevance is given to the problem of detecting shared hierarchies and convergence of dependencies, and of modeling many-to-many relationships. The approach is implemented in a prototype that reads an XML Schema and produces in output the logical schema of the warehouse.

Designing web warehouses from XML schemas

The accuracy and relevance of Business Intelligence & Analytics (BI&A) rely on the ability to bring high data quality to the data warehouse from both internal and external sources using the ETL process. The latter is complex and time-consuming as it manages data with heterogeneous content and diverse quality problems. Ensuring data quality requires tracking quality defects along the ETL process. In this paper, we present the main ETL quality characteristics. We provide an overview of the existing ETL process data quality approaches. We also present a comparative study of some commercial ETL tools to show how much these tools consider data quality dimensions. To illustrate our study, we carry out experiments using an ETL dedicated solution (Talend Data Integration) and a data quality dedicated solution (Talend Data Quality). Based on our study, we identify and discuss quality challenges to be addressed in our future research.

https://hal.archives-ouvertes.fr/hal-02424279/document

Data quality in ETL process: A preliminary study

Current Issue on Knowledge Management System for future research: a Systematic Literature Review

https://hal.archives-ouvertes.fr/hal-01060817/document

The hierarchical agglomerative clustering with Gower index: A methodology for automatic design of OLAP cube in ecological data processing context

Business Intelligence (BI) can assist in agricultural enterprises to strengthen their production potential and tech- nical effi ciency due to its eff ective support to the managerial, analytical, planning, and decision-making activities of man- agers and specialists. However, the state of the BI in the Czech Republic is not still completely understood. In this context, this paper aims at the evaluation of the current state of the art of the BI among small Czech farms. Th e focus of the re search was put on the evaluation of both the state of the BI, and the relevant business information systems and software for agri- culture. Th ere was a survey among 135 agricultural entrepreneurs from various regions in the Czech Republic. Th e survey results are presented by the descriptive statistics and frequency tables. Th ere is an examination of the relationship between the agricultural enterprise structure and the use of the BI. Dependencies among the examined characteristics were sought by the means of the analysis of qualitative variables. With 95% probability, it could be claimed that the type of production, the size of farmed land, the number of employees and the level of fi nancial subsidies have no signifi cant impact on using the BI, the expert and analytical systems in agricultural enterprises.

/pdf/evaluation-of-the-state-of-the-business-intelligence-among-384krkpi9z.pdf

Evaluation of the State of the Business Intelligence among Small Czech Farms

It is widely accepted today that relational systems are not appropriate to handle Big Data. This has led to a new category of databases commonly known as NoSQL databases that were created in response to the needs for better scalability, higher flexibility and faster data access. These systems have proven their efficiency to store and query Big Data. Unfortunately, only few works have presented approaches to implement conceptual models describing Big Data in NoSQL systems. This paper proposes an automatic MDA-based approach that provides a set of transformations, formalized with the QVT language, to translate UML conceptual models into NoSQL models. In our approach, we build an intermediate logical model compatible with column, document and graph oriented systems. The advantage of using a unified logical model is that this model remains stable, even though the NoSQL system evolves over time which simplifies the transformation process and saves developers efforts and time.

https://hal.archives-ouvertes.fr/hal-01873732/document

MDA-based approach for NoSQL Databases Modelling

In 2014, Big Data has passed the top of the Gartner Hype Cycle, proving that Big Data technologies and

application start to be mature, becoming more realistic about how Big Data can be useful for organizations.

NoSQL data stores are becoming widely used to handle Big Data; these databases operate on schema-less

data model enabling users to incorporate new data into their applications without using a predefined

schema. But, there is still a need for a conceptual model to define how data will be structured in the

database. In this paper, we show how to store Big Data within NoSQL systems. For this, we use the Model

Driven Architecture (MDA) that provides a framework for models automatic transformation. Starting from

a conceptual model that describes a set of complex objects, we propose transformation rules formalized with

QVT to generate a column-oriented NoSQL model. To ensure efficient automatic transformation, we use a

logical model that limits the impacts related to technical aspects of column-oriented platforms. We provide

experiments of our approach using a case study example taken from the health care domain. The results of

our experiments show that the proposed logical model can be effectively implemented in different columnoriented

systems independently of their specific technical details.

https://hal.archives-ouvertes.fr/hal-03193283/document

Big Data and Knowledge Management: How to Implement Conceptual Models in NoSQL Systems?

Volume, Variety and Velocity are the three dimensions that have definitely changed the tools we need to store and process Big Data effectively, giving rise to NoSQL systems for faster data access, better scalability and higher flexibility. While NoSQL systems have proven their efficiency to handle Big Data, it is still an unsolved problem how the automatic storage of Big Data in NoSQL systems could be done. One solution for addressing this problem is to model Big Data, and then define mapping rules towards the physical level. This paper proposes an automatic MDAbased approach that translates conceptual models expressed using the Unified Modeling Language (UML) into NoSQL physical models. Our approach rely on an intermediate logical model compatible with column, document and graph oriented systems which allows to choose the system type that suits the best with business rules and technical constraints.

https://hal.archives-ouvertes.fr/hal-01914003/document

UMLtoNoSQL: Automatic Transformation of Conceptual Schema to NoSQL Databases

NoSQL data stores are becoming widely used to handle Big Data; these systems operate on schema-less data model enabling users to incorporate new data into their applications without using a predefined schema But, there is still a need for a conceptual model to define how data will be structured in the database In this paper, we show how to store Big Data described by conceptual model within NoSQL systems For this, we use the Model Driven Architecture (MDA) that provides a framework for models automatic transformation Starting from a conceptual model describing a set of complex objects, we propose transformation rules formalized with QVT to generate NoSQL physical models To ensure efficient automatic transformation and to limit the impacts related to technical aspects of NoSQL systems, we propose a generic logical model that is compatible with the three types of NoSQL systems (column, document and graph) We provide experiments of our approach using a case study related to the hea lth care field The results of our experiments show that the proposed logical model can be effectively transformed into different NoSQL physical models independently of their specific details

https://hal.archives-ouvertes.fr/hal-01782574/document

Logical Unified Modeling For NoSQL DataBases

With decision support systems, decisionmakers analyse data in data marts extracted from production bases. The data-mart sche ma design is generally performed by expert designers (administrator or com puter specialist). With data-driven, requirement-driven or hybrid-driven ap proaches, this designer builds a data-mart defining facts (analysis subject s) and analysis axes. This process, based on data sources and decision-makers requirements, often turns out to be approximate and complex. We propose to de sign a data-mart schema by the decision-maker himself, following a hybrid-d riven approach. Using an assistance process that visualises successively int rmediate schemas built from data sources, the decision-maker gradually builds h is multidimensional schema. He determines measures to be analysed, dimensions h ierarchies within dimensions. A CASE tool based on this concept has be en d veloped.

Fatma Abdelhedi

Papers

MDA-based approach for NoSQL Databases Modelling

Big Data and Knowledge Management: How to Implement Conceptual Models in NoSQL Systems?

UMLtoNoSQL: Automatic Transformation of Conceptual Schema to NoSQL Databases

Logical Unified Modeling For NoSQL DataBases

Computer-aided data-mart design