Abstract Agile development is an approach for managing and building software projects and teams, in order to satisfy customers by providing adaptive planning, evolutionary development, early delivery and continuous improvement. It is a collection of methodologies such as Extreme Programming (XP), Scrum and Feature Driven Development (FDD). Even if agile methods are rapid and flexible to change, they still suffer from some problems example,... Agile development is an approach for managing and building software projects and teams, in order to satisfy customers by providing adaptive planning, evolutionary development, early delivery and continuous improvement. It is a collection of methodologies such as Extreme Programming (XP), Scrum and Feature Driven Development (FDD). Even if agile methods are rapid and flexible to change, they still suffer from some problems example, difficulty of estimating the effort required at the beginning of the development software life cycle, especially for large projects. Also, it is important to keep design phases sufficiently long to allow for a real maturation of needs and avoid reflection based only on reactions to what has been developed, which can lead to a lot of reworking. As a first step towards a new agile method, this paper aims to provide a comparative study of available agile methods in order to propose a new model-based method. Read More Read Less

Abstract Big Data is a concept popularized in recent years to reflect the fact that organizations are confronted with large volumes of data to be processed and this, of course, presents a strong commercial and marketing challenge. This trend around the analysis and collection of Big Data has given rise to new solutions that combine traditional data warehouse technologies with Big Data systems in... Big Data is a concept popularized in recent years to reflect the fact that organizations are confronted with large volumes of data to be processed and this, of course, presents a strong commercial and marketing challenge. This trend around the analysis and collection of Big Data has given rise to new solutions that combine traditional data warehouse technologies with Big Data systems in a logical architecture. It is important to note here that several distributions ready to use for managing a system Big Data are available in the market, namely HortonWorks, Cloudera, MapR, IBM Infosphere BigInsights, Pivotal HD, Microsoft HD Insight, etc. The different distributions have an approach and different positioning in relation to the vision of a platform Hadoop. In this article, we shall first explain the architectures and components of the five distributions of Hadoop solutions for Big Data. Then we shall present our comparative study in which we shall use 34 relevant criteria to define the strengths and weaknesses of the main Hadoop distribution providers. Read More Read Less

Abstract Despite the emergence of the Semantic Web and its performance, relational databases are still the most used for data manipulation, making the mapping of the relational world to the semantic world a very pertinent research topic to fill the gap between these two heterogeneous systems and ensure better semantic interoperability without physical transformation of data represented with different syntaxes. Regarding the transformation... Despite the emergence of the Semantic Web and its performance, relational databases are still the most used for data manipulation, making the mapping of the relational world to the semantic world a very pertinent research topic to fill the gap between these two heterogeneous systems and ensure better semantic interoperability without physical transformation of data represented with different syntaxes. Regarding the transformation of SQL queries to SPARQL queries semantically equivalent, several algorithms have been developed to ensure this conversion; unfortunately, most of these algorithms have shortcomings and weaknesses in the consideration of the Left Outer Join commands in SQL queries. This problem has motivated us to write this paper so as to remedy this lack. Our main contribution is as follows: (a) establish an effective semantic transformation algorithm for Left Outer Join commands (in their simple and nested structures) to their equivalent in the SPARQL language, (b) integrate this result to RETRO (A Framework for Semantics Preserving SQL-to-SPARQL Translation) in order to have a richer algorithm that contribute to treatment of a maximum of SQL commands. Read More Read Less