Mohammed Ramdani

A New Method for Constructing Concept Maps in Adaptive E-Learning Systems

With the enormously progress of information technology, more and more adaptive learning and testing systems based on concept map have been proposed. Because the existing method to construct concept maps is time consuming and needs extra space to store the result of calculations or dependents on the threshold value given by learner that sometimes constructs incorrectly relationships or miss some relationships. In this paper, we present a new method based on Chen and Bai’s method to automatically construct concepts maps. Firstly, we apply the Apriori Algorithm to mine some information then we construct two kinds of questions-relationship maps and combine them into the one combined questions-relationship map. Finally, we calculate the relevance degree between any two concepts to construct concepts map. The proposed method overcomes the drawbacks of Chen and Bai’s method and gets more reasonable results than the one constructed by Chen and Bai’s method.

The automatic creation of OLAP cube using an MDA approach

The Model‐Driven Architecture (MDA) is an approach that aligns modeling and automation for software development. By applying such an approach to data warehouse (DW) projects, we can minimize a great deal of time and cost. Furthermore, most of OnLine Analytical Processing (OLAP) platforms seem to be like black boxes that provide wizards only to business intelligence developers to create and manipulate OLAP objects without allowing their sustainability and migration from a platform to another. That is why many works in the literature have proposed using the MDA approach in DW projects. However, most of them have mainly focused on the generation of the DW relational model from the conceptual one, and they overlooked the OLAP model and the cube implementation.

A CIM for Data Warehouse Requirements Using an UML Profile

Different modelling approaches have been proposed to automate the process of design and development of data warehouse. Some of them use the model driven architecture, however they are more focused on designing platform independent model (PIM) and platform specific model (PSM) and reserved unfortunately few efforts for requirements modelling and design computational independent model (CIM). This situation can be explained essentially by the presence of mature technologies and methods used for the representation of PIM and PSM models. Moreover, there are no standards to build CIM. Within this context, data warehousing projects fail due to not much attention paid on the requirement analysis phase. To overcome this problem, this study provides an approach to build a CIM architecture for modelling data warehouse requirements using an unified modelling language (UML) profile that extends the use cases and business process models, and proposes requirements specifications using SBVR structured English.

A novel approach for open domain event schema discovery from twitter

Open domain event extraction is a recently-introduced type of event extraction that extracts, aggregate and categorize important events. This is done without any domain specific guidance such as special training data or extraction rules. Because OEE is domain-independent, it helps the final users managing the unstructured data in an easy way. OEE help users creating complex queries or finding a new domain when they have an unknown structure of the explored events. We can help the user by generating a simplified relational schema that describes the extracted events in any given domain. For systems that extract events within a narrow domain, the schema is easily specified in advance. While, the events and types extracted with OEE do not fit with full schema information: we cant know in advance what schema is appropriate for each discovered type. In this paper, we introduce a novel approach of schema discovery based on probabilistic generative models especially LinkLDA for open-domain event extraction. This approach aims to develop an algorithm to automatically derive high quality smart schemas from the extracted events. To evaluate the quality of our results, we will carry out our experiments on a set of events extracted from twitter, the most up-to-date stream of current events.

OLAP cube partitioning based on association rules method

Partitioning is an optimization method used in Business intelligence (BI) systems to improve query and processing performances. That is why most of BI vendors integrate partitioning functionality in their solutions. However, they do not provide partitioning strategies which remain a serious challenge for BI administrators. Some works in the literature have proposed algorithms and strategies for Data warehouse partitioning. Nevertheless, most of them focused on the relational data warehouse partitioning and ignore the OLAP cubes although they are the first concerned by the user multidimensional queries. To deal with this, we propose in this paper a dynamic partitioning strategy for OLAP cubes based on the association rules algorithm. The first step in the proposal consists on analyzing the user queries for a specific period with a view to finding the frequent predicates itemsets. Afterwards, we use our proposed algorithm based on the association rules method to partition the data cube according to the frequent predicates itemsets obtained in the first step. Finally, we present a case study and experiences results to evaluate and validate our approach.

Towards an MDA-oriented UML profiles for data warehouses design and development

Nowadays, many approaches have been proposed to design and develop Data Warehouse (DW). However, there are neither standard method that addresses the design of all DW layers nor a software process prescribed for this kind of engineering domain. Generally, most of those approaches are interested in a particular aspect of DW (Storage, ETL processing, OLAP analysis, reporting, etc.) and dont cover its whole life-cycle. On the other hand, the Model Driven Architecture (MDA) is a standard approach which intended to support the whole phases of software manufacturing, by promoting the usage of models and transformations between them up to code generation. The MDA aims to automate the software engineering process, reducing thus the software development cost and improving its productivity. In this paper, we describe how to apply MDA throughout the entire development process of DW. In fact, the proposal is an MDA-oriented UML profiles. It starts by eliciting the DW requirements and then deduces the multidimensional conceptual model. The first two steps are both based on UML profiles. Afterwards, a model transformation is performed from the conceptual model to get the relational and OLAP models. At last, the SQL code and XMLA are generated for building respectively the DW and its corresponding OLAP cubes. All model transformations are performed using the transformation language ATL.

Towards minimizing author's effort during designing the domain model of adaptive web-based educational system

Over the past thirty years, considerable research has been invested in the development of computer systems for effectively teaching students. Among these systems, the adaptive web-based educational systems (AESs) were a promising research field. Designing and authoring such systems is not easy task. Thus, several tools were proposed in order to facilitate authors jobs. However, in particular, authoring domain model still suffers the manual work. This paper presents an approach for minimizing effort of authoring process by allowing system being part of construction.

Fuzzy Galois connections for the extraction of temporal association rules

In this paper, we propose a new method able to correct errors the dematerialization the documents, especially the invoice document. We use on Electronic document readers able to read and recognize the relevant information. The process consists on: digitizing an important quantity of documents and passing them through an electronic documents reader. Then treating (correcting) the error recognition. The final objective is to find an electronic document reflecting all information included in the original document (physical document). To achieve this issue we develop a new parser which is applicable to a large kind of documents, especially correcting the detected errors. This parser is based on the formal concept analysis.

Fuzzy Closed Concept for the Correction of Errors in the Dematerialization Process

In this paper, we propose a new method able to correct errors related to the recognition of invoice type documents. We rely on automated document readers that can read and recognize the various, relevant information in a scanned document. The process consists of digitizing a large volume of documents, and passes them through automatic documents readers, then carries out the correction of the different errors. The final objective is to find an electronic document reflecting all information included in the background document. The main goal is the generation of organized electronic documents, like a data basis or files XML. To achieve this issue we develop a new parser which is applicable to a various kind of documents, especially the corrections for the recognition errors by using the fuzzy closed concept, this formalism allow the detection of the set of rules to be applied in a set of documents.