In Business Intelligence (BI) systems, decision-making is based on data warehouses (DW), alimented, generally, from internal sources to the organization. Decision-making based solely on this data, sometimes, gives a partial and limited view of certain activities. Consequently, the decision maker finds himself constrained to search additional information on the Web in order to understand the external environment. This information can be found in external data sources as the Open Data (OD), to complete a decisional analysis. Therefore, it is valuable to enrich DWs with external data sources. In this context, we propose, in this paper, an approach that aims to explore the Web in order to search and select appropriate OD sources for DW enrichment. This approach is carried out on the basis of Natural Language Processing (NLP) techniques as well as a scrapping process related to the Google Dataset Search engine, in order to provide an efficient solution for making decisions to validate this approach, a tool called "Open Data Search" is developed and the obtained results are presented.

The security of personal data is crucial for a company or any individual. Phishing is one of the most common and dangerous cybercrime attacks. These attacks aim to steal information used by individuals and organizations using social engineering, which is a key point for the success of the phishing attack. Even though there are several systems and solutions, the amount of personal information stolen continues to increase as cyberattacks become more difficult to detect. This paper consists of a broad review to study the work carried out in the fight against phishing and the identification of vulnerabilities in existing systems to achieve better efficiency. The authors focused on the social medium Twitter to study the phishing attacks passing through this medium, and they present their new design, which is based on new features. The classification of the approach includes 23 features and uses the MLP artificial neural network (ANN MLP) algorithm. Experiments show that the system is effective at detecting phishing sites, with a 96% success rate using recent data.

Cloud federation enables service providers to collaborate to provide better services to customers. For cloud storage services, optimizing customer object placement for a member of a federation is a real challenge. Storage, migration, and latency costs need to be considered. These costs are contradictory in some cases. In this article, we modeled object placement as a multi-objective optimization problem. The proposed model takes into account parameters related to the local infrastructure, the federated environment, customer workloads, and their SLAs. For resolving this problem, we propose CDP-NSGAII IR , a Constraint Data Placement matheuristic based on NSGAII with Injection and Repair functions. The injection function aims to enhance the solutions’ quality. It consists to calculate some solutions using an exact method then inject them into the initial population of NSGAII. The repair function ensures that the solutions obey the problem constraints and so prevents from exploring large sets of unfeasible solutions. It reduces drastically the execution time of NSGAII. Experimental results show that the injection function improves the HV of NSGAII and the exact method by up to 94% and 60%, respectively, while the repair function reduces the execution time by an average of 68%.

A cloud federation gives to cloud service providers (CSP) the opportunity to collaborate in order to offer a better QoS to customers at a lower cost.To do so, CSPs make some spare resources available to others at a reduced cost.One of the most critical resources is the storage system as it represents the main system bottleneck.From this point of view, how to efficiently place data in a federation of Clouds with heterogeneous storage systems is a real challenge.To address this issue, one needs to accurately estimate the data placement cost.In this paper, we propose a cost model for hybrid storage systems in a cloud federation for a Database as a Service (DBaaS) application.It takes into account the storage system characteristics, customers I/O workloads and SLA.The proposed cost model considers both 1) Internal customers data placement cost including local placement, outsourcing, back-migration and penalty costs, and 2) External customers data placement cost including insourcing and geo-migration costs.It can be used to help in the decision-making process which aims to enhance customers QoS and reduce CSPs costs in a federation.Simulation results showed the relevance of the considered costs.We have shown that mis-considering some sub-costs may lead to a 95% cost error for external customers data placement and 80% for outsourcing customers.This may cause significant financial loss.

The Cloud Computing technology is evolving constantly but essence remains the same that is to offer distinct cost saving opportunities by consolidating and restructuring information technology as a service. With the continuously increasing cloud provisions, cloud consumers start to have difficulties to find the best relevant services that suit their requirements. Therefore, selecting best services by cloud users is becoming a greater challenge. In this paper, we present a framework of services’ recommendation system in a Cloud environment, using automatic learning techniques. The system aims at finding the services that suit the interests and preferences of cloud consumers by combining content based and behaviour based recommendations. In this paper, we present, USTHBCLOUD, a cloud services recommendation prototype evaluated with an experimental study.

Solid State Drives (SSD) are integrated together with Hard Disk Drives (HDD) in Hybrid Storage Systems (HSS) for Cloud environment. When it comes to storing data, some placement strategies are used to find the best location (SSD or HDD). These strategies should minimize the cost of data placement while satisfying Service Level Objectives (SLO). This paper presents two Cost based Object Placement Strategies (COPS) for DBaaS objects in HSS: a Genetic based approach (G-COPS) and an ad-hoc Heuristic approach (H-COPS) based on incremental optimization. While G-COPS proved to be closer to the optimal solution in case of small instances, H-COPS showed a better scalability as it approached the exact solution even for large instances (by 10% in average). In addition, H-COPS showed small execution times (few seconds) even for large instances which makes it a good candidate to be used in runtime. Both H-COPS and G-COPS performed better than state-of-the-art solutions as they satisfied SLOs while reducing the overall cost by more than 40% for problems of small and large instances.

Business Intelligence systems provide an effective solution for multidimensional online computing and analysis from large volumes of data. In the decision making process, analyzed data are typically stored in a set of cubes often heterogeneous. Most of the time, the structure of these cubes is unknown by decision makers. Our goal is to enable decision makers to express their need via a query in natural language. In this paper, we deal with the problem of data cube design and construction according to a user query where the expressed need is dispersed on more than one cube. We propose a cubes design and construction approach based on fusion of cubes containing a part of the user's need. We validate our approach via a tool, called “Design-Cubes-Query”, that implements our approach and we show its use through a case study.

In the current Big Data era, systems for collecting, storing and efficiently exploiting huge amounts of data are continually introduced, such as Hadoop, Apache Spark, Dremel, etc. Druid is one of theses systems especially designed to manage such data quantities, and allows to perform detailed real-time analysis on terabytes of data within sub-second latencies. One of the important Druid's requirements is fast data filtering. To insure that, Druid makes an extensive use of bitmap indexes. Previously, we introduced a new compressed bitmap index scheme called Roaring bitmap that has shown interesting results when compared to the bitmap compression scheme adopted by Druid: Concise. Since, Roaring bitmap has been integrated to Druid as an indexing solution. In this work, we produce an extensive series of experiments in order to compare Roaring bitmap and Concise time-space performances when used to accelerate Druid's OLAP queries and other kinds of operations Druid realizes on bitmaps, like: retrieving set bits from bitmaps, computing bitmap complements, aggregating several bitmaps with logical ORs and ANDs operations. Roaring bitmap has shown to improve up to ≈ 5× analytical queries response times under Druid compared to Concise.