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Business Intelligence (BI) is a collection of tools, technologies, and practices that include the entire process of collecting, processing, and analyzing qualitative information, to help entrepreneurs better understand their business and marketplace. Every day, social networks expand at a faster rate and pace, which sees them as a source of Big Data. Therefore, BI is developed in the same way on VoC (Voice of Customer) expressed in social media as qualitative data for company decision-makers, who desire to have a clear perception of customers’ behaviour. In this article, we present a comparative study between traditional BI and social BI, then examine an approach to social business intelligence. Next, we are going to demonstrate the power of Big Data that can be integrated into BI so that we can finally describe in detail how Big Data technologies, like Apache Flume, help to collect unstructured data from various sources such as social media networks and store it in Hadoop storage.

A data warehouse (DW) is a vast repository of data that facilitates decision-making for businesses and companies. This concept dates back to the 1980s and it has been widely accepted. One of the key points for the success of the process of data warehousing lies in the definition of the warehouse model depending on data sources and analysis needs. Once the data warehouse is designed, the content and structure of the data sources, as well as the requirements analysis are required to evolve, therefore, an evolution of the model must take place (diagram and data). In this context, several approaches have been developed to design and implement data warehouses. Nevertheless, there is no standard process that deals with designing all of the data warehouse layers, also, there is no software that encompasses this type of problem. In general, the majority of these approaches focus on a particular aspect of data warehouse such as data storage, ETL process, OLAP, reporting, etc, and does not cover its entire lifecycle. A Model-Driven Architecture (MDA) is a standard approach, its aims to support all phases of software manufacturing by promoting the use of models and the transformations between them. Moreover, this approach aims to automate the process of software engineering, thereby decreasing the cost of software development and enhancing its productivity. In this study, we present a systematic review of various works on the data warehouse design methods. We compare and discuss these works according to the criteria that seem relevant for this issue. We present a new design approach for multidimensional schemas construction from relational models using MDA techniques, we also develop the resulting research perspectives.

The objective of this paper is to propose a hybrid decision-making methodology based on affinity diagram, fuzzy analytic hierarchy process (FAHP) and fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) to evaluate, rank and select the most appropriate cloud solutions to accommodate and manage big data projects. In fact, the strategic priority of many corporations consists in the creation of competitive advantages by using new available technologies, processes and governance mechanisms, such as big data and cloud computing. Since the technology is permanently subject to advances and developments, the question for many businesses is how to benefit from big data using the power of technical flexibility that cloud computing can provide. In this context, selecting the most adequate cloud solution to host big data projects is a complex issue that requires an extensive evaluation process. Thus, to assist users to efficiently select their most preferred cloud solution, we propose a hybrid decision-making methodology that meets these requirements in four stages. In the first stage, the identification of evaluation criteria is performed by a decision-making committee using Affinity Diagram. Due to the varied importance of the selected criteria, a FAHP process is used in the second stage to assign the importance weights for each criterion, while FTOPSIS process, in the third stage, employs these weighted criteria as inputs to evaluate and measure the performance of each alternative. In the last step, a sensitivity analysis is performed to evaluate the impact of criteria weights on the final rankings of alternatives.

The location selection for Landfill of Industrial Wastes (LIW) is a very significant task in waste management studies, which has significant impacts on sustainable development of the region. Furthermore, the selection of the appropriate and efficient sites for LIWs is an important multi-criteria decision making problem. The present document suggests an OLAP/GIS-Fuzzy AHP-TOPSIS based methodology for evaluation and selection of best sites for LIWs. In this respect, the candidate locations are specified based on the combination of On-Line Analytical Processing and Geographic Information System (OLAP/GIS). The Fuzzy Analytical Hierarchy Process (Fuzzy-AHP), a multi-criteria decision-making method is applied to analyze the structure of the problem and obtain the weights of the qualitative and quantitative criteria, by incorporating the uncertainty values in decision-making. Then, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is taken into account to assess and rank the alternative locations. Finally, a hypothetical application of the proposed approach is illustrated by a case study of location selection for LIWs in Morocco. The results show that the proposed methodology can successfully achieve the aim of this work.

Geospatial Business Intelligence (Geospatial BI) is a system that combines multidimensional analysis and cartographic visualization. It plays an important role in decision making process for enterprises. Adopting such a comprehensive solution may result in the great investment decision for them, so great deal of attention should be given in the selection of the optimal system. As there are many impacting factors in the selection of Geospatial BI system, the same process is considered as a complex multi-criteria decision making problem. In this paper, we explore the application of an integrated methodology for the evaluation of various Geospatial BI alternatives. The proposed methodology integrates the three well-known decision-making techniques, namely Modified Delphi, fuzzy analytic hierarchical process (fuzzy-AHP), and preference ranking organization method for enrichment evaluations (PROMETHEE). In this respect, the modified Delphi is used to select the most impacting factors by a few decision-makers. The fuzzy-AHP is employed to analyze the structure of the problem and to obtain the weights of the qualitative and quantitative criteria, by incorporating the uncertainty values. Then, the PROMETHEE technique is used for optimal ranking of the alternative system choices. A step-by-step, numerical study is illustrated by using the proposed methodology on the decision making problem of a company that is faced to five Geospatial BI solutions. The results demonstrate that the proposed methodology can successfully accomplish our goal of this study.

The location selection for landfill municipal solid waste is an important issue in waste management. Selection of the optimal location requires consideration of multiple alternative solutions and evaluation factors because of system complexity. Further, ranking of the alternative locations and selection of the most optimal and efficient locations for landfill waste are an important multi-criteria decision-making problem. In this study, the candidate locations for landfill are determined based on Political, Economical, Environmental and Social factors are assessed through decision-makers’ opinion and by the methodology that incorporates fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS). Specifically, the fuzzy AHP technique is applied to determine the weights of selected criteria impacting the location selection process, and the fuzzy AHP is adapted to model the linguistic vagueness and ambiguity, which can also be expressed as triangular fuzzy numbers. Furthermore, fuzzy TOPSIS technique is employed to rank the alternative locations. The applicability of this methodology is demonstrated by a case study of landfill waste location selection in the region of Casablanca, Morocco, and the results are compared with other techniques. Finally, to complete the treatment, a sensitivity analysis is performed to examine the impact of the preferences given by decision-makers to choose the best location.

Recently, Cloud computing has known a fast growth in term of applications and the end users. In addition to the growth and evolution of the Cloud environment, many challenges that impact the performances of the Cloud applications emerged. One of these challenges is the Load Balancing between the virtual machines of a Datacenter, which is needed to balance the workload of each virtual machine while hoping to get a better Quality of services (QoS). Many approaches were proposed in hope of offering a good QoS. But due to the fact that the Cloud environment is exponentially evolving, these approaches became outdated. In this axis of research, we are proposing a new approach based on the Simulated Annealing and different parameters that affect the distribution of the tasks between the virtual machines. A simulation is also done to compare our approach with other existing algorithms using Cloudsim.

OLAP (Online Analytical Processing) system appears as a revolutionary technology that provides adequate analytic solutions for decision support. Using OLAP, analysts and policymakers are able to process and analyze data in an interactive, fast, and effective way according to several axes. This provides a clear vision of their business at any time and in real time. However, these systems suffer from certain limitations related to the consideration of both the multicriteria and fuzzy aspects of multidimensional data when making decisions. To overcome these limitations, we propose an integrated decision-making prototype based on OLAP system and multicriteria analysis (MCA) to generate a hybrid analysis process dealing with complex multicriteria decision-making situations. This proposed solution, based on our previous contributions, allows the analyst to extract data from OLAP data cube model and analyze them using OLAP operators and MCA methods.

Actually, a set of ETL software (Extract, Transform and Load) is available to constitute a major investment market. Each ETL uses its own techniques for extracting, transforming and loading data into data warehouse, which makes the task of evaluating ETL software very difficult. However, choosing the right software of ETL is critical to the success or failure of any Business Intelligence project. As there are many impacting factors in the selection of ETL software, the same process is considered as a complex multi-criteria decision making (MCDM) problem. In this study, an application of decision-making methodology that employs the two well-known MCDM techniques, namely Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods is designed. In this respect, the aim of using AHP is to analyze the structure of the ETL software selection problem and obtain weights of the selected criteria. Then, TOPSIS technique is used to calculate the alternatives’ ratings. An example is given to illustrate the proposed methodology. Finally, a software prototype for demonstrating both methods is implemented.