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Predicting stock market price is considered as a challenging task of financial time series analysis, which is of great interest to stock investors, stock traders and applied researchers. Many machine learning techniques have been used in this area to predict the stock market price, including regression algorithms which can be useful tools to provide good performance of financial time series prediction...
The decision making process is mainly data driven, which makes Data Warehousing play a crucial role in providing a reliable and efficient tool to meet analytical needs and provide end users and decision makers with useful insights. However, several surveys show that a significant percentage of Data Warehouse (DW) projects fail to meet their business goals. Indeed, it is mainly due to the lack of any...
Decision Tree is one of the most popular supervised Machine Learning algorithms; it is also the easiest to understand. But finding an optimal decision tree for a given data is a harder task and the use of multiple performance metrics adds some complexity to the problem of selecting the most appropriate DT.
to facilitate the composition of UML models in the context of model driven architecture, the development of an automatic composition operator is necessary. In order to automatically apply the composition models, we propose the application of a prototype oriented model composition on the-two hemisphere model driven approach. In this paper, we define a process for adapting this prototype to manage different...
The step of mining frequent itemsets in database is the essential step and most expensive in the process of mining association rules in data mining task, many algorithms of mining frequent itemsets have been proposed to improve the performance of Apriori Algorithm. In this paper, we have introduced an optimization in the phase of generation pruning of candidates by a new strategy for the calculation...
Predicting stock prices is an important task of financial time series forecasting, which is of great interest to stock investors, stock traders and applied researchers. Many machine learning techniques have been used in recent times to predict the stock price, including regression algorithms which can be useful tools to provide good accuracy of financial time series forecasting. In this paper, we...
Several studies conducted in Morocco and other developing countries have shown that Small and Medium Enterprises (SMEs) fail to achieve business goals for reasons related principally to lack of entrepreneurship knowledge and poor financial management. Indeed, most managers do not have the required educational background and skills, precisely in accounting and cash flow management. To manage these...
We have developed and deployed an e-Government system named E-FES that facilitates citizens' and employees' access to local government information and services in a local registry office (Bureau d'état Civil “BEC”). E-FES system is running since 2006 in different municipalities in both rural and urban areas. Accordingly, we have been able to use a considerable amount of data which enabled us to design...
Stock market prediction is regarded as a challenging task of financial time-series prediction. There have been many studies using machine learning techniques in this area. A large number of successful applications have shown that regression algorithms can be very useful tools for time-series modelling and forecasting. In this paper we run a comparative study of three of these algorithms: Multiple...
The information systems and ERP adopted by small and medium companies are often viewed as key differentiators for their survival in today's competitive and challenging market. However, the integration of information technologies by firms in countries at the development stage is confronted with many barriers that are inherent to their specific cultural, educational and socio-economic environment. Our...
Facing complex systems, i∗ needs mechanism of managing complexity and granularity. This paper proposes a holonic architecture to address this weakness of i∗. Our solution is based on the business unit, business services and business goal as basic granules that encapsulates the intentional units, intentions, goals of the organization. The architecture is based on three conceptual levels which are the...
The multidimensional conceptual model is an important artifact that fixes the role of the future data warehouse. Its design quality is indicated by the conformity rate with the requirements expressed by the decision makers and the end-users. Existing proposals for conceptual multidimensional modeling do not systematically take into consideration the end-users queries neither at the formal level nor...
Data warehouse (DW) architectures, which are at the core of many new generation information systems, are designed to provide decision makers and professional analysts with intuitive and performing means to explore and get intelligent data from large heterogeneous databases. However, the data quality is not the only concern; requirements related for example to security, performance and operability...
The 2012 IEEE Colloquium in Information Science and Technology (CIST) is part of the IEEE CONFERENCE SERIES that are held in Fez, Morocco, and is sponsored by the IEEE Morocco Section and the IEEE Morocco Computer & Communication Joint Chapter, and the USMBA IEEE Student Branch. The 2012 edition was co-organized by the Faculty of Sciences Dhar Mahraz, the Faculty of Technical Sciences of Fez and...
Telecommunication companies have developed very large, heterogeneous and complex technical infrastructures to support the variety of services they offer like landline calls, mobile calls, adsl, and wimax. Efficient monitoring and maintenance of operational stability of these infrastructures are critical for delivering high quality services to the end users. The management of the data generated by...
Extracting structured information from pages published on the worldwide web is a problem with many facets that has gained growing interest in recent years. We propose a novel ontology-based approach that can achieve both the extraction and the semantic description of data contained in a web page. Existing methods addressing these issues range from pure manual methods based on rules to systems that...
State of the art organizations produce models of their business processes to ensure continuous improvement of their activities which help to achieve high revenue performance. They also use these models to build up quality and preserve knowledge. Business processes are determined according to the organization's function and they consist of a completely closed, timely and logical sequence of structured...
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