Dataware House

Les systèmes de transport intelligents (STI) (en anglais Intelligent
Transportation Systems (ITS)) désignent les applications des
nouvelles technologies de l'information et de la communication au domaine des transports. On les dit "Intelligents" parce que leur développement repose sur des fonctions généralement associées à l'intelligence : capacités sensorielles, mémoire, communication, traitement de l'information et comportement adaptatif. On trouve les STI dans plusieurs champs d'activité :
dans l'optimisation de l'utilisation des infrastructures de transport, dans l'amélioration de la sécurité (notamment de la sécurité routière) et de la sûreté ainsi que dans le développement des services. L'utilisation des STI s'intègre aussi dans un contexte de développement durable : ces nouveaux systèmes concourent à la maîtrise de la mobilité en favorisant entre autres le report de la voiture vers des modes plus respectueux de l'environnement. Ils
font l'objet d'une compétition économique serrée au niveau mondial.[3] Plus particulièrement, il s'agit de l'ensemble des systèmes permettant de collecter, stocker, traiter et distribuer de l'information relative au mouvement des personnes et des marchandises et notamment des systèmes d'information
voyageur, de paiements électroniques, de gestion de fret, de gestion de flottes de transport collectif, d'aide à la gestion du trafic, d'aide à la conduite. Les systèmes de transport intelligent (STI) regroupent donc l'ensemble des applications et équipements permettant aux gestionnaires de mieux gérer et aux usagers (particuliers et professionnels) de mieux utiliser les infrastructures et réseaux de transport terrestres de fret et de personnes, y compris dans leurs interfaces avec les modes non terrestres. [1] Ce papier présente la conception et la réalisation d’un système d’information géographique interactif de transport intelligent pour la réservation express prenant en compte le profil du voyageur.

The goal of the book is to present the latest research on the new challenges of data technologies. It will offer an overview of the social, ethical and legal problems posed by group profiling, big data and predictive analysis and of the different approaches and methods that can be used to address them. In doing so, it will help the reader to gain a better grasp of the ethical and legal conundrums posed by group profiling. The volume first maps the current and emerging uses of new data technologies and clarifies the promises and dangers of group profiling in real life situations. It then balances this with an analysis of how far the current legal paradigm grants group rights to privacy and data protection, and discusses possible routes to addressing these problems. Finally, an afterword gathers the conclusions reached by the different authors and discuss future perspectives on regulating new data technologies.

Resumen. El presente trabajo tiene como fin mostrar las ventajas que tiene aplicar herramientas de Inteligencia de Negocios en instituciones chilenas, tomando como caso en particular La Universidad de La Serena, la cual pretende desarrollar un sistema informático de Inteligencia Empresarial, mediante la utilización de herramientas técnicas y tecnologías de Business Intelligence (BI) de última generación, a través de su Centro de Informática. De esta forma se proveerán herramientas avanzadas de análisis y correlación multidimensional de datos tales como cubos OLAP, mejorando ostensiblemente la provisión de los datos de información a la alta administración de la Institución, apoyados fuertemente en tecnología, obteniendo así una segunda versión más avanzada y moderna al actual Sistema de Apoyo a la Gestión Institucional (SAGI).

These last years we have been witnessing a tremendous growth in the volume and availability of data. This fact results primarily from the emergence of a multitude of sources (e.g. computers, mobile devices, sensors or social networks) that are continuously producing either structured, semi-structured or unstructured data. Database Management Systems and Data Warehouses are no longer the only technologies used to store and analyze datasets, namely due to the volume and complex structure of nowadays data that degrade their performance and scalability. Big Data is one of the recent challenges, since it implies new requirements in terms of data storage, processing and visualization. Despite that, analyzing properly Big Data can constitute great advantages because it allows discovering patterns and correlations in datasets. Users can use this processed information to gain deeper insights and to get business advantages. Thus, data modeling and data analytics are evolved in a way that we are able to process huge amounts of data without compromising performance and availability, but instead by “relaxing” the usual ACID properties. This paper provides a broad view and discussion of the current state of this subject with a particular focus on data modeling and data analytics, describing and clarifying the main differences between the three main approaches in what concerns these aspects, namely: operational databases, decision support databases and Big Data technologies.

Forged documents specifically passport, driving licence and VISA stickers are used for fraud purposes including robbery, theft and many more. So detecting forged characters from documents is a significantly important and challenging task in digital forensic imaging. Forged characters detection has two big challenges. First challenge is, data for forged characters detection is extremely difficult to get due to several reasons including limited access of data, unlabeled data or work is done on private data. Second challenge is, deep learning (DL) algorithms require labeled data, which poses a further challenge as getting labeled is tedious, time-consuming, expensive and requires domain expertise. To end these issues, in this paper we propose a novel algorithm, which generates the three datasets namely forged characters detection for passport (FCD-P), forged characters detection for driving licence (FCD-D) and forged characters detection for VISA stickers (FCD-V). To the best of our knowledge, we are the first to release these datasets. The proposed algorithm starts by reading plain document images, simulates forging simulation tasks on five different countries' passports, driving licences and VISA stickers. Then it keeps the bounding boxes as a track of the forged characters as a labeling process. Furthermore, considering the real world scenario, we performed the selected data augmentation accordingly. Regarding the stats of datasets, each dataset consists of 15000 images having size of 950 x 550 of each. For further research purpose we release our algorithm code 1 and, datasets i.

Kitchen Gardening Project is the revolutionary step to increase vegetables production as well as provision of cheap vegetables to the consumers. The main focus of the study was to assess the impact of kitchen gardening training given by Water Resources Research Institute (WRRI) under watershed project in Arokas and Ghoragali. Capacity building of rural women in Kitchen Gardening was the focus and twenty trainees of kitchen gardening were selected randomly from each location to assess the impact of their livelihood. The study finding reveals that Kitchen gardening had increase environmental beauty and income of the growers to some extent in the targeted area. It was recommended that longer-term interventions required to support livelihoods with links to the market and to make strategies with communities to improve access to products and services.

Data warehouses (DW) play a decisive role in providing analytical information for decision making. Multidimensional modeling is a special approach to modeling data, considered the foundation for building data warehouses. With the explosive growth in the amount of heterogeneous data (most of which external to the organization) in the latest years, the DW has been impacted by the need to interoperate and deal with the complexity of this new type of information, such as big data, data lakes and cognitive computing platforms, becoming evident the need to improve the semantic expressiveness of the DW. Research has shown that ontological theories can play a fundamental role in improving the quality of conceptual models, reinforcing their potential to support semantic interoperability in its various manifestations. In this paper we propose the application of ontological patterns, grounded in the Unified Foundational Ontology (UFO), for conceptual modeling in multidimen-sional models, in order to improve the semantic expressiveness of the models used to represent analytical data in a DW.

The house fly Musca domestica L. (Diptera: Muscidae) and stable fly Stomoxys calcitrans (L.) (Diptera: Muscidae) are major pests of livestock. Biological control is an important tool in an integrated control framework. Increased mortality in filth flies has been documented with entomopathogenic fungi, several strains of which are commercially available. Three strains of Beauveria bassiana (Balsamo-Crivelli) Vuillemin (Hypocreales: Cordycipitaceae) and one strain of Metarhizium brunneum (Petch) (Hypocreales: Clavicipitaceae) were tested in commercial formulations for pathogenicity against house flies and stable flies. There was a significant increase in mortality of house flies with three of the formulations, BotaniGard ® ES, Mycotrol ® O, and Met52 ® EC, during days 4–9 in comparison with balEnce™ and the control. In stable flies, mortality rates were highest with Met52 ® EC, followed by Mycotrol ® O, BotaniGard ® ES and, finally, balEnce™. There was a significant fungal effect on sporulation in both house flies and stable flies. Product formulation, species differences and fungal strains may be responsible for some of the differences observed. Future testing in field situations is necessary. These commercial biopesticides may represent important tools in integrated fly management programmes.

The data warehouse (DW) is a modern proven technique of handling and managing diversity in data sources, format and structure. Recent advances in database technologies are leading to the proliferation of different kinds of information design with independent supporting hardware and software. This technology was developed therefore to integrate heterogeneous information sources for analysis purposes. Information sources are progressively becoming autonomous and they change their content rapidly due to perpetual transactions (data changes) and may change their structure due to continual users' requirements evolving (schema changes). Handling correctly all type of these changes however is a real challenge. Thus, strategic decision making is an ongoing process and part of human life. Businesses all over the world are faced with challenging decision making which is the very livelihood to their very existence. There has been intensive research in the field of databases including the grandfather of data warehousing Inmon which they offered tremendous insight into how the power of computing can be harnessed in strategic decision making. There achievement in the petroleum industry has been highlighted and written about by Nimmagadda and his fellow professionals. However how this can be implemented in Africa is remain to be solved. In this paper we investigated the various data capturing systems which they currently in use at the Ghana National Petroleum Cooperation (GNPC) -a state agency responsible for the overall management and supervision of the hydrocarbon endowment of the sub-region and recommending a tree tier level petroleum data warehouse architecture for cutting-edge decision making on petroleum resources.