Big Data is changing the way businesses compete and operate. However, the Big Data revolution is happening differently in the mining sector . The idea of creating value from predictive analytics is not new, however, the effective use of data is becoming the basis of competition . The massive scale, growth and variety of mining engineering data are simply too much and too expensive for traditional methodologies to handle. For this reason, it is proposed here the application of an innovative Business Intelligence software platform known as Tableau  in order to open a new range of possibilities for the mining sector to get maximum value from the data. To achieve this, it will be developed a practical example about maintenance optimization for heavy construction equipment using Tableau and their multiple functionalities. One of these solutions consists on the design of a maintenance business dashboard for hydraulic excavators and mining trucks that combines high performance and ease of use to let even non-technical users to explore and drill down into specific information. This approach centralizes access to heavy equipment data in the cloud at the same time that harnesses people’s natural ability to spot visual patterns building powerful interative calculations and forecasts with data mining, trend analysis and correlations for tried and true statistical understanding. The results are expected to bring engineers and technical staff a new opportunity to publish data models and make it easy for everyone to work from a curated, single source of truth. It combines advances in database and computer graphics technology without writing code so the mining sector can pivot, split and manage metadata to optimize technical problems in real time better than ever before.
The aim of this paper is to study regional gradient observability for a hyperbolic system in the case where the subregion of interest is a part of the boundary, and the reconstruction of the state gradient without the knowledge of the state. First, we give definitions and characterizations of these new concepts and establish necessary conditions for the sensor structure in order to obtain regional boundary gradient observability. The developed approach, based on the Hilbert uniqueness method , leads to a reconstruction algorithm. The obtained results are illustrated with numerical examples and simulations.
Cloud Computing is the most agressively growing computing model in the last decade due to convenience, flexibility, agility and methods of transforming enterprises operational reach. Cloud computing makes IT based scalable resource provisioning (i.e. compute, network, storage, memory, etc.) flexible and cost convenient. Cloud computing offers different architectures (i.e. public, private, hybrid and community) and services (IaaS, PaaS, SaaS) which need to be closely assessed by enterprises to align and understand their business model with the cloud architecture. Migrating inhouse IT based applications and services into the cloud may lead the enterprise susceptible to various risks such as: Governance, Compliance, Risk management, Data Control, applications performance, compatibility, failover, disaster recovery, etc. This is where Cloud Economics aims at merging the cloud and enterprises business model by analysing the internal and external variables affecting the cloud operations, management, cost, agility and growth. It also helps in merging the business strategy with the cloud model focusing on the critical success factors (CSFs) and key performance indicators (KPIs). These quantitative benchmarked metrics (KPIs) evaluate the threshold of further improvement, average or perfect state. Cloud Service Level Agreement is the only method to control the outsources IT based services and its Quality of Service. The SLA may describe the service performances at different level (i.e. compliance, insights, visibility, control, etc.). Integrating these enterprsies KPIs into the SLA may benefit the enterprise in being proactive, improved QoS and overcome the common cloud vendor lock-in issues and additional cloud based costs.
Lubna Luxmi Dhirani is a PhD student in the Department of Electronic and Computer Engineering at University of Limerick, Ireland. Her PhD research project is based on designing a System for Securing the Hybrid Cloud in a tenant-vendor-third party situation. She currently holds 4 publications supporting her PhD research. She has done MSc in Business Information Technology (2008) from United Kingdom and B.Eng in Computer Systems (2006) frim Pakistan. Lubna has worked as a lecturer for 3 years and taught various IT-based courses at SZABIST – Dubai, UAE Campus and ISRA University, Pakistan.
Smart Home has become a real topical issue deserving more research and work. It is a kind of evolution that will change the habit of housing in the next years. It can provide a kind of easier and effective life style to people. The smart home/smart phone system is built by using technologies to control the electric devices and sensors (as temperature and PIR motion sensors). This paper aims to provide a model of Smart Home/ Smart phone system which is a low costeffective and flexible home monitoring. The proposed system may allow home functions to be controlled remotely from anywhere in the world using an Android app downloaded for a smartphone.
Khaoula Karimi received the Engineer Degree in Software engineering from Faculty of Sciences and Technologies, Settat, Morocco, in 2015. She is currently a PhD student in Polydisciplinary Faculty of Ouarzazate, Department Mathematics and Informatics and Management, Ibn Zohr University Agadir, Morocco. Her research interests design and implementation of Smarthome/Smartphone systems.
The management of vehicle flows in large cities is an important issue, which gives rise to many studies. The new information and communication technologies have been a major development providing solutions to the various problems related to traffic management providing solutions to the various problems related to traffic management. This article describes a recent and efficient technology based on wireless sensor networks. These latter, Collect and transmit data autonomously to the traffic management system (Traffic Light Controller) in an infrastructure (intersection or roundabout). The management techniques deployed in smart cities aim to minimize traffic congestion and consequently the environmental costs of road traffic. Our solution shows the value of this intelligent technology in detecting the presence of a vehicle at a given point in the infrastructure and also to communicate the traffic status (dense or fluid) and obtaining the optimum traffic light sequence and minimizing the waiting time of vehicles in order to offer the longest possible crossing time.
Mustapha Kabrane received his the first Master’s degree in Electronics, Automatics and Computer, from the Faculty of Sciences, University of Perpignan Via Domitia , France, in 2012, and his the second Master’s degree in Computer Sciences from Institute of Sciences and Technology, University of Valenciennes, France, In 2013. He is currently a PhD student. His research interests include wireless sensor Networks implemented in the management and control of urban traffic at the Polydisciplinary Faculty of Ouarzazate, Ibn Zohr university, Agadir, Morroco