Constructing a Global and Integral Model of Business Management Using a CBR System

Knowledge has become the most strategic resource in the new business environment. A case-based reasoning system, which incorporates a novel clustering and retrieval method, has been developed for identifying critical situations in business processes. The proposed method is based on a Cooperative Maximum Likelihood Hebbian Learning model, which can be used to categorize the necessities for the Acquisition, Transfer and Updating of Knowledge of the different departments of a firm. This technique is used as a tool to develop a part of a Global and Integral Model of business Management, which brings about a global improvement in the firm, adding value, flexibility and competitiveness. From this perspective, the model tries to generalise the hypothesis of organizational survival and competitiveness, so that the organisation that is able to identify, strengthen, and use key knowledge will reach a pole position

Smart Buildings

This talk presents an efficient cyberphysical platform for the smart management of smart buildings http://www.deepint.net. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart building is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study at Salamanca - Ecocasa. This platform could enable smart building to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Smart territories

The concept of smart cities is relatively new in research. Thanks to the colossal advances in Artificial Intelligence that took place over the last decade we are able to do all that that we once thought impossible; we build cities driven by information and technologies. In this keynote, we are going to look at the success stories of smart city-related projects and analyse the factors that led them to success. The development of interactive, reliable and secure systems, both connectionist and symbolic, is often a time-consuming process in which numerous experts are involved. However, intuitive and automated tools like “Deep Intelligence” developed by DCSc and BISITE, facilitate this process. Furthermore, in this talk we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems, as well as the use of edge platforms or fog computing

Methodologies for innovation and best practices in Industry 4.0 for SMEs

Today, cyber physical systems are transforming the way in which industries operate, we call this Industry 4.0 or the fourth industrial revolution. Industry 4.0 involves the use of technologies such as Cloud Computing, Edge Computing, Internet of Things, Robotics and most of all Big Data. Big Data are the very basis of the Industry 4.0 paradigm, because they can provide crucial information on all the processes that take place within manufacturing (which helps optimize processes and prevent downtime), as well as provide information about the employees (performance, individual needs, safety in the workplace) as well as clients/customers (their needs and wants, trends, opinions) which helps businesses become competitive and expand on the international market. Current processing capabilities thanks to technologies such as Internet of Things, Cloud Computing and Edge Computing, mean that data can be processed much faster and with greater security. The implementation of Artificial Intelligence techniques, such as Machine Learning, can enable technologies, can help machines take certain decisions autonomously, or help humans make decisions much faster. Furthermore, data can be used to feed predictive models which can help businesses and manufacturers anticipate future changes and needs, address problems before they cause tangible harm

Building Efficient Smart Cities

Current technological developments offer promising solutions to the challenges faced by cities such as crowding, pollution, housing, the search for greater comfort, better healthcare, optimized mobility and other urban services that must be adapted to the fast-paced life of the citizens. Cities that deploy technology to optimize their processes and infrastructure fit under the concept of a smart city. An increasing number of cities strive towards becoming smart and some are even already being recognized as such, including Singapore, London and Barcelona. Our society has an ever-greater reliance on technology for its sustenance. This will continue into the future, as technology is rapidly penetrating all facets of human life, from daily activities to the workplace and industries. A myriad of data is generated from all these digitized processes, which can be used to further enhance all smart services, increasing their adaptability, precision and efficiency. However, dealing with large amounts of data coming from different types of sources is a complex process; this impedes many cities from taking full advantage of data, or even worse, a lack of control over the data sources may lead to serious security issues, leaving cities vulnerable to cybercrime. Given that smart city infrastructure is largely digitized, a cyberattack would have fatal consequences on the city’s operation, leading to economic loss, citizen distrust and shut down of essential city services and networks. This is a threat to the efficiency smart cities strive for

Efficient Digital Management in Smart Cities

The concept of smart cities puts the citizen at the center of all processes. It is the citizen who decides what kind of city they live in. Their opinions and attitudes towards technologies and the solutions they would like to see in their cities must be listened to. With Deep Intelligence, cities will be able to create more optimal citizen-centered services as, as the tool can collect data from multiple sources, such as databases and social networks, from which valuable information on citizens’ opinions and attitudes regarding technology, smart city services and urban problems, may be extracted

IoT and Blockchain for Smart Cities

Blockchain is a Distributed Ledger Technology (DLT) that makes it possible to secure any type of transaction. This is because the information stored on the Blockchain is immutable, impeding any type of fraud or modification of the data. It was first created for Bitcoin transactions; however, the research community has realized its potential quickly, and started using it for purposes other than cryptocurrency transactions. Blockchain may even be used to secure and provide reliability to the data being transmitted between computational systems, ensuring their immutability. Given the amount of data produced within a smart city, the use of Blockchain is imperative in smart cities, as it protects them from cyberattacks and fraud. Moreover, the transparency of the information stored on Blockchain means that it helps create a more just and democratic society

Artificial Intelligence, social changes and impact on the world of education

The way in which humans acquire and share knowledge has been under constant evolution throughout times. Since the appearance of the first computers, education has changed dramatically. Now, as disruptive technologies are in full development, new opportunities arise for taking education to levels that have never been seen before. Ever since the coronavirus pandemic, the use of online teaching modalities has become widespread all over the world and the situation has caused the development of robust digital learning solutions an urgent need. At present, primary, secondary, third-level teaching and all sorts of courses may be delivered online, either in real-time or recorded for later viewing. Classes can be complemented with videos, documents or even interactive exercises. However, the institutions that used little or no technology prior to Covid-19 have found this situation overwhelming. The lack of knowledge regarding the digital teaching/learning tools available on the market and/or lack of knowledge regarding their use, means that educational institutions will not be able to take full advantage of the opportunities offered; poor use of technology in online classrooms may hinder the students’ progress

AIoT for Achieving Sustainable Development Goals

Artificial Intelligence of Things (AIoT) is a relatively new concept that involves the merging of Artificial Intelligence (AI) with the Internet of Things (IoT). It has emerged from the realization that Internet of Things networks could be further enhanced if they were also provided with Artificial Intelligence, enhancing the extraction of data and network operation. Prior to AIoT, the Internet of Things would consist of networks of sensors embedded in a physical environment, that collected data and sent them to a remote server. Upon reaching the server, a data analysis would be carried out which normally involved the application of a series of Artificial Intelligence techniques by experts. However, as Internet of Things networks expand in smart cities, this workflow makes optimal operation unfeasible. This is because the data that is captured by IoT is increasing in size continually. Sending such amounts of data to a remote server becomes costly, time-consuming and resource inefficient. Moreover, dependence on a central server means that a server failure, which would be imminent if overloaded with data, would lead to a halt in the operation of the smart service for which the IoT network had been deployed. Thus, decentralizing the operation becomes a crucial element of AIoT. This is done through the Edge Computing paradigm which takes the processing of data to the edge of the network. Artificial Intelligence is found at the edge of the network so that the data may be processed, filtered and analyzed there. It is even possible to equip the edge of the network with the ability to make decisions through the implementation of AI techniques such as Machine Learning. The speed of decision making at the edge of the network means that many social, environmental, industrial and administrative processes may be optimized, as crucial decisions may be taken faster. Deep Intelligence is a tool that employs disruptive Artificial Intelligence techniques for data analysis i.e., classification, clustering, forecasting, optimization, visualization. Its strength lies in its ability to extract data from virtually any source type. This is a very important feature given the heterogeneity of the data being produced in the world today. Another very important characteristic is its intuitiveness and ability to operate almost autonomously. The user is guided through the process which means that anyone can use it without any knowledge of the technical, technological and mathematical aspects of the processes performed by the platform. This means that the Deepint.net platform integrates functionalities that would normally take years to implement in any sector individually and that would normally require a group of experts in data analysis and related technologies [1-322]. The Deep Intelligence platform can be used to easily operate Edge Computing architectures and IoT networks. The joint characteristics of a well-designed Edge Computing platform (that is, one which brings computing resources to the edge of the network) and of the advanced Deepint.net platform deployed in a cloud environment, mean that high speed, real-time response, effective troubleshooting and management, as well as precise forecasting can be achieved. Moreover, the low cost of the solution, in combination with the availability of low-cost sensors, devices, Edge Computing hardware, means that deployment becomes a possibility for developing countries, where such solutions are needed most