Affective computing for smart operations: a survey and comparative analysis of the available tools, libraries and web services

In this paper, we make a deep search of the available tools in the market, at the current state of the art of Sentiment Analysis. Our aim is to optimize the human response in Datacenter Operations, using a combination of research tools, that allow us to decrease human error in general operations, managing Complex Infrastructures. The use of Sentiment Analysis tools is the first step for extending our capabilities for optimizing the human interface. Using different data collections from a variety of data sources, our research provides a very interesting outcome. In our final testing, we have found that the three main commercial platforms (IBM Watson, Google Cloud and Microsoft Azure) get the same accuracy (89-90%). for the different datasets tested, based on Artificial Neural Network and Deep Learning techniques. The other stand-alone Applications or APIs, like Vader or MeaninCloud, get a similar accuracy level in some of the datasets, using a different approach, semantic Networks, such as Concepnet1, but the model can easily be optimized above 90% of accuracy, just adjusting some parameter of the semantic model. This paper points to future directions for optimizing DataCenter Operations Management and decreasing human error in complex environments

Emotion-Aware Cyber-Physical Systems

Los actuales gestores de infraestructuras complejas no son capaces de optimizar con éxito la resolución de los problemas causados por la excesiva cantidad de datos a gestionar, entre otras cosas, debido a la monotonía que ello produce en el personal de operación o la falta de priorización en las tareas a realizar ante una posible incidencia. Esto produce efectos negativos como caídas de los sistemas o cortes de suministro del servicio que proporcionan dichas infraestructuras a otras organizaciones o clientes. Esta tesis aborda este problema de la gestión óptima de infraestructuras complejas desde un enfoque nuevo, que considera estas infraestructuras como sistemas ciberfísicos con conciencia emocional. La introducción del uso de esos factores emocionales como una variable más de monitorización y gestión aporta una visión más avanzada, realista y completa de los entornos de producción. Para poder realizar estas tareas necesitamos apoyarnos en los últimos avances en cuanto a Inteligencia Artificial, creando nuevos métodos para la extracción y la toma de decisiones que nos ayuden a conseguir nuestros objetivos en un entorno con información heterogénea. Nos centraremos en un caso particular de estos sistemas debido a su especial relevancia: los grandes centros de datos. Este es un hecho muy relevante a nivel global, puesto que el coste medio de una caída de un centro de datos está aumentando desde $0.5M en 2010 a$0.75M en 2016, según el Ponemon Institute[63]. Además, la duración de las caídas de la mayoría de los negocios basados en el procesado de datos (Industria 4.0) está aumentando muy por encima de la media de otros negocios. Es un problema que requiere de acción inmediata, puesto que el 50% de los centros de datos no están preparados para los equipos de alta densidad actuales, y los ataques cibernéticos están aumentando espectacularmente (de ser la causa de un 0.2% de las caídas de centros de datos en 2010 a un 22% en 2016). Ya en la actualidad, en media, cada centro de datos tiene unas pérdidas anuales de más de 2M$por caídas totales o parciales. Además, los sistemas de gestión actuales son claramente insuficientes. Según el estudio citado[63], más de la mitad de las caídas de centros de datos podían haberse evitado y el 22$0.5M in 2010 to $0.75M in 2016, as suggested in the Ponemon institute study[63]. In addition, the duration of the outages in most businesses based on data processing (Industry 4.0) is increasing well above the average of other businesses. It is a problem that requires immediate action, since 50$2M due those total or partial outages. In addition, current management systems are clearly insufficient. According to the aforementioned study, more than half of data center crashes could have been avoided and 22% were due to human errors of operation and another 22 % more due to cyber attacks, a cause that also involves preventive tasks carried out by staff. The problem can be complicated by the introduction, or rather the proliferation and standardization of the so-called Big Data, the Internet of Things and Smart Cities, since they increase exponentially the data processing needs. (Smart Cities), as data processing needs increase exponentially. Throughout the text of this dissertation we will introduce these concepts and their crossed implications. Both attacks and human errors involve people, these people in turn make decisions in an environment of high complexity and with a high degree of uncertainty. The market of software manufacturers dedicated to the management of data centers, such as the DCIM (Data Center Infrastructure Management) is in full expansion, is expected to exceed USD 4.5 billion by 2026[84]. However, current solutions are focused on gathering the logs and the availability of tools to analyze such information. The information could be available, but decisions are left to the operations personnel. And many times, these decisions are complex and the information available is too broad and very low level to facilitate the decision making. Some notable examples are the decisions to: - Optimize the energy consumption of the data center. - Minimize the risks of attacks. - Optimize preventive maintenance. - Maximize the benefit, taking into account aspects of the business, resources and status. - Make the most of renewable energy generation capacities. The efficient and eéctive management of current data centers requires more powerful tools, aware that there are people making decisions, capable of automatically carrying out the necessary analysis, capable of oéring operations personnel all the information needed, and at the appropriate abstraction level and in the appropriate format, capable of automating decision making totally or partially. On the other hand, until now the diérent variables have been optimized independently. In fact, in many data centers, the management of the infrastructure and the servers is carried out by diérent companies and with scarce communication. However, the diérent data center variables are often related to each other, with complex modeling relationships that may change over time. Therefore, a holistic approach to resource management is essential, where other data center management systems not only aggregate information from all elements of the data center, but are also aware of: - What is happening in the data center, of the services demand, of the appropriated resources, of consumption, of thermal evolution, of possible attacks; - The circumstances of the context in the operations team, their state of mind, their objectives, the uncertainties that may occur. This is a problem that takes into account the computational infrastructure system but is managed by human operators who supervise and manage the operations following business guidelines. Throughout this thesis, some initiatives have been collected to limit and control the possible causes of problems, highlighting the following contributions: - The analysis of the diérent procedures of multimodal communication (text, audio or video) and of the variables involved directly or indirectly in a cyber-physical system where the emotions of the workforce are captured of in order to be able to use them as another source of information. - To study the trends, taking into account other disciplines of health sciences, such as Psychology, that may aéct decision-making and therefore productivity, directly or indirectly. - The construction of a prototype for the control of a cyber-physical system that involves data from infrastructure monitoring, and data from other sources such as emotional or biometric measures, from the personnel responsible for managing operations. These are some of the original contributions of this Ph.D. Thesis: - A model for the extraction of emotional information through biometric and audiovisual sensors in the operating environment of data centers and validation in a real environment. - The design and implementation of a method of extraction and analysis to associate the measured variables with values of excitation and valence of emotions in the operating environment of data centers and validation in real environment. - The development of an algorithm to determine a corrective action based on the current operating status and the predominant emotion of the operator in the data center operation environment and validation in real environment. - The analysis, design and implementation of a display module configured to provide corrective action to the operator in the operating environment of data centers and validation in real environment. - The development of an algorithm for predicting bias in decision making in the Cyber-Physical Systems Operations Center. - The development of a management system for operations personnel based on the combined use of environmental, telemetric, biometric, audiovisual and emotional information. The key contribution of this PhD thesis is to address the situation of managing complex infrastructures from an optimization approach of the Cyber-Physical System, where the behavior and management of machine and human are aligned and optimized using emotional awareness as a driver

Affective computing for smart operations: a survey and comparative analysis of the available tools, libraries and web services

In this paper, we make a deep search of the available tools in the market, at the current state of the art of Sentiment Analysis. Our aim is to optimize the human response in Datacenter Operations, using a combination of research tools, that allow us to decrease human error in general operations, managing Complex Infrastructures. The use of Sentiment Analysis tools is the first step for extending our capabilities for optimizing the human interface. Using different data collections from a variety of data sources, our research provides a very interesting outcome. In our final testing, we have found that the three main commercial platforms (IBM Watson, Google Cloud and Microsoft Azure) get the same accuracy (89-90%). for the different datasets tested, based on Artificial Neural Network and Deep Learning techniques. The other stand-alone Applications or APIs, like Vader or MeaninCloud, get a similar accuracy level in some of the datasets, using a different approach, semantic Networks, such as Concepnet1, but the model can easily be optimized above 90% of accuracy, just adjusting some parameter of the semantic model. This paper points to future directions for optimizing DataCenter Operations Management and decreasing human error in complex environments

Emotion-driven system for data center management

Complex Information Systems and infrastructures, like Smart Cities, must be efficiently operated, minimizing inefficiencies and maximizing productivity. Traditional approaches are focused on improving the systems, automating processes and services, leaving aside human and emotions considerations. To achieve this efficient operation, we attempted to cover both sides. We found new ways to capture the information coming from the workforce, in our case, the operations management team, and, merged this information with the data from the IoT sensors from the systems, enabling aholistic view of the entire operations occurring in real-time. In a Data Center environment, we havedeveloped a set of tools for capturing the emotional data in order to detect potential biases caused by the specific mood of the person inside the operations team. We used Artificial Intelligence algorithms for finding the patterns that will help us to manage the system in the future. We compared and verified our findings with the existing references from other disciplines, e.g., Psychology. In this article, we expose some methods to be developed in future studies for supervising and increasing productivity in Data Centers, as a useful example for Smart Cities. Our research focuses on monitoring the mood and the emotional status of the personnel responsible for operating the system. We use this emotional data as an input for measurement