Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures

Modeling metadata of CCTV systems and Indoor Location Sensors for automatic filtering of relevant video content

The following topics are dealt with: formal specification; social networking (online); Internet of Things; data analysis; business data processing; human factors; Internet; data mining; learning (artificial intelligence); and decision making

Weaving Cognition into the Internet-of-Things: Application to Water Leaks

Despite the growing interest in the Internet-of-Things, many organizations remain reluctant to integrating things into their business processes. Different reasons justify this reluctance including things’ limited capabilities to act upon the cyber-physical environment in which they operate. To address this specific limitation, this paper examines thing empowerment with cognitive capabilities that would make them for instance, selective of the forthcoming business processes in which they would participate. The selection is based on things’ restrictions like limitedness and goals to achieve like improved reputation. For demonstration and implementation purposes, water leaks are used as a case study. A BPEL-based business process driving the fixing of water leaks is implemented involving different cognitive-empowered things like moisture sensor

Cyber resilience and incident response in smart cities: A systematic literature review

© 2020 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/smartcities3030046The world is experiencing a rapid growth of smart cities accelerated by Industry 4.0, including the Internet of Things (IoT), and enhanced by the application of emerging innovative technologies which in turn create highly fragile and complex cyber–physical–natural ecosystems. This paper systematically identifies peer-reviewed literature and explicitly investigates empirical primary studies that address cyber resilience and digital forensic incident response (DFIR) aspects of cyber–physical systems (CPSs) in smart cities. Our findings show that CPSs addressing cyber resilience and support for modern DFIR are a recent paradigm. Most of the primary studies are focused on a subset of the incident response process, the “detection and analysis” phase whilst attempts to address other parts of the DFIR process remain limited. Further analysis shows that research focused on smart healthcare and smart citizen were addressed only by a small number of primary studies. Additionally, our findings identify a lack of available real CPS-generated datasets limiting the experiments to mostly testbed type environments or in some cases authors relied on simulation software. Therefore, contributing this systematic literature review (SLR), we used a search protocol providing an evidence-based summary of the key themes and main focus domains investigating cyber resilience and DFIR addressed by CPS frameworks and systems. This SLR also provides scientific evidence of the gaps in the literature for possible future directions for research within the CPS cybersecurity realm. In total, 600 papers were surveyed from which 52 primary studies were included and analysed.Published onlin

Improving data preparation for the application of process mining

Immersed in what is already known as the fourth industrial revolution, automation and data exchange are taking on a particularly relevant role in complex environments, such as industrial manufacturing environments or logistics. This digitisation and transition to the Industry 4.0 paradigm is causing experts to start analysing business processes from other perspectives. Consequently, where management and business intelligence used to dominate, process mining appears as a link, trying to build a bridge between both disciplines to unite and improve them. This new perspective on process analysis helps to improve strategic decision making and competitive capabilities. Process mining brings together data and process perspectives in a single discipline that covers the entire spectrum of process management. Through process mining, and based on observations of their actual operations, organisations can understand the state of their operations, detect deviations, and improve their performance based on what they observe. In this way, process mining is an ally, occupying a large part of current academic and industrial research. However, although this discipline is receiving more and more attention, it presents severe application problems when it is implemented in real environments. The variety of input data in terms of form, content, semantics, and levels of abstraction makes the execution of process mining tasks in industry an iterative, tedious, and manual process, requiring multidisciplinary experts with extensive knowledge of the domain, process management, and data processing. Currently, although there are numerous academic proposals, there are no industrial solutions capable of automating these tasks. For this reason, in this thesis by compendium we address the problem of improving business processes in complex environments thanks to the study of the state-of-the-art and a set of proposals that improve relevant aspects in the life cycle of processes, from the creation of logs, log preparation, process quality assessment, and improvement of business processes. Firstly, for this thesis, a systematic study of the literature was carried out in order to gain an in-depth knowledge of the state-of-the-art in this field, as well as the different challenges faced by this discipline. This in-depth analysis has allowed us to detect a number of challenges that have not been addressed or received insufficient attention, of which three have been selected and presented as the objectives of this thesis. The first challenge is related to the assessment of the quality of input data, known as event logs, since the requeriment of the application of techniques for improving the event log must be based on the level of quality of the initial data, which is why this thesis presents a methodology and a set of metrics that support the expert in selecting which technique to apply to the data according to the quality estimation at each moment, another challenge obtained as a result of our analysis of the literature. Likewise, the use of a set of metrics to evaluate the quality of the resulting process models is also proposed, with the aim of assessing whether improvement in the quality of the input data has a direct impact on the final results. The second challenge identified is the need to improve the input data used in the analysis of business processes. As in any data-driven discipline, the quality of the results strongly depends on the quality of the input data, so the second challenge to be addressed is the improvement of the preparation of event logs. The contribution in this area is the application of natural language processing techniques to relabel activities from textual descriptions of process activities, as well as the application of clustering techniques to help simplify the results, generating more understandable models from a human point of view. Finally, the third challenge detected is related to the process optimisation, so we contribute with an approach for the optimisation of resources associated with business processes, which, through the inclusion of decision-making in the creation of flexible processes, enables significant cost reductions. Furthermore, all the proposals made in this thesis are validated and designed in collaboration with experts from different fields of industry and have been evaluated through real case studies in public and private projects in collaboration with the aeronautical industry and the logistics sector

Técnicas para realizar a validação de requisitos no contexto de internet das coisas (IoT)

Trabalho de conclusão de curso (graduação) — Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2021.A internet das coisas vem ocupando um espaço cada vez maior em equipes de desenvolvi mento de software e na sociedade. O nível de aplicação da IoT é abrangente. Tráfego de pessoas, casas inteligentes, ambientes otimizados e gestão de água/energia são alguns dos exemplos da sua aplicabilidade. Nesse universo de possibilidades, desenvolvedores e empresas de tecnologia devem estar preparados para adaptar seus projetos e absorver essa tecnologia em expansão. Como essa tecnologia é recente, falhas de projeto e retrabalho acontecem com frequência e dificultam o desenvolvimento de produtos de alta qualidade atualmente. O objetivo deste trabalho é identificar por meio de uma pesquisa explo ratória, processos e técnicas de validação, voltadas ao contexto da internet das coisas. Além disso, investigamos a percepção dos desenvolvedores de software IoT sobre as suas atividades relacionadas a Engenharia de Requisitos em seus projetos. A percepção dos profissionais foi coletada através de entrevistas onde eles relataram as dificuldades e de safios que enfrentam durante suas atividades diárias. Foram encontrados 22 processos e 9 técnicas de validação para o contexto de IoT na literatura. A partir das entrevistas, foi possível perceber que stakeholders de projetos IoT não utilizam um processo formal de engenharia de requisitos. Normalmente, são utilizadas técnicas distintas como reuniões e diagramas, sempre com base na demanda e na necessidade do projeto. Apesar dos profissionais e stakeholders acharem importante a Engenharia de Requisitos, a adesão à processos e técnicas voltadas a IoT não é unânime devido a curva de aprendizado para adotar novos métodos e a falta de maleabilidade nos processos durante o desenvolvimento de software.Internet of things occupies more and more space in development teams and in society in general. The applicability that IoT covers is huge. Smart houses, water/energy consup tion, traffic management and smart buildings are some examples of what has been made in this context. In this vast universe of possibilities, developers and tech companies need to be prepared and adapt their projects to cover it. With that in mind, failures/reworks in projects happens more easily and makes it more difficult to produce high standards products. The objective of this paper is to identify, based on a exploratory research, processes and validation techniques in IoT context. Furthermore, this work investigates the professionals‘ perception in their activities with requirenment engineering in IoT projects. Their reports were collected through interviews so they could explain the difficulties and problems that arise in their daily work. In total, 22 processes and 9 validation techniques has been found in literature. From the interviews, it had been realized that stakeholders don´t use formal processes in their IoT projects. Usually, single techniques are used, like reunions and diagramans, to handle the requirements engineering.The stakeholders implement these methods based on the demand and size of the project. Although stakeholders thinks that RE is a important part inside a project, the use of processes and techniques for IoT development isn´t unanimous due to the learning curve to adopt such methods and the lack of flexibility in these processes during the development phase

Processos da engenharia de requisitos no contexto de internet das coisas (IoT) e técnicas de validação de requisitos

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2021.A Internet das Coisas possibilitou um engrandecimento nas possibilidades de automação e de facilitação do cotidiano das pessoas. Desde automação residencial até a edifícios inteligentes, o aumento da popularidade da IoT traz um desafio para o desenvolvimento de software e a engenharia de requisitos. Desenvolvedores e empresas não estão familiarizados com os processos e técnicas de validação de requisitos existentes no contexto de sistema IoT. Por conta disso, possíveis falhas de projeto e retrabalhos durante o desenvolvimento de software são problemas a serem considerados pelas equipes de desenvolvimento. O objetivo desse artigo é investigar na literatura os processos de engenharia de requisitos no contexto de IoT e as técnicas de validação de requisitos utilizadas. Além disso, apresentar um guia para apoiar as equipes de desenvolvimento de software a ter acesso fácil aos processos e técnicas propostas na literatura para este contexto. Nós realizamos um survey com os practitioners da indústria para investigar se eles usam e conhecem os processos e técnicas identificadas na literatura. Nossos achados revelam que a técnica mais utilizada pelos practitioners para realizar a especificação de requisitos são as reuniões com as partes interessadas e brainstorming e para validar requisitos são utilizados os protótipos e casos de uso.The Internet of Things made possible an increase in the possibilities of automation and facilitation of people’s daily lives. From home automation to smart buildings, the rise in IoT’s popularity brings a challenge to software development and requirements engineering. Developers and companies are not familiar with the requirements validation processes and techniques that exist in the context of an IoT system. Therefore, possible project failures and rework during software development are issues to be considered by development teams. The aim of this article is to investigate the requirements engineering processes in the IoT context and the requirements validation techniques used in the literature. Also, present a guide to support software development teams to have easy access to the processes and techniques proposed in the literature for this context. We conducted a survey of industry practitioners to investigate whether they use and know the processes and techniques identified in the literature. Our findings reveal that the technique most used by practitioners to perform requirements specification are stakeholders meeting and brainstorming and to validate requirements are prototypes and use cases

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

The evaluation of ontologies: quality, reuse and social factors

Finding a “good” or the “right” ontology is a growing challenge in the ontology domain, where one of the main aims is to share and reuse existing semantics and knowledge. Before reusing an ontology, knowledge engineers not only have to find a set of appropriate ontologies for their search query, but they should also be able to evaluate those ontologies according to different internal and external criteria. Therefore, ontology evaluation is at the heart of ontology selection and has received a considerable amount of attention in the literature.Despite the importance of ontology evaluation and selection and the widespread research on these topics, there are still many unanswered questions and challenges when it comes to evaluating and selecting ontologies for reuse. Most of the evaluation metrics and frameworks in the literature are mainly based on a limited set of internal characteristics, e.g., content and structure of ontologies and ignore how they are used and evaluated by communities. This thesis aimed to investigate the notion of quality and reusability in the ontology domain and to explore and identify the set of metrics that can affect the process of ontology evaluation and selection for reuse. [Continues.