A survey on pre-processing techniques: relevant issues in the context of environmental data mining

One of the important issues related with all types of data analysis, either statistical data analysis, machine learning, data mining, data science or whatever form of data-driven modeling, is data quality. The more complex the reality to be analyzed is, the higher the risk of getting low quality data. Unfortunately real data often contain noise, uncertainty, errors, redundancies or even irrelevant information. Useless models will be obtained when built over incorrect or incomplete data. As a consequence, the quality of decisions made over these models, also depends on data quality. This is why pre-processing is one of the most critical steps of data analysis in any of its forms. However, pre-processing has not been properly systematized yet, and little research is focused on this. In this paper a survey on most popular pre-processing steps required in environmental data analysis is presented, together with a proposal to systematize it. Rather than providing technical details on specific pre-processing techniques, the paper focus on providing general ideas to a non-expert user, who, after reading them, can decide which one is the more suitable technique required to solve his/her problem.Peer ReviewedPostprint (author's final draft

DECISION MAKING SUPPORT THROUGH A KNOWLEDGE MANAGEMENT FRAMEWORK FOR COMPLEX IT SYSTEMS DEVELOPMENT PROJECTS IN THE KINGDOM OF SAUDI ARABIA

Recent research reveals a narrow, rational model of problem- solving and decision-making in complex IT systems development projects. This creates problems that are identified in the thesis. The aim of this study is to develop a novel decision-making framework to support the decision-making process of managers of complex IT systems development projects by focusing on knowledge management frameworks. The objectives for the research were determined through a critical review of the existing research on decision-making in IT projects, primarily to discover how project managers’ decision-making can be supported through project-specific knowledge management. A qualitative research approach was then designed to investigate the phenomenon in its context by conducting in-depth semi-structured interviews. This study used qualitative data, through expert participants’ observations and opinions on IT systems development, particularly by understanding project management issues. The expert participants expressed their experiences through in-depth interviews. The collected data was then analysed using the thematic analysis technique and the findings were used to develop the IT Systems Development Decision-Making Support Framework. The Framework was then validated through focus group interviews. The main contribution of this research is based on the application of knowledge creation and knowledge management theories to decision-making frameworks for IT systems projects through the IT Systems Development Decision-Making Support Framework. The Framework is expected to enable decision evaluation and project-specific knowledge generation and sharing in IT systems development projects. This is vital for the type of contextual knowledge required for project-specific knowledge creation and management. Since IT systems development projects tend to be unique and their development process is complex, it is contended that an effective novel approach for modelling the expert decision-making process and assessing the defined model through project-specific knowledge activities is essential. This approach should help to deal with high level of complexity that is normally found in IT systems development projects

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities