Unraveling the capabilities that enable digital transformation: A data-driven methodology and the case of artificial intelligence

Digital transformation (DT) is prevalent in businesses today. However, current studies to guide DT are mostly qualitative, resulting in a strong call for quantitative evidence of exactly what DT is and the capabilities needed to enable it successfully. With the aim of filling the gaps, this paper presents a novel bibliometric framework that unearths clues from scientific articles and patents. The framework incorporates the scientific evolutionary pathways and hierarchical topic tree to quantitatively identify the DT research topics’ evolutionary patterns and hierarchies at play in DT research. Our results include a comprehensive definition of DT from the perspective of bibliometrics and a systematic categorization of the capabilities required to enable DT, distilled from over 10,179 academic papers on DT. To further yield practical insights on technological capabilities, the paper also includes a case study of 9,454 patents focusing on one of the emerging technologies - artificial intelligence (AI). We summarized the outcomes with a four-level AI capabilities model. The paper ends with a discussion on its contributions: presenting a quantitative account of the DT research, introducing a process based understanding of DT, offering a list of major capabilities enabling DT, and drawing the attention of managers to be aware of capabilities needed when undertaking their DT journey

GPS Anomaly Detection And Machine Learning Models For Precise Unmanned Aerial Systems

The rapid development and deployment of 5G/6G networks have brought numerous benefits such as faster speeds, enhanced capacity, improved reliability, lower latency, greater network efficiency, and enablement of new applications. Emerging applications of 5G impacting billions of devices and embedded electronics also pose cyber security vulnerabilities. This thesis focuses on the development of Global Positioning Systems (GPS) Based Anomaly Detection and corresponding algorithms for Unmanned Aerial Systems (UAS). Chapter 1 provides an overview of the thesis background and its objectives. Chapter 2 presents an overview of the 5G architectures, their advantages, and potential cyber threat types. Chapter 3 addresses the issue of GPS dropouts by taking the use case of the Dallas-Fort Worth (DFW) airport. By analyzing data from surveillance drones in the (DFW) area, its message frequency, and statistics on time differences between GPS messages were examined. Chapter 4 focuses on modeling and detecting false data injection (FDI) on GPS. Specifically, three scenarios, including Gaussian noise injection, data duplication, data manipulation are modeled. Further, multiple detection schemes that are Clustering-based and reinforcement learning techniques are deployed and detection accuracy were investigated. Chapter 5 shows the results of Chapters 3 and 4. Overall, this research provides a categorization and possible outlier detection to minimize the GPS interference for UAS enhancing the security and reliability of UAS operations

SHELDON Smart habitat for the elderly.

An insightful document concerning active and assisted living under different perspectives: Furniture and habitat, ICT solutions and Healthcare

Development of a real-time business intelligence (BI) framework based on hex-elementization of data points for accurate business decision-making

The desire to use business intelligence (BI) to enhance efficiency and effectiveness of business decisions is neither new nor revolutionary. The promise of BI is to provide the ability to capture interrelationship from data and information to guide action towards a business goal. Although BI has been around since the 1960s, businesses still cannot get competitive information in the form they want, when they want and how they want. Business decisions are already full of challenges. The challenges in business decision-making include the use of a vast amount of data, adopting new technologies, and making decisions on a real-time basis. To address these challenges, businesses spend valuable time and resources on data, technologies and business processes. Integration of data in decision-making is crucial for modern businesses. This research aims to propose and validate a framework for organic integration of data into business decision-making. This proposed framework enables efficient business decisions in real-time. The core of this research is to understand and modularise the pre-established set of data points into intelligent and granular “hex-elements” (stated simply, hex-element is a data point with six properties). These intelligent hex-elements build semi-automatic relationships using their six properties between the large volume and high-velocity data points in a dynamic, automated and integrated manner. The proposed business intelligence framework is called “Hex-Elementization” (or “Hex-E” for short). Evolution of technology presents ongoing challenges to BI. These challenges emanate from the challenging nature of the underlying new-age data characterised by large volume, high velocity and wide variety. Efficient and effective analysis of such data depends on the business context and the corresponding technical capabilities of the organisation. Technologies like Big Data, Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML), play a key role in capitalising on the variety, volume and veracity of data. Extricating the “value” from data in its various forms, depth and scale require synchronizing technologies with analytics and business processes. Transforming data into useful and actionable intelligence is the discipline of data scientists. Data scientists and data analysts use sophisticated tools to crunch data into information which, in turn, are converted into intelligence. The transformation of data into information and its final consumption as actionable business intelligence is an end-to-end journey. This end-to-end transformation of data to intelligence is complex, time-consuming and resource-intensive. This research explores approaches to ease the challenges the of end-to-end transformation of data into intelligence. This research presents Hex-E as a simplified and semi-automated framework to integrate, unify, correlate and coalesce data (from diverse sources and disparate formats) into intelligence. Furthermore, this framework aims to unify data from diverse sources and disparate formats to help businesses make accurate and timely decisions

A DATA-DRIVEN APPROACH TO SUPPORTING USERS’ ADAPTATION TO SMART IN-VEHICLE SYSTEMS

The utilization of data to understand user behavior and support user needs began to develop in areas such as internet services, smartphone apps development, and the gaming industry. This bloom of data-driven services and applications forced OEMs to consider possible solutions for better in-vehicle connectivity. However, digital transformation in the automotive sector presents numerous challenges. One of those challenges is identifying and establishing the relevant user-related data that will cover current and future needs to help the automotive industry cope with the digital transformation pace. At the same time, this development should not be sporadic, without a clear purpose or vision of how newly-generated data can support engineers to create better systems for drivers. The important issue is to learn how to extract the knowledge from the immense data we possess, and to understand the extent to which this data can be used.Another challenge is the lack of established approaches towards vehicle data utilization for user-related studies. This area is relatively new to the automotive industry. Despite the positive examples from other fields that demonstrate the potential for data-driven context-aware applications, automotive practices still have gaps in capturing the driving context and driver behavior. This lack of user-related data can partially be explained by the multitasking activities that the driver performs while driving the car and the higher complexity of the automotive context compared to other domains. Thus, more research is needed to explore the capacity of vehicle data to support users in different tasks.Considering all the interrelations between the driver and in-vehicle system in the defined context of use helps to obtain more comprehensive information and better understand how the system under evaluation can be improved to meet driver needs. Tracking driver behavior with the help of vehicle data may provide developers with quick and reliable user feedback on how drivers are using the system. Compared to vehicle data, the driver’s feedback is often incomplete and perception-based since the driver cannot always correlate his behavior to complex processes of vehicle performance or clearly remember the context conditions. Thus, this research aims to demonstrate the ability of vehicle data to support product design and evaluation processes with data-driven automated user insights. This research does not disregard the driver’s qualitative input as unimportant but provides insights into how to better combine quantitative and qualitative methods for more effective results.According to the aim, the research focuses on three main aspects:•\ua0\ua0\ua0\ua0\ua0 Identifying the extent to which vehicle data can contribute to driver behavior understanding.\ua0 •\ua0\ua0\ua0\ua0\ua0 Expanding the concepts for vehicle data utilization to support drivers.•\ua0\ua0\ua0\ua0\ua0 Developing the methodology for a more effective combination of quantitative (vehicle data-based) and qualitative (based on users’ feedback) studies. Additionally, special consideration is given to describing the drawbacks and limitations, to enhance future data-driven applications