Trajectory Data Analysis in Support of Understanding Movement Patterns: A Data Mining Approach

Recent developments in wireless technology, mobility and networking infrastructures increased the amounts of data being captured every second. Data captured from the digital traces of moving objects and devices is called trajectory data. With the increasing volume of spatiotemporal trajectories, constructive and meaningful knowledge needs to be extracted. In this paper, a conceptual framework is proposed to apply data mining techniques on trajectories and semantically enrich the extracted patterns. A design science research approach is followed, where the framework is tested and evaluated using a prototypical instantiation, built to support decisions in the context of the Egyptian tourism industry. By applying association rule mining, the revealed time-stamped frequently visited regions of interest (ROI) patterns show that specific semantic annotations are required at early stages in the process and on lower levels of detail, refuting the presumption of cross-application usable patterns

Towards a Taxonomy for Data-Driven Digital Services

Digitization is transforming every domain nowadays, leading to a growing body of knowledge on digital service innovation. Coupled with the generation and collection of big data, data-driven digital services are becoming of great importance to business, economy and society. This paper aims to classify the different types of data-driven digital services, as a first step to understand their characteristics and dynamics. A taxonomy is developed and the emerging characteristics include data acquisition mechanisms, data exploitation, insights utilization, and service interaction characteristics. The examined services fall into 15 distinct types and are further clustered into 3 classes of types: distributed analytics intermediaries, visual data-driven services, and analytics-embedded services. Such contribution enables service designers and providers to understand the key aspects in utilizing data and analytics in the design and delivery of their services. The taxonomy is set out to shape the direction and scope of scholarly discourse around digital service innovation research and practice

A Sustainable synthesis, eco-safe approach efficiency and DFT study of novel 5,6,7,8-Tetrahyroquinazolin-2(1H)-one derivatives as antioxidant reagents

5,6,7,8-Tetrahydroquinazolin-2-(thio)-ones (THQ) fits the class of N-heterocycles as a structural core in numerous bioactive compounds. They promptly extended previous decades. They were significantly recognized in combinatorial chemistry and materials science to determine the drug discovery, antioxidants, and pharmaceuticals fields. In the present work, one-pot multicomponent sustainable synthesis of THQ with easily accessible starting materials, i.e., cyclohexanone, different aromatic aldehydes and (thio)urea, has been performed to determine the proposed Biginelli mechanism that is supported by DFT. It is found that the THQs are synthesized by a mechano-chemical (grinding) tool to achieve a yield of 85.2% within 3.5 min, i.e., YE (% yield/time) 24.34 differs from the conventional method in which lower % yield (YE = 0.72) of THQ was achieved. This confirmed that in the green chemistry principle, the determination of % yield according to saving reaction time must be considered. Moreover, DFT-based antioxidant properties of the THQ were also studied in which the most potent antioxidant compounds were 7b > 6d > 2f. Softness (σ, eV−1) and hardness (η, eV mol−1) can approve the soft molecule that stays more reactive as a result of decreasing the energy gap along heterocyclic with values 0.1491 > 0.1300 > 0.1168 eV−1 one-to-one with the efficiency of antioxidant

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Data-driven Innovation : An exploration of outcomes and processes within federated networks

The emergence and pervasiveness of digital technologies are changing many aspects of our lives, including what and how we innovate. Industries and societies are competing to embrace this wave of digitalization by developing the right infrastructures and ecosystems for innovation. Similarly, innovation managers and entrepreneurs are using digital technologies to develop novel products, services, processes, business models, etc. One of the major consequences of digitalization is the massive amounts of machine-readable data generated through digital interactions. But this is not only a consequence, it is also a driver for other innovations to emerge. Employing analytical techniques on data to extract useful patterns and insights enables different aspects of innovation. During the last decade, scholars within digital innovation have started to explore this relationship between analytics and innovation, a phenomenon referred to as data-driven innovation (DDI). Most theories to date view analytics as variable that affects innovation in performative terms and treats it as a black-box. However, if the innovation managers and entrepreneurs are to manage and navigate DDI, and for the investors, funders and policymakers to take informed decisions, they need a better understanding of how DDI outcomes (i.e. market offerings such as products and services) are shaped and how they emerge from a process perspective. This dissertation explores this research gap by addressing two research questions: “What characterizes data-driven innovation outcomes?” and “How do data-driven innovations emerge in federated networks?” A federated network is a type of – increasingly common – contemporary innovation structure that is also enabled by digital technology. The dissertation is based on a compilation of five articles addressing these questions. The overall research approach follows a multiple case study design and the empirical investigation takes place in two case sites corresponding to two EU-funded projects. As a result, a classification taxonomy is developed for data-driven digital services. This taxonomy contributes to the conceptualization of DDI outcomes grounded on static and dynamic characteristics. In addition, a DDI process framework is proposed that highlights the importance of exploration, the temporal relationship between data acquisition and innovation development, and the various factors that influence the process along with examples of their contextual manifestations. Finally, social and cognitive interactions within federated networks of DDI are explored to reveal that the innovation teams rely on data-driven representations to facilitate various stakeholders’ engagement and contribution throughout the process. These representations eventually stabilize into boundary objects that retain the factual integrity of the data and analytical models but are also flexible for contextual interpretation and use. These findings contribute to the current discourse within digital innovation by introducing the lens of data analytics to conceptualize a specific type of digital artifacts, and well as providing a rich descriptive account of an extended digital innovation process. They also contribute to the discourse on data-driven innovation by providing an empirical account of DDI from a process viewpoint

Data science : developing theoretical contributions in information systems via text analytics

Scholars have been increasingly calling for innovative research in the organizational sciences in general, and the information systems (IS) field in specific, one that breaks from the dominance of gap-spotting and specific methodical confinements. Hence, pushing the boundaries of information systems is needed, and one way to do so is by relying more on data and less on a priori theory. Data, being considered one of the most important resources in research, and society at large, requires the application of scientific methods to extract valuable knowledge towards theoretical development. However, the nature of knowledge varies from a scientific discipline to another, and the views on data science (DS) studies are substantially diverse. These views vary from being seen as a new scientific (fourth) paradigm, to an extension of existing paradigms with new tools and methods, to a phenomenon or object of study. In this paper, we review these perspectives and expand on the view of data science as a methodology for scientific inquiry. Motivated by the IS discipline’s history and accumulated knowledge in using DS methods for understanding organizational and societal phenomena, IS theory and theoretical contributions are given particular attention as the key outcome of adopting such methodology. Exemplar studies are analyzed to show how rigor can be achieved, and an illustrative example using text analytics to study digital innovation is provided to guide researchers.Validerad;2020;Nivå 1;2020-01-24 (johcin)</p

A Framework for Informal Learning Analytics - Evidence from the Literacy Domain

Multidisciplinary approaches to learning analytics (LA) have the potential to provide important insights into student learning beyond interactions within learning management systems (LMS). In this paper we demonstrate the benefits of such an approach by proposing a framework that adds the contextual elements of task design, tools and technologies and datasets to established LA processes. Our framework was developed as a design science research (DSR) artifact, working with teachers of English at two Swedish secondary schools. The results highlight the importance of valid task design for generating relevant, useful insights and provide a basis for simplifying and automating in-situ LA that can be used by teachers in their everyday work. The study also provided important insights for the field of online research and comprehension (ORC) both in relation to methodology and how students engage with a task that requires locating and synthesizing information on the open Internet in a second language.ISBN för värdpublikation: 978-0-9981331-4-0</p

Data-driven innovation processes within federated networks

Purpose Within digital innovation, there are two significant consequences of the pervasiveness of digital technology: (1) the increasing connectivity is enabling a wider reach and scope of innovation structures, such as innovation networks and (2) the unprecedented availability of digital data is creating new opportunities for innovation. Accordingly, there is a growing domain for studying data-driven innovation (DDI), especially in contemporary contexts of innovation networks. The purpose of this study is to explore how DDI processes take form in a specific type of innovation networks, namely federated networks. Design/methodology/approach A multiple case study design is applied in this paper. We draw our analysis from data collected over six months from four cases of DDI. The within-analysis is aimed at constructing the DDI process instance in each case, while the crosscase analysis focuses on pattern matching and cross-case synthesis of common and unique characteristics in the constructed processes. Findings Evidence from the crosscase analysis suggests that the widely accepted four-phase digital innovation process (including discovery, development, diffusion and post-diffusion) does not account for the explorative nature of data analytics and DDI. We propose an extended process comprising an explicit exploration phase before development, where refinement of the innovation concept and exploring social relationships are essential. Our analysis also suggests two modes of DDI: (1) asynchronous, i.e. data acquired before development and (2) synchronous, i.e. data acquired after (or during) development. We discuss the implications of these modes on the DDI process and the participants in the innovation network. Originality/value The paper proposes an extended version of the digital innovation process that is more specifically suited for DDI. We also provide an early explanation to the variation in DDI process complexities by highlighting the different modes of DDI processes. To the best of our knowledge, this is the first empirical investigation of DDI following the process from early stages of discovery till postdiffusion.Validerad;2022;Nivå 2;2022-04-13 (sofila)</p

Data-driven innovation processes within federated networks

Purpose Within digital innovation, there are two significant consequences of the pervasiveness of digital technology: (1) the increasing connectivity is enabling a wider reach and scope of innovation structures, such as innovation networks and (2) the unprecedented availability of digital data is creating new opportunities for innovation. Accordingly, there is a growing domain for studying data-driven innovation (DDI), especially in contemporary contexts of innovation networks. The purpose of this study is to explore how DDI processes take form in a specific type of innovation networks, namely federated networks. Design/methodology/approach A multiple case study design is applied in this paper. We draw our analysis from data collected over six months from four cases of DDI. The within-analysis is aimed at constructing the DDI process instance in each case, while the crosscase analysis focuses on pattern matching and cross-case synthesis of common and unique characteristics in the constructed processes. Findings Evidence from the crosscase analysis suggests that the widely accepted four-phase digital innovation process (including discovery, development, diffusion and post-diffusion) does not account for the explorative nature of data analytics and DDI. We propose an extended process comprising an explicit exploration phase before development, where refinement of the innovation concept and exploring social relationships are essential. Our analysis also suggests two modes of DDI: (1) asynchronous, i.e. data acquired before development and (2) synchronous, i.e. data acquired after (or during) development. We discuss the implications of these modes on the DDI process and the participants in the innovation network. Originality/value The paper proposes an extended version of the digital innovation process that is more specifically suited for DDI. We also provide an early explanation to the variation in DDI process complexities by highlighting the different modes of DDI processes. To the best of our knowledge, this is the first empirical investigation of DDI following the process from early stages of discovery till postdiffusion

Digital Service Innovation Enabled by Big Data Analytics : A Review and the Way Forward

Service innovation is attracting attention with the expanding service industries and economies. Accompanied by major developments in ICT and sensory and digital technologies, the interest in digital service innovation (DSI), both from academia and industry, is increasing. Digitization and the accompanying technological advancements are leading to phenomena that call for extensive research in relation to service innovation; one of which is big data analytics (BDA). In this paper, we review the DSI literature and explore how BDA can contribute along the different dimensions of DSI. The ex post literature suffers from the lack of such studies. Accordingly, we suggest a research agenda for BDA-enabled DSI, motivated by emerging research gaps, as well as opportunities and guiding research questions. It is expected that such research agenda will contribute to shape an ex ante research efforts in an attempt to advance the state-of-the-art in BDA-enabled DSI