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

Healthcare analytics—A literature review and proposed research agenda

This research addresses the demanding need for research in healthcare analytics, by explaining how previous studies have used big data, AI, and machine learning to identify, address, or solve healthcare problems. Healthcare science methods are combined with contemporary data science techniques to examine the literature, identify research gaps, and propose a research agenda for researchers, academic institutions, and governmental healthcare organizations. The study contributes to the body of literature by providing a state-of-the-art review of healthcare analytics as well as proposing a research agenda to advance the knowledge in this area. The results of this research can be beneficial for both healthcare science and data science researchers as well as practitioners in the field

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

The use of experts panels in ERP cost estimation research

Published version of a chapter in the book: Enterprise information systems. The original publication is available at www.springerlink.com, http://dx.doi.org/10.1007/978-3-642-16419-4_10This paper is an effort towards illustrating the use of expert panel (EP) as a mean of eliciting knowledge from a group of enterprise resource planning (ERP) experts as an exploratory research. The development of a cost estimation model (CEM) for ERP adoptions is very crucial for research and practice, and that was the main reason behind the willingness of experts to participate in this research. The use of EP was very beneficial as it involved various data collection and visualisation techniques, as well as data validation and confirmation. Beside its advantages, one of the main motives for using a group technique is that it is difficult to find a representative sample for a casual survey method, as ERP experts and consultants are rare to find, especially in the scope of SMEs’ ERP implementations. It is worth noting that the panel reached consensus regarding the results of the EP. The experts modified and enhanced the initial cost drivers (CD) list largely, as they added, modified, merged and split different costs drivers. In addition, the experts added CF (sub-factors) that could influence or affect each cost driver. Moreover, they ranked the CD according to their weight on total costs. All of this helped the authors to better understand relationships among various CF.This paper is an effort towards illustrating the use of expert panel (EP) as a mean of eliciting knowledge from a group of enterprise resource planning (ERP) experts as an exploratory research. The development of a cost estimation model (CEM) for ERP adoptions is very crucial for research and practice, and that was the main reason behind the willingness of experts to participate in this research. The use of EP was very beneficial as it involved various data collection and visualisation techniques, as well as data validation and confirmation. Beside its advantages, one of the main motives for using a group technique is that it is difficult to find a representative sample for a casual survey method, as ERP experts and consultants are rare to find, especially in the scope of SMEs’ ERP implementations. It is worth noting that the panel reached consensus regarding the results of the EP. The experts modified and enhanced the initial cost drivers (CD) list largely, as they added, modified, merged and split different costs drivers. In addition, the experts added CF (sub-factors) that could influence or affect each cost driver. Moreover, they ranked the CD according to their weight on total costs. All of this helped the authors to better understand relationships among various CF

Design Science Research: Evaluation in the Lens of Big Data Analytics

Given the different types of artifacts and their various evaluation methods, one of the main challenges faced by researchers in design science research (DSR) is choosing suitable and efficient methods during the artifact evaluation phase. With the emergence of big data analytics, data scientists conducting DSR are also challenged with identifying suitable evaluation mechanisms for their data products. Hence, this conceptual research paper is set out to address the following questions. Does big data analytics impact how evaluation in DSR is conducted? If so, does it lead to a new type of evaluation or a new genre of DSR? We conclude by arguing that big data analytics should influence how evaluation is conducted, but it does not lead to the creation of a new genre of design research

Opening Digital Archives and Collections With Emerging Data Analytics Technology : A Research Agenda

In the public sector, the EU legislation requires preservation and opening of increasing amounts of heterogeneous digital information that should be utilized by citizens and businesses. While technologies such as big data analytics (BDA) have emerged, opening of digital archives and collections at a large scale is in its infancy. Opening archives and collections involve also particular requirements for recognizing and managing issues of privacy and digital rights. As well, ensuring the sustainability of the opened materials and economical appraisal of digital materials for preservation require robust digital preservation practices. We need to proceed beyond the state-of-the-art in opening digital archives and collections through the means of emerging big data analytics and validating a novel concept for analytics which then enables delivering of knowledge for citizens and the society. We set out an agenda for using BDA as our strategy for research and enquiry and for demonstrating the benefit of BDA for opening digital archives by civil servants and for citizens. That will –eventually -transform the preservation practices, and delivery and use opportunities of public digital archives. Our research agenda suggests a framework integrating four domains of inquiry, analytics-enhanced appraisal, analytics-prepared preservation, analytics-enhanced opening, and analytics-enhanced use, for utilizing the BDA technologies in the domain of digital archives and collections. The suggested framework and research agenda identifies initially particular BDA technologies to be utilized in each of the four domains, and contributes by highlighting a need for an integrated “public understanding of big data” in the domain of digital preservation.Validerad; 2017; Nivå 1; 2017-02-15 (andbra)</p

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