Orchestrating Economic, Socio-Technical and Technical Validation Using Visual Modelling

The paper presents an approach for orchestrating validation of project results from different perspectives by using visual modelling techniques. The context for the paper is the FP6 project MAPPER. Validation in MAPPER covers economic, socio-technical and technical viewpoints. The economic viewpoint mainly focuses on business value and coherence with business drivers like reduced lifecycle time or increased flexibility. Sustainable collaboration for joint value creation of various units in a networked organisation is the main aspect of the socio-technical viewpoint. From a technical point of view, usability of IT-infrastructure and services is a key aspect. The MAPPER Validation Framework includes and orchestrates approaches and methodologies from these three viewpoints and defines the validation actions to be performed. The main contributions of the paper to research in the field are (1) the structure of the MAPPER Validation Framework integrating different validation perspectives, (2) experiences from using a visual modelling environment for framework development and (3) experiences from orchestrating different validation perspectives

Workflows for Quantitative Data Analysis in The Social Sciences

The background is given to how statistical analysis is used by quantitative social scientists. Developing statistical analyses requires substantial effort, yet there are important limitations in current practice. This has motivated the authors to create a more systematic and effective methodology with supporting tools. The approach to modelling quantitative data analysis in the social sciences is presented. Analysis scripts are treated abstractly as mathematical functions and concretely as web services. This allows individual scripts to be combined into high-level workflows. A comprehensive set of tools allows workflows to be defined, automatically validated and verified, and automatically implemented. The workflows expose opportunities for parallel execution, can define support for proper fault handling, and can be realised by non-technical users. Services, workflows and datasets can also be readily shared. The approach is illustrated with a realistic case study that analyses occupational position in relation to health

Problem Conceptualization as a Foundation of Data Analytics in Local Governments: Lessons from the City of Syracuse, New York

The use data and data analytics (DA) has been attracting the attention of academics and practitioners in the public sector and is sometimes seen as a potential strategy for process and service innovation. While research on the many possible uses of data have clearly increased - open data, big data, data analytics- empirical research on the socio-technical process that local governments followed when using data analytics to improve services and policies is still scarce. Based on existing literature about data analytics in the public sector and the data lifecycle concept, this paper examines how data analytics is actually used in a local government and what are the main steps in this process. It analyzes the experience of a mid-size American city that had a dedicated task force to data analytics use to support decision making at the local level – Syracuse, New York. Findings suggest that data analytics as a process not only involves data analysis and representations (such as visualizations), but also data collection and cleaning. Further, it seems clear that the conceptualization of the problem is a critical step in producing meaningful data analytics, but also in thinking about innovations even when data is not readily available

An exploration of IoT platform development

IoT (Internet of Things) platforms are key enablers for smart city initiatives, targeting the improvement of citizens\u27 quality of life and economic growth. As IoT platforms are dynamic, proactive, and heterogeneous socio-technical artefacts, systematic approaches are required for their development. Limited surveys have exclusively explored how IoT platforms are developed and maintained from the perspective of information system development process lifecycle. In this paper, we present a detailed analysis of 63 approaches. This is accomplished by proposing an evaluation framework as a cornerstone to highlight the characteristics, strengths, and weaknesses of these approaches. The survey results not only provide insights of empirical findings, recommendations, and mechanisms for the development of quality aware IoT platforms, but also identify important issues and gaps that need to be addressed

Data-Intensive architecture for scientific knowledge discovery

This paper presents a data-intensive architecture that demonstrates the ability to support applications from a wide range of application domains, and support the different types of users involved in defining, designing and executing data-intensive processing tasks. The prototype architecture is introduced, and the pivotal role of DISPEL as a canonical language is explained. The architecture promotes the exploration and exploitation of distributed and heterogeneous data and spans the complete knowledge discovery process, from data preparation, to analysis, to evaluation and reiteration. The architecture evaluation included large-scale applications from astronomy, cosmology, hydrology, functional genetics, imaging processing and seismology

Designing Activities for Collaboration at Classroom Scale Using Shared Technology

Although researchers, teachers and policy makers broadly agree on the benefits of collaborative learning, there appears to be less clarity regarding how effective collaboration can be realised at classroom scale. Research in Computer-Supported Collaborative Learning (CSCL), Human-Computer Interaction (HCI), simulation-based learning and related fields has produced a considerable range of applications that aim to support collaboration in classrooms. Grounded in well-established theories of how humans learn, many such applications have shown promising results within the context of small research studies. However, most of those research-driven applications never matured beyond the prototype stage and few are available today as products that schools can easily use and adopt. Many systems lack flexibility or require too much time, hardware, technical skills or other resources to be effectively implemented. Furthermore, teachers can be overwhelmed by managing large groups of students engaged in complex, computer-supported tasks. This thesis investigates how forms of whole-classroom activity can be supported by combining shareable technologies with simulation, team play and orchestration. New designs are explored to help large groups engage and discuss at multiple scales (from pairs and small groups to the entire classroom) in ways that effectively include each student and use the teacher's limited resources efficiently. Moreover, this research aims to devise and validate a conceptual framework that can guide future design, orchestration and evaluation of such activities. Three in-situ studies were conducted to address these goals. The first study involved the design of a climate change simulation to support a professional training course. Iterative design and video analysis resulted in the formulation of the Collaborative Learning Orchestration for Verbal Engagement and Reflection (CLOVER) framework. This framework comprises a suite of conceptual tools and recommendations that aim to help designers and teachers create, orchestrate and evaluate decision-based simulations for whole-classroom use. Two follow-up studies were conducted to validate the usability and usefulness of CLOVER. One of them aimed to replicate the previous findings in a similar context and resulted in the design of a sustainable, whole-classroom simulation for students to discuss finance decisions. The other used CLOVER to expand an existing desktop application (a~language comprehension task for children) to classroom scale. In sum, the three studies provide substantial empirical evidence, suggesting that CLOVER-based applications can effectively reconcile learning needs (collaboration) and technological affordances (shareable devices) with the inherent benefits and constraints of teacher-driven, co-located environments. Furthermore, the findings contribute to a better understanding of what it means to design for sustainability in this context

Navigating AI innovation ecosystems in manufacturing: Shaping factors and their implications

Manufacturers often encounter challenges when implementing artificial intelligence (AI) in their manufacturing operations. Similar challenges with other digital transformation technologies have resulted in the emergence of innovation ecosystems. In this paper, we aim to demonstrate the emergence of AI innovation ecosystems and highlight the factors that influence their structure in manufacturing. To achieve this, we conducted a qualitative study of ten manufacturing case studies, analyzing different value propositions, activities, actors, and modules in AI ecosystems in the manufacturing sector. We first visualize the AI innovation ecosystems to showcase their structure and then discuss factors such as trustworthiness, scalability, simulation, and cloud that impact the ecosystem structure. Our study provides practitioners with a better understanding of the structure of AI ecosystems and their influencing factors. For researchers, we introduce influencing factors as a new part of the ecosystem-as-structure concept, which can lead to new research opportunities