THE LIFE CYCLE OF DATA LABELS IN ORGANIZATIONAL LEARNING: A CASE STUDY OF THE AUTOMOTIVE INDUSTRY

Data labels are an integral input to develop machine learning (ML) models. In complex domains, labels represent the externalized product of complex knowledge. While prior research discussed labels typically as input of ML models, we explore their role in organizational learning (OL). Based on a case study of a German car manufacturer, we contextualize a framework of OL to the use of labels in organizations informing about organizational members who work with labels, requirements of label-based tools, label-related tasks, and impediments of label-related task performance. From our findings, we derive propositions about the role of labels in OL and outline future research opportunities. Our results inform theory about the role of labels in OL and can guide practitioners leveraging labels to create and transfer knowledge within organizations

Visual Analytics for IoT Data From Large-Scale Manufacturing Processes

Advances in technologies, such as the Internet of Things (IoT) and Visual Analytics (VA), are enabling a new generation of smart manufacturing. These technologies enable the efficient tracking of the quality of produced parts along every step in the manufacturing process. However, the challenge remains that distinct IoT data sources must be connected, harmonized, and made readily accessible to human experts for analyses. In this paper, we followed a design science research approach to develop a VA artifact supporting engineering experts in analyzing IoT data from interconnected stations of a manufacturing process for electrical engines. We developed our artifact in collaboration with an industrial partner from the automotive sector and evaluated it with five engineering experts. Results indicate high usability and usefulness of the artifact as part of a real-world manufacturing process. Our instantiated artifact can serve as guidance to researchers and practitioners, who work in similar manufacturing domains

VISUAL ANALYTICS IN ORGANIZATIONAL KNOWLEDGE CREATION: A CASE STUDY

The conversion between tacit and explicit knowledge remains an often-discussed and highly relevant topic in organizational knowledge creation. Although prior research addresses this process, it primarily focuses on the conversion between tacit and explicit knowledge through social processes. This work discusses theories of organizational knowledge creation in the light of sociotechnical systems, and specifically extends them to the interaction between individuals and visual analytics systems that afford analytical decision making based on interactive visualization and knowledge discovery mechanisms. Based on related work, we develop a theoreti-cal framework to explain novel mechanism for knowledge creation afforded by visual analytics systems. We evaluate our framework with a case study with one of the leading organizations in the automotive industry. Over the course of the case study, we observe and analyze interactions between domain experts and a newly introduced visual analytics system. Through our case study findings, we reveal novel mechanisms of organiza-tional knowledge creation and discuss their implications

ManuKnowVis: How to Support Different User Groups in Contextualizing and Leveraging Knowledge Repositories

We present ManuKnowVis, the result of a design study, in which we contextualize data from multiple knowledge repositories of a manufacturing process for battery modules used in electric vehicles. In data-driven analyses of manufacturing data, we observed a discrepancy between two stakeholder groups involved in serial manufacturing processes: Knowledge providers (e.g., engineers) have domain knowledge about the manufacturing process but have difficulties in implementing data-driven analyses. Knowledge consumers (e.g., data scientists) have no first-hand domain knowledge but are highly skilled in performing data-driven analyses. ManuKnowVis bridges the gap between providers and consumers and enables the creation and completion of manufacturing knowledge. We contribute a multi-stakeholder design study, where we developed ManuKnowVis in three main iterations with consumers and providers from an automotive company. The iterative development led us to a multiple linked view tool, in which, on the one hand, providers can describe and connect individual entities (e.g., stations or produced parts) of the manufacturing process based on their domain knowledge. On the other hand, consumers can leverage this enhanced data to better understand complex domain problems, thus, performing data analyses more efficiently. As such, our approach directly impacts the success of data-driven analyses from manufacturing data. To demonstrate the usefulness of our approach, we carried out a case study with seven domain experts, which demonstrates how providers can externalize their knowledge and consumers can implement data-driven analyses more efficiently

Dynamically Adapting the Environment for Elderly People Through Smartwatch-based Mood Detection

none5siThe ageing population and age-related diseases are some of the most urgent challenges in healthcare. This leads to an increasing demand in innovative solutions to afford a healthy and safe lifestyle to the elderly. Towards this goal, the City4Age project, funded by the Horizon 2020 Programme of the European Commission, focuses on IoT-based personal data capture, supporting smart cities to empower social/health services. This paper describes the combination of the smartwatch-based Happimeter with City4Age data capture technology. Through measuring the mood of the wearer of the smartwatch, a signal is transmitted to the Philips Hue platform, enabling mood controlled lighting. Philips Hue allows the wireless remote control of energy-efficient LED light bulbs. Thus, measuring the mood through the Happimeter, the living environment for elderly people can be dynamically adapted. In our experiments, by changing colors and brightness of light bulbs using the Philips Hue platform, their quality of life can be improved. A validation test will be done in the context of the City4Age project, involving 31 elderly people living in a Southern Italian city.restrictedCapodieci, A.; Budner, P.; Eirich, J.; Gloor, P.; Mainetti, L.Capodieci, Antonio; Budner, P.; Eirich, J.; Gloor, P.; Mainetti, Luc

Aristotle Said Happiness is a State of Activity Predicting Mood Through Body Sensing with Smartwatches

We measure and predict states of Activation and Happiness using a body sensing application connected to smartwatches. Through the sensors of commercially available smartwatches we collect individual mood states and correlate them with body sensing data such as acceleration, heart rate, light level data, and location, through the GPS sensor built into the smartphone connected to the smartwatch. We polled users on the smartwatch for seven weeks four times per day asking for their mood state. We found that both Happiness and Activation are negatively correlated with heart beats and with the levels of light. People tend to be happier when they are moving more intensely and are feeling less activated during weekends. We also found that people with a lower Conscientiousness and Neuroticism and higher Agreeableness tend to be happy more frequently. In addition, more Activation can be predicted by lower Openness to experience and higher Agreeableness and Conscientiousness. Lastly, we find that tracking people's geographical coordinates might play an important role in predicting Happiness and Activation. The methodology we propose is a first step towards building an automated mood tracking system, to be used for better teamwork and in combination with social network analysis studies

ManEx: The Visual Analysis of Measurements for the Assessment of Errors in Electrical Engines

Electrical engines are a key technology all automotive manufacturers must master to stay competitive. Engineers need to analyze an overwhelming number of engine measurements to improve the manufacturing for this technology. They are hindered in the task of analyzing large numbers of engines, however, by the following challenges: 1) Engines comprise a complex hierarchical structure of subcomponents. 2) Locating the cause of errors along manufacturing processes is a difficult procedure. 3) Large numbers of heterogeneous measurements impair the ability to explain errors in engines. We address these challenges in a design study with automotive engineers and by developing the visual analytics system Manufacturing Explorer (ManEx), which provides interactive interfaces to analyze measurements of engines across the manufacturing process. ManEx was validated by five experts. Our results suggest high usability and usefulness scores and the improvement of a real-world manufacturing process. Specifically, with ManEx, experts reduced scraped parts by over 3%

IRVINE: A Design Study on Analyzing Correlation Patterns of Electrical Engines

In this design study, we present IRVINE, a Visual Analytics (VA) system, which facilitates the analysis of acoustic data to detect and understand previously unknown errors in the manufacturing of electrical engines. In serial manufacturing processes, signatures from acoustic data provide valuable information on how the relationship between multiple produced engines serves to detect and understand previously unknown errors. To analyze such signatures, IRVINE leverages interactive clustering and data labeling techniques, allowing users to analyze clusters of engines with similar signatures, drill down to groups of engines, and select an engine of interest. Furthermore, IRVINE allows to assign labels to engines and clusters and annotate the cause of an error in the acoustic raw measurement of an engine. Since labels and annotations represent valuable knowledge, they are conserved in a knowledge database to be available for other stakeholders. We contribute a design study, where we developed IRVINE in four main iterations with engineers from a company in the automotive sector. To validate IRVINE, we conducted a field study with six domain experts. Our results suggest a high usability and usefulness of IRVINE as part of the improvement of a real-world manufacturing process. Specifically, with IRVINE domain experts were able to label and annotate produced electrical engines more than 30% faster