8,580 research outputs found
Guided Interaction Exploration in Artifact-centric Process Models
Artifact-centric process models aim to describe complex processes as a
collection of interacting artifacts. Recent development in process mining allow
for the discovery of such models. However, the focus is often on the
representation of the individual artifacts rather than their interactions.
Based on event data we can automatically discover composite state machines
representing artifact-centric processes. Moreover, we provide ways of
visualizing and quantifying interactions among different artifacts. For
example, we are able to highlight strongly correlated behaviours in different
artifacts. The approach has been fully implemented as a ProM plug-in; the CSM
Miner provides an interactive artifact-centric process discovery tool focussing
on interactions. The approach has been evaluated using real life data sets,
including the personal loan and overdraft process of a Dutch financial
institution.Comment: 10 pages, 4 figures, to be published in proceedings of the 19th IEEE
Conference on Business Informatics, CBI 201
Guided Interaction Exploration and Performance Analysisin Artifact-Centric Process Models
Artifact-centric process models aim to describecomplex processes as a collection of interacting artifacts.Recent development in process mining allow for the dis-covery of such models. However, the focus is often on therepresentation of the individual artifacts rather than theirinteractions. Based on event data, composite state machi-nes representing artifact-centric processes can be discov-ered automatically. Moreover, the study provides ways ofvisualising and quantifying interactions among differentartifacts. For example, strongly correlated behaviours indifferent artifacts can be highlighted. Interesting correla-tions can be subsequently analysed to identify potentialcauses of process performance issues. The study provides astrategy to explore the interactions and performance dif-ferences in this context. The approach has been fullyimplemented as a ProM plug-in; the CSM Miner providesan interactive artifact-centric process discovery toolfocussing on interactions. The approach has been evaluatedusing real life data, to show that the guided exploration ofartifact interactions can successfully identify process per-formance issues
Advancements and Challenges in Object-Centric Process Mining: A Systematic Literature Review
Recent years have seen the emergence of object-centric process mining
techniques. Born as a response to the limitations of traditional process mining
in analyzing event data from prevalent information systems like CRM and ERP,
these techniques aim to tackle the deficiency, convergence, and divergence
issues seen in traditional event logs. Despite the promise, the adoption in
real-world process mining analyses remains limited. This paper embarks on a
comprehensive literature review of object-centric process mining, providing
insights into the current status of the discipline and its historical
trajectory
Interactions in Visualizations to Support Knowledge Activation
Humans have several exceptional abilities, one of which is the perceptual tasks of their visual sense. Humans have the unique ability to perceive data and identify patterns, trends, and outliers. This research investigates the design of interactive visualizations to identify the benefits of interacting with information. The research question leading the investigation is how does interacting with visualizations support analytical reasoning of emergent information to activate knowledge? The study uses the theory of distributed cognition and human-information interaction to apply the design science research framework. The motivation behind the research is to identify guidelines for interactive visualizations to enhance a user’s ability to make decisions in dynamic situations and apply knowledge gleaned from the visualization. An experiment is used to analyze the use of an interactive dashboard in a dynamic decision-making situation. The results of this experiment specifically look at the combination of interactions as they support the distribution of cognition over three spaces of a human-visualization cognitive system. The results provide insight into the benefits that interactions have for enhancing analytical reasoning, expanding the use of visualizations beyond communicating or disseminating information. Providing a broad range of interactions that work with multiple views of information increases the opportunities that users have to complete tasks. This research contributes to the information visualization discipline by expanding the focus from representing data to representing and interacting with information. Secondly, my results provide an example of a qualitative assessment based on the value of visualization, in comparison to traditional usability assessment
From Personal Data to Service Innovation – Guiding the Design of New Service Opportunities
Stimulated by an ongoing digital transformation, companies obtain a new source for digital service innovation: The use of personal data has the potential to build deeper customer relationships and to develop individualized services. However, methodological support for the systematic application of personal data in innovation processes is still scarce. This paper suggests a comprehensive approach for service design tools that enable collaborative design activities by participants with different data skills to identify new service opportunities. This approach includes the systematic development of customer understanding as well as a process to match customer needs to existing personal data resources. Following a design science research approach, we develop design principles for service design tools and build and evaluate a service opportunity canvas as a first instantiation
Designing Attention-Centric Notification Systems: Five HCI Challenges
Through an examination of the emerging domain of cognitive systems, with a
focus on attention-centric cognitive systems used for notification, this document explores
the human-computer interaction challenges that must be addressed for successful
interface design. This document asserts that with compatible tools and methods, user
notification requirements and interface usability can be abstracted, expressed, and
compared with critical parameter ratings; that is, even novice designers can assess
attention cost factors to determine target parameter levels for new system development.
With a general understanding of the user tasks supported by the notification system, a
designer can access the repository of design knowledge for appropriate information and
interaction design techniques (e.g., use of color, audio features, animation, screen size,
transition of states, etc), which have analytically and empirically derived ratings.
Furthermore, usability evaluation methods, provided to designers as part of the integrated
system, are adaptable to specific combinations of targeted parameter levels. User testing
results can be conveniently added back into the design knowledge repository and
compared to target parameter levels to determine design success and build reusable HCI
knowledge.
This approach is discussed in greater detail as we describe five HCI challenges
relating to cognitive system development: (1) convenient access to basic research and
guidelines, (2) requirements engineering methods for notification interfaces, (3) better
and more usable predictive modeling for pre-attentive and dual-task interfaces, (4)
standard empirical evaluation procedures for notification systems, and (5) conceptual
frameworks for organizing reusable design and software components.
This document also describes our initial work toward building infrastructure to
overcome these five challenges, focused on notification system development. We
described LINK-UP, a design environment grounded on years of theory and method
development within HCI, providing a mechanism to integrate interdisciplinary expertise
from the cognitive systems research community. Claims allow convenient access to
basic research and guidelines, while modules parallel a lifecycle development iteration
and provide a process for requirements engineering guided by this basic research. The
activities carried out through LINK-UP provide access to and interaction with reusable
design components organized based on our framework. We think that this approach may
provide the scientific basis necessary for exciting interdisciplinary advancement through
many fields of design, with notification systems serving as an initial model.
A version of this document will appear as chapter 3 in the book Cognitive
Systems: Human Cognitive Models in Systems Design edited by Chris Forsythe, Michael
Bernard, and Timothy Goldsmith resulting from a workshop led by the editors in summer
2003. The authors are grateful for the input of the workshop organizers and conference
attendees in the preparation of this document
Measuring Trustworthiness of AI Systems: A Holistic Maturity Model
Artificial intelligence (AI) has an impact on business and society at large while posing challenges and risks. For AI adoption, trustworthiness is paramount, yet there appears to be a gap between theory and practice. Organizations need guidance in quantitatively assessing and improving the trustworthiness of AI systems. To address such challenges, maturity models have shown to be a valuable instrument. However, recent AI maturity models address trustworthiness only at the maturest level. As a response, we propose a model to integrate the concept of trustworthiness across the AI lifecycle management. In doing so, we follow Design Science Research to develop a holistic model highlighting the importance of trustworthiness throughout the AI adoption journey to realize the real value potential. This research-in-progress contributes to the emerging research on human-AI systems and managing AI. Our objective is to use the model for assessing, evaluating, and improving trustworthy AI on an organizational level
Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry
Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results
- …