Abstract

Abstract
Monitoring a system over time is as important as ever with the increasing use of cloud-native software architectures. This paper expands the set of patterns published in a previous paper (Liveness Endpoint, Readiness Endpoint and Synthetic Testing) with two solutions for supporting teams in diagnosing occurring issues — Deployment Tracking and Exception Tracking. These patterns advise tracking relevant events that occur in the system. The Deployment Tracking pattern provides means to limit the sources of an anomaly, and the Exception Tracking pattern makes a specific class of anomalies visible so that a team can act on them. Both patterns help practitioners identify the root cause of an issue, which is instrumental in fixing it. They can help even less experienced professionals to improve monitoring processes, and reduce the mean time to resolve problems with their application. These patterns draw on documented industry best practices and existing tools. In order to help the reader find other patterns that supplement the ones suggested in this study, relations to already-existing monitoring patterns are also examined. © 2023 Copyright held by the owner/author(s).

Abstract

Abstract
The rise of social media has brought a great transformation to the way news are discovered and shared. Unlike traditional news sources, social media allows anyone to cover a story. Therefore, sometimes an event is already discussed by people before a journalist turns it into a news article. Twitter is a particularly appealing social network for discussing events, since its posts are very compact and, therefore, contain colloquial language and abbreviations. However, its large volume of tweets also makes it impossible for a user to keep up with an event. In this work, we present TweetStream2Story, a web app for extracting narratives from tweets posted in real time, about a topic of choice. This framework can be used to provide new information to journalists or be of interest to any user who wishes to stay up-to-date on a certain topic or ongoing event. As a contribution to the research community, we provide a live version of the demo, as well as its source code.

Abstract
Incidental visualizations are meant to be seen at-a-glance, on-the-go, and during short exposure times. They will always appear side-by-side with an ongoing primary task while providing ancillary information relevant to those tasks. They differ from glanceable visualizations because looking at them is never their major focus, and they differ from ambient visualizations because they are not embedded in the environment, but appear when needed. However, unlike glanceable and ambient visualizations that have been studied in the past, incidental visualizations have yet to be explored in-depth. In particular, it is still not clear what is their impact on the users' performance of primary tasks. Therefore, we conducted an empirical online between-subjects user study where participants had to play a maze game as their primary task. Their goal was to complete several mazes as quickly as possible to maximize their score. This game was chosen to be a cognitively demanding task, bound to be significantly affected if incidental visualizations have a meaningful impact. At the same time, they had to answer a question that appeared while playing, regarding the path followed so far. Then, for half the participants, an incidental visualization was shown for a short period while playing, containing information useful for answering the question. We analyzed various metrics to understand how the maze performance was impacted by the incidental visualization. Additionally, we aimed to understand if working memory would influence how the maze was played and how visualizations were perceived. We concluded that incidental visualizations of the type used in this study do not disrupt people while they played the maze as their primary task. Furthermore, our results strongly suggested that the information conveyed by the visualization improved their performance in answering the question. Finally, working memory had no impact on the participants' results.

Abstract
Remote collaborative work has become pervasive in many settings, ranging from engineering to medical professions. Users are immersed in virtual environments and communicate through life-sized avatars that enable face-to-face collaboration. Within this context, users often collaboratively view and interact with virtual 3D models, for example to assist in the design of new devices such as customized prosthetics, vehicles or buildings. Discussing such shared 3D content face-to-face, however, has a variety of challenges such as ambiguities, occlusions, and different viewpoints that all decrease mutual awareness, which in turn leads to decreased task performance and increased errors. To address this challenge, we introduce MAGIC, a novel approach for understanding pointing gestures in a face-to-face shared 3D space, improving mutual understanding and awareness. Our approach distorts the remote user's gestures to correctly reflect them in the local user's reference space when face-to-face. To measure what two users perceive in common when using pointing gestures in a shared 3D space, we introduce a novel metric called pointing agreement. Results from a user study suggest that MAGIC significantly improves pointing agreement in face-toface collaboration settings, improving co-presence and awareness of interactions performed in the shared space. We believe that MAGIC improves remote collaboration by enabling simpler communication mechanisms and better mutual awareness.