Data-driven Personal Cartographic Perspectives — An Overview of Applied, Artistic, and Academic Visualization Projects for Egocentric Retrospective Analysis of Personal Spatia-Temporal Behavior

In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events—anomaly detection in time-series—can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research.

Download Full-text

sGrapp: Butterfly Approximation in Streaming Graphs

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3495011 ◽

2022 ◽

Vol 16 (4) ◽

pp. 1-43

Author(s):

Aida Sheshbolouki ◽

M. Tamer Özsu

Keyword(s):

Fundamental Problem ◽

Experimental Studies ◽

Data Driven ◽

Superior Performance ◽

Temporal Behavior ◽

Large Graphs ◽

Adaptive Window ◽

Computationally Expensive ◽

Dynamic Phenomena ◽

Organizing Principles

We study the fundamental problem of butterfly (i.e., (2,2)-bicliques) counting in bipartite streaming graphs. Similar to triangles in unipartite graphs, enumerating butterflies is crucial in understanding the structure of bipartite graphs. This benefits many applications where studying the cohesion in a graph shaped data is of particular interest. Examples include investigating the structure of computational graphs or input graphs to the algorithms, as well as dynamic phenomena and analytic tasks over complex real graphs. Butterfly counting is computationally expensive, and known techniques do not scale to large graphs; the problem is even harder in streaming graphs. In this article, following a data-driven methodology, we first conduct an empirical analysis to uncover temporal organizing principles of butterflies in real streaming graphs and then we introduce an approximate adaptive window-based algorithm, sGrapp, for counting butterflies as well as its optimized version sGrapp-x. sGrapp is designed to operate efficiently and effectively over any graph stream with any temporal behavior. Experimental studies of sGrapp and sGrapp-x show superior performance in terms of both accuracy and efficiency.

Download Full-text

Interactive data-driven discovery of temporal behavior models from events in media streams

Proceedings of the 20th ACM international conference on Multimedia - MM '12 ◽

10.1145/2393347.2393413 ◽

2012 ◽

Cited By ~ 2

Author(s):

Chreston Miller ◽

Francis Quek

Keyword(s):

Data Driven ◽

Temporal Behavior ◽

Interactive Data ◽

Behavior Models

Download Full-text

A Retrospective Analysis of the COVID-19 Infodemic in Saudi Arabia

Future Internet ◽

10.3390/fi13100254 ◽

2021 ◽

Vol 13 (10) ◽

pp. 254

Author(s):

Ashwag Alasmari ◽

Aseel Addawood ◽

Mariam Nouh ◽

Wajanat Rayes ◽

Areej Al-Wabil

Keyword(s):

Social Media ◽

Retrospective Analysis ◽

Public Discourse ◽

Data Driven ◽

Pharmaceutical Companies ◽

Combined System ◽

The Social ◽

Data Driven Approach ◽

Social Media Platforms ◽

Qualitative Methodologies

COVID-19 has had broad disruptive effects on economies, healthcare systems, governments, societies, and individuals. Uncertainty concerning the scale of this crisis has given rise to countless rumors, hoaxes, and misinformation. Much of this type of conversation and misinformation about the pandemic now occurs online and in particular on social media platforms like Twitter. This study analysis incorporated a data-driven approach to map the contours of misinformation and contextualize the COVID-19 pandemic with regards to socio-religious-political information. This work consists of a combined system bridging quantitative and qualitative methodologies to assess how information-exchanging behaviors can be used to minimize the effects of emergent misinformation. The study revealed that the social media platforms detected the most significant source of rumors in transmitting information rapidly in the community. It showed that WhatsApp users made up about 46% of the source of rumors in online platforms, while, through Twitter, it demonstrated a declining trend of rumors by 41%. Moreover, the results indicate the second-most common type of misinformation was provided by pharmaceutical companies; however, a prevalent type of misinformation spreading in the world during this pandemic has to do with the biological war. In this combined retrospective analysis of the study, social media with varying approaches in public discourse contributes to efficient public health responses.

Download Full-text

Sound Production Treatment for Acquired Apraxia of Speech: An Examination of Dosage in Relation to Probe Performance

American Journal of Speech-Language Pathology ◽

10.1044/2020_ajslp-19-00110 ◽

2020 ◽

pp. 1-16

Author(s):

Julie L. Wambaugh ◽

Lydia Kallhoff ◽

Christina Nessler

Keyword(s):

Retrospective Analysis ◽

Sound Production ◽

Apraxia Of Speech ◽

Practice Schedule ◽

Practice Schedules ◽

Baseline Performance ◽

The Mean ◽

Number Of Treatment ◽

The Impact

Purpose This study was designed to examine the association of dosage and effects of Sound Production Treatment (SPT) for acquired apraxia of speech. Method Treatment logs and probe data from 20 speakers with apraxia of speech and aphasia were submitted to a retrospective analysis. The number of treatment sessions and teaching episodes was examined relative to (a) change in articulation accuracy above baseline performance, (b) mastery of production, and (c) maintenance. The impact of practice schedule (SPT-Blocked vs. SPT-Random) was also examined. Results The average number of treatment sessions conducted prior to change was 5.4 for SPT-Blocked and 3.9 for SPT-Random. The mean number of teaching episodes preceding change was 334 for SPT-Blocked and 179 for SPT-Random. Mastery occurred within an average of 13.7 sessions (1,252 teaching episodes) and 12.4 sessions (1,082 teaching episodes) for SPT-Blocked and SPT-Random, respectively. Comparisons of dosage metric values across practice schedules did not reveal substantial differences. Significant negative correlations were found between follow-up probe performance and the dosage metrics. Conclusions Only a few treatment sessions were needed to achieve initial positive changes in articulation, with mastery occurring within 12–14 sessions for the majority of participants. Earlier occurrence of change or mastery was associated with better follow-up performance. Supplemental Material https://doi.org/10.23641/asha.12592190

Download Full-text