Authors:
Eduardo Duarte
1
;
Diogo Gomes
2
;
David Campos
3
and
Rui L. Aguiar
4
Affiliations:
1
Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro and Portugal
;
2
Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal, Institute of Telecommunications, University of Aveiro, Aveiro and Portugal
;
3
Bosch Thermotechnology, Aveiro 3800-533 and Portugal
;
4
Institute of Telecommunications, University of Aveiro, Aveiro and Portugal
Keyword(s):
Time Series, Annotations, Annotation Systems, Collaborative Software, Data Analysis, Information Science, Data Modeling, Knowledge Management, Database Management Systems, Distributed Systems, Information Visualization.
Related
Ontology
Subjects/Areas/Topics:
Architectural Concepts
;
Artificial Intelligence
;
Business Analytics
;
Computer Vision, Visualization and Computer Graphics
;
Data Analytics
;
Data Engineering
;
Data Management and Quality
;
Data Modeling and Visualization
;
Databases and Data Security
;
General Data Visualization
;
Information and Scientific Visualization
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Modeling and Managing Large Data Systems
;
Open Source Databases
;
Symbolic Systems
Abstract:
The recent expansion of metrification on a daily basis has led to the production of massive quantities of data, which in many cases correspond to time series. To streamline the discovery and sharing of meaningful information within time series, a multitude of analysis software tools were developed. However, these tools lack appropriate mechanisms to handle massive time series data sets and large quantities of simultaneous requests, as well as suitable visual representations for annotated data. We propose a distributed, scalable, secure and high-performant architecture that allows a group of researchers to curate a mutual knowledge base deployed over a network and to annotate patterns while preventing data loss from overlapping contributions or unsanctioned changes. Analysts can share annotation projects with peers over a reactive web interface with a customizable workspace. Annotations can express meaning not only over a segment of time but also over a subset of the series that coexi
st in the same segment. In order to reduce visual clutter and improve readability, we propose a novel visual encoding where annotations are rendered as arcs traced only over the affected curves. The performance of the prototype under different architectural approaches was benchmarked.
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