Distributed and scalable platform for collaborative analysis of massive time series data sets

A presentation at DATA 2019: International Conference on Data Science, E-learning and Information Systems in in Prague, Czechia by Ed Duarte

Introduction

Introduction

Introduction - Time series analysis

Introduction - Time series analysis

Introduction - Time series visualization

Introduction - Time series visualization

Introduction - Annotation

Introduction - Annotation

Proposal

Proposal

Proposal - Data model

Proposal - Data model

Proposal - Data management (1/2)

Proposal - Data management (1/2)

Proposal - Data management (2/2)

Proposal - Data management (2/2)

Proposal - Architecture (1/7)

Proposal - Architecture (1/7)

Proposal - Architecture (2/7)

Proposal - Architecture (2/7)

Proposal - Architecture (3/7)

Proposal - Architecture (3/7)

Proposal - Architecture (4/7)

Proposal - Architecture (4/7)

Proposal - Architecture (5/7)

Proposal - Architecture (5/7)

Proposal - Architecture (6/7)

Proposal - Architecture (6/7)

Proposal - Architecture (7/7)

Proposal - Architecture (7/7)

Proposal - Annotations

Proposal - Annotations

DEMO

DEMO

Evaluation - Time series in PostgreSQL (1/3)

Evaluation - Time series in PostgreSQL (1/3)

Evaluation - Time series in PostgreSQL (2/3)

Evaluation - Time series in PostgreSQL (2/3)

Evaluation - Time series in PostgreSQL (3/3)

Evaluation - Time series in PostgreSQL (3/3)

Conclusion

Conclusion

END

END

High-performant webapp that allows researchers to annotate time series patterns while preventing data loss from overlapping contributions or unsanctioned changes.

Resources

The following resources were mentioned during the presentation or are useful additional information.