Orca-SR

Reconstructing a high dimensional unknown signal, using lower dimensional observations is a challenging problem, known as signal reconstruction problem (SRP), with diverse applications including network traffic engineering, medical image reconstruction, and astronomy. Recently the database community...

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Bibliographic Details
Published in:Proceedings of the VLDB Endowment
Main Authors: Augustine, Jees, Shetiya, Suraj, Asudeh, Abolfazl, Thirumuruganathan, Saravanan, Nazi, Azade, Zhang, Nan, Das, Gautam, Srivastava, Divesh
Format: Article in Journal/Newspaper
Language:English
Published: Association for Computing Machinery (ACM) 2020
Subjects:
Orca
Online Access:http://dx.doi.org/10.14778/3415478.3415523
https://dl.acm.org/doi/pdf/10.14778/3415478.3415523
Description
Summary:Reconstructing a high dimensional unknown signal, using lower dimensional observations is a challenging problem, known as signal reconstruction problem (SRP), with diverse applications including network traffic engineering, medical image reconstruction, and astronomy. Recently the database community has shown significant advancements in solving the SRP problem efficiently, effectively, and in scale by leveraging database techniques such as similarity joins. In this demo, we demonstrate Orca-SR that highlights the benefits of signal reconstruction in scale by demonstrating real-time network traffic flow analysis on large networks that were not possible before. Orca-SR is a web application that enables a user to generate network flow and load the network for interactive analysis of the impact of different traffic patterns on signal reconstruction.

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