Orca-SR

• 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

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.