Anand Padmanabha Iyer |


Mitigating the Latency-Accuracy Trade-off in Mobile Data Analytics Systems

ASAP: Fast, Approximate Graph Pattern Mining at Scale

Towards Fast and Scalable Graph Pattern Mining

Monarch: Gaining Command on Geo-Distributed Graph Analytics

Bridging the GAP: Towards Approximate Graph Analytics

A Scalable Distributed Spatial Index for the Internet-of-Things

Automating Diagnosis of Cellular Radio Access Network Problems

Blog Posts

RISELab publication wins best paper award at SIGMOD GRADES-NDA 2018

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RISELab publication “Bridging the GAP: Towards Approximate Graph Analytics“, authored by Anand Iyer, Aurojit Panda, Shivaram Venkataraman, Mosharaf Chowdhury, Prof. Aditya Akella, Prof. Scott Shenker and Prof. Ion Stoica, has won the best paper award at ACM GRADES-NDA 2018, co-located with SIGMOD. The paper proposes the use of approximation to speed up distributed graph processing.