3 citations · 3 across the 4 of their papers we have counts for
16 papers
DriftSurf: A Risk-competitive Learning Algorithm under Concept Drift
Ashraf Tahmasbi, Ellango Jothimurugesan, Srikanta Tirthapura +1
When learning from streaming data, a change in the data distribution, also known as concept drift, can render a previously-learned model inaccurate and require training a new model…
Shared-Memory Parallel Maximal Clique Enumeration from Static and Dynamic Graphs
Apurba Das, Seyed-Vahid Sanei-Mehri, Srikanta Tirthapura
Maximal Clique Enumeration (MCE) is a fundamental graph mining problem, and is useful as a primitive in identifying dense structures in a graph. Due to the high computational cost…
Random Sampling for Group-By Queries
Trong Duc Nguyen, Ming-Hung Shih, Sai Sree Parvathaneni +3
Random sampling has been widely used in approximate query processing on large databases, due to its potential to significantly reduce resource usage and response times, at the cost…
Parallel Streaming Random Sampling
Kanat Tangwongsan, Srikanta Tirthapura
This paper investigates parallel random sampling from a potentially-unending data stream whose elements are revealed in a series of element sequences (minibatches). While sampling…
Weighted Reservoir Sampling from Distributed Streams
Rajesh Jayaram, Gokarna Sharma, Srikanta Tirthapura +1
We consider message-efficient continuous random sampling from a distributed stream, where the probability of inclusion of an item in the sample is proportional to a weight associat…
Optimal Random Sampling from Distributed Streams Revisited
Srikanta Tirthapura, David P. Woodruff
We give an improved algorithm for drawing a random sample from a large data stream when the input elements are distributed across multiple sites which communicate via a central coo…