32 citations · 74 across the 12 of their papers we have counts for
17 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…
FLEET: Butterfly Estimation from a Bipartite Graph Stream
Seyed-Vahid Sanei-Mehri, Yu Zhang, Ahmet Erdem Sariyuce +1
We consider space-efficient single-pass estimation of the number of butterflies, a fundamental bipartite graph motif, from a massive bipartite graph stream where each edge represen…
Enumerating Top-k Quasi-Cliques
Seyed-Vahid Sanei-Mehri, Apurba Das, Srikanta Tirthapura
Quasi-cliques are dense incomplete subgraphs of a graph that generalize the notion of cliques. Enumerating quasi-cliques from a graph is a robust way to detect densely connected st…
Shared-Memory Parallel Maximal Clique Enumeration
Apurba Das, Seyed-Vahid Sanei-Mehri, Srikanta Tirthapura
We present shared-memory parallel methods for Maximal Clique Enumeration (MCE) from a graph. MCE is a fundamental and well-studied graph analytics task, and is a widely used primit…
Variance-Optimal Offline and Streaming Stratified Random Sampling
Trong Duc Nguyen, Ming-Hung Shih, Divesh Srivastava +2
Stratified random sampling (SRS) is a fundamental sampling technique that provides accurate estimates for aggregate queries using a small size sample, and has been used widely for…
Onion Curve: A Space Filling Curve with Near-Optimal Clustering
Pan Xu, Cuong Nguyen, Srikanta Tirthapura
Space filling curves (SFCs) are widely used in the design of indexes for spatial and temporal data. Clustering is a key metric for an SFC, that measures how well the curve preserve…
Butterfly Counting in Bipartite Networks
Seyed-Vahid Sanei-Mehri, Ahmet Erdem Sariyuce, Srikanta Tirthapura
We consider the problem of counting motifs in bipartite affiliation networks, such as author-paper, user-product, and actor-movie relations. We focus on counting the number of occu…
Learning Graphical Models from a Distributed Stream
Yu Zhang, Srikanta Tirthapura, Graham Cormode
A current challenge for data management systems is to support the construction and maintenance of machine learning models over data that is large, multi-dimensional, and evolving.…
A Change-Sensitive Algorithm for Maintaining Maximal Bicliques in a Dynamic Bipartite Graph
Apurba Das, Srikanta Tirthapura
We consider the maintenance of maximal bicliques from a dynamic bipartite graph that changes over time due to the addition or deletion of edges. When the set of edges in a graph ch…
Streaming k-Means Clustering with Fast Queries
Yu Zhang, Kanat Tangwongsan, Srikanta Tirthapura
We present methods for k-means clustering on a stream with a focus on providing fast responses to clustering queries. Compared to the current state-of-the-art, our methods provide…
Work-Efficient Parallel and Incremental Graph Connectivity
Natcha Simsiri, Kanat Tangwongsan, Srikanta Tirthapura +1
On an evolving graph that is continuously updated by a high-velocity stream of edges, how can one efficiently maintain if two vertices are connected? This is the connectivity probl…
Incremental Maintenance of Maximal Cliques in a Dynamic Graph
Apurba Das, Michael Svendsen, Srikanta Tirthapura
We consider the maintenance of the set of all maximal cliques in a dynamic graph that is changing through the addition or deletion of edges. We present nearly tight bounds on the m…