arXiv subjects

Srikanta Tirthapura

Publications and source records attributed to Srikanta Tirthapura.

23 records · Page 2Linked to original sources

arXiv2014

Enumerating Maximal Bicliques from a Large Graph using MapReduce

We consider the enumeration of maximal bipartite cliques (bicliques) from a large graph, a task central to many practical data mining problems in social network analysis and bioinformatics. We present novel parallel algorithms for the MapReduce platform, and an experimental evaluation using Hadoop MapReduce. Our algorithm is based on clustering the input graph into smaller sized subgraphs, followed by processing different subgraphs in parallel. Our algorithm uses two ideas that enable it to scale to large graphs: (1) the redundancy in work between different subgraph explorations is minimized through a careful pruning of the search space, and (2) the load on different reducers is balanced through the use of an appropriate total order among the vertices. Our evaluation shows that the algorithm scales to large graphs with millions of edges and tens of mil- lions of maximal bicliques. To our knowledge, this is the first work on maximal biclique enumeration for graphs of this scale.

cs.DC↗

arXiv2013

Identifying Correlated Heavy-Hitters in a Two-Dimensional Data Stream

We consider online mining of correlated heavy-hitters from a data stream. Given a stream of two-dimensional data, a correlated aggregate query first extracts a substream by applying a predicate along a primary dimension, and then computes an aggregate along a secondary dimension. Prior work on identifying heavy-hitters in streams has almost exclusively focused on identifying heavy-hitters on a single dimensional stream, and these yield little insight into the properties of heavy-hitters along other dimensions. In typical applications however, an analyst is interested not only in identifying heavy-hitters, but also in understanding further properties such as: what other items appear frequently along with a heavy-hitter, or what is the frequency distribution of items that appear along with the heavy-hitters. We consider queries of the following form: In a stream S of (x, y) tuples, on the substream H of all x values that are heavy-hitters, maintain those y values that occur frequently with the x values in H. We call this problem as Correlated Heavy-Hitters (CHH). We formulate an approximate formulation of CHH identification, and present an algorithm for tracking CHHs on a data stream. The algorithm is easy to implement and uses workspace which is orders of magnitude smaller than the stream itself. We present provable guarantees on the maximum error, as well as detailed experimental results that demonstrate the space-accuracy trade-off.

cs.DB↗

arXiv2013

Parallel Triangle Counting in Massive Streaming Graphs

The number of triangles in a graph is a fundamental metric, used in social network analysis, link classification and recommendation, and more. Driven by these applications and the trend that modern graph datasets are both large and dynamic, we present the design and implementation of a fast and cache-efficient parallel algorithm for estimating the number of triangles in a massive undirected graph whose edges arrive as a stream. It brings together the benefits of streaming algorithms and parallel algorithms. By building on the streaming algorithms framework, the algorithm has a small memory footprint. By leveraging the paralell cache-oblivious framework, it makes efficient use of the memory hierarchy of modern multicore machines without needing to know its specific parameters. We prove theoretical bounds on accuracy, memory access cost, and parallel runtime complexity, as well as showing empirically that the algorithm yields accurate results and substantial speedups compared to an optimized sequential implementation. (This is an expanded version of a CIKM'13 paper of the same title.)

cs.DB↗

arXiv2011

A novel graph-based formulation for characterizing morphology with application to organic solar cells

Organic solar cells have the potential for widespread usage due to their promise of low cost, roll-to-roll manufacturability, and mechanical flexibility. However, deployment is impeded by their relatively low power conversion efficiencies. The last decade has seen significant progress in enhancing the power conversion of these devices through various strategies. One such approach is based on morphology control. This is because morphology affects all phenomena involved in solar conversion: light absorption and electron-hole pair (exciton) generation; exciton diffusion and dissociation into free charges; and transport of charges to the electrodes. Progress in experimental characterization and computational modeling now allow reconstruction and imaging of the thin film morphology. This opens up the possibility of rationally linking fabrication with morphology, as well as morphology with performance. In this context, a comprehensive set of computational tools to rapidly quantify and classify the heterogeneous internal structure of thin films will be invaluable in linking process, structure and property. We present a novel graph-based framework to efficiently construct a broad suite of physically meaningful morphology descriptors. These morphology descriptors are further classified according to the physical subprocesses within an organic solar cells. The approach is motivated by the equivalence between a discretized morphology and a labeled, weighted, undirected graph. We utilize this approach to pose key questions related to structure characterization. We subsequently construct estimates and upper bounds of various efficiencies. The approach is showcased by characterizing the effect of thermal annealing on time-evolution of a thin film morphology. We conclude by formulating natural extensions to characterize crystallinity and anisotropy of the morphology using the framework.

cond-mat.mtrl-sci↗

arXiv2010

A Streaming Approximation Algorithm for Klee's Measure Problem

The efficient estimation of frequency moments of a data stream in one-pass using limited space and time per item is one of the most fundamental problem in data stream processing. An especially important estimation is to find the number of distinct elements in a data stream, which is generally referred to as the zeroth frequency moment and denoted by $F_0$. In this paper, we consider streams of rectangles defined over a discrete space and the task is to compute the total number of distinct points covered by the rectangles. This is known as the Klee's measure problem in 2 dimensions. We present and analyze a randomized streaming approximation algorithm which gives an $(ε, δ)$-approximation of $F_0$ for the total area of Klee's measure problem in 2 dimensions. Our algorithm achieves the following complexity bounds: (a) the amortized processing time per rectangle is $O(\frac{1}{ε^4}\log^3 n\log\frac{1}δ)$; (b) the space complexity is $O(\frac{1}{ε^2}\log n \log\frac{1}δ)$ bits; and (c) the time to answer a query for $F_0$ is $O(\log\frac{1}δ)$, respectively. To our knowledge, this is the first streaming approximation for the Klee's measure problem that achieves sub-polynomial bounds.

cs.DS↗