Department of Computer Science and Information Systems

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Author: search.filters.author.Goyal, PoonamSubject: search.filters.subject.Algorithm

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    Designing self-adaptive websites using online hotlink assignment algorithm
    (ACM Digital Library, 2009-12) Goyal, Navneet; Goyal, Poonam
    An online hotlink assignment algorithm is proposed for designing adaptive websites. The objective is to reach desired pages on a website in minimum number of clicks, thereby reducing the load on the web server. As a consequence, the traffic on the internet is also reduced. The hotlinks are assigned based on the frequency of access of pages. We model a website as a single source directed graph. Optimal hotlink assignment problem is NP-hard for general graphs. The website graph is reduced to a Breadth First Search (BFS) tree which maintains the semantic relationships between web pages. The proposed online algorithm can place at most k hotlinks per page with a maximum of l hotlinks on the entire website, where k«l. The input stream is simulated using the Zipf distribution. The results presented in the paper compare the performance of the online algorithm with the optimal offline algorithm.
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    A concurrent k-NN search algorithm for R-tree
    (ACM Digital Library, 2015-10) Goyal, Navneet; Goyal, Poonam; Challa, Jagat Sesh
    k-nearest neighbor (k-NN) search is one of the commonly used query in database systems. It has its application in various domains like data mining, decision support systems, information retrieval, multimedia and spatial databases, etc. When k-NN search is performed over large data sets, spatial data indexing structures such as R-trees are commonly used to improve query efficiency. The best-first k-NN (BF-kNN) algorithm is the fastest known k-NN over R-trees. We present CBF-kNN, a concurrent BF-kNN for R-trees, which is the first concurrent version of k-NN we know of for R-trees. CBF-kNN uses one of the most efficient concurrent priority queues known as mound. CBF-kNN overcomes the concurrency limitations of priority queues by using a tree-parallel mode of execution. CBF-kNN has an estimated speedup of O(p/k) for p threads. Experimental results on various real datasets show that the speedup in practice is close to this estimate.
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    Spatial Locality Aware, Fast, and Scalable SLINK Algorithm for Commodity Clusters
    (IEEE, 2016) Goyal, Navneet; Goyal, Poonam
    Single linkage (SLINK) hierarchical clustering algorithm is a preferred clustering algorithm over traditional partitioning-based clustering as it does not require the number of clusters as input. But, due to its high time complexity and inherent data dependencies, it does not scale well for large datasets. In this paper, we parallelize an efficient implementation of SLINK algorithm to leverage a commodity cluster of multicore workstations. We present, dGridSlink, a distributed algorithm, which outperforms the best existing parallel solution in literature for all the real datasets considered. We also propose a hybrid parallel algorithm hGridSLINK for a cluster of multicore nodes. The proposed parallel algorithms are scalable and can cluster (several) millions of data points efficiently, without compromising the quality of clustering.