Authors: R. Anitha; Saswati Mukherjee
Addresses: Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Sriperumbudur-602117, India ' Department of Information Science and Technology, CEG Campus, Anna University, Chennai 25, India
Abstract: As the data in cloud computing environment grows exponentially over the past few years, retrieving required data in shorter time becomes tedious. This paper proposes a probabilistic framework for efficient retrieval of data from huge datasets using combined approach of clustering and frequent pattern analysis using maximum frequent transaction (MFT) set algorithm based on similarity of transactions provided by a novel data structure called Bloomier matrix filter (BMF). In the proposed model clustering the metadata file is done on two levels. The first level of cluster is a base cluster which is created in an offline mode, while uploading the data based on keyword using tf-idf and second level of cluster is a derived cluster which is created in an online mode, while downloading the data. Frequent transactions are generated based on the run time statistics of the transaction provided by the Bloomier matrix filter analysis. Based on the run time statistics of the BMF the dynamic cluster is derived. We have implemented the model in a cloud environment and the experimental results shows that our approach is more efficient than the existing search technology and increases throughput by handling more number of queries efficiently with reduced latency.
Keywords: cloud storage; clustering; metadata databases; Bloom filter; frequent itemsets; cache management; cloud computing; data retrieval; frequent pattern analysis; maximum frequent transaction; MFT; search technology.
International Journal of High Performance Computing and Networking, 2017 Vol.10 No.1/2, pp.148 - 155
Available online: 13 Mar 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article