MANISH BABASAHEB GUDADHE
@stvincentngp.edu.in
Associate Professor , Department of Computer Engineering
St. Vincent Pallotti College of Engineering & Technology
EDUCATION
Ph. D. (Submiited) Comp. Sci. & Engg RTM Nagpur University, Nagpur Dec 2021 -- --
M. E. (Wireless Comm. & Camp.) RTM Nagpur University, Nagpur 2008 Distinction 75.40 %
B. E. (Computer Technology) Nagpur University, Nagpur 2000 Firstclass 64%
H.S.S.C. Maharashtra Board, Nagpur 1996 Firstclass 67.33%
S.S.C. Maharashtra Board, Nagpur 1994 Distinction 81.71%
RESEARCH INTERESTS
Database, Data Analytics, Data Replication, Cloud Computing
Scopus Publications
Scopus Publications
Abhinav Muley and Manish Gudadhe
Springer Berlin Heidelberg
High-utility patterns generated from mining the unknown and different databases can be clustered to identify the most valid patterns. Sources include the internet, journals, and enterprise data. Here, a grid-based clustering method (CLIQUE) is used to aggregate patterns mined from multiple databases. The proposed model forms the clusters based on all the utilities of patterns to determine the interestingness and the correct interval of its utility measure. The set of all patterns is collected by first mining the databases individually, at the local level. The problem arises when the same pattern is identified by all of the databases but with different utility factors. In this case, it becomes difficult to decide whether the pattern should be considered as a valid or not, due to the presence of multiple utility values. Hence, an aggregation model is applied to test whether a pattern satisfies the utility threshold set by a domain expert. We found that the proposed aggregation model effectively clusters all of the interesting patterns by discarding those patterns that do not satisfy the threshold condition. The proposed model accurately optimizes the utility interval of the valid patterns.
Abhinav Muley and Manish Gudadhe
MDPI AG
In large organizations, it is often required to collect data from the different geographic branches spread over different locations. Extensive amounts of data may be gathered at the centralized location in order to generate interesting patterns via mono-mining the amassed database. However, it is feasible to mine the useful patterns at the data source itself and forward only these patterns to the centralized company, rather than the entire original database. These patterns also exist in huge numbers, and different sources calculate different utility values for each pattern. This paper proposes a weighted model for aggregating the high-utility patterns from different data sources. The procedure of pattern selection was also proposed to efficiently extract high-utility patterns in our weighted model by discarding low-utility patterns. Meanwhile, the synthesizing model yielded high-utility patterns, unlike association rule mining, in which frequent itemsets are generated by considering each item with equal utility, which is not true in real life applications such as sales transactions. Extensive experiments performed on the datasets with varied characteristics show that the proposed algorithm will be effective for mining very sparse and sparse databases with a huge number of transactions. Our proposed model also outperforms various state-of-the-art distributed models of mining in terms of running time.
Manish B. Gudadhe and Avinash J. Agrawal
ACM Press
Data replication in cloud computing emerged as a popular alternative to the traditional cluster based replication because of the exponential growth in usage of the internet with an explosion of data sources and usability of data. To provide high data availability, improved performance and sustaining growing demand of data, cloud paradigm became a popular platform for replication of the frequently used data. This research article mainly concentrates on the availability of replicated data in the cloud under various situations. The variations in popularity of content, the number of replicas, placement of replicas, distribution of data nodes and workload will be analyzed to their impacts on the serviceability and data availability. The paper will investigate replication strategies based on heterogeneous cloud storage, explosive query load outburst, network bandwidth and varying content popularity. This work is an attempt to provide a comparative analysis of performance under different values of number of replicas, network bandwidth and content popularity.