Main Article Content
Generally, Users perform searches to satisfy their information needs. Now a day’s lots of people are using search engine to satisfy information need. Server search is one of the techniques of searching the information. the Growth of data brings new changes in Server. The data usually proposed in timely fashion in server. If there is increase in latency then it may cause a massive loss to the enterprises. The similarity detection plays very important role in data. while there are many algorithms are used for similarity detection such as Shingle, Simhas TSA and Position Aware sampling algorithm. The Shingle Simhash and Traits read entire files to calculate similar values. It requires the long delay in growth of data set value. instead of reading entire Files PAS sample some data in the form of Unicode to calculate similarity characteristic value.PAS is the advance technique of TSA. However slight modification of file will trigger the position of file content .Therefore the failure of similarity identification is there due to some modifications.. This paper proposes an Enhanced Position-Aware Sampling algorithm (EPAS) to identify file similarity for the Server. EPAS concurrently samples data blocks from the modulated file to avoid the position shift by the modifications. While there is an metric is proposed to measure the similarity between different files and make the possible detection probability close to the actual probability. In this paper describes a PAS algorithm to reduce the time overhead of similarity detection. Using PAS algorithm we can reduce the complication and time for identifying the similarity. Our result demonstrate that the EPAS significantly outperforms the existing well known algorithms in terms of time. Therefore, it is an effective approach of similarity identification for the Server.
How to Cite
, S. S. B. A. P. A. L. K. “PAS: A Sampling Based Similarity Identification Algorithm for Compression of Unicode Data Content”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 6, no. 6, June 2018, pp. 254-9, doi:10.17762/ijritcc.v6i6.1669.