Deduplication is mainly used to solve the problem of space and is known as a space-efficient technique. A two step algorithm called ‘brushing’ has been proposed in this paper to solve individual file deduplication. The main aim of the algorithm is to overcome the space related problem, at the same time the algorithm also takes care of time complexity problem. The proposed algorithm has extremely low RAM overhead. The first phase of the algorithm checks the similar entities and removes them thus grouping only unique entities and in the second phase while the unique file is hashed, the unique entities are represented as index values thereby reducing the size of the file to a great extent. Test results shows that if a file contains 40–50 % duplicate data, then this technique reduces the size up to 2/3 of the file. This algorithm has a high deduplication throughput on the file system.

The world today is facing the problem of ever-increasing level of pollution of water bodies. The seriousness of this problem calls for specialized attention to predict future trend. Data mining is the most popular technique for handling huge amount of geo-spatial data. The variety and size of information to be processed for proper trend analysis towards water quality management makes the use of data mining concepts all the more relevant. This paper summarizes the on-going researches involving data mining applications in this area. The applications have been observed to use the techniques of ANN, Fuzzy, or GIS modeling for featurewise classification of water pollutants. The parameters need to be clustered depending upon their area of application. The respective advantages of each model in classifying the parameters have been identified. The authors have arrived at a set of parameters best suited for analyzing the effect of pollutants on the river Churni, an effluent of the river Bhagirathi. Copyright 2012 ACM.