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Sangeetha, A.
- PAM:An Efficient and Privacy-Aware Monitoring Framework for Continuously Moving Object
Authors
1 Department of Computer Science and Engineering, Alagappa University, Karaikudi, Tamilnadu, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 11 (2011), Pagination: 684-689Abstract
Efficiency and privacy are two fundamental issues in moving object monitoring. This paper proposes a privacy-aware monitoring (PAM) framework that addresses both issues. The framework distinguishes itself from the existing work by being the first to holistically address the issues of location updating in terms of monitoring accuracy, efficiency and privacy, in particular when and how mobile clients should send location updates to the server. Based on the notions of safe region and most probable result, PAM performs location updates only when they would likely alter the query results. Furthermore, by designing various client update strategies, the framework is flexible and able to optimize accuracy, privacy or efficiency. We develop efficient query evaluation/reevaluation and safe region computation algorithms in the framework. The experimental results show that PAM substantially outperforms traditional schemes in terms of monitoring accuracy, CPU cost and scalability while achieving close-to-optimal communication cost.Keywords
Spatial Databases, Location-Dependent and Sensitive, Mobile Applications.
- A Survey on Data Mining Approaches to Handle Agricultural Data
Authors
1 Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore, Tamilnadu, IN
Source
Data Mining and Knowledge Engineering, Vol 8, No 9 (2016), Pagination: 286-290Abstract
Agriculture is the backbone of our country, where every activities and events in the agriculture depends on the area or locality. This variation creates huge number of data’s, and that to be maintained effectively. These uncertain and dynamic data’s are very tedious to maintain and to manipulate. To overcome the above issues, several studies introduced numerous techniques in data mining. This paper gives a survey about the data mining techniques and tools used in agriculture. The data mining techniques used in agriculture which includes clustering techniques such as K-Means, Fuzzy, KNN, and classification techniques such as Bayesian, Artificial Neural network, SVM and Decision Tree etc. This also makes discussion about the problems of those techniques in the real time analysis.