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Lakshmi Devi, S.
- An Adaptive Resource Management for Adjust Window Sizes & Time Granularities in Data Stream Management Systems
Authors
1 Ponnaiyah Ramajayam College of Engineering & Technology/Department of CSE, Thanjavur, IN
2 Ponnaiyah Ramajayam Engineering College/Department of CSE, Thanjavur, IN
3 Ponnaiyah Ramajayam Engineering College/Department of PG CS, Thanjavur, IN
Source
Data Mining and Knowledge Engineering, Vol 2, No 4 (2010), Pagination:Abstract
DSMS need to control its resources adaptively as stream characteristics and its query workload vary over time. To analysis an approach to adaptive resource management to adjusts window sizes and time granularities for continuous sliding window queries to keep resource usage within limits. In order to quantify the impact of the two techniques on a query plan, to evaluating the cost based system for the resource allocation. An throughout the experimental analyzes to demonstrate the effectiveness, scalability and accuracy of the cost based system.Keywords
DSMS, Window Size, Sliding Window, Time Granularity.
- An Appraisal and Proportional Study of Data Mining Techniques for Web Intrusion Detection Methods
Authors
1 Department of Computer Science, Rathinam College of Arts and Science, Coimbatore, IN
Source
Data Mining and Knowledge Engineering, Vol 10, No 2 (2018), Pagination: 31-34Abstract
Despite of growing statistics technology widely, security has remained one stimulating area for computers and webs. In statistics security, intrusion detection is the act of detecting actions that attempt to compromise the confidentiality, integrity or availability of a resource. Currently many researchers have focused on intrusion detection methods based on data mining techniques as an efficient artifice. Data mining is one of the technologies applied to intrusion detection to invent a new pattern from the massive web data as well as to reduce the strain of the manual compilations of the intrusion and normal behavior patterns. This article reviews the current state of art data mining techniques, compares various data mining techniques used to implement an intrusion detection methods such as Decision Trees, Artificial Neural Web, Naive Bayes, Support Vector Machine and K-Nearest Neighbour Algorithm by highlighting advantages and disadvantages of each of the techniques. Finally, a discussion of the future technologies and methodologies which promise to enhance the ability of computer methods to detect intrusion is provided and current research challenges are pointed out in the field of intrusion detection methods.