Redundancy detection in semistructured case bases

2001 ◽  
Vol 13 (3) ◽  
pp. 513-518 ◽  
Author(s):  
K. Racine ◽  
Qiang Yang
Keyword(s):  
Redundancy Detection

Minimum attribute reduction algorithm based on quick extraction and multi-strategy social spider optimization

2021 ◽  
pp. 1-16
Author(s):  
Qianjin Wei ◽  
Chengxian Wang ◽  
Yimin Wen
Keyword(s):  
Rough Set Theory ◽  
Attribute Reduction ◽  
Search Space ◽  
Initial State ◽  
Solution Quality ◽  
Social Spider ◽  
Opposition Based Learning ◽  
Social Spider Optimization ◽  
Intelligent Optimization Algorithm ◽  
Redundancy Detection

Intelligent optimization algorithm combined with rough set theory to solve minimum attribute reduction (MAR) is time consuming due to repeated evaluations of the same position. The algorithm also finds in poor solution quality because individuals are not fully explored in space. This study proposed an algorithm based on quick extraction and multi-strategy social spider optimization (QSSOAR). First, a similarity constraint strategy was called to constrain the initial state of the population. In the iterative process, an adaptive opposition-based learning (AOBL) was used to enlarge the search space. To obtain a reduction with fewer attributes, the dynamic redundancy detection (DRD) strategy was applied to remove redundant attributes in the reduction result. Furthermore, the quick extraction strategy was introduced to avoid multiple repeated computations in this paper. By combining an array with key-value pairs, the corresponding value can be obtained by simple comparison. The proposed algorithm and four representative algorithms were compared on nine UCI datasets. The results show that the proposed algorithm performs well in reduction ability, running time, and convergence speed. Meanwhile, the results confirm the superiority of the algorithm in solving MAR.

scholarly journals Combinatorial redundancy detection

2016 ◽  
Vol 265 (1) ◽  
pp. 47-65 ◽  
Author(s):  
Komei Fukuda ◽  
Bernd Gärtner ◽  
May Szedlák
Keyword(s):  
Redundancy Detection

Dynamic video summarization using two-level redundancy detection

2008 ◽  
Vol 42 (2) ◽  
pp. 233-250 ◽  
Author(s):  
Yue Gao ◽  
Wei-Bo Wang ◽  
Jun-Hai Yong ◽  
He-Jin Gu
Keyword(s):  
Video Summarization ◽  
Redundancy Detection

scholarly journals Probability Model for Data Redundancy Detection in Sensor Networks

2009 ◽  
Vol 5 (2) ◽  
pp. 195-204 ◽  
Author(s):  
Suman Kumar ◽  
Seung-Jong Park
Keyword(s):  
Sensor Networks ◽  
Data Dissemination ◽  
Probability Model ◽  
Poisson Processes ◽  
Sensor Nodes ◽  
Data Redundancy ◽  
Share Data ◽  
Degree Of Confidence ◽  
Redundancy Detection ◽  
Bivariate Poisson Process

Sensor networks are made of autonomous devices that are able to collect, store, process and share data with other devices. Large sensor networks are often redundant in the sense that the measurements of some nodes can be substituted by other nodes with a certain degree of confidence. This spatial correlation results in wastage of link bandwidth and energy. In this paper, a model for two associated Poisson processes, through which sensors are distributed in a plane, is derived. A probability condition is established for data redundancy among closely located sensor nodes. The model generates a spatial bivariate Poisson process whose parameters depend on the parameters of the two individual Poisson processes and on the distance between the associated points. The proposed model helps in building efficient algorithms for data dissemination in the sensor network. A numerical example is provided investigating the advantage of this model.

scholarly journals A Predictive Acknowledgement Model for Enhancing Traffic Redundancy Elimination in Cloud

2019 ◽  
Vol 8 (9) ◽  
pp. 2571-2580
Keyword(s):  
Cloud Storage ◽  
Smart Phones ◽  
Data Traffic ◽  
Bandwidth Utilization ◽  
Redundancy Elimination ◽  
Massive Growth ◽  
Simulation Purpose ◽  
Redundancy Detection ◽  
Cloud Users ◽  
Cloud User

Due to the huge increase in the utilization of cloud storage in recent days, it leads to a massive growth in data traffic from application based servers to applications like smart phones, which not only influences batteries and computational capacities but as well swamp down multi-hopping strategies during data transmission. To resolve this crisis, traffic redundancy elimination (TRE) is an effectual solution, where the chunks to be transmitted will be directly fetched out from the receivers’ cache. Moreover, prevailing solutions cannot be directly applied or it is not appropriate for smart phones owing to its energy overhead ad high computation that is imposed on the applications. In order to overcome this problem, in this investigation, a novel a Predictive Acknowledgement for Eliminating Traffic (PACKET) is proposed which comprises of three significant elements. Initially, every application possess a clone in cloud that are responsible for calculating intensive tasks like detecting redundancy and parsing traffic. Secondly, consider that every cloud user has some specific applications like Facebook to be used in regular day to day life, every clones of cloud has to selectively determine the applications that are most frequently utilized and also reduce the high redundancy ratio. Thirdly, some cloud users always possess certain common applications; the proposed PACKET clusters those clones to co-operatively perform redundancy detection so as to diminish cache resource consumption in cloud. The simulation is carried out in MATLAB environment; the traces of applications are collected from online available data and are utilized for simulation purpose. Experimental outcomes demonstrates that PACKET can attain much higher hit ratio, reduced E2E delay, increased E2E throughput, energy efficiency and effectual bandwidth utilization in contrast to existing approaches. The proposed PACKET shows better and efficient trade-off than prevailing techniques.

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