OPTIMIZED FEATURE SELECTION BASED PREDICTIVE ROUND ROBIN SCHEDULING (OFS-PRRS)
Main Article Content
Abstract
Optimized Feature Selection based Predictive Round Robin Scheduling (OFS-PRRS) Technique for stream data in big data analytics with higher prediction accuracy and lesser scheduling time. In OFS-PRRS technique, Least Absolute Shrinkage and Selection Operator (LASSO) function is used for feature selection. LASSO function in big data analytics is used based on assumption of linear dependency between input features and output value.
Downloads
Download data is not yet available.
Article Details
Section
Articles
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.