Database workload management through CBR and fuzzy based characterization

Abstract

Database Management System (DBMS) is used as a data source with financial, educational, web and other applications from last many years. Users are connected with the DBMS to update existing records and retrieving reports by executing workloads that consist of complex queries. In order to get the sufficient level of performance, arrangement of workloads is necessary. Rapid growth in data, maximum functionality and changing behavior tends the database workload to be more complex and tricky. Each DBMS experiences complex workloads that are difficult to manage by the humans; human experts take much time to manage database workload efficiently; even in some cases it may become impossible and leads toward malnourishment. This problem leads database practitioners, vendors and researchers toward new challenges. To achieve a satisfactory level of performance, either Database Administrator (DBA) or DBMSs must have the knowledge about the workload shifts. Efficient execution and resource allocation of workload is dependent on the workload type that may be either On Line Transaction Processing (OLTP) or Decision Support System (DSS). The research introduces a way to manage the workload in DBMSs on the basis of the workload type. The main goal of the research is to manage the workload in DBMSs through characterization, scheduler and idleness detection modules. The database workload management is performed by using the case based reasoning characterization; Fuzzy logic based scheduling and finally detection of CPU Idleness. Results are validated through experiments that are performed on real time and benchmark workload to reveal effectiveness and efficiency.

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

“…FL facilitates the knowledge elicitation from a domain expert, eases the transfer of knowledge between domains, and enhances the similarity measurement. Fuzzy logic has been integrated with CBR in hybrid systems [37,38] and used for calculating the fuzzy similarity between cases [22]. However, there are no real studies in the literature for fuzzy-CBR systems for diabetes diagnosis.…”

Prediction Based Workload Performance Evaluation for Disaster Management Spatial Database

“…After taking the differences between the variables used for workload prediction into account, research by Zewdu et al (2009) took 4 of 10 status variable before and after the experiment of another study. Abdul et al (2014) have come up with three variables that give more information about the type of workload. The paper states that three variables can be gained from the process of key write, key read and Table lock.…”

“…The CBR (Cased Based Reasoning) is currently the most popular machine learning technique. CBR has been involved in earlier Workload prediction work (Abdul et al, 2014). It is believed that CBR can provide a suitable paradigm for microarray analysis of prediction, where the rules that define the domain knowledge are difficult to obtain because usually, only a small number of training samples are available.…”

“…Sarwat (2015) elaborates on spatial data investigation session in which the user in the research would not tolerate delays introduced by the fundamental spatial data management system in the execution of effective and efficient queries. Abdul et al (2014) utilised Artificial Intelligent (AI) technique in the prediction of workload. This specific AI technique used Fuzzy Logic.…”

“…Although the implementation of Decision and Classification trees as in Elnaffar et al (2007) was very promising especially after pruning the tree to handle such large data, unfortunately, it led to inaccurate results as the value was cut leaf (knowledge) that was rarely used to improve the speed of analysis. Besides Decision and Classification trees, there are various approaches to machine learning but the most promising algorithm to be implemented is Case-Based Reasoning (CBR) as mentioned by Abdul et al (2014).…”