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.

The trustworthiness of nodes in Vehicular Ad-Hoc Networks (VANETs) is essential for disseminating truthful event messages. False messages may cause vehicles to behave in unintended ways, creating an unreliable transportation system. The efficiency and reliability of the transportation system can be obtained through trustworthy vehicular nodes providing correct event messages. In a VANET, the consensus issue can be resolved by employing blockchain. Even if we employ blockchain in a VANET, the trustworthiness of each message recorded needs to be verified separately since the blockchain itself does not guarantee the trust level of each event message. For instance, when there are multiple conflicting messages associated with a single accident on the road, a vote based on majority opinion can be considered one option for making a decision regarding the accident. In this work, we design the VANET event message clustering algorithm (VEMCA) to resolve the conflicting message problem. Furthermore, we develop a simulator for the VANET environment that demonstrates how the clustering algorithm can be used for event message validation. Experimental results show that our algorithm outperforms state-of-the-art clustering algorithms in terms of accuracy, precision, recall, f1-score, and computational time. INDEX TERMS VANET, clustering algorithm, trustworthiness, blockchain, simulator I. INTRODUCTION A. BACKGROUND VOLUME 4, 2016