Maruthi Rohit Ayyagari

arXiv2019

Cache Contention on Multicore Systems: An Ontology-based Approach

Multicore processors have proved to be the right choice for both desktop and server systems because it can support high performance with an acceptable budget expenditure. In this work, we have compared several works in cache contention and found that such works have identified several techniques for cache contention other than cache size including FSB, Memory Controller and prefetching hardware. We found that Distributed Intensity Online (DIO) is a very promising cache contention algorithm since it can achieve up to 2% from the optimal technique. Moreover, we propose a new framework for cache contention based on resource ontologies. In which ontologies instances will be used for communication between diverse processes instead of grasping schedules based on hardware.

cs.OS↗

arXiv2019

Integrating Association Rules with Decision Trees in Object-Relational Databases

Research has provided evidence that associative classification produces more accurate results compared to other classification models. The Classification Based on Association (CBA) is one of the famous Associative Classification algorithms that generates accurate classifiers. However, current association classification algorithms reside external to databases, which reduces the flexibility of enterprise analytics systems. This paper implements the CBA in Oracle database using two variant models: hardcoding the CBA in Oracle Data Mining (ODM) package and Integrating Oracle Apriori model with the Oracle Decision tree model. We compared the proposed model performance with Naive Bayes, Support Vector Machine, Random Forests, and Decision Tree over 18 datasets from UCI. Results showed that our models outperformed the original CBA model with 1 percent and is competitive to chosen classification models over benchmark datasets.

cs.IR↗