Implementing bi-temporal properties into various NoSQL database categories

© Research Institute for Intelligent Computer Systems, 2019. NoSQL database systems have emerged and developed at an accelerating rate in the last years. Attractive properties such as scalability and performance, which are needed by many applications today, contributed to their increasing popularity. Time is very important aspect in many applications. Many NoSQL database systems do not offer built in management for temporal properties. In this paper, we discuss how we can embed temporal properties in NoSQL databases. We review and differentiate between the most popular NoSQL stores. Moreover, we propose various solutions to modify data models for embedding bitemporal properties in two of the most popular categories of NoSQL databases (Key-value stores and Column stores). In addition, we give examples of how to represent bitemporal properties using Redis Key-value store and Cassandra column oriented store. This work can be used as basis for designing and implementing temporal operators and temporal data management in NoSQL databases

Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns

Software defect prediction is a promising approach aiming to improve software quality and testing efficiency by providing timely identification of defect-prone software modules before the actual testing process begins. These prediction results help software developers to effectively allocate their limited resources to the modules that are more prone to defects. In this paper, a hybrid heterogeneous ensemble approach is proposed for the purpose of software defect prediction. Heterogeneous ensembles consist of set of classifiers of different learning base methods in which each of them has its own strengths and weaknesses. The main idea of the proposed approach is to develop expert and robust heterogeneous classification models. Two versions of the proposed approach are developed and experimented. The first is based on simple classifiers, and the second is based on ensemble ones. For evaluation, 21 publicly available benchmark datasets are selected to conduct the experiments and benchmark the proposed approach. The evaluation results show the superiority of the ensemble version over other well-regarded basic and ensemble classifiers