In modern society, our everyday life has a close connection with traffic issues. One of the most burning issues is about predicting traffic accidents. Predicting accidents on the road can be achieved by classification analysis, a data mining procedure requiring enough data to build a learning model. Regarding building such a predicting system, there are several problems. It requires lots of hardware resources to collect traffic data and analyze it for predicting traffic accidents since the data is very huge. Furthermore, data related to traffic accidents is few comparing with data which is not related to them. The numbers of two types of data are imbalanced. The purpose of this paper is to build a predicting model that can resolve all these problems. This paper suggests using Hadoop framework to process and analyze big traffic data efficiently and a sampling method to resolve the problem of data imbalance. Based on this, the predicting system, first of all, preprocess traffic big data and analyzes it to create data for the learning system. The imbalance of created data is corrected by a sampling method. To improve predicting accuracy, corrected data is classified into several groups, to which classification analysis is applied. These analysis steps are processed by Hadoop framework.

During the development of the software, a variety of bugs are reported. Several bug tracking systems, such as, Bugzilla, MantisBT, Trac, JIRA, are used to deal with reported bug information in many open source development projects. Bug reports in bug tracking system would be triaged to manage bugs and determine developer who is responsible for resolving the bug report. As the size of the software is increasingly growing and bug reports tend to be duplicated, bug triage becomes more and more complex and difficult. In this paper, we present an approach to assign bug reports to appropriate developers, which is a main part of bug triage task. At first, words which have been included the resolved bug reports are classified according to each developer. Second, words in newly bug reports are selected. After first and second steps, vectors whose items are the selected words are generated. At the third step, TF-IDF(Term frequency -Inverse document frequency) of the each selected words are computed, which is the weight value of each vector item. Finally, the developers are recommended based on the similarity between the developer's word vector and the vector of new bug report. We conducted an experiment on Eclipse JDT and CDT project to show the applicability of the proposed approach. We also compared the proposed approach with an existing study which is based on machine learning. The experimental results show that the proposed approach is superior to existing method.