This study presents a data segmentation method, which was intended to improve the performance of the k‐nearest neighbours algorithm for making short‐term traffic volume predictions. According to the introduced method, selected segments of vehicle detector data are searched for records similar to the current traffic conditions, instead of the entire database. The data segments are determined on the basis of a segmentation procedure, which aims to select input data that are useful for the prediction algorithm. Advantages of the proposed method were demonstrated in experiments on real‐world traffic data. Experimental results show that the proposed method not only improves the accuracy of the traffic volume prediction, but also significantly reduces its computational cost.

The Electronic Health Record (EHR) groups all digital documents related to a given patient such as anamnesis, results of the laboratory tests, prescriptions, recorded medical signals as ECG or images, etc. Dealing with such data representation incurs a plethora of problems, such as different data types, even unstructured data (i.e., doctor’s notes), huge and fast-growing volume, etc. Therefore. EHR should be considered as one of the most complex data objects in the information processing industry. Accordingly, taking into consideration its complexity, heterogeneity, fast growth, and size, the analysis of EHR data increasingly needs big data tools. Such tools should be able to analyze datasets characterized by the so-called 4Vs (volume, velocity, variety, and veracity). These notwithstanding, we should also add the fifth V—value—because analytics tool deployment makes sense only if it leads to health-care improvement (as personalized patient care, decreasing unnecessary hospitalization, or reducing patient readmissions). In this study, we focus on the selected aspects of EHR analysis from the big data perspective.