Abstract

Abstract

Abstract

Abstract
Nowadays information is generated and gathered from distributed streaming data sources, stressing communications and computing infrastructure, making it hard to transmit, compute, and store. Knowledge discovery from ubiquitous data streams has become a major goal for all sorts of applications, mostly based on unsupervised techniques such as clustering. Two subproblems exist: clustering streaming data observations and clustering streaming data sources. The former searches for dense regions of the data space, identifying hot spots where data sources tend to produce data, while the latter finds groups of sources that behave similarly over time. In order to better assess the current status of this topic, this article presents a thorough review on distributed algorithms addressing either of the subproblems. We characterize clustering algorithms for ubiquitous data streams, discussing advantages and disadvantages of distributed procedures. Overall, distributed stream clustering methods improve communication ratios, processing speed, and resources consumption, while achieving similar clustering validity as the centralized counterparts. (C) 2013 John Wiley & Sons, Ltd.

Abstract
Incremental learning from data streams is increasingly attracting research focus due to many real streaming problems (such as learning from transactions, sensors or other sequential observations) that require processing and forecasting in the real time. In this paper we deal with two issues related to incremental learning - prediction accuracy and prediction explanation - and demonstrate their applicability on several streaming problems for predicting electricity load in the future. For improving prediction accuracy we propose and evaluate the use of two reliability estimators that allow us to estimate prediction error and correct predictions. For improving interpretability of the incremental model and its predictions we propose an adaptation of the existing prediction explanation methodology, which was originally developed for batch learning from stationary data. The explanation methodology is combined with a state-of-the-art concept drift detector and a visualization technique to enhance the explanation in dynamic streaming settings. The results show that the proposed approaches can improve prediction accuracy and allow transparent insight into the modeled concept.

Abstract
Patients' privacy is critical in healthcare but users of Electronic Health Records (EHR) frequently circumvent existing security rules to perform their daily work. Users are so-called the weakest link in security but they are, many times, part of the solution when they are involved in systems' design. In the healthcare domain, the focus is to treat patients (many times with scarce technological, time and human resources) and not to secure their information. Therefore, security must not interfere with this process but be present, nevertheless. Security usability issues must also be met with interdisciplinary knowledge from human-computer-interaction, social sciences and psychology. The main goal of this paper is to raise security and usability awareness with the analysis of users' interaction logs of a BreakTheGlass (BTG) feature. This feature is used to restrict access to patient reports to a group of healthcare professionals within an EHR but also permit access control override in emergency and/or unexpected situations. The analysis of BTG user interaction logs allows, in a short time span and transparently to the user, revealing security and usability problems. This log analysis permits a better choice of methodologies to further apply in the investigation and resolution of the encountered problems.