Monitoring data streams

Stream monitoring is concerned with analyzing data that is represented in the form of infinite streams. This field has gained prominence in recent years, as streaming data is generated in increasing volume and dimension in a variety of areas. It finds application in connection with monitoring industrial sensors, "smart" technology like smart houses and smart cars, wearable devices used for medical and physiological monitoring, but also in environmental surveillance or finance. However, stream monitoring is a challenging task due to the diverse and changing nature of the streaming data, its high volume and high dimensionality with thousands of sensors producing streams with millions of measurements over short time spans. Automated, scalable and efficient analysis of these streams can help to keep track of important events, highlight relevant aspects and provide better insights into the monitored system. In this thesis, we propose techniques adapted to these tasks in supervised and unsupervised settings, in particular Stream Classification and Stream Dependency Monitoring. After a motivating introduction, we introduce concepts related to streaming data and discuss technological frameworks that have emerged to deal with streaming data in the second chapter of this thesis. We introduce the notion of information theoretical entropy as a useful basis for data monitoring in the third chapter. In the second part of the thesis, we present Probabilistic Hoeffding Trees, a novel approach towards stream classification. We will show how probabilistic learning greatly improves the flexibility of decision trees and their ability to adapt to changes in data streams. The general technique is applicable to a variety of classification models and fast to compute without significantly greater memory cost compared to regular Hoeffding Trees. We show that our technique achieves better or on-par results to current state-of-the-art tree classification models on a variety of large, synthetic and real life data sets. In the third part of the thesis, we concentrate on unsupervised monitoring of data streams. We will use mutual information as entropic measure to identify the most important relationships in a monitored system. By using the powerful concept of mutual information we can, first, capture relevant aspects in a great variety of data sources with different underlying concepts and possible relationships and, second, analyze theoretical and computational complexity. We present the MID and DIMID algorithms. They perform extremely efficient on high dimensional data streams and provide accurate results, outperforming state-of-the-art algorithms for dependency monitoring. In the fourth part of this thesis, we introduce delayed relationships as a further feature in the dependency analysis. In reality, the phenomena monitored by e.g. some type of sensor might depend on another, but measurable effects can be delayed. This delay might be due to technical reasons, i.e. different stream processing speeds, or because the effects actually appear delayed over time. We present Loglag, the first algorithm that monitors dependency with respect to an optimal delay. It utilizes several approximation techniques to achieve competitive resource requirements. We demonstrate its scalability and accuracy on real world data, and also give theoretical guarantees to its accuracy

Monitoring data streams

Stream monitoring is concerned with analyzing data that is represented in the form of infinite streams. This field has gained prominence in recent years, as streaming data is generated in increasing volume and dimension in a variety of areas. It finds application in connection with monitoring industrial sensors, "smart" technology like smart houses and smart cars, wearable devices used for medical and physiological monitoring, but also in environmental surveillance or finance. However, stream monitoring is a challenging task due to the diverse and changing nature of the streaming data, its high volume and high dimensionality with thousands of sensors producing streams with millions of measurements over short time spans. Automated, scalable and efficient analysis of these streams can help to keep track of important events, highlight relevant aspects and provide better insights into the monitored system. In this thesis, we propose techniques adapted to these tasks in supervised and unsupervised settings, in particular Stream Classification and Stream Dependency Monitoring. After a motivating introduction, we introduce concepts related to streaming data and discuss technological frameworks that have emerged to deal with streaming data in the second chapter of this thesis. We introduce the notion of information theoretical entropy as a useful basis for data monitoring in the third chapter. In the second part of the thesis, we present Probabilistic Hoeffding Trees, a novel approach towards stream classification. We will show how probabilistic learning greatly improves the flexibility of decision trees and their ability to adapt to changes in data streams. The general technique is applicable to a variety of classification models and fast to compute without significantly greater memory cost compared to regular Hoeffding Trees. We show that our technique achieves better or on-par results to current state-of-the-art tree classification models on a variety of large, synthetic and real life data sets. In the third part of the thesis, we concentrate on unsupervised monitoring of data streams. We will use mutual information as entropic measure to identify the most important relationships in a monitored system. By using the powerful concept of mutual information we can, first, capture relevant aspects in a great variety of data sources with different underlying concepts and possible relationships and, second, analyze theoretical and computational complexity. We present the MID and DIMID algorithms. They perform extremely efficient on high dimensional data streams and provide accurate results, outperforming state-of-the-art algorithms for dependency monitoring. In the fourth part of this thesis, we introduce delayed relationships as a further feature in the dependency analysis. In reality, the phenomena monitored by e.g. some type of sensor might depend on another, but measurable effects can be delayed. This delay might be due to technical reasons, i.e. different stream processing speeds, or because the effects actually appear delayed over time. We present Loglag, the first algorithm that monitors dependency with respect to an optimal delay. It utilizes several approximation techniques to achieve competitive resource requirements. We demonstrate its scalability and accuracy on real world data, and also give theoretical guarantees to its accuracy

L1-determined ideals in group algebras of exponential Lie groups

A locally compact group $G$ is said to be $\ast$-regular if the natural map \Psi:\Prim C^\ast(G)\to\Prim_{\ast} L^1(G) is a homeomorphism with respect to the Jacobson topologies on the primitive ideal spaces \Prim C^\ast(G) and \Prim_{\ast} L^1(G). In 1980 J. Boidol characterized the $\ast$-regular ones among all exponential Lie groups by a purely algebraic condition. In this article we introduce the notion of $L^1$-determined ideals in order to discuss the weaker property of primitive $\ast$-regularity. We give two sufficient criteria for closed ideals $I$ of $C^\ast(G)$ to be $L^1$-determined. Herefrom we deduce a strategy to prove that a given exponential Lie group is primitive $\ast$-regular. The author proved in his thesis that all exponential Lie groups of dimension $\le 7$ have this property. So far no counter-example is known. Here we discuss the example $G=B_5$, the only critical one in dimension $\le 5$

Long-term outcomes of cardiac resynchronization therapy are worse in patients who require atrioventricular junction ablation for atrial fibrillation than in those with sinus rhythm

Background: The aim of the study was to assess the impact of atrial fibrillation (AF) with and without the need for atrioventricular junction (AVJ) ablation on outcomes in patients undergoing cardiac resynchronization therapy (CRT).Methods: A single center cohort of 200 consecutive CRT patients was divided into three groups: 1) AF with CRT pacing < 95% in which AVJ ablation was performed (AF-ABL, n = 40; 20%), 2) AF without the need for AVJ ablation (AF-non ABL, n = 40; 20%), 3) sinus rhythm (SR, n = 120; 60%). All patients were assessed before CRT implantation and at 6-month follow-up. Positive clinical response to CRT was considered alive status without the need for heart transplantation and improvement ≥ 1 NYHA after 6 months. The comparative analysis among all study groups with respect to response-rate and long-term survival was performed.Results: The 6-month response-rate in both AF-ABL and AF-nonABL was significantly lower than in SR (52.5 and 50 vs.77.5%, respectively; both p < 0.017), though there were no differences in baseline characteristics among study groups apart from higher baseline NT-proBNP levels in AF-ABL. However, after adjustment for this confounder, and despite optimal CRT pacing burden in study groups, the remote all-cause mortality during median follow-up of 36.1 months was significantly higher in AF-ABL than in SR (adjusted HR = 2.57, 95% CI 1.09–6.02, p = 0.03). What is more, no difference in long-term survival between SR and AF-nonABL was observed.Conclusions: Despite the improvement of CRT pacing burden and thus response-rate up to the level of AF subjects without the need for ablation, the long-term survival of AF patients requiring AVJ ablation remains still worse than in SR

∗*- regularity of exponential Lie groups

Boidol J. $*$- regularity of exponential Lie groups. Bielefeld; 1979