Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

Economic Control of Quality of Manufactured Product.

C4.5: Programs for Machine Learning (書評)

Kieker is an extensible framework for monitoring and analyzing the runtime behavior of concurrent or distributed software systems. It provides measurement probes for application performance monitoring and control-flow tracing. Analysis plugins extract and visualize architectural models, augmented by quantitative observations. Configurable readers and writers allow Kieker to be used for online and offline analysis. This paper reviews the Kieker framework focusing on its features, its provided extension points for custom components, as well the imposed monitoring overhead.

Kieker: a framework for application performance monitoring and dynamic software analysis

Kieker is an extensible framework for monitoring and analyzing the runtime behavior of concurrent or distributed software systems. It provides measurement probes for application performance monitoring and control-ow tracing. Analysis plugins extract and visualize architectural models, augmented by quantitative observations. Congurable readers and writers allow Kieker to be used for online and oine analysis. This paper reviews the Kieker framework focusing on its features, its provided extension points for custom components, as well the imposed monitoring overhead.

Kieker: A Framework for Application Performance Monitoring and Dynamic Software Analysis — Invited Demo Paper —

In addition to studying the construction and evolution of software services, the software engineering discipline needs to address the operation of continuously running software services. A requirement for its robust operation are means for effective monitoring of software runtime behavior. In contrast to profiling for construction activities, monitoring of operational services should only impose a small performance overhead. Furthermore, instrumentation should be non-intrusive to the business logic, as far as possible.

We present the Kieker framework for monitoring software runtime behavior, e.g., internal performance or (distributed) trace data. The flexible architecture allows to replace or add framework components, including monitoring probes, analysis components, and monitoring record types shared by logging and analysis. As a non-intrusive instrumentation technique, Kieker currently employs, but is not restricted to, aspect-oriented programming. An extensive lab study evaluates and quantifies the low overhead caused by the framework components. Qualitative evaluations provided by industrial case studies demonstrate the practicality of the approach with a telecommunication customer self service and a digital photo submission service. Kieker is available as open-source software, where both the academic and industrial partners contribute to the code. Our experiment data is publicly available, allowing interested researchers to repeat and extend our lab experiments.

/pdf/continuous-monitoring-of-software-services-design-and-51fcww0kvs.pdf

Continuous Monitoring of Software Services: Design and Application of the Kieker Framework

Microservices complement approaches like DevOps and continuous delivery in terms of software architecture. Along with this architectural style, several important deployment technologies, such as container-based virtualization and container orchestration solutions, have emerged. These technologies allow to efficiently exploit cloud platforms, providing a high degree of scalability, availability, and portability for microservices.Despite the obvious importance of a sufficient level of performance, there is still a lack of performance engineering approaches explicitly taking into account the particularities of microservices. In this paper, we argue why new solutions to performance engineering for microservices are needed. Furthermore, we identify open issues and outline possible research directions with regard to performance-aware testing, monitoring, and modeling of microservices.

Performance Engineering for Microservices: Research Challenges and Directions

Autonomic computing components and services require continuous monitoring capabilities for collecting and analyzing data of runtime behavior. Particularly for software systems, a trade-off between monitoring coverage and performance overhead is necessary.In this paper, we propose an approach for localizing performance anomalies in software systems employing self-adaptive monitoring. Time series analysis of operation response times, incorporating architectural information about the diagnosed software system, is employed for anomaly localization. Comprising quality of service data, such as response times, resource utilization, and anomaly scores, OCL-based monitoring rules specify the adaptive monitoring coverage. This enables to zoom into a system's or component's internal realization in order to locate root causes of software failures and to prevent failures by early fault determination and correction.The approach has been implemented as part of the Kieker monitoring and analysis framework. The evaluation presented in this paper focuses on monitoring overhead, response time forecasts, and the anomaly detection process.

http://eprints.uni-kiel.de/14422/1/icac2011_jeh_avh_jwa_wha.pdf

André van Hoorn

Papers

Kieker: a framework for application performance monitoring and dynamic software analysis

Kieker: A Framework for Application Performance Monitoring and Dynamic Software Analysis — Invited Demo Paper —

Continuous Monitoring of Software Services: Design and Application of the Kieker Framework

Performance Engineering for Microservices: Research Challenges and Directions

Self-adaptive software system monitoring for performance anomaly localization