Sürekli Zaman Markov Karar Süreçlerinin Özgüleştirilmesi

Özgüleştirme tekniğinin amacı, bir üstel yarı-Markov karar sürecini (ÜYMKS) kendine özdeş, ama farklı formülasyona sahip bir başka ÜYMKS’ye dönüştürmektir. Bu sayede, özellikle optimal politikaların yapısal özelliklerini daha kolay bir şekilde ispatlamak mümkündür. Özgüleştirme tekniğinin literatürdeki mevcut hali, beklenen toplam indirgenmiş maliyeti en küçüklemeye çalışan ÜYMKS’lere uygulanmaktadır. Bu makale, ÜYMKS’ler için önerilmiş olan özgüleştirme tekniğinin sürekli zaman Markov karar süreçlerine (SZMKS) nasıl uygulanabileceğini, sınırlı maliyet fonksiyonu ve üstten sınırlı geçiş hızları varsayımları altında, göstermeyi hedeflemektedir. Bu amaçla, verilen SZMKS, öncelikle bir ÜYMKS’ye dönüştürülmüştür ve daha sonra bu yeni ÜYMKS özgüleştirilmiştir.

End-to-end anomaly detection in stream data

Nowadays, huge volumes of data are generated with increasing velocity through various systems, applications, and activities. This increases the demand for stream and time series analysis to react to changing conditions in real-time for enhanced efficiency and quality of service delivery as well as upgraded safety and security in private and public sectors. Despite its very rich history, time series anomaly detection is still one of the vital topics in machine learning research and is receiving increasing attention. Identifying hidden patterns and selecting an appropriate model that fits the observed data well and also carries over to unobserved data is not a trivial task. Due to the increasing diversity of data sources and associated stochastic processes, this pivotal data analysis topic is loaded with various challenges like complex latent patterns, concept drift, and overfitting that may mislead the model and cause a high false alarm rate. Handling these challenges leads the advanced anomaly detection methods to develop sophisticated decision logic, which turns them into mysterious and inexplicable black-boxes. Contrary to this trend, end-users expect transparency and verifiability to trust a model and the outcomes it produces. Also, pointing the users to the most anomalous/malicious areas of time series and causal features could save them time, energy, and money. For the mentioned reasons, this thesis is addressing the crucial challenges in an end-to-end pipeline of stream-based anomaly detection through the three essential phases of behavior prediction, inference, and interpretation. The first step is focused on devising a time series model that leads to high average accuracy as well as small error deviation. On this basis, we propose higher-quality anomaly detection and scoring techniques that utilize the related contexts to reclassify the observations and post-pruning the unjustified events. Last but not least, we make the predictive process transparent and verifiable by providing meaningful reasoning behind its generated results based on the understandable concepts by a human. The provided insight can pinpoint the anomalous regions of time series and explain why the current status of a system has been flagged as anomalous. Stream-based anomaly detection research is a principal area of innovation to support our economy, security, and even the safety and health of societies worldwide. We believe our proposed analysis techniques can contribute to building a situational awareness platform and open new perspectives in a variety of domains like cybersecurity, and health

Modeling and Simulating Causal Dependencies on Process-aware Information Systems from a Cost Perspective

Providing effective IT support for business processes has become crucial for enterprises to stay competitive in their market. Business processes must be defined, implemented, enacted, monitored, and continuously adapted to changing situations. Process life cycle support and continuous process improvement become critical success factors in contemporary and future enterprise computing. In this context, process-aware information systems (PAISs) adopt a key role. Thereby, organization-specific and generic process support systems are distinguished. In the former case, the PAIS is build "from scratch" and incorporates organization-specific information about the structure and processes to be supported. In the latter case, the PAIS does not contain any information about the structure and processes of a particular organization. Instead, an organization needs to configure the PAIS by specifying processes, organizational entities, and business objects. To enable the realization of PAISs, numerous process support paradigms, process modeling standards, and business process management tools have been introduced. The application of these approaches in PAIS engineering projects is not only influenced by technological, but also by organizational and project-specific factors. Between these factors there exist numerous causal dependencies, which, in turn, often lead to complex and unexpected effects in PAIS engineering projects. In particular, the costs of PAIS engineering projects are significantly influenced by these causal dependencies. What is therefore needed is a comprehensive approach enabling PAIS engineers to systematically investigate these causal dependencies as well as their impact on the costs of PAIS engineering projects. Existing economic-driven IT evaluation and software cost estimation approaches, however, are unable to take into account causal dependencies and resulting effects. In response, this thesis introduces the EcoPOST framework. This framework utilizes evaluation models to describe the interplay of technological, organizational, and project-specific evaluation factors, and simulation concepts to unfold the dynamic behavior of PAIS engineering projects. In this context, the EcoPOST framework also supports the reuse of evaluation models based on a library of generic, predefined evaluation patterns and also provides governing guidelines (e.g., model design guidelines) which enhance the transfer of the EcoPOST framework into practice. Tool support is available as well. Finally, we present the results of two online surveys, three case studies, and one controlled software experiment. Based on these empirical and experimental research activities, we are able to validate evaluation concepts underlying the EcoPOST framework and additionally demonstrate its practical applicability

Probabilistic Models for Life Cycle Management of Energy Infrastructure Systems

The degradation of aging energy infrastructure systems has the potential to increase the risk of failure, resulting in power outage and costly unplanned maintenance work. Therefore, the development of scientific and cost-effective life cycle management (LCM) strategies has become increasingly important to maintain energy infrastructure. Since degradation of aging equipment is an uncertain process which depends on many factors, a risk-based approach is required to consider the effect of various uncertainties in LCM. The thesis presents probabilistic models to support risk-based life cycle management of energy infrastructure systems. In addition to uncertainty in degradation process, the inspection data collected by the energy industry is often censored and truncated which make it difficult to estimate the lifetime probability distribution of the equipment. The thesis presents modern statistical techniques in quantifying uncertainties associated with inspection data and to estimate the lifetime distributions in a consistent manner. Age-based and sequential inspection-based replacement models are proposed for maintenance of component in a large-distribution network. A probabilistic lifetime model to consider the effect of imperfect preventive maintenance of a component is developed and its impact to maintenance optimization is illustrated. The thesis presents a stochastic model for the pitting corrosion process in steam generators (SG), which is a serious form of degradation in SG tubing of some nuclear generating stations. The model is applied to estimate the number of tubes requiring plugging and the probability of tube leakage in an operating period. The application and benefits of the model are illustrated in the context of managing the life cycle of a steam generator

Customizing exponential semi-Markov decision processes under the discounted cost criterion

Çekyay, Bora (Dogus Author)The uniformization technique is a widely used method for establishing the existence of optimal policies with certain monotonicity properties. This technique converts a semi-Markov decision process with exponential sojourn times (ESMDP) into an equivalent discrete-time Markov decision process by defining some fictitious jumps. This study proposes a new device, called customization, which can convert a given ESMDP into another equivalent ESMDP whose formulation possibly simplifies mathematical analysis. The customization technique uses the fictitious jump idea to establish the equivalence under deterministic stationary policies just like the uniformization technique. However, it allows the transition rates of the new ESMDP to be different. Moreover, it can be applied even when the transition rates of the initial ESMDP are unbounded. This flexibility can be very useful in analyzing the problems where the uniformization is not applicable or not so helpful. We analyze a complex optimal replacement problem and an infinite server queueing problem with unbounded transition rates to demonstrate the applicability and advantages of customization