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In prior research we explored the use of time series analysis methods to detect one class of information technology (IT) infrastructure anomalies - Distributed Denial of Service (DDoS) attacks. The results of this prior work were a mathematical model and a prototype implementation that were concretely trialed and operated in the data centers of Germany's Federal Employment Agency (FEA). With this paper, we go one step further and generalize as well as optimize the mathematical model and create higher performance and scalability for an updated prototype through targeted use of cloud technologies. The starting point of our generalization is the Exponential Smoothing (E-S) approach, which underlies, for example, the well-known Holt-Winters method. This method is used to predict univariate time series. To detect anomalies (such as DDoS attacks) in infrastructure data, we extend the E-S approach to enable it to forecast multivariate time series. In this optimization of our method and our prototype, we take an exploratory, agile approach. Furthermore, we present a cloud-native architecture stack which we pilot in Azure.

Open Data impliziert die freie Zugänglichkeit, Verfügbarkeit und Wiederverwendbarkeit von Datensätzen. Obwohl hochwertige Datensätze öffentlich verfügbar sind, ist der Zugang zu diesen und die Transparenz über die Formate nicht immer gegeben. Dies mindert die optimale Nutzung des Potenzials zur Wertschöpfung, trotz der vorherrschenden Einigkeit über ihre Chancen. Denn Open Data ermöglicht das Vorantreiben von Compliance-Themen wie Transparenz und Rechenschaftspflicht bis hin zur Förderung von Innovationen. Die Nutzung von Open Data erfordert Mut und eine gemeinsame Anstrengung verschiedener Akteure und Branchen. Im Rahmen des vorliegenden Beitrags werden auf Grundlage des Design Science-Ansatzes eine Open Data Capability Map sowie darauf aufbauend eine Datenarchitektur für Open Data in der Luftfahrtindustrie an einem Beispiel entwickelt.

The Mobius function is commonly used to define Euler’s totient function and the Mangoldt function. Similarly, the summatory Mobius function (the Mertens function) is used to define the summatory totient function and the summatory Mangoldt function (the second Chebyshev function). Analogues of the product formula for the totient function are introduced. An analogue of the summatory totient function with many additive properties is introduced.

The Collatz dynamic is known to generate a complex quiver of sequences over natural numbers which inflation propensity remains so unpredictable it could be used to generate reliable proof of work algorithms for the cryptocurrency industry; it has so far resisted every attempt at linearizing its behavior. Here we establish an ad hoc equivalent of modular arithmetic for Collatz sequences, based on five arithmetic rules we prove apply on the entire Collatz dynamical system and which iteration exactly define the full basin of attraction leading to any odd number. We further simulate these rules to gain insight on their quiver geometry and computational properties, and observe they allow to linearize the proof of convergence of the full rows of the binary tree over odd numbers in their natural order, a result which, along with the full description of the basin of any odd number, has never been achieved before. We then provide two theoretical programs to explain why the five rules allow to linearize Collatz convergence, one in ZFC and one in Peano arithmetic.

The Mertens function is the summatory Mobius function but the Mertens function can be generated recursively without using this definition. This recursive definition is the basis of autocorrelations that can be done on sequences of Mertens function values. Fourier transforms of the autocorrelations result in the energy spectral density. A likely upper bound of the absolute value of the Mertens function is determined.

Chatbots have the potential to significantly increase the efficiency of banks and public institutions. Both sectors, however, are subject to special regulations and restrictions in areas such as information security and data protection. The policies of these organizations therefore, in some cases, reject the use of cloud and proprietary products because in their view they lack transparency. As a result, the implementation of chatbots in banks and public institutions often focuses on open-source and on-premises solutions; however, there are hardly any scientific guidelines on how to implement these systems. Our paper aims to close this research gap. The article proposes a reference architecture for chatbots in banks and public institutions that are a.) based on open-source software and b.) are hosted on-premises. The framework is validated by case studies at TeamBank AG and the German Federal Employment Agency. Even if our architecture is designed for these specific industries, it may also add value in other sectors ��� as chatbots are expected to become increasingly important for the practical application of artificial intelligence in enterprises.

The data centers of Germany���s Federal Employment Agency (FEA) provide an information technology (IT) infrastructure that is critical for both external stakeholders and internal processes. For FEA and many other organizations like it, it is essential that any IT infrastructure anomalies - deviations from normal behavior - are detected and their underlying causes are understood and, if appropriate, addressed. In this paper we develop a solution that can help increase the availability of an IT landscape such as FEA���s that is characterized by increasing technical complexity and increasing relevance of its applications. The solution detects IT service anomalies based on IT service access logs analyzed with time series methods. The solution also provides visualizations to support further analyses.

Künstliche Intelligenz ist in aller Munde. Immer mehr Anwendungsbereiche werden durch die Auswertung von vorliegenden Daten mit Algorithmen und Frameworks z.B. des Maschinellen Lernens erschlossen. Dieses Buch hat das Ziel, einen Überblick über gegenwärtig vorhandene Lösungen zu geben und darüber hinaus konkrete Hilfestellung bei der Auswahl von Algorithmen oder Tools bei spezifischen Problemstellungen zu bieten. Um diesem Anspruch gerecht zu werden, wurden 90 Lösungen mittels einer systematischen Literaturrecherche und Praxissuche identifiziert sowie anschließend klassifiziert. Mit Hilfe dieses Buches gelingt es, schnell die notwendigen Grundlagen zu verstehen, gängige Anwendungsgebiete zu identifizieren und den Prozess zur Auswahl eines passenden ML-Tools für das eigene Projekt systematisch zu meistern.

AI systems are increasingly evolving from laboratory experiments in data analysis to increments of productive software products. A professional AI platform must therefore not only function as a laboratory environment but must be designed and procured as a workbench for the development, productive implementation, operation and maintenance of ML models. Subsequently, it needs to integrate within a global software engineering approach. This way, Enterprise Architecture Management (EAM) must implement efficient governance of the development cycle, to enable organization-wide collaboration, to accelerate the go-live and to standardize operations. In this paper we highlight obstacles and show best practices on how architects can integrate data science and AI in their environment. Additionally, we suggest an integrated approach adapting the best practices from both the data science and DevOps.

In this paper we argue that the Enterprise Architect (EA) should be considered as a crisis manager. In times of a crisis organizations must create a holistic view on the situation and evaluate the proposed measures. Evaluation will be based on experience, data, upfront-created scenarios and assessments of risks, cost and benefits. The EA is already engaged in processes such as Continuity Management, Risk Management, IT Security, Business Process Management, IT Strategy, and others. Therefore, we propose that the EA is one good candidate (not the only one) for handling organizational crises. This paper presents a model of overall crisis management that incorporates an enterprise architecture view as well as related dimensions of crisis management: the ability to control a crisis, the level of communication during and after a crisis, the type of change brought by a crisis, the crisis' outcomes, its distribution within an enterprise, and an organization's criticality of business functions. Finally, we highlight how the EA role supports crisis management at Lufthansa, a top German aviation company.