004 Informatik
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Die digitale Transformation bezieht sich auf die zunehmende Digitalisierung von Inhalten und Prozessen und die steigende Bedeutung digitaler Medien in Wirtschaft und Gesellschaft. Dabei wird der Wandel u. a. durch die Evolution in der Nutzung des Internets getrieben. Während in der Phase des so genannten Web 1.0 die Publikation und Verbreitung statischer Inhalte im Fokus stand, werden durch das Web 2.0 überwiegend Prozesse der dezentralen Erzeugung und einfachen Verbreitung von User Generated Content stimuliert. Unternehmen müssen auf diese Veränderungen reagieren, um die eigene Wettbewerbsfähigkeit nachhaltig abzusichern. Der vorliegende Beitrag konzentriert sich auf die Weiterentwicklung des Kundenservice. Dieser wurde in den zurückliegenden Jahren von vielen Unternehmen überwiegend als Kostenfaktor mit geringer strategischer Bedeutung eingestuft. Diese Sichtweise hat sich in der digitalen Transformation grundlegend geändert. Kunden können heute Mängel an Produkten und Dienstleistungen durch Foren und Social Media Kanäle sofort und mit hoher Reichweite adressieren. Unternehmen müssen auf den gleichen Kanälen reagieren, um die Multiplikation negativer Sichtweisen einzudämmen und Übertragungseffekte auf traditionelle Medien zu vermeiden. Gleichzeitig entstehen durch digitale Kanäle völlig neue Serviceangebote, die sich nachhaltig auf die unternehmerische Wettbewerbsfähigkeit auswirken. Der vorliegende Beitrag gibt zunächst einen Überblick zu wesentlichen Entwicklungslinien der digitalen Transformation. Auf dieser Grundlage werden die Perspektiven für Unternehmen zur Integration digitaler Medien in die eigene Wertschöpfungskette skizziert. Darüber hinaus steht v. a. die Veränderung des Kundenservice im so genannten Web 2.0 zur Diskussion. Ein Ausblick auf zukünftige Entwicklungen der Digitalisierung rundet den Beitrag entsprechend ab.
The evolution of Services Oriented Architectures (SOA) presents many challenges due to their complex, dynamic and heterogeneous nature. We describe how SOA design principles can facilitate SOA evolvability and examine several approaches to support SOA evolution. SOA evolution approaches can be classified based on the level of granularity they address, namely, service code level, service interaction level and model level. We also discuss emerging trends, such as microservices and knowledge-based support, which can enhance the evolution of future SOA systems.
The digital transformation of our society changes the way we live, work, learn, communicate, and collaborate. The digitization of software-intensive products and services is enabled basically by four megatrends: Cloud computing, big data mobile systems, and social technologies. This disruptive change interacts with all information processes and systems that are important business enablers for the current digital transformation. The internet of things, social collaboration systems for adaptive case management, mobility systems and services for big data in cloud services environments are emerging to support intelligent user-centered and social community systems. Modern enterprises see themselves confronted with an ever growing design space to engineer business models of the future as well as their IT support, respectively. The decision analytics in this field becomes increasingly complex and decision support, particularly for the development and evolution of sustainable enterprise architectures (EA), is duly needed. With the advent of intelligent user-centered and social community systems, the challenging decision processes can be supported in more flexible and intuitive ways. Tapping into these systems and techniques, the engineers and managers of the enterprise architecture become part of a viable enterprise, i.e. a resilient and continuously evolving system that develops innovative business models.
Digital companies need information systems to implement their business processes end-to-end. BPM systems are promising candidates for that, because they are highly adaptable due to their business process model-driven operation mode. End-to-end processes contain different types of sub-processes that are either procedural, data-driven or business rule-based. Modern BPM systems support modeling notations for all these types of sub-processes. Moreover, end-to-end processes contain parts of shadow processing, so consequently, they must be supported in a performant way, too. BPMN seems to be the adequate notation for modeling these parts due to its procedural nature. Further, BPMN provides several elements that enable the modeling of parallel executions which are very interesting for accelerating shadow processing parts of the process. The present paper will observe the limitations and potentials of BPM systems for a high-performance execution of BPMN models representing shadow processing parts of a business process.
Reality mining refers to an application of data mining, using sensor data to drive behavioral patterns in the real world. However, research in this field started a decade ago when technology was far behind today's state of the art. This paper discusses which requirements are now posed to applications in the context of reality mining. A survey has shown which sensors are available in state-of-the-art smartphones and usable to gather data for reality mining. As another contribution of this paper, a reality mining application architecture is proposed to facilitate the implementation of such applications. A proof of concept verifies the assumptions made on reality mining and the presented architecture.
Nowadays almost every major company has a monitoring system and produces log data to analyse their systems. To perform analysation on the log data and to extract experience for future decisions it is important to transform and synchronize different time series. For synchronizing multiple time series several methods are provided so that they are leading to a synchronized uniform time series. This is achieved by using discretisation and approximation methodics. Furthermore the discretisation through ticks is demonstrated, as well as the respectivly illustrated results.
Many organizations identified the opportunities of big data analytics to support the business with problem-specific insights through the exploitation of generated data. Socio-technical solutions are developed in big data projects to reach competitive advantage. Although these projects are aligned to specific business needs, common architectural challenges are not addressed in a comprehensive manner. Enterprise architecture management is a holistic approach to tackle the complex business and IT architecture. The transformation of an organization's EA is influenced by big data projects and their data-driven approach on all layers. To enable strategy oriented development of the EA it is essential to synchronize these projects supported by EA management. In
this paper, we conduct a systematic review of big data literature to analyze which requirements for the EA management discipline are proposed. Thereby, a broad overview about existing research is presented to facilitate a more detailed exploration and to foster the evolution o the EA management discipline.
Rapidly growing data volumes push today's analytical systems close to the feasible processing limit. Massive parallelism is one possible solution to reduce the computational time of analytical algorithms. However, data transfer becomes a significant bottleneck since it blocks system resources moving data-to-code. Technological advances allow to economically place compute units close to storage and perform data processing operations close to data, minimizing data transfers and increasing scalability. Hence the principle of Near Data Processing (NDP) and the shift towards code-to-data. In the present paper we claim that the development of NDP-system architectures becomes an inevitable task in the future. Analytical DBMS like HPE Vertica have multiple points of impact with major advantages which are presented within this paper.
The question of why individuals adopt information technology has been present in the information systems research since the past quarter century. One of the most used models for predicting the technology usage was introduced by Fred David: The Technology Acceptance Model (TAM). It describes the influence of perceived usefulness and perceived ease of use on attitude, behavioral intention and system usage. The first two mentioned factors in turn are influenced by external variables. Although a plethora of papers exists about the TAM , an extensive analysis of the role of the external variables in the model is still missing. This paper aims to give an overview ove the most important variables. In an extensive literature review, we identified 763 relevant papers, found 552 unique single extenal variables, characterized the most important of them, and described the frequency of their appearance. Additionally, we grouped these variables into four categories (organizational characteristis, system characteristics, user personal characteristics, and other variables). Afterwards we discuss the results and show implications for theory and practice.
The Internet of Things (IoT) refers to the interconnectedness of physical objects, and works by equipping the latter with sensors and actuators as a means to connect to the internet. The number of connected things has increased threefold over the past five years. Consequently, firms expect the IoT to become a source of new business models driven by technology. However, only a few early adopters have started to install and use IoT appliances on a frequent basis. So it is still unclear which factors drive technological acceptance of IoT appliances. Confronting this gap in current research, the present paper explores how IoT appliances are conceptually defined, which factors drive technological acceptance of IoT appliances, and how firms can use results in order to improve value propositions in corresponding business models. lt is discovered that IoT appliance vendors need to support a broad focus as the potential buyers expose a large variety. As conclusions from this insight, the paper illustrates some flexible marketing strategies.
The acquisition of data for reality mining applications is a critical factor, since many mobile devices, e.g. smartphones, must be capable of capturing the required data. Otherwise, only a small target group would be able to use the reality mining application. In the course of a survey, we have identified smartphone features which might be relevant for various reality mining applications. The survey classifies these features and shows how the support of each feature has changed over the years by analyzing 143 smartphones released between 2004 and 2015. All analyzed devices can be ranked by their number of provided features. Furthermore, this paper deals with quality issues which have occurred during carrying out the survey.
Converting users into customers : the role of user profile information and customer journey analysis
(2016)
Due to the digital transformation, the importance of web analysis and user profiling for enterprises is increasing rapidly as customers focus on digital channels to obtain information about products and brands. While there exists a lot research on these topics, only a minority of firms use them to their advantage. This study aims to tighten the link between research and business such that experimental methods can be used for the improvement of communication strategies in practice. Therefore, a systematic literature analysis is conducted, workshops are observed and documented and an empirical study is used to integrate single steps into a framework for the
practical usage of user profiling and customer journey analysis.
Business process management and IT supported processes are an actual topic. The procedure of finding a business process system that implements your processes the best way is not easy and takes a lot of time. In this article you will find a recommendation for an open source system. Four selected open source workflow management systems are tested and analyzed. Mean criteria for the evaluation are listed in a criteria catalogue and rated by experts by their importance. Finally, the systems are evaluated by the criteria and the best evaluated system can be recommended.
The troubles began when Tom, the business analyst, asked the customer what he wants. The customer came up with good ideas for software features. Tom created a brilliant roadmap and defined the requirements for a new software product. Mary, the development team leader, was already eager to start developing and happy when she got the requirements. She and her team went ahead and created the software right away. Afterwards, Paul tested the software against the requirements. As soon as the software fulfilled the requirements, Linda, the product manager, deployed it to the customer. The customer did not like the software and ignored it. Ringo, the head of software development, was fired. How come? Nowadays, we have tremendous capabilities for creating nearly all kinds of software to fulfill the needs of customers. We can apply agile practices for reacting flexibly to changing requirements, we can use distributed development, open source, or other means for creating software at low cost, we can use cloud technologies for deploying software rapidly, and we can get enormous amounts of data showing us how customers actually use software products. However, the sad reality is that around 90% of products fail, and more than 60% of the features of a typical software product are rarely or never used. But there is a silver lining – an insight regarding successful features: Around 60% of the successes stem from a significant change of an initial idea. This gives us a hint on how to build the right software for users and customers.
Digitale Selbstvermessungstechnologien wurden in den letzten Jahren zum Sinnbild rationaler und optimierter Lebensführung. Mit dem digitalen Prosumenten sind jedoch auch zahlreiche Pathologien verbunden. Der Beitrag verortet Lifelogging - digitalisierte Lebensprotokollierung - als auf Algorithmen basierende Form von Alchemie und Surrogat für echte soziale Utopien.
This chapter presents an introduction to the emerging trends for architecting the digital transformation having a strong focus on digital products, intelligent services, and related systems together with methods, models and architectures. The primary aim of this book is to highlight some of the most recent research results in the field. We are providing a focused set of brief descriptions of the chapters included in the book.
Selecting a suitable development method for a specific project context is one of the most challenging activities in process design. To extend the so far statistical construction of hybrid development methods, we analyze 829 data points to investigate which context factors influence the choice of methods or practices. Using exploratory factor analysis, we derive five base clusters consisting of up to 10 methods. Logistic regression analysis then reveals which context factors have an influence on the integration of methods from these clusters in the development process. Our results indicate that only a few context factors including project/product size and target application domain significantly influence the choice. This summary refers to the paper “Determining Context Factors for Hybrid Development Methods with Trained Models”. This paper was published in the proceedings of the International Conference on Software and System Process in 2020.
In a networked world, companies depend on fast and smart decisions, especially when it comes to reacting to external change. With the wealth of data available today, smart decisions can increasingly be based on data analysis and be supported by IT systems that leverage AI. A global pandemic brings external change to an unprecedented level of unpredictability and severity of impact. Resilience therefore becomes an essential factor in most decisions when aiming at making and keeping them smart. In this chapter, we study the characteristics of resilient systems and test them with four use cases in a wide-ranging set of application areas. In all use cases, we highlight how AI can be used for data analysis to make smart decisions and contribute to the resilience of systems.