Informatik
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The digital transformation of our society changes the way we live, work, learn, communicate, and collaborate. This disruptive change drive current and next information processes and systems that are important business enablers for the context of digitization since years. Our aim is to support flexibility and agile transformations for both business domains and related information technology with more flexible enterprise information systems through adaptation and evolution of digital architectures. The present research paper investigates the continuous bottom-up integration of micro-granular architectures for a huge amount of dynamically growing systems and services, like microservices and the Internet of Things, as part of a new composed digital architecture. To integrate micro-granular architecture models into living architectural model versions we are extending enterprise architecture reference models by state of art elements for agile architectural engineering to support digital products, services, and processes.
With the Internet of Things being one of the most discussed trends in the computer world lately, many organizations find themselves struggling with the great paradigm shift and thus the implementation of IoT on a strategic level. The Ignite methodoogy as a part of the Enterprise-IoT project promises to support organizations with these strategic issues as it combines best practices with expert knowledge from diverse industries helping to create a better understanding of how to transform into an IoT driven business. A framework that is introduced within the context of IoT business model development is the Bosch IoT Business Model Builder. In this study the provided framework is compared to the Osterwalder Business Model Canvas and the St. Gallen Business Model Navigator, the most commonly used and referenced frameworks according to a quantitative literature analysis.
The third Digital Enterprise Computing Conference DEC 17 at the Herman Hollerith Center in Böblingen brings together students, researchers, and practitioners to discuss solutions, experiences, and future developments for the digital transformation. Digitization of business and IT defines the conference agenda: digital models & architecture, digital marketing, agility & innovation.
Software process improvement (SPI) is around for decades: frameworks are proposed, success factors are studied, and experiences have been reported. However, the sheer mass of concepts, approaches, and standards published over the years overwhelms practitioners as well as researchers. What is out there? Are there new emerging approaches? What are open issues? Still, we struggle to answer the question for what is the current state of SPI and related research? We present initial results from a systematic mapping study to shed light on the field of SPI and to draw conclusions for future research directions. An analysis of 635 publications draws a big picture of SPI-related research of the past 25 years. Our study shows a high number of solution proposals, experience reports, and secondary studies, but only few theories. In particular, standard SPI models are analyzed and evaluated for applicability, especially from the perspective of SPI in small-to-medium-sized companies, which leads to new specialized frameworks. Furthermore, we find a growing interest in success factors to aid companies in conducting SPI.
This summary refers to the paper Software process improvement : where is the evidence? [Ku15].
This paper was published as full research paper in the ICSSP’2015 proceedings.
A software process is the game plan to organize project teams and run projects. Yet, it still is a challenge to select the appropriate development approach for the respective context. A multitude of development approaches compete for the users’ favor, but there is no silver bullet serving all possible setups. Moreover, recent research as well as experience from practice shows companies utilizing different development approaches to assemble the best-fitting approach for the respective company: a more traditional process provides the basic framework to serve the organization, while project teams embody this framework with more agile (and/or lean) practices to keep their flexibility. The paper at hand provides insights into the HELENA study with which we aim to investigate the use of “Hybrid dEveLopmENt Approaches in software systems development”. We present the survey design and initial findings from the survey’s test runs. Furthermore, we outline the next steps towards the full survey.
Although still in the early stages of diffusion, smartwatches represent the most popular type of wearable devices. Yet, little is known why some people are more likely to adopt smartwatches than others. To deepen the understanding of underlying factors prompting adoption behavior, the authors develop a theoretical model grounded in technology acceptance and social psychology literature. Empirical results reveal perceived usefulness and visibility as important factors that drive intention. The magnitude of these antecedents is influenced by an individual’s perception of viewing smartwatches as a technology and/or as a fashion accessory. Theoretical and managerial implications are discussed.
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.
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.
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.
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.