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The Internet of Things (IoT) fundamentally influences today’s digital strategies with disruptive business operating models and fast changing markets. New business information systems are integrating emerging Internet of Things infrastructures and components. With the huge diversity of Internet of Things technologies and products organizations have to leverage and extend previous enterprise architecture efforts to enable business value by integrating the Internet of Things into their evolving Enterprise Architecture Management environments. Both architecture engineering and management of current enterprise architectures is complex and has to integrate beside the Internet of Things synergistic disciplines like EAM - Enterprise Architecture and Management with disciplines like: services & cloud computing, semantic-based decision support through ontologies and knowledge-based systems, big data management, as well as mobility and collaboration networks. To provide adequate decision support for complex business/IT environments, it is necessary to identify affected changes of Internet of Things environments and their related fast adapting architecture. We have to make transparent the impact of these changes over the integral landscape of affected EAM-capabilities, like directly and transitively impacted IoT-objects, business categories, processes, applications, services, platforms and infrastructures. The paper describes a new metamodel-based approach for integrating partial Internet of Things objects, which are semi-automatically federated into a holistic Enterprise Architecture Management environment.
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.
The digital transformation of our society changes the way we live, work, learn, communicate, and collaborate. This disruptive change interacts with all information processes and systems that are important business enablers for the digital transformation since years. 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. They will shape future trends of business innovation and the next wave of information and communication technology. Biological metaphors of living and adaptable ecosystems provide the logical foundation for self-optimizing and resilient run-time environments for intelligent business services and related distributed information systems with service-oriented enterprise architectures. The present research investigates mechanisms for flexible adaptation and evolution of digital enterprise architectures in the context of integrated synergistic disciplines like distributed service-oriented architectures and information systems, EAM - Enterprise Architecture and Management, metamodeling, semantic echnologies, web services, cloud computing and Big Data technology. Our aim is to support flexibility and agile transformations for both business domains and related enterprise systems through adaptation and evolution of digital enterprise architectures. The present research paper investigates digital transformations of business and IT and integrates fundamental mappings between adaptable digital enterprise architectures and service-oriented information systems.
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.
Regardless of company size or industry sector, a majority of project teams and companies use customized processes that combine different development methods-so-called hybrid development methods. Even though such hybrid development methods are highly individualized, a common understanding of how to systematically construct synergetic practices is missing. Based on 1,467 data points from a large-scale online survey among practitioners, we study the current state of practice in process use to answer the question: What are hybrid development methods made of? Our findings reveal that only eight methods and few practices build the core of modern software development. This small set allows for statistically constructing hybrid development methods.
In recent times, enterprises have been increasingly dealing with the use of social media in internal communication and collaboration. In particular, so-called Enterprise Social Networks (ESN) promise meaningful benefits for the nature of work in corporations. However, these platforms often suffer from poor degrees of use. This raises the question of what initiatives enterprise can launch in order to stimulate the vitality of ESN. Since the use of ESN is often voluntary, individual adoption by employees need to be examined to find an answer. Therefore, the Unified Theory of Acceptance and Use of Technology (UTAUT) model was selected for the theoretical foundation of this paper. Following a qualitative research approach, the available research provides an analysis of expert interviews on specific ESN implementation strategies and included factors. In order to extensively conceptualize and generalize these strategic considerations, we conducted an inductive coding process. The results reveal that ESN implementation strategies can be understood as a multi-level construct (individual vs. group vs. organizational level) containing different factors dependent on the degree of documentation and intensity. This research in progress describes a qualitative evaluation as a preliminary study for further quantitative analysis of an ESN adoption model.
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.
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.
Business processes are important knowledge resources of a company. The knowledge contained in business processes impart procedures used to create products and services. However, modelling and application of business processes are affected by problems connected to knowledge transfer. This paper presents and implements a layered model to improve the knowledge transfer. Thus modelling and understanding of business process models is supported. An evaluation of the approach is presented and results and other areas of application are discussed.
A sequence of transactions represents a complex and multi dimensional type of data. Feature construction can be used to reduce the data´s dimensionality to find behavioural patterns within such sequences. The patterns can be expressed using the blue prints of the constructed relevant features. These blue prints can then be used for real time classification on other sequences.