Author Supplied: In the last decades, architecture has emerged as a discipline in the domain of Information Technology (IT). A well-accepted definition of architecture is from ISO/IEC 42010: "The fundamental organization of a system, embodied in its components, their relationships to each other and the environment, and the principles governing its design and evolution." Currently, many levels and types of architecture in the domain of IT have been defined. We have scoped our work to two types of architecture: enterprise architecture and software architecture. IT architecture work is demanding and challenging and includes, inter alia, identifying architectural significant requirements (functional and non-functional), designing and selecting solutions for these requirements, and ensuring that the solutions are implemented according to the architectural design. To reflect on the quality of architecture work, we have taken ISO/IEC 8402 as a starting point. It defines quality as "the totality of characteristics of an entity that bear on its ability to satisfy stated requirements". We consider architecture work to be of high quality, when it is effective; when it answers stated requirements. Although IT Architecture has been introduced in many organizations, the elaboration does not always proceed without problems. In the domain of enterprise architecture, most practices are still in the early stages of maturity with, for example, low scores on the focus areas ‘Development of architecture’ and ‘Monitoring’ (of the implementation activities). In the domain of software architecture, problems of the same kind are observed. For instance, architecture designs are frequently poor and incomplete, while architecture compliance checking is performed in practice on a limited scale only. With our work, we intend to contribute to the advancement of architecture in the domain of IT and the effectiveness of architecture work by means of the development and improvement of supporting instruments and tools. In line with this intention, the main research question of this thesis is: How can the effectiveness of IT architecture work be evaluated and improved?
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Author supplied: Teaching software architecture (SA) in a bachelor computer science curriculum can be challenging, as the concepts are on a high abstraction level and not easy to grasp for students. Good techniques and tools that help with addressing the challenging SA aspects in a didactically responsible way are needed. In this tool demo we show how we used the software architecture compliance checking tool HUSACCT for addressing various concepts of SA in our courses on software architecture. The students were introduced to architectural reconstruction and architecture compliance checking, which helped them to gain important insights in aspects such as the relation between architectural models and code and the specification of dependency relations between architecture elements as concrete rules.
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Enterprise Architecture has been developed in order to optimize the alignment between business needs and the (rapidly changing) possibilities of information technology. But do organizations indeed benefit from the application of Enterprise Architecture according to those who are in any way involved in architecture? To answer this question, a model has been developed (the Enterprise Architecture Value Framework) to organize the benefits of Enterprise Architecture. Based on this model, a survey has been conducted among the various types of stakeholders of Enterprise Architecture, such as architects, project managers, developers and business or IT managers. In the survey the respondents were asked to what extent they perceive various benefits of Enterprise Architecture in their organization. The results of this survey (with 287 fully completed responses) are analyzed and presented in this paper. In all categories of the framework benefits are perceived, though to different extent. Very few benefits are perceived in relation to the external orientation of the organization. Few statistically significant correlations were found in relation to the background of the respondents: the overall view on benefits of Enterprise Architecture appeared independent of the role of the respondents, the economic sector and the number of years of experience with architecture.
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Smart home technologies are a large potential market for the construction and building services industry. This chapter discusses the topics consultants, installers, and suppliers of home automation systems encounter when working in the field. Improved communication skills and more flexible approaches to the design and installing of building services leads to many new opportunities for new products and services. There are a large number of requirements from the perspective of architectural design and building services engineering, which relate to the infrastructure that is needed for smart homes. An overview of these electrical engineering and ICT requirements is discussed. When working with clients, it is important to consider the additional set of rules of working in their homes. Clients may have additional needs in the field of home modifications that can also be addressed when doing retrofitting projects. An outline of steps to get stared and essential questions for professional care organization is given.
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With the development of Enterprise Architecture (EA) as a discipline, measuring and understanding its value for business and IT has become relevant. In this paper a framework for categorizing the benefits of EA, the Enterprise Architecture Value Framework (EAVF), is presented and based on this framework, a measurability maturity scale is introduced. In the EAVF the value aspects of EA are expressed using the four perspectives of the Balanced Scorecard with regard to the development of these aspects over time, defining sixteen key areas in which EA may provide value. In its current form the framework can support architects and researchers in describing and categorizing the benefits of EA. As part of our ongoing research on the value of EA, two pilots using the framework have been carried out at large financial institutions. These pilots illustrate how to use the EAVF as a tool in measuring the benefits of EA.
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Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and metereological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be GatedRecurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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Ageing-in-place is the preferred way of living for older individuals in an ageing society. It can be facilitated through architectural and technological solutions in the home environment. Dementia poses additional challenges when designing, constructing, or retrofitting housing facilities that support ageing-in-place. Older adults with dementia and their partners ask for living environments that support independence, compensate for declining and vitality, and lower the burden of family care. This study reports the design process of a demonstration home for people with dementia through performing a literature review and focus group sessions. This design incorporates modifications in terms of architecture, interior design, the indoor environment, and technological solutions. Current design guidelines are frequently based on small-scale studies, and, therefore, more systematic field research should be performed to provide further evidence for the efficacy of solutions. The dwellings of people with dementia are used to investigate the many aspects of supportive living environments for older adults with dementia and as educational and training settings for professionals from the fields of nursing, construction, and building services engineering.
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Business Rule Management (BRM) is a means to make decision-making within organizations explicit and manageable. BRM functions within the context of an Enterprise Architecture (EA). The aim of EA is to enable the organization to achieve its strategic goals. Ideally, BRM and EA should be well aligned. This paper explores through study of case study documentation the BRM design choices that relate to EA and hence might influence the organizations ability to achieve a digital business strategy. We translate this exploration into five propositions relating BRM design choices to EA characteristics.
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Ageing-in-place is the preferred way of living for older individuals in an ageing society. It can be facilitated through architectural and technological solutions in the home environment. Dementia poses additional challenges when designing, constructing, or retrofitting housing facilities that support ageing-in-place. Older adults with dementia and their partners ask for living environments that support independence, compensate for declining and vitality, and lower the burden of family care. This study reports the design process of a demonstration home for people with dementia through performing a literature review and focus group sessions. This design incorporates modifications in terms of architecture, interior design, the indoor environment, and technological solutions. Current design guidelines are frequently based on small-scale studies, and, therefore, more systematic field research should be performed to provide further evidence for the efficacy of solutions. The dwellings of people with dementia are used to investigate the many aspects of supportive living environments for older adults with dementia and as educational and training settings for professionals from the fields of nursing, construction, and building services engineering.
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Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and meteorological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be Gated Recurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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