Pub Date : 2021-04-30DOI: 10.7250/csimq.2021-26.01
Julia Kaidalova, K. Sandkuhl, U. Seigerroth
. Products have evolved from being solely composed of mechanical and electrical parts to complex systems that are connected to the Internet and form the basis for new kinds of functionalities and services. Such smart connected products include a substantial amount of software and information technology which can be called the “product-IT”. Enterprise architecture (EA) management is an established function in enterprises providing methods and tools for a systematic alignment of business and IT in an organization. However, EA models capture the details of business, information and technology architecture of an enterprise (i.e., the enterprise-IT) but usually do not include the product-IT. When value creation and delivery in an enterprise’s business model also includes the product-IT, a product-IT inclusive view on EA urgently is required. The focus of this article is the methodical support for integration of product-IT and enterprise-IT using enterprise architectures. The main contributions in this article are the conceptual expansion of EA management to become product-IT inclusive that is manifested in a proposal for an extended EA meta-model, and an approach and method components to handle product-IT inclusive EAM.
{"title":"Product-IT Inclusive Enterprise Architecture Management: An Approach Based on Ecosystems, Customer Journey and Data-Driven Business Opportunities","authors":"Julia Kaidalova, K. Sandkuhl, U. Seigerroth","doi":"10.7250/csimq.2021-26.01","DOIUrl":"https://doi.org/10.7250/csimq.2021-26.01","url":null,"abstract":". Products have evolved from being solely composed of mechanical and electrical parts to complex systems that are connected to the Internet and form the basis for new kinds of functionalities and services. Such smart connected products include a substantial amount of software and information technology which can be called the “product-IT”. Enterprise architecture (EA) management is an established function in enterprises providing methods and tools for a systematic alignment of business and IT in an organization. However, EA models capture the details of business, information and technology architecture of an enterprise (i.e., the enterprise-IT) but usually do not include the product-IT. When value creation and delivery in an enterprise’s business model also includes the product-IT, a product-IT inclusive view on EA urgently is required. The focus of this article is the methodical support for integration of product-IT and enterprise-IT using enterprise architectures. The main contributions in this article are the conceptual expansion of EA management to become product-IT inclusive that is manifested in a proposal for an extended EA meta-model, and an approach and method components to handle product-IT inclusive EAM.","PeriodicalId":416219,"journal":{"name":"Complex Syst. Informatics Model. Q.","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133826668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-31DOI: 10.7250/csimq.2020-25.04
Natalie Grufman, Sinéad Lyons, E. Sneiders
Industry 4.0 is considered to be the fourth industrial revolution and involves virtual and physical systems that are interconnected and collaborate in an autonomous way. Industry 4.0 is a relatively new concept within computer science and raises interest on how to make use of technologies included in the concept and profit from them. This article investigates Industry 4.0 in the context of SMEs: the opportunities and challenges that Industry 4.0 poses upon SMEs, as well as readiness of SMEs for Industry 4.0 are considered. The data collection and analysis methods were literature review with grounded theory. In the result, the main challenges proved being of organizational nature: SMEs need help with company-specific strategies for implementing Industry 4.0; and SMEs need skilled employees. The opportunities are flexibility and openness to innovation, which are pertinent to SMEs; cloud computing; and public investments into technology and adoption of Industry 4.0 by companies. The readiness of SMEs for Industry 4.0 is still somewhat low – they are still learners.
{"title":"Exploring Readiness of SMEs for Industry 4.0","authors":"Natalie Grufman, Sinéad Lyons, E. Sneiders","doi":"10.7250/csimq.2020-25.04","DOIUrl":"https://doi.org/10.7250/csimq.2020-25.04","url":null,"abstract":"Industry 4.0 is considered to be the fourth industrial revolution and involves virtual and physical systems that are interconnected and collaborate in an autonomous way. Industry 4.0 is a relatively new concept within computer science and raises interest on how to make use of technologies included in the concept and profit from them. This article investigates Industry 4.0 in the context of SMEs: the opportunities and challenges that Industry 4.0 poses upon SMEs, as well as readiness of SMEs for Industry 4.0 are considered. The data collection and analysis methods were literature review with grounded theory. In the result, the main challenges proved being of organizational nature: SMEs need help with company-specific strategies for implementing Industry 4.0; and SMEs need skilled employees. The opportunities are flexibility and openness to innovation, which are pertinent to SMEs; cloud computing; and public investments into technology and adoption of Industry 4.0 by companies. The readiness of SMEs for Industry 4.0 is still somewhat low – they are still learners.","PeriodicalId":416219,"journal":{"name":"Complex Syst. Informatics Model. Q.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114256640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-31DOI: 10.7250/csimq.2020-25.03
Ibrahim Koura, F. Bénaben, J. Gou, Audrey Fertier
The concept of collaborative networks has been encountered very often lately as the answer when trying to adapt and improve enterprises in these highly competitive business environments, therefore the urge for constantly addressing this topic. A lot of work-related to collaborative networks has been done so far, from defining network types to leveling partnerships and proposing models for partnership developments. But the lack of tackling a very important obstacle, which is the difficulty of detecting and anticipating collaboration opportunities between enterprises, inspired this research. In this article, a new theoretical opportunity detection approach is proposed based on enterprise characterization concept, KPI classification as well as collaboration types. This detection approach is a table of industrial classifications that imitates the Mendeleev periodic table from the concept point of view. A fictional example from an industrial context is shown to explain the usage of this approach accompanied by discussion about future work and limitations.
{"title":"The Periodic Table of Industries: Detection of Collaboration Opportunities Based on an Imitation of the Mendeleev Periodic Table of Elements","authors":"Ibrahim Koura, F. Bénaben, J. Gou, Audrey Fertier","doi":"10.7250/csimq.2020-25.03","DOIUrl":"https://doi.org/10.7250/csimq.2020-25.03","url":null,"abstract":"The concept of collaborative networks has been encountered very often lately as the answer when trying to adapt and improve enterprises in these highly competitive business environments, therefore the urge for constantly addressing this topic. A lot of work-related to collaborative networks has been done so far, from defining network types to leveling partnerships and proposing models for partnership developments. But the lack of tackling a very important obstacle, which is the difficulty of detecting and anticipating collaboration opportunities between enterprises, inspired this research. In this article, a new theoretical opportunity detection approach is proposed based on enterprise characterization concept, KPI classification as well as collaboration types. This detection approach is a table of industrial classifications that imitates the Mendeleev periodic table from the concept point of view. A fictional example from an industrial context is shown to explain the usage of this approach accompanied by discussion about future work and limitations.","PeriodicalId":416219,"journal":{"name":"Complex Syst. Informatics Model. Q.","volume":"420 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123036292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-31DOI: 10.7250/csimq.2020-25.02
Carmen Ioana Gog
The Design Science research method was hereby employed to develop an artifact that demonstrates the experimental “model-aware” software engineering methodology in the context of PHP Web development – a “low code” development approach with code templates generated from technology-specific models. The proof-of-concept consists of two interacting components: a custom diagrammatic modeling environment and model-driven generated PHP pages. The interaction between the two components conforms the engineering method labelled as “Model-aware software engineering” (MASE) – a flavor of model-driven engineering recently introduced in research projects as a hybridization of the Agile Modeling Method Engineering (AMME) framework and the Resource Description Framework (RDF). The experimental MASE method is employed here to demonstrate its feasibility for the common Model-View-Controller (MVC) website development pattern, thus showing potential to support common Web development work.
{"title":"Agile Development of PHP Websites: A Model-Aware Approach","authors":"Carmen Ioana Gog","doi":"10.7250/csimq.2020-25.02","DOIUrl":"https://doi.org/10.7250/csimq.2020-25.02","url":null,"abstract":"The Design Science research method was hereby employed to develop an artifact that demonstrates the experimental “model-aware” software engineering methodology in the context of PHP Web development – a “low code” development approach with code templates generated from technology-specific models. The proof-of-concept consists of two interacting components: a custom diagrammatic modeling environment and model-driven generated PHP pages. The interaction between the two components conforms the engineering method labelled as “Model-aware software engineering” (MASE) – a flavor of model-driven engineering recently introduced in research projects as a hybridization of the Agile Modeling Method Engineering (AMME) framework and the Resource Description Framework (RDF). The experimental MASE method is employed here to demonstrate its feasibility for the common Model-View-Controller (MVC) website development pattern, thus showing potential to support common Web development work.","PeriodicalId":416219,"journal":{"name":"Complex Syst. Informatics Model. Q.","volume":"10 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123692433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-30DOI: 10.7250/csimq.2020-24.03
Johannes Wichmann, K. Sandkuhl, N. Shilov, A. Smirnov, F. Timm, M. Wißotzki
Enterprise Architecture (EA) management has been discussed as being supportive for implementation of regulations in enterprises and organizations, but the role of EA frameworks in this context has not been addressed intensely. The EU General Data Protection Regulation (GDPR) is one of the most frequently discussed regulation in industry and research, and expected to cause a shift in viewpoint of enterprises from a technological perspective dominated by information security issues to an organizational perspective governed by GDPR-compliant organizational structures and processes. A well-documented Enterprise Architecture (EA) and a working Enterprise Architecture Management (EAM) organization are expected to significantly ease the roadmap planning for GDPR implementation. Therefore, this article focuses on the practice of EA use for GDPR implementation. The main contributions of this article are (a) an analysis and comparison of existing architecture frameworks and how they address security-related issues, and (b) a case study from financial industries illustrating the use of EA for implementing GDPR compliance.
{"title":"Enterprise Architecture Frameworks as Support for Implementation of Regulations: Approach and Experiences from GDPR","authors":"Johannes Wichmann, K. Sandkuhl, N. Shilov, A. Smirnov, F. Timm, M. Wißotzki","doi":"10.7250/csimq.2020-24.03","DOIUrl":"https://doi.org/10.7250/csimq.2020-24.03","url":null,"abstract":"Enterprise Architecture (EA) management has been discussed as being supportive for implementation of regulations in enterprises and organizations, but the role of EA frameworks in this context has not been addressed intensely. The EU General Data Protection Regulation (GDPR) is one of the most frequently discussed regulation in industry and research, and expected to cause a shift in viewpoint of enterprises from a technological perspective dominated by information security issues to an organizational perspective governed by GDPR-compliant organizational structures and processes. A well-documented Enterprise Architecture (EA) and a working Enterprise Architecture Management (EAM) organization are expected to significantly ease the roadmap planning for GDPR implementation. Therefore, this article focuses on the practice of EA use for GDPR implementation. The main contributions of this article are (a) an analysis and comparison of existing architecture frameworks and how they address security-related issues, and (b) a case study from financial industries illustrating the use of EA for implementing GDPR compliance.","PeriodicalId":416219,"journal":{"name":"Complex Syst. Informatics Model. Q.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126329335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-30DOI: 10.7250/csimq.2020-24.04
Ralf-Christian Härting, Christopher Reichstein, K. Sandkuhl, Nathalie Hoppe, Hakan Yesilay
Digital transformation has an increasing influence on business processes and new business models. A successful digital transformation in enterprises requires a holistic IT infrastructure in order to meet changing business requirements. Enterprises face the challenge of combining business and IT to benefit from existing technological attainments in the digital age. As Enterprise Architecture Management (EAM) is supposed to support companies’ transformation processes, it has consequently moved into the focus of large companies and small and medium-sized enterprises. Previous studies have considered the benefits of EAM taking not into account factors regarding the digital transformation process. The present study is therefore intended to close this gap. This article builds on a conceptual model based on a qualitative design with case studies. It presents a quantitative study that investigates the empirical relation between several indicators and the dependent variable “Benefits of EAM” in the digital transformation process. The results show that the indicators “IT Landscape”, “Internal Business” and “EAM Establishment” positively and significantly influence the benefits of EAM in the digital transformation process.
{"title":"Potential Benefits of Enterprise Architecture Management in the Digital Transformation Process","authors":"Ralf-Christian Härting, Christopher Reichstein, K. Sandkuhl, Nathalie Hoppe, Hakan Yesilay","doi":"10.7250/csimq.2020-24.04","DOIUrl":"https://doi.org/10.7250/csimq.2020-24.04","url":null,"abstract":"Digital transformation has an increasing influence on business processes and new business models. A successful digital transformation in enterprises requires a holistic IT infrastructure in order to meet changing business requirements. Enterprises face the challenge of combining business and IT to benefit from existing technological attainments in the digital age. As Enterprise Architecture Management (EAM) is supposed to support companies’ transformation processes, it has consequently moved into the focus of large companies and small and medium-sized enterprises. Previous studies have considered the benefits of EAM taking not into account factors regarding the digital transformation process. The present study is therefore intended to close this gap. This article builds on a conceptual model based on a qualitative design with case studies. It presents a quantitative study that investigates the empirical relation between several indicators and the dependent variable “Benefits of EAM” in the digital transformation process. The results show that the indicators “IT Landscape”, “Internal Business” and “EAM Establishment” positively and significantly influence the benefits of EAM in the digital transformation process.","PeriodicalId":416219,"journal":{"name":"Complex Syst. Informatics Model. Q.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124458110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-30DOI: 10.7250/csimq.2020-24.00
Erika Nazaruka
Complex systems consist of multiple interacting parts; some of them (or even all of them) may also be systems. While performing their tasks, these parts operate with multiple data and information flows. Data are gathered, created, transferred, and analyzed. Information based on the analyzed data is assessed and taken into account during decision making. Different types of data and a large number of data flows can be considered as one of the sources of system complexity. Thus, information management, including data control, is an important aspect of complex systems development and management. According to ISO/IEC/IEEE 15288:2015, “the purpose of the Information Management Process is to generate, obtain, confirm, transform, retain, retrieve, disseminate and dispose of information, to designated stakeholders…”. Information management strategies consider the scope of information, constrains, security controls and information life cycle. This means that information management activities should be implemented starting from the level of primitive data gathering and ending with enterprise-level decision making. The articles, which have been recommended by reviewers for this issue of CSIMQ, present contributions in different aspects of information management in complex systems, namely, implementation of harmful environment monitoring and data transmitting by Internet-of-Things (IoT) systems, analysis of technological and organizational means for mitigating issues related to information security and users’ privacy that can lead to changes in corresponding systems’ processes, organization and infrastructure, as well as assessment of potential benefits that a controlled (i.e. based on the up-to-date information) change process can bring to an enterprise.
{"title":"Selected Topics on Information Management in Complex Systems: Editorial Introduction to Issue 24 of CSIMQ","authors":"Erika Nazaruka","doi":"10.7250/csimq.2020-24.00","DOIUrl":"https://doi.org/10.7250/csimq.2020-24.00","url":null,"abstract":"Complex systems consist of multiple interacting parts; some of them (or even all of them) may also be systems. While performing their tasks, these parts operate with multiple data and information flows. Data are gathered, created, transferred, and analyzed. Information based on the analyzed data is assessed and taken into account during decision making. Different types of data and a large number of data flows can be considered as one of the sources of system complexity. Thus, information management, including data control, is an important aspect of complex systems development and management. According to ISO/IEC/IEEE 15288:2015, “the purpose of the Information Management Process is to generate, obtain, confirm, transform, retain, retrieve, disseminate and dispose of information, to designated stakeholders…”. Information management strategies consider the scope of information, constrains, security controls and information life cycle. This means that information management activities should be implemented starting from the level of primitive data gathering and ending with enterprise-level decision making. The articles, which have been recommended by reviewers for this issue of CSIMQ, present contributions in different aspects of information management in complex systems, namely, implementation of harmful environment monitoring and data transmitting by Internet-of-Things (IoT) systems, analysis of technological and organizational means for mitigating issues related to information security and users’ privacy that can lead to changes in corresponding systems’ processes, organization and infrastructure, as well as assessment of potential benefits that a controlled (i.e. based on the up-to-date information) change process can bring to an enterprise.","PeriodicalId":416219,"journal":{"name":"Complex Syst. Informatics Model. Q.","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121649543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-30DOI: 10.7250/csimq.2020-24.02
Khairunisa Sharif, B. Tenbergen
Intelligent voice assistants are internet-connected devices, which listen to their environment and react to spoken user commands in order to retrieve information from the internet, control appliances in the household, or notify the user of incoming messages, reminders, and the like. With their increasing ubiquity in smart homes, their application seems only limited by the imagination of developers, who connect these off-the-shelf devices to existing apps, online services, or appliances. However, since their inherent nature is to observe the user in their home, their ubiquity also raises concern of security and user privacy. To justify the trust placed into the devices, the devices must be secure from unauthorized access and the back-end infrastructure tasked with speech-to-text analysis, command interpretation, and connection to other services and appliances must maintain confidentiality of data. To investigate existing possible vulnerabilities, approaches to mitigate them, as well as general considerations in this emerging field, we supplement the findings of a recent study with results from a systematic literature review. We were able to compile a list of six main types of user privacy vulnerabilities, partially confirming previous findings, but also finding additional issues. We discuss these vulnerabilities, their associated attack vectors, and possible mitigations users can take to protect themselves.
{"title":"Smart Home Voice Assistants: A Literature Survey of User Privacy and Security Vulnerabilities","authors":"Khairunisa Sharif, B. Tenbergen","doi":"10.7250/csimq.2020-24.02","DOIUrl":"https://doi.org/10.7250/csimq.2020-24.02","url":null,"abstract":"Intelligent voice assistants are internet-connected devices, which listen to their environment and react to spoken user commands in order to retrieve information from the internet, control appliances in the household, or notify the user of incoming messages, reminders, and the like. With their increasing ubiquity in smart homes, their application seems only limited by the imagination of developers, who connect these off-the-shelf devices to existing apps, online services, or appliances. However, since their inherent nature is to observe the user in their home, their ubiquity also raises concern of security and user privacy. To justify the trust placed into the devices, the devices must be secure from unauthorized access and the back-end infrastructure tasked with speech-to-text analysis, command interpretation, and connection to other services and appliances must maintain confidentiality of data. To investigate existing possible vulnerabilities, approaches to mitigate them, as well as general considerations in this emerging field, we supplement the findings of a recent study with results from a systematic literature review. We were able to compile a list of six main types of user privacy vulnerabilities, partially confirming previous findings, but also finding additional issues. We discuss these vulnerabilities, their associated attack vectors, and possible mitigations users can take to protect themselves.","PeriodicalId":416219,"journal":{"name":"Complex Syst. Informatics Model. Q.","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117136204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-30DOI: 10.7250/csimq.2020-24.01
I. Skarga-Bandurova, Yana Krytska, Artem Velykzhanin, Lina Barbaruk, O. Suvorin, Mikhail Shorokhov
The article provides a conceptual framework for developing real-time water monitoring system based on IoT technology. The process, strategy and knowledge base for multidisciplinary research on IoT systems and prerequisites for real-world application of IoT technology into continuous water quality monitoring are discussed. The study expands current efforts aimed at leveraging customized IoT solutions for better instrumentation and the continued integration of sensor data into networks. The process of system design from scratch and base components of IoT-based water quality monitoring system for surface water are described. While the focus of this article is on system design, opportunities to improve the system components for the management of water resources with continuous water quality monitoring are much broader. In this view, perspectives and development issues of IoT-based water quality monitoring are also discussed.
{"title":"Emerging Tools for Design and Implementation of Water Quality Monitoring Based on IoT","authors":"I. Skarga-Bandurova, Yana Krytska, Artem Velykzhanin, Lina Barbaruk, O. Suvorin, Mikhail Shorokhov","doi":"10.7250/csimq.2020-24.01","DOIUrl":"https://doi.org/10.7250/csimq.2020-24.01","url":null,"abstract":"The article provides a conceptual framework for developing real-time water monitoring system based on IoT technology. The process, strategy and knowledge base for multidisciplinary research on IoT systems and prerequisites for real-world application of IoT technology into continuous water quality monitoring are discussed. The study expands current efforts aimed at leveraging customized IoT solutions for better instrumentation and the continued integration of sensor data into networks. The process of system design from scratch and base components of IoT-based water quality monitoring system for surface water are described. While the focus of this article is on system design, opportunities to improve the system components for the management of water resources with continuous water quality monitoring are much broader. In this view, perspectives and development issues of IoT-based water quality monitoring are also discussed.","PeriodicalId":416219,"journal":{"name":"Complex Syst. Informatics Model. Q.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115253607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-31DOI: 10.7250/csimq.2020-23.00
K. Sandkuhl
Complex systems and their analysis, construction, management or application are the motivation of all articles in this issue of CSIMQ. Different perspectives exist on what actually causes “complexity” in systems. In systems theory, a widely spread view is that complex systems have many components with emergent behavior, i.e. the large number and the dynamics of components are decisive. In business informatics, the complexity of information systems is attributed to their socio-technical nature, which acknowledges the interaction between the human actors and the information technology in an enterprise. Understanding the context of complex systems or their components is supported by modeling and is a key aspect of preparing organizational solutions. Models do not remove the complexity of the real world but help to understand it and to design and develop solutions. All articles in this issue are in some respect concerned with models or modeling. The articles also reflect recent trends in industry and society, such as digital transformation and applications of artificial intelligence, and show that these trends will not necessarily reduce complexity in systems but rather require the combination of proven approaches, such as modeling, and new methods for managing this complexity.
{"title":"Selected Topics on Complex Systems Informatics: Editorial Introduction to Issue 23 of CSIMQ","authors":"K. Sandkuhl","doi":"10.7250/csimq.2020-23.00","DOIUrl":"https://doi.org/10.7250/csimq.2020-23.00","url":null,"abstract":"Complex systems and their analysis, construction, management or application are the motivation of all articles in this issue of CSIMQ. Different perspectives exist on what actually causes “complexity” in systems. In systems theory, a widely spread view is that complex systems have many components with emergent behavior, i.e. the large number and the dynamics of components are decisive. In business informatics, the complexity of information systems is attributed to their socio-technical nature, which acknowledges the interaction between the human actors and the information technology in an enterprise. Understanding the context of complex systems or their components is supported by modeling and is a key aspect of preparing organizational solutions. Models do not remove the complexity of the real world but help to understand it and to design and develop solutions. All articles in this issue are in some respect concerned with models or modeling. The articles also reflect recent trends in industry and society, such as digital transformation and applications of artificial intelligence, and show that these trends will not necessarily reduce complexity in systems but rather require the combination of proven approaches, such as modeling, and new methods for managing this complexity.","PeriodicalId":416219,"journal":{"name":"Complex Syst. Informatics Model. Q.","volume":"46 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113989669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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