@inbook{SSM*24,

title = {Guided Visual Analytics-A Visual Analytics Guidance Approach for Systematic Reviews in Research},

author = {Lennart B. Sina and Cristian A. Secco and Midhad Blazevic and Kawa Nazemi},

editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},

url = {https://doi.org/10.1007/978-3-031-46549-9_11},

doi = {10.1007/978-3-031-46549-9_11},

isbn = {978-3-031-46549-9},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery},

pages = {319–343},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Visual Analytics systems are often complex expert systems that require high expertise. The simplification of the interaction with such systems in order to make them usable for novices is one subject of current research works. One way to ease the user's interaction with the systems is through guidance approaches. Guidance approaches aim to support the user while working with the system by providing targeted assistance. We present in this work a stepwise guidance approach for Visual Analytics. For that, we use the domain of literature search and exploration exemplary. The underlying system allows researchers to visually search and explore scientific publications and automatically generate systematic review protocols. To accomplish this, we present a stepwise visual guidance system approach that combines automatic steps and manual user validation to unify the systematic literature review (SLR) creation process. Based on a design study we conducted, we present our proposed AI-based assistant (MAIA) that assists users in the various steps required to create systematic literature reviews. According to the PRISMA statement, we describe the process of SLR creation exemplary and present the different screens that guide the user through SLR creation.},

keywords = {},

pubstate = {published},

tppubtype = {inbook}

}

@inbook{Blazevic2024b,

title = {Integrating Machine Learning in Visual Analytics for Supporting Collaboration in Science},

author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Kawa Nazemi},

editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},

url = {https://doi.org/10.1007/978-3-031-46549-9_12},

doi = {10.1007/978-3-031-46549-9_12},

isbn = {978-3-031-46549-9},

year  = {2024},

date = {2024-01-01},

booktitle = {Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery},

pages = {345–373},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Studies have shown rising interest in scientific collaborations throughout the past decades. The challenges throughout various studies show an emerging need for research and development in methods and systems that utilize artificial intelligence to provide research communities with adequate tools that facilitate and encourage collaborative research. Many platforms focus on listing authors' publications and showcasing them with citation scores. They neglect the possibility of creating a holistic assistance and collaborative approach that covers the entire scientific research process using adequate intelligence methods. We introduce in this chapter a novel approach to visual collaboration. Our approach covers the entire process of scientific paper writing through real-time visual recommendations. It combines on-the-fly similarity measurements, ideation assistance based on group constellations, visual exploration, and stimuli promotion for the different stages of collaborative writing. Our research into collaborative research applications also led us to examine the adverse effects of multitasking and multi-application usage on researchers. These effects on human cognition require the integration of visual analytics that combines artificial intelligence with interactive visualizations. Thereby the interaction design and the ease of use are essential. Our approach presents a single-source AI-driven visual collaborative research platform for the entire research community.},

keywords = {},

pubstate = {published},

tppubtype = {inbook}

}

@inbook{Blazevic2024,

title = {Similarity in Visual Analytics-A Visual Analytics Approach for Finding Similar Publications},

author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Kawa Nazemi},

editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},

url = {https://doi.org/10.1007/978-3-031-46549-9_16},

doi = {10.1007/978-3-031-46549-9_16},

isbn = {978-3-031-46549-9},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery},

pages = {443–468},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Recent studies show that the search for relevant publications requires researchers to invest a significant amount of time and work; some studies even underline that this task requires the most time and work out of all the tasks in the entire research process. To reduce the search time in the research process, we propose a visual analytics approach that combines models and methods of natural language process, machine learning, similarity measures, and interactive visual representations. The proposed approach is based on our previous works and enhances those with additional automatic assistance during the entire search process. Our visual analytics approach facilitates the presentation of large amounts of relevant results similar to the identified topics of interest and tailored to the needs of researchers during their research process. The proposed method enables annotation in the context of exploration, allowing researchers to quickly find and bookmark relevant publications during exploration and, by doing so, improve the publication recommendations. We analyze the annotations researchers make throughout their research journey to identify the topics of interest and use them as input for our learning and measurement methods. By utilizing researchers' commonly observed annotation and exploration behavior, our approach counters information overload by generating labeled vectors of interest and providing similar publications.},

keywords = {},

pubstate = {published},

tppubtype = {inbook}

}

@inbook{Nazemi2022,

title = {Visual Analytics for Strategic Decision Making in Technology Management},

author = {Kawa Nazemi and Tim Feiter and Lennart B. Sina and Dirk Burkhardt and Alexander Kock},

editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},

url = {https://doi.org/10.1007/978-3-030-93119-3_2},

doi = {10.1007/978-3-030-93119-3_2},

isbn = {978-3-030-93119-3},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},

pages = {31--61},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {Strategic foresight, corporate foresight, and technology management enable firms to detect discontinuous changes early and develop future courses for a more sophisticated market positioning. The enhancements in machine learning and artificial intelligence allow more automatic detection of early trends to create future courses and make strategic decisions. Visual Analytics combines methods of automated data analysis through machine learning methods and interactive visualizations. It enables a far better way to gather insights from a vast amount of data to make a strategic decision. While Visual Analytics got various models and approaches to enable strategic decision-making, the analysis of trends is still a matter of research. The forecasting approaches and involvement of humans in the visual trend analysis process require further investigation that will lead to sophisticated analytical methods. We introduce in this paper a novel model of Visual Analytics for decision-making, particularly for technology management, through early trends from scientific publications. We combine Corporate Foresight and Visual Analytics and propose a machine learning-based Technology Roadmapping based on our previous work.},

keywords = {},

pubstate = {published},

tppubtype = {inbook}

}

@inbook{Kaupp2022b,

title = {Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model},

author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},

editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},

doi = {10.1007/978-3-030-93119-3_16},

isbn = {978-3-030-93119-3},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},

pages = {403–436},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {The integration of cyber-physical systems accelerates Industry 4.0. Smart factories become more and more complex, with novel connections, relationships, and dependencies. Consequently, complexity also rises with the vast amount of data. While acquiring data from all the involved systems and protocols remains challenging, the assessment and reasoning of information are complex for tasks like fault detection and diagnosis. Furthermore, through the risen complexity of smart manufacturing, the diagnosis process relies even more on the current situation, the context. Current Visual Analytics models prevail only a vague definition of context. This chapter presents an updated and extended version of the TAOISM Visual Analytics model based on our previous work. The model defines the context in smart manufacturing that enables context-aware diagnosis and analysis. Additionally, we extend our model in contrast to our previous work with context hierarchies, an applied use case on open-source data, transformation strategies, an algorithm to acquire context information automatically and present a concept of context-based information aggregation as well as a test of context-aware diagnosis with latest advances in neural networks. We fuse methodologies, algorithms, and specifications of both vital research fields, Visual Analytics and Smart Manufacturing, together with our previous findings to build a living Visual Analytics model open for future research.},

keywords = {Artificial Intelligence, Machine Leanring, Machine learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery},

pubstate = {published},

tppubtype = {inbook}

}

@inbook{Kovalerchuk2022b,

title = {Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions},

author = {Boris Kovalerchuk and Răzvan Andonie and Nuno Datia and Kawa Nazemi and Ebad Banissi},

editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},

url = {https://doi.org/10.1007/978-3-030-93119-3_1},

doi = {10.1007/978-3-030-93119-3_1},

isbn = {978-3-030-93119-3},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},

pages = {1--27},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {Integrating artificial intelligence (AI) and machine learning (ML) methods with interactive visualization is a research area that has evolved for years. With the rise of AI applications, the combination of AI/ML and interactive visualization is elevated to new levels of sophistication and has become more widespread in many domains. Such application drive has led to a growing trend to bridge the gap between AI/ML and visualizations. This chapter summarizes the current research trend and provides foresight to future research direction in integrating AI/ML and visualization. It investigates different areas of integrating the named disciplines, starting with visualization in ML, visual analytics, visual-enabled machine learning, natural language processing, and multidimensional visualization and AI to illustrate the research trend towards visual knowledge discovery. Each section of this chapter presents the current research state along with problem statements or future directions that allow a deeper investigation of seamless integration of novel AI methods in interactive visualizations.},

keywords = {},

pubstate = {published},

tppubtype = {inbook}

}

@inbook{Kaupp2022,

title = {Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model},

author = {Lukas Kaupp and Kawa Nazemi and Bernhard Humm},

editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi},

url = {https://doi.org/10.1007/978-3-030-93119-3_16},

doi = {10.1007/978-3-030-93119-3_16},

isbn = {978-3-030-93119-3},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery},

pages = {403--436},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {The integration of cyber-physical systems accelerates Industry 4.0. Smart factories become more and more complex, with novel connections, relationships, and dependencies. Consequently, complexity also rises with the vast amount of data. While acquiring data from all the involved systems and protocols remains challenging, the assessment and reasoning of information are complex for tasks like fault detection and diagnosis. Furthermore, through the risen complexity of smart manufacturing, the diagnosis process relies even more on the current situation, the context. Current Visual Analytics models prevail only a vague definition of context. This chapter presents an updated and extended version of the TAOISM Visual Analytics model based on our previous work. The model defines the context in smart manufacturing that enables context-aware diagnosis and analysis. Additionally, we extend our model in contrast to our previous work with context hierarchies, an applied use case on open-source data, transformation strategies, an algorithm to acquire context information automatically and present a concept of context-based information aggregation as well as a test of context-aware diagnosis with latest advances in neural networks. We fuse methodologies, algorithms, and specifications of both vital research fields, Visual Analytics and Smart Manufacturing, together with our previous findings to build a living Visual Analytics model open for future research.},

keywords = {Artificial Intelligence, Machine Leanring, Machine learning, mobility indicators for visual analytics, smart factory, Smart manufacturing, Visual Analytical Reasoning, Visual analytics, Visual Knowledge Discovery},

pubstate = {published},

tppubtype = {inbook}

}