@inproceedings{9582711,

title = {Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data},

author = {Midhad Blazevic and Lennart B. Sina and Dirk Burkhardt and Melanie Siegel and Kawa Nazemi},

url = {https://ieeexplore.ieee.org/document/9582711, IEEE Xplore},

doi = {10.1109/IV53921.2021.00041},

year  = {2021},

date = {2021-03-01},

urldate = {2021-03-01},

booktitle = {2021 25th International Conference Information Visualisation (IV)},

pages = {211-217},

publisher = {IEEE},

abstract = {Visual Analytics enables solving complex analytical tasks by coupling interactive visualizations and machine learning approaches. Besides the analytical reasoning enabled through Visual Analytics, the exploration of data plays an essential role. The exploration process can be supported through similarity-based approaches that enable finding similar data to those annotated in the context of visual exploration. We propose in this paper a process of annotation in the context of exploration that leads to labeled vectors-of-interest and enables finding similar publications based on interest vectors. The generation and labeling of the interest vectors are performed automatically by the Visual Analytics system and lead to finding similar papers and categorizing the annotated papers. With this approach, we provide a categorized similarity search based on an automatically labeled interest matrix in Visual Analytics.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sina2021,

title = {Visual Dashboards in Trend Analytics to Observe Competitors and Leading Domain Experts},

author = {Lennart Sina and Dirk Burkhardt and Kawa Nazemi},

editor = {Haithem Afli and Udo Bleimann and Dirk Burkhardt and Robert Loew and Stefanie Regier and Ingo Stengel and Haiying Wang and Huiru (Jane) Zheng},

url = {http://ceur-ws.org/Vol-2815/CERC2020_paper14.pdf, Paper on CEUR-WS, Full PDF},

issn = {1613-0073},

year  = {2021},

date = {2021-02-17},

booktitle = {Proceedings of the 6th Collaborative European Research Conference (CERC 2020)},

volume = {Vol. 2815},

pages = {222-235},

publisher = {CEUR-WS.org},

address = {Aachen, Germany},

series = {CEUR Workshop Proceedings},

abstract = {The rapid change due to digitalization challenge a variety of market players and force them to find strategies to be aware of developments in these markets, particularly those that impact their business. The main challenge is what a practical solution could look like and how technology can support market players in these trend observation tasks. The paper outlines therefore a technological solution to observe specific authors e.g. researchers who influence a certain market or engineers of competitors. In many branches both are well-known groups to market players and there is almost always the need of a technology that supports the topical observation. This paper focuses on the concept of how a visual dashboard could enable a market observation and how data must be processed for it and its prototypical implementation which enables an evaluation later. Furthermore, the definition of a principal technological analysis for innovation and technology management is created and is also an important contribution to the scientific community that specifically considers the technology perspective and its corresponding requirements.},

note = {urn:nbn:de:0074-2815-0},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2020c,

title = {Visual Analytics Indicators for Mobility and Transportation},

author = {Kawa Nazemi and Matthias Kowald and Till Dannewald and Dirk Burkhardt and Egils Ginters},

editor = {Janis Grabis and Andrejs Romanovs and Galina Kulesova},

doi = {10.1109/ITMS51158.2020.9259321},

isbn = {978-1-7281-9105-8},

year  = {2020},

date = {2020-09-10},

booktitle = {2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)},

pages = {1-6},

publisher = {IEEE},

abstract = {Visual Analytics enables a deep analysis of complex and multivariate data by applying machine learning methods and interactive visualization. These complex analyses lead to gain insights and knowledge for a variety of analytics tasks to enable the decision-making process. The enablement of decision-making processes is essential for managing and planning mobility and transportation. These are influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans’ mobility behaviour. New technologies will lead to a different mobility behaviour with other constraints. These changes in mobility behaviour require analytical systems to forecast the required information and probably appearing changes. These systems must consider different perspectives and employ multiple indicators. Visual Analytics enable such analytical tasks. We introduce in this paper the main indicators for Visual Analytics for mobility and transportation that are exemplary explained through two case studies.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Burkhardt2020cb,

title = {Innovations in Mobility and Logistics: Assistance of Complex Analytical Processes in Visual Trend Analytics},

author = {Dirk Burkhardt and Kawa Nazemi and Egils Ginters},

editor = {Janis Grabis and Andrejs Romanovs and Galina Kulesova},

doi = {10.1109/ITMS51158.2020.9259309},

isbn = {978-1-7281-9105-8},

year  = {2020},

date = {2020-09-09},

booktitle = {2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)},

pages = {1-6},

publisher = {IEEE},

abstract = {A variety of new technologies and ideas for businesses are arising in the domain of logistics and mobility. It can be differentiated between fundamental new approaches, e.g. central packaging stations or deliveries via drones and minor technological advancements that aim on more ecologically and economic transportation. The need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance is growing. The users’ behavior is commonly investigated in adaptive systems, which is considering the induvial preferences of users, but neglecting often the tasks and goals of the analysis. A process-related supports could assist to solve an analytical task in a more efficient and effective way. We introduce in this paper an approach that enables non-professionals to perform visual trend analysis through an advanced process assistance based on process mining and visual adaptation. This allows generating a process model based on events, which is the baseline for process support feature calculation. These features in form of visual adaptations and the process model enable assisting non-experts in complex analytical tasks.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Kaupp_IV2020,

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

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

doi = {10.1109/IV51561.2020.00064},

issn = {2375-0138},

year  = {2020},

date = {2020-09-01},

booktitle = {2020 24th International Conference Information Visualisation (IV)},

pages = {350-359},

publisher = {IEEE Computer Society},

abstract = {The integrated cyber-physical systems in Smart Manufacturing generate continuously vast amount of data. These complex data are difficult to assess and gather knowledge about the data. Tasks like fault detection and diagnosis are therewith difficult to solve. Visual Analytics mitigates complexity through the combined use of algorithms and visualization methods that allow to perceive information in a more accurate way. Thereby, reasoning relies more and more on the given situation within a smart manufacturing environment, namely the context. Current general Visual Analytics approaches only provide a vague definition of context. We introduce in this paper a model that specifies the context in Visual Analytics for Smart Manufacturing. Additionally, our model bridges the latest advances in research on Smart Manufacturing and Visual Analytics. We combine and summarize methodologies, algorithms and specifications of both vital research fields with our previous findings and fuse them together. As a result, we propose our novel industry 4.0-ready Visual Analytics model for context-aware diagnosis in Smart Manufacturing.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi_IV2020,

title = {Comparison of Full-text Articles and Abstracts for Visual Trend Analytics through Natural Language Processing},

author = {Kawa Nazemi and Maike J. Klepsch and Dirk Burkhardt and Lukas Kaupp},

doi = {10.1109/IV51561.2020.00065},

issn = {2375-0138},

year  = {2020},

date = {2020-09-01},

booktitle = {2020 24th International Conference Information Visualisation (IV)},

pages = {360-367},

publisher = {IEEE Computer Society},

abstract = {Scientific publications are an essential resource for detecting emerging trends and innovations in a very early stage, by far earlier than patents may allow. Thereby Visual Analytics systems enable a deep analysis by applying commonly unsupervised machine learning methods and investigating a mass amount of data. A main question from the Visual Analytics viewpoint in this context is, do abstracts of scientific publications provide a similar analysis capability compared to their corresponding full-texts? This would allow to extract a mass amount of text documents in a much faster manner. We compare in this paper the topic extraction methods LSI and LDA by using full text articles and their corresponding abstracts to obtain which method and which data are better suited for a Visual Analytics system for Technology and Corporate Foresight. Based on a easy replicable natural language processing approach, we further investigate the impact of lemmatization for LDA and LSI. The comparison will be performed qualitative and quantitative to gather both, the human perception in visual systems and coherence values. Based on an application scenario a visual trend analytics system illustrates the outcomes.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-030-39688-6_12,

title = {Visual Analytics in Mobility, Transportation and Logistics},

author = {Kawa Nazemi and Dirk Burkhardt and Lukas Kaupp and Till Dannewald and Matthias Kowald and Egils Ginters},

editor = {Egils Ginters and Mario Arturo Ruiz Estrada and Miquel Angel Piera Eroles},

url = {https://rd.springer.com/chapter/10.1007%2F978-3-030-39688-6_12, Springer},

doi = {10.1007/978-3-030-39688-6_12},

isbn = {978-3-030-39688-6},

year  = {2020},

date = {2020-01-31},

booktitle = {ICTE in Transportation and Logistics 2019},

pages = {82--89},

publisher = {Springer International Publishing. Lecture Notes in Intelligent Transportation and Infrastructure},

address = {Cham},

abstract = {Mobility, transportation and logistics are more and more influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans' mobility behavior. These indicators will lead to massive changes in our daily live with regards to mobility, transportation and logistics. New technologies will lead to a different mobility behavior with new constraints. These changes in mobility behavior and logistics require analytical systems to forecast the required information and probably appearing changes. These systems have to consider different perspectives and employ multiple indicators. Visual Analytics provides both, the analytical approaches by including machine learning approaches and interactive visualizations to enable such analytical tasks. In this paper the main indicators for Visual Analytics in the domain of mobility transportation and logistics are discussed and followed by exemplary case studies to illustrate the advantages of such systems. The examples are aimed to demonstrate the benefits of Visual Analytics in mobility.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-030-39688-6_40,

title = {Process Support and Visual Adaptation to Assist Visual Trend Analytics in Managing Transportation Innovations},

author = {Dirk Burkhardt and Kawa Nazemi and Egils Ginters},

editor = {Egils Ginters and Mario Arturo Ruiz Estrada and Miquel Angel Piera Eroles},

url = {https://rd.springer.com/chapter/10.1007%2F978-3-030-39688-6_40, Springer},

doi = {10.1007/978-3-030-39688-6_40},

isbn = {978-3-030-39688-6},

year  = {2020},

date = {2020-01-30},

booktitle = {ICTE in Transportation and Logistics 2019},

pages = {319--327},

publisher = {Springer International Publishing. Lecture Notes in Intelligent Transportation and Infrastructure},

address = {Cham},

abstract = {In the domain of mobility and logistics, a variety of new technologies and business ideas are arising. Beside technologies that aim on ecologically and economic transportation, such as electric engines, there are also fundamental different approaches like central packaging stations or deliveries via drones. Yet, there is a growing need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance. Commonly adaptive systems investigate only the users' behavior, while a process-related supports could assist to solve an analytical task more efficient and effective. In this article an approach that enables non-experts to perform visual trend analysis through an advanced process support based on process mining is described. This allow us to calculate a process model based on events, which is the baseline for process support feature calculation. These features and the process model enable to assist non-expert users in complex analytical tasks.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi_ISVC2019,

title = {A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management},

author = {Kawa Nazemi and Dirk Burkhardt},

editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Daniela Ushizima and Sek Chai and Shinjiro Sueda and Xin Lin and Aidong Lu and Daniel Thalmann and Chaoli Wang and Panpan Xu},

url = {https://rd.springer.com/chapter/10.1007/978-3-030-33723-0_23, Springer LNCS},

doi = {10.1007/978-3-030-33723-0_23},

isbn = {978-3-030-33723-0},

year  = {2019},

date = {2019-10-09},

booktitle = {Advances in Visual Computing},

pages = {283--294},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {Visual Analytics provides with a combination of automated techniques and interactive visualizations huge analysis possibilities in technology and innovation management. Thereby not only the use of machine learning data mining methods plays an important role. Due to the high interaction capabilities, it provides a more user-centered approach, where users are able to manipulate the entire analysis process and get the most valuable information. Existing Visual Analytics systems for Trend Analytics and technology and innovation management do not really make use of this unique feature and almost neglect the human in the analysis process. Outcomes from research in information search, information visualization and technology management can lead to more sophisticated Visual Analytics systems that involved the human in the entire analysis process. We propose in this paper a new interaction approach for Visual Analytics in technology and innovation management with a special focus on technological trend analytics.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi-IV2019,

title = {Visual Analytics for Analyzing Technological Trends from Text},

author = {K Nazemi and D Burkhardt},

doi = {10.1109/IV.2019.00041},

issn = {2375-0138},

year  = {2019},

date = {2019-07-01},

booktitle = {2019 23rd International Conference Information Visualisation (IV)},

pages = {191-200},

abstract = {The awareness of emerging technologies is essential for strategic decision making in enterprises. Emerging and decreasing technological trends could lead to strengthening the competitiveness and market positioning. The exploration, detection and identification of such trends can be essentially supported through information visualization, trend mining and in particular through the combination of those. Commonly, trends appear first in science and scientific documents. However, those documents do not provide sufficient information for analyzing and identifying emerging trends. It is necessary to enrich data, extract information from the integrated data, measure the gradient of trends over time and provide effective interactive visualizations. We introduce in this paper an approach for integrating, enriching, mining, analyzing, identifying and visualizing emerging trends from scientific documents. Our approach enhances the state of the art in visual trend analytics by investigating the entire analysis process and providing an approach for enabling human to explore undetected potentially emerging trends.},

note = {Best Paper Award},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Burkhardt2017,

title = {Informationsvisualisierung und Visual Analytics zur Unterstützung von E-Government Prozessen},

author = {Dirk Burkhardt and Kawa Nazemi},

editor = {Korinna Bade and Matthias Pietsch and Susanne Raabe and Lars Schütz},

url = {https://www.shaker.de/de/content/catalogue/index.asp?lang=de&ID=8&ISBN=978-3-8440-5439-2&search=yes},

doi = {10.2370/9783844054392},

isbn = {978-3844054392},

year  = {2017},

date = {2017-01-05},

booktitle = {Technologische Trends im Spannungsfeld von Beteiligung – Entscheidung – Planung},

pages = {29-38},

publisher = {Shaker Verlag},

abstract = {Politische und gesellschaftliche Prozesse werden durch Informationen sehr stark geprägt, wie auch die jüngsten Ereignisse aufzeigen. Diese Informationen können, trotz enormer Fortschritte, nicht immer aus den sehr großen, heterogenen und verteilten Daten entnommen werden. „Big Data“ stellt somit auch in der öffentlichen Verwaltung eine immer größere Herausforderung dar. Sowohl durch eine umfangreiche Erhebung von Statistiken, als auch durch Dokumente wie Berichte und Studien, wachsen in Behörden die zu bewältigenden Informationsaufgaben. Darüber hinaus spielt die Berücksichtigung von Bürgermeinungen, vor allem auf kommunaler Ebene, eine immer größere Rolle. Eine Auswertung ohne moderne Informationstechnik ist dabei kaum mehr möglich. Damit aber aus diesen Daten tatsächlich die relevanten Informationen extrahiert werden, bedarf es Informationsvisualisierung und Visual Analytics Systeme die sehr detaillierte, aber dennoch einfache und schnelle Analysen für den Menschen erlauben. Dies stellt aber sehr hohe Anforderungen an die visuellen Systeme, da sie gleichzeitig auch den Nutzer und dessen Fähigkeiten berücksichtigen müssen.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2015b,

title = {Visual Trend Analysis with Digital Libraries},

author = {Kawa Nazemi and Reimond Retz and Dirk Burkhardt and Arjan Kuijper and Jörn Kohlhammer and Dieter W Fellner},

url = {https://doi.acm.org/10.1145/2809563.2809569},

doi = {10.1145/2809563.2809569},

isbn = {978-1-4503-3721-2},

year  = {2015},

date = {2015-10-01},

booktitle = {Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business.},

pages = {14:1--14:8},

publisher = {ACM},

address = {Graz, Austria},

series = {i-KNOW '15},

abstract = {The early awareness of new technologies and upcoming trends is essential for making strategic decisions in enterprises and research. Trends may signal that technologies or related topics might be of great interest in the future or obsolete for future directions. The identification of such trends premises analytical skills that can be supported through trend mining and visual analytics. Thus the earliest trends or signals commonly appear in science, the investigation of digital libraries in this context is inevitable. However, digital libraries do not provide sufficient information for analyzing trends. It is necessary to integrate data, extract information from the integrated data and provide effective interactive visual analysis tools. We introduce in this paper a model that investigates all stages from data integration to interactive visualization for identifying trends and analyzing the market situation through our visual trend analysis environment. Our approach improves the visual analysis of trends by investigating the entire transformation steps from raw and structured data to visual representations.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-319-20612-7_4,

title = {Explorative Visualization of Impact Analysis for Policy Modeling by Bonding Open Government and Simulation Data},

author = {Dirk Burkhardt and Kawa Nazemi and Egils Ginters and Artis Aizstrauts and Jörn Kohlhammer},

editor = {Sakae Yamamoto},

url = {https://link.springer.com/chapter/10.1007/978-3-319-20612-7_4. Springer Link},

doi = {doi.org/10.1007/978-3-319-20612-7_4},

isbn = {978-3-319-20612-7},

year  = {2015},

date = {2015-03-01},

booktitle = {International Conference on Human Interface and the Management of Information (HIMI 2015). Information and Knowledge Design.},

pages = {34--45},

publisher = {Springer International Publishing},

address = {Cham},

series = {LNCS 9172},

abstract = {Problem identification and solution finding are major challenges in policy modeling. Statistical indicator-data build the foundation for most of the required analysis work. In particular finding effective and efficient policies that solve an existing political problem is critical, since the forecast validation of the effectiveness is quite difficult. Simulation technologies can help to identify optimal policies for solutions, but nowadays many of such simulators are stand-alone technologies. In this paper we introduce a new visualization approach to enable the coupling of statistical indicator data from Open Government Data sources with simulators and especially simulation result data with the goal to provide an enhanced impact analysis for political analysts and decision makers. This allows, amongst others a more intuitive and effective way of solution finding.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}