Scientific Publications

@inproceedings{Burkhardt2025b,

title = {DPPviewer: A Visual Analytics Approach for Optimizing Production Chains on Digital Product Passports},

author = {Dirk Burkhardt and Moritz Bock and Arjan Kuijper},

editor = {Ebad Banissi and Nuno Datia and João Moura Pires and Anna Ursyn and Kawa Nazemi and Cristian A. Secco and Boris Kovalerchuk and Razvan Andonie and Marina Gavrilova and Minoru Nakayama and Mountaz Hascoet and Amalia Rusu and Quang Vinh Nguyen and Mabule Samuel Mabakane and Adrian Rusu and Jie Hua and Fatma Bouali and Gilles Venturini and Alice Dong and Sebastian Kernbach and Gaetano Cimin and Urska Cvek and and Tony Huang},

url = {https://dirk.burkhardt.xyz/wp-content/uploads/2025/11/2025iv-ii.pdf, Paper as PDF},

doi = {10.1109/IV68685.2025.00061},

isbn = {979-8-3315-7741-4},

year  = {2025},

date = {2025-10-31},

urldate = {2025-08-08},

booktitle = {2025 29th International Conference Information Visualisation (IV)},

pages = {311-319},

publisher = {IEEE},

abstract = {The development of a Digital Product Passport (DPP) is an important step in the process of sustainable production and an increasingly mandatory tool for the manufacturing industry, particularly in the EU. However, DPPs have so far been understood in the industry as a purely technical implementation for the exchange of relevant data toward product manufacturing, which is not suitable for reading and understanding by humans. This paper, therefore, describes an approach for a visual analytics system that enables human decision making through the visualization and analysis of DPPs, with a particular focus on optimizing CO2 efficiency. The system uses intuitive analytical visualizations to enable quick understanding and actionable insights. The main contribution is the concrete data processing and visualization of DPP information for human access, and is so far one of the first available approaches for a visual representation of DPPs in general.},

keywords = {Carbon Footprint, Circular Economy, Data Visualization, Industry 4.0, Internet of Things, Manufacturing Industries, Optimization, Product Carbon Footprint, Production, Regulation, Sustainable Development, Visual Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Burkhardt2025a,

title = {Process Mining for Production Optimization in Smart Manufacturing},

author = {Dirk Burkhardt and Juliane Harbarth and Andreas Görmer},

editor = {Ebad Banissi and Nuno Datia and João Moura Pires and Anna Ursyn and Kawa Nazemi and Cristian A. Secco and Boris Kovalerchuk and Razvan Andonie and Marina Gavrilova and Minoru Nakayama and Mountaz Hascoet and Amalia Rusu and Quang Vinh Nguyen and Mabule Samuel Mabakane and Adrian Rusu and Jie Hua and Fatma Bouali and Gilles Venturini and Alice Dong and Sebastian Kernbach and Gaetano Cimin and Urska Cvek and and Tony Huang},

url = {https://dirk.burkhardt.xyz/wp-content/uploads/2025/11/2025iv-i.pdf, Paper as PDF},

doi = {10.1109/IV68685.2025.00058},

isbn = {979-8-3315-7741-4},

year  = {2025},

date = {2025-10-31},

urldate = {2025-10-31},

booktitle = {2025 29th International Conference Information Visualisation (IV)},

pages = {288-295},

publisher = {IEEE},

abstract = {Smart Manufacturing is currently the main objective when production manufacturers digitalize their plants to face current regulations and requirements to optimize costs, consumed resources, and sustainability. The focus is usually on extracting data from production and making it "analyzable". However, the results often neglect advanced options to optimize the production, may it in regards to the production process itself or in regards to consumed resources of the produced goods in specific. As the main contribution, this paper describes a novel approach to consider process mining appending to plant digitization and IoT analytics. As a result, the entire production process becomes transparent and therewith analyzable, but also the concrete consumed resources per produced good, per group, or as a whole can be analyzed. As the application benefit, the paper also outlines some advanced analysis capabilities to identify production optimizations based on process mining.},

keywords = {Fourth Industrial Revolution, Industry 4.0, IoT Analytics, Optimization, Process Mining, Production, Smart Manufacturing, Sustainable Development, Visual Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Burkhardt2025,

title = {Enabling Smart Manufacturing with Visual Analytics for Plant Workers},

author = {Dirk Burkhardt and Gerald Ristow},

editor = {Meike Klettke and Ralf Schenkel and Andreas Henrich and Daniela Nicklas and Maximilian E. Schüle and Klaus Meyer-Wegener},

url = {https://dirk.burkhardt.xyz/wp-content/uploads/2025/03/2025btw.pdf, Paper as PDF},

doi = {10.18420/BTW2025-33},

issn = {2944-7682},

year  = {2025},

date = {2025-02-28},

urldate = {2025-02-28},

booktitle = {Datenbanksysteme für Business, Technologie und Web (BTW 2025)},

pages = {665-678},

publisher = {Gesellschaft für Informatik},

address = {Bonn},

series = {Lecture Notes in Informatics (LNI) - Proceedings, Volume P-361},

abstract = {Smart manufacturing is increasingly making use of visual analytics to optimize production or to identify early problem signs. However, current solutions and approaches require professionals, especially from the data science area, to make use of it, which is for most production companies not affordable. In this paper, we describe first a best practice to sensorize plants from the wood and beverage industry to enable smart manufacturing in general. Second, we describe a new approach that aims at providing easy-to-use visual analytics functionalities that are designed to be used directly by plant workers. Plant workers usually have encompassing experience in the production and the plant, but lack of computer experience and corresponding mathematical knowledge for data analysis. Through lowering the barriers for plant workers in performing data analysis of the IoT sensors with simplified and almost automated analysis functions would give them the ability to gain insights into the production and achieve similar production optimizations and problem preventions as data science experts could. The main contributions of this article are on the one hand the best practice of how production lines of the wood and beverage industry could be made ready for smart manufacturing, but also an approaches that enable non-data scientists, especially plant workers, to perform sufficient analysis about optimal production settings and early problem cause identification.},

keywords = {Human-Computer Interaction, Internet of Things, Smart Manufacturing, User-Centered Design, Visual Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Blazevic2021,

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://dirk.burkhardt.xyz/wp-content/uploads/2021/11/2021iV.pdf, Paper as PDF},

doi = {10.1109/IV53921.2021.00041},

isbn = {978-1-6654-3827-8},

year  = {2021},

date = {2021-10-28},

urldate = {2021-10-28},

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

pages = {211-217},

publisher = {IEEE},

address = {New York, USA},

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 similaritybased 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 = {Collaborative Systems, iV, Similarity, Trend Analytics, Visual Analytics, Visual Business Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Burkhardt2020b,

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},

url = {https://dirk.burkhardt.xyz/wp-content/uploads/2020/11/2020itms-ii.pdf, Paper as PDF},

doi = {10.1109/ITMS51158.2020.9259309},

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

year  = {2020},

date = {2020-11-19},

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 = {Adaptive Visualization, Logistics, Process Mining, Transportation, Trend Analytics, Visual Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2020b,

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},

url = {https://dirk.burkhardt.xyz/wp-content/uploads/2020/11/2020itms-i.pdf, Paper as PDF},

doi = {10.1109/ITMS51158.2020.9259321},

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

year  = {2020},

date = {2020-11-19},

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 = {mobility analytics, mobility behaviour, mobility indicators for visual analytics, Visual Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sina2020,

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 = {https://dirk.burkhardt.xyz/wp-content/uploads/2021/02/2020cerc-ii.pdf, Paper as PDF

http://ceur-ws.org/Vol-2815/CERC2020_paper14.pdf, Paper on CEUR-WS

http://ceur-ws.org/Vol-2815/, Proceedings on CEUR-WS},

issn = {1613-0073},

year  = {2020},

date = {2020-09-11},

urldate = {2020-09-11},

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 = {Business Intelligence, CERC, Information Exploration, Innovation Management, Visual Analytics, Visual Trend Analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2020c,

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},

url = {https://dirk.burkhardt.xyz/wp-content/uploads/2021/03/2020iv.pdf, Paper as PDF},

doi = {10.1109/IV51561.2020.00065},

isbn = {978-1-7281-9134-8},

year  = {2020},

date = {2020-09-01},

urldate = {2020-09-01},

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

pages = {360-367},

publisher = {IEEE},

address = {New York, USA},

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 = {Data Science, iV, Natural Language Processing, Visual Analytics, Visual Trend Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2020,

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://dirk.burkhardt.xyz/wp-content/uploads/2020/02/2019icte-i.pdf, Paper as PDF},

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},

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 = {Data Analytics, Mobility Behavior, Visual Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2019c,

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://dirk.burkhardt.xyz/wp-content/uploads/2019/10/2019isvc.pdf, Paper as PDF

https://dx.doi.org/10.5281/zenodo.3473065, doi:10.5281/zenodo.3473065 (Poster)},

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 = {Artificial Intelligence, Data Analytics, Human Factors, Human-Centered Interfaces, Human-Computer Interaction, Information Visualization, Intelligent Systems, maschine learning, Visual Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2018b,

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

author = {Kawa Nazemi and Dirk Burkhardt},

url = {https://dirk.burkhardt.xyz/wp-content/uploads/2019/08/2019iV.pdf, Paper as PDF},

doi = {10.1109/IV.2019.00041},

issn = {2375-0138},

year  = {2019},

date = {2019-07-02},

urldate = {2019-07-02},

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

pages = {191-200},

publisher = {IEEE},

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 = {Emerging Trend Identification, Information Visualization, iV, Trend Analytics, Visual Analytics, Visual Business Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Burkhardt2018,

title = {Juxtaposing Visual Layouts – An Approach for Solving Analytical and Exploratory Tasks through Arranging Visual Interfaces},

author = {Kawa Nazemi and Dirk Burkhardt},

url = {https://dirk.burkhardt.xyz/wp-content/uploads/2018/11/2018vare_ii.pdf, Paper as PDF

http://www.scopus.com/inward/record.url?eid=2-s2.0-85056741373&partnerID=MN8TOARS, Link to Publisher},

doi = {10.5281/zenodo.2542952},

isbn = {978-1-5108-7222-6},

year  = {2018},

date = {2018-09-21},

booktitle = {4th International Conference of the Virtual and Augmented Reality in Education (VARE 2018)},

pages = {144-153},

publisher = {CAL-TEK SRL},

address = {Rende, Italy},

series = {4},

abstract = {Interactive visualization and visual analytics systems enables solving a variety of tasks. Starting with simple search tasks for outliers, anomalies etc. in data to analytical comparisons, information visualizations may lead to a faster and more precise solving of tasks. There exist a variety of methods to support users in the process of task solving, e.g. superimposing, juxtaposing or partitioning complex visual structures. Commonly all these methods make use of a single data source that is visualized at the same time. We propose in this paper an approach that goes beyond the established methods and enables visualizing different databases, data-sets and sub-sets of data with juxtaposed visual interfaces. Our approach should be seen as an expandable method. Our main contributions are an in-depth analysis of visual task models and an approach for juxtaposing visual layouts as visual interfaces to enable solving complex tasks.},

keywords = {Information Visualization, Visual Analytics, Visual Interfaces, Visual Tasks},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Burkhardt2017b,

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://dirk.burkhardt.xyz/wp-content/uploads/2018/11/2017kat.pdf, Paper as PDF

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

https://dx.doi.org/10.2370/9783844054392, doi:10.2370/9783844054392 (Full Proceedings)},

doi = {10.5281/zenodo.2576074},

isbn = {978-3844054392},

year  = {2017},

date = {2017-01-05},

booktitle = {Technologische Trends im Spannungsfeld von Beteiligung – Entscheidung – Planung: Fachforum DIGITALES PLANEN und GESTALTEN 2017},

pages = {29--38},

publisher = {Shaker Verlag},

address = {Aachen, Germany},

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 = {e-Government, e-Participation, Information Visualization, Interactive Visualization, Policy Modeling, Visual Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2015c,

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://dirk.burkhardt.xyz/wp-content/uploads/2018/11/2015iknow.pdf, Paper as PDF},

doi = {10.1145/2809563.2809569},

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

year  = {2015},

date = {2015-10-21},

urldate = {2015-10-21},

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

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

publisher = {ACM},

address = {New York, NY, USA},

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.},

note = {Honorable Mention of the Demo},

keywords = {Data Mining, Information Extraction, Information Visualization, Trend Analysis, Visual Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}