@article{Nazemi2019b,

title = {Visual analytical dashboards for comparative analytical tasks – a case study on mobility and transportation},

author = {Kawa Nazemi and Dirk Burkhardt},

url = {https://www.sciencedirect.com/science/article/pii/S1877050919301243

https://www.sciencedirect.com/science/article/pii/S1877050919301243/pdf?md5=2ab7848a8c215fa8998ada154e55b2c5&pid=1-s2.0-S1877050919301243-main.pdf, full text},

doi = {10.1016/j.procs.2019.01.117},

issn = {1877-0509},

year  = {2019},

date = {2019-03-01},

journal = {Procedia Computer Science},

volume = {149},

pages = {138 - 150},

abstract = {Mobility, logistics and transportation are emerging fields of research and application. Humans’ mobility behavior plays an increasing role for societal challenges. Beside the societal challenges these areas are strongly related to technologies and innovations. Gathering information about emerging technologies plays an increasing role for the entire research in this ares. Humans’ information processing can be strongly supported by Visual Analytics that combines automatic modelling and interactive visualizations. The juxtapose orchestration of interactive visualization enables gathering more information in a shorter time. We propose in this paper an approach that goes beyond the established methods of dashboarding 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 dashboards to enable solving complex tasks. We illustrate our main outcome through a case study that investigates the area of mobility and illustrates how complex analytical tasks can be performed easily by combining different visual interfaces.},

note = {ICTE in Transportation and Logistics 2018 (ICTE 2018)},

keywords = {Information visualization},

pubstate = {published},

tppubtype = {article}

}

@article{Burkhardt2019b,

title = {Visual legal analytics – A visual approach to analyze law-conflicts of e-Services for e-Mobility and transportation domain},

author = {Dirk Burkhardt and Kawa Nazemi},

url = {https://www.sciencedirect.com/science/article/pii/S1877050919301784

https://www.sciencedirect.com/science/article/pii/S1877050919301784/pdf?md5=754eea9a3a7282f84c582efd6e7d0479&pid=1-s2.0-S1877050919301784-main.pdf, full text},

doi = {https://doi.org/10.1016/j.procs.2019.01.170},

issn = {1877-0509},

year  = {2019},

date = {2019-01-01},

journal = {Procedia Computer Science},

volume = {149},

pages = {515 - 524},

abstract = {The impact of the electromobility has next to the automotive industry also an increasing impact on the transportation and logistics domain. In particular the today’s starting switches to electronic trucks/scooter lead to massive changes in the organization and planning in this field. Public funding or tax reduction for environment friendly solutions forces also the growth of new mobility and transportation services. However, the vast changes in this domain and the high number of innovations of new technologies and services leads also into a critical legal uncertainty. The clarification of a legal status for a new technology or service can become cost intensive in a dimension that in particular startups could not invest. In this paper we therefore introduce a new approach to identify and analyze legal conflicts based on a business model or plan against existing laws. The intention is that an early awareness of critical legal aspect could enable an early adoption of the planned service to ensure its legality. Our main contribution is distinguished in two parts. Firstly, a new Norm-graph visualization approach to show laws and legal aspects in an easier understandable manner. And secondly, a Visual Legal Analytics approach to analyze legal conflicts e.g. on the basis of a business plans. The Visual Legal Analytics approach aims to provide a visual analysis interface to validate the automatically identified legal conflicts resulting from the pre-processing stage with a graphical overview about the derivation down to the law roots and the option to check the original sources to get further details. At the end analyst can so verify conflicts as relevant and resolve it by advancing e.g. the business plan or as irrelevant. An evaluation performed with lawyers has proofed our approach.},

note = {ICTE in Transportation and Logistics 2018 (ICTE 2018)},

keywords = {eGovernance, Information visualization, Law visualization, Mobility, Ontology visualization, Semantic visualization, Semantics visualization},

pubstate = {published},

tppubtype = {article}

}

@article{Burkhardt2017c,

title = {Search Intention Analysis for Task- and User-Centered Visualization in Big Data Applications},

author = {Dirk Burkhardt and Sachin Pattan and Kawa Nazemi and Arjan Kuijper},

url = {https://www.sciencedirect.com/science/article/pii/S1877050917301710, Elsevier Science Direct

https://www.sciencedirect.com/science/article/pii/S1877050917301710/pdf?md5=505e85e86e138c532368faf70d2ab1e2&pid=1-s2.0-S1877050917301710-main.pdf, full text},

doi = {https://doi.org/10.1016/j.procs.2017.01.170},

issn = {1877-0509},

year  = {2017},

date = {2017-12-01},

journal = {Procedia Computer Science},

volume = {104},

pages = {539 - 547},

abstract = {A new approach for classifying users’ search intentions is described in this paper. The approach uses the parameters: word frequency, query length and entity matching for distinguishing the user's query into exploratory, targeted and analysis search. The approach focuses mainly on word frequency analysis, where different sources for word frequency data are considered such as the Wortschatz frequency service by the University of Leipzig and the Microsoft Ngram service (now part of the Microsoft Cognitive Services). The model is evaluated with the help of a survey tool and few machine learning techniques. The survey was conducted with more than one hundred users and on evaluating the model with the collected data, the results are satisfactory. In big data applications the search intention analysis can be used to identify the purpose of a performed search, to provide an optimal initially set of visualizations that respects the intended task of the user to work with the result data.},

note = {ICTE 2016, Riga Technical University, Latvia},

keywords = {Information visualization, Intelligent Systems, User behavior, User Interactions, User Interface, User-centered design, Visual analytics},

pubstate = {published},

tppubtype = {article}

}

@article{Nazemi2017,

title = {Analyzing the Information Search Behavior and Intentions in Visual Information Systems},

author = {Kawa Nazemi and Dirk Burkhardt and Arjan Kuijper},

url = {https://www.cosmosscholars.com/images/JCSTU/JCSTU-V4N2A2-Nazemi.pdf, full text},

doi = {10.15379/2410-2938.2017.04.02.02},

year  = {2017},

date = {2017-01-01},

journal = {Journal of Computer Science Technology Updates},

volume = {4},

abstract = {Visual information search systems support different search approaches such as targeted, exploratory or analytical search. Those visual systems deal with the challenge of composing optimal initial result visualization sets that face the search intention and respond to the search behavior of users. The diversity of these kinds of search tasks require different sets of visual layouts and functionalities, e.g. to filter, thrill-down or even analyze concrete data properties. This paper describes a new approach to calculate the probability towards the three mentioned search intentions, derived from users’ behavior. The implementation is realized as a web-service, which is included in a visual environment that is designed to enable various search strategies based on heterogeneous data sources. In fact, based on an entered search query our developed search intention analysis web-service calculates the most probable search task, and our visualization system initially shows the optimal result set of visualizations to solve the task. The main contribution of this paper is a probability-based approach to derive the users’ search intentions based on the search behavior enhanced by the application to a visual system.},

keywords = {Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},

pubstate = {published},

tppubtype = {article}

}

@article{Burkhardt2015eb,

title = {Visual Correlation Analysis to Explain Open Government Data based on Linked-Open Data for Decision Making},

author = {Dirk Burkhardt and Kawa Nazemi and Mohsen Parisay and Jörn Kohlhammer},

editor = {René Lozi and Aspen Olmsted},

url = {https://infonomics-society.org/ijds/published-papers/volume-5-2014/, Journal Site},

doi = {10.20533/ijds.2040.2570.2014.0116},

issn = {2040-2570},

year  = {2015},

date = {2015-09-30},

journal = {International Journal of Digital Society (IJDS)},

volume = {5},

number = {3},

pages = {915--923},

abstract = {Public authorities normally consider statistical data about indicators in their decision makings. Such valid kind of data allows an objective observation about indicator developments over time. In case of a significant deviation from the normal indicator level, it is difficult to understand the reasons for upcoming problems. In this article we present an approach that allows an enhanced information gathering through an improved information overview about the depending aspects to such an indicator by considering governmental data-sources that provide also other types of data than just statistics. Even more, our approach integrates a system that allows generating explanations for Open Government Data, especially to specific indicators, based on Linked-Open Data and shows it in graphical form to enable a fast overview gathering. This allows decision-makers to get hints for unexpected reasons of concrete problems that may influence an indicator.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nazemi2015d,

title = {Web-based Evaluation of Information Visualization},

author = {Kawa Nazemi and Dirk Burkhardt and David Hoppe and Mariam Nazemi and Jörn Kohlhammer},

url = {https://www.sciencedirect.com/science/article/pii/S2351978915007192, Elsevier Science Direct

https://www.sciencedirect.com/science/article/pii/S2351978915007192/pdf?md5=ee6ef6cc5f2f761a33314ffc3ee12445&pid=1-s2.0-S2351978915007192-main.pdf, full text},

doi = {https://doi.org/10.1016/j.promfg.2015.07.718},

issn = {2351-9789},

year  = {2015},

date = {2015-03-01},

journal = {Procedia Manufacturing},

volume = {3},

pages = {5527 - 5534},

abstract = {Information visualization is strongly related to human perception, human behavior, and in particular human interaction. It is a discipline that focuses on human to enable him gathering insights, knowledge, and solving various and heterogeneous tasks. The human-centered characteristic of information visualization requires valid and proper user studies that improve the system or validate their benefits. New methods, techniques, or approaches of information visualization are commonly evaluated. However, the evaluation is either time and cost consuming or they are made minimum resources that leads to results, which may not be valid. In particular the number of participants is commonly restricted and does not enable a valid assumption about the results. Thus performance measures plays a key role in information visualization, existing web-survey tools are not convenient. We introduce in this paper a new method that enables web-based evaluations of information visualization systems. Our main contribution is the enhancement of web-based survey tools with performance measures. Our approach enables the measurement of task-completion time, correctness of solved tasks, and includes a number of pre- and post-questionnaires.},

note = {6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015},

keywords = {Evaluation Methods, Evaluation Tools, Human Perception, Information visualization, User Study, User-centered design, Web-based Evaluation},

pubstate = {published},

tppubtype = {article}

}

@article{Burkhardt2015b,

title = {Best-practice Piloting of Integrated Social Media Analysis Solution for E-Participation in Cities},

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

editor = {Jimson Mathew and Ashutosh K. singh},

url = {https://www.sciencedirect.com/science/article/pii/S1877050915038648, Elsevier Science Direct

https://www.sciencedirect.com/science/article/pii/S1877050915038648/pdf?md5=169ec82a0af0b5b8e740685f17683d0a&pid=1-s2.0-S1877050915038648-main.pdf, full text},

doi = {https://doi.org/10.1016/j.procs.2015.12.354},

issn = {1877-0509},

year  = {2015},

date = {2015-01-01},

journal = {Procedia Computer Science},

volume = {77},

pages = {11 - 21},

abstract = {Goal definitions and developments are challenging in large-scale projects, because of the different expertise and skills of the stakeholders. Development often fails its intended goal because of misunderstandings and unclear definitions and descriptions during the planning phase. The paper describes a novel approach to collecting requirements and defining development plans by provisioning a guideline which informs what has to be done, when and in what form. The User Case Requirement Analysis model was applied in the large-scale European project FUPOL during the development of a Social Media Analysis System. Based on this a successful task-based evaluation could be performed that shows the benefit of the model and the software.},

note = {ICTE in regional Development 2015 Valmiera, Latvia},

keywords = {Decision Making, E-Governmant, Evaluation, Information Communication Technologies, Piloting, Policy modeling},

pubstate = {published},

tppubtype = {article}

}

@article{NAZEMI201575,

title = {Semantics Visualization – Definition, Approaches and Challenges},

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

url = {https://www.sciencedirect.com/science/article/pii/S1877050915036777, Elsevier Science Direct

https://www.sciencedirect.com/science/article/pii/S1877050915036777/pdf?md5=b7e921e7548cdf69e35324864b0b2ea1&pid=1-s2.0-S1877050915036777-main.pdf, full text

},

doi = {https://doi.org/10.1016/j.procs.2015.12.216},

issn = {1877-0509},

year  = {2015},

date = {2015-01-01},

journal = {Procedia Computer Science},

volume = {75},

pages = {75 - 83},

abstract = {The visualization of the simulation results must be done in conformity with beneficiaries perception and professional domain understanding. It means that right data must be identified before. Semantic technologies provide new ways for accessing data and acquiring knowledge. The underlying structures allow finding information easier, gathering meanings and associations of the data entities and associating the data to users’ knowledge. Even though the focus of the research in this area is more to provide “machine readable” data, human-centered systems benefit from the technologies too. Especially graphical representations of the semantically structured data play a key-role in today's research. The meaningful relations of data entities and the meaningful and labeled clustering of data in form of semantic concepts enable new ways to visualize data. With these new ways, various challenges are related with deploying semantics visualizations beyond analytical search and simulation. The goal is to give a common understanding of the term semantics as it is used in semantic web. This paper dealt with the general idea of semantics visualization. First a short introduction to semantic formalisms is given followed by a general definition. Subsequently approaches and techniques of existing semantics visualizations are presented, where-as a new classification is introduced to describe the techniques. The article concludes with future challenges in semantics visualization focusing on users, data and tasks.},

note = {2015 International Conference Virtual and Augmented Reality in Education},

keywords = {Semantic visualization, Simulation, Visual analytics, Visualization and virtualization},

pubstate = {published},

tppubtype = {article}

}