Oliver Zimmert - Academia.edu (original) (raw)

Papers by Oliver Zimmert

Research paper thumbnail of A knowledge discovery in community contributions of big data technologies

Proceedings of the 10th International Conference on Management of Digital EcoSystems, 2018

The increasing variety of big data technologies in open source communities is challenging organiz... more The increasing variety of big data technologies in open source communities is challenging organizations to generate value from those advancements. The technology landscape is missing an overall perspective that clarifies the fragmented understanding of technologies, unpredictable lifecycles, and the unknown adoption for organizations to enable their business with useful technologies. More than one million contributions of features, bugs, and changes were pushed on public available code repositories to develop big data technologies with hidden understanding of the underlying data basis. Using this source could help to identify insights about technological domains as well as their adoption process of contributors to new uprising big data technologies. A knowledge discovery process provided the potential to analyze 269 big data technologies regarding their contribution behavior of over 21,000 contributors. As a result, investigations show an ecosystem of structuring big data technologies based on dynamic contributor networks that have implications on organizations adoption.

Research paper thumbnail of Shaping Data : Scaling Data Visualizations

Generating the maximum number of visual patterns by uncovering the entire space of possible visua... more Generating the maximum number of visual patterns by uncovering the entire space of possible visual designs remains a challenge within the construction process of information visualization. Users interact with different mindsets consisting of design, data analysis, application development, and hardware resource usage. Therefore, they desire a flexible and productive interface that keeps them clued into the design process without requiring knowledge of the underlying technical system. A general model was applied to a prototype to demonstrate the benefits and restrictions of this construction process and to contribute toward bringing different mindsets together. Type of presentation: Research contribution – Visual analytics

Research paper thumbnail of Früherkennung von Erfolgsfaktoren im unternehmerischen Alltag

Research paper thumbnail of Shaping data: Visualization under construction

2015 IEEE International Conference on Big Data (Big Data), 2015

Generating the maximum number of visual patterns by uncovering the entire space of possible visua... more Generating the maximum number of visual patterns by uncovering the entire space of possible visual designs remains a challenge within the construction process of information visualization. Users interact with different mindsets consisting of design, data analysis, application development, and hardware resource usage. Therefore, they desire a flexible and productive interface that keeps them clued into the design process without requiring knowledge of the underlying technical system. This was summarized under the no developing, more modeling paradigm, which is described through a visual theory as the basis of the shaping process. A general model was applied to a prototype to demonstrate the benefits and restrictions of this construction process and to contribute toward bringing different mindsets together.

Research paper thumbnail of Die Vitalität des Unternehmens – Entwicklung eines taktischen Frühwarnsystems

Wertschöpfungsmanagement im Mittelstand, 2010

Research paper thumbnail of Shaping Unlimited Patterns

Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems, 2014

Scaling data visualizations to represent large data sets remains one of the top challenges from t... more Scaling data visualizations to represent large data sets remains one of the top challenges from the last decade. The fast growing amount of data is facing limitations of human perception and of technology as bottlenecks to exploring hidden patterns. This literature review summarizes the boundaries of visual scalability and their influencing factors, addressing state-of-the-art challenges. In a semantic differential approach, contrary characteristics were studied to support the classification of low and highly scalable data visualizations. As a result, the literature supports a vision of a shift toward adapting the taxonomy of visual scalability facing emergent technologies.

Research paper thumbnail of Representing Multidimensional Cancer Registry Data

Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies, 2013

ABSTRACT Epidemiology requires the analysis and visualization of massive data sets. The field of ... more ABSTRACT Epidemiology requires the analysis and visualization of massive data sets. The field of cancer statistics in particular is facing the challenging task of visualizing a large data set that contains a wide range of available dimensions. The existing work of epidemiologists has been time-consuming because of visualization techniques that could not be scaled to support an unguided exploration process. This limitation has led to the inefficient use of data representations that are mainly used for detailed analysis. Our goal was to find a scalable visualization technique that focused on covering a wide range of categorical information. For this purpose, a task by data type taxonomy is used to analyze the existing data visualization techniques. The chosen representation was based on the implemented flow visualization and provided an overview for exploring the data by epidemiologists. In this way, a more scalable visualization delivered the ability to support the creation of hypotheses by finding relationships of interest.

Research paper thumbnail of A knowledge discovery in community contributions of big data technologies

Proceedings of the 10th International Conference on Management of Digital EcoSystems, 2018

The increasing variety of big data technologies in open source communities is challenging organiz... more The increasing variety of big data technologies in open source communities is challenging organizations to generate value from those advancements. The technology landscape is missing an overall perspective that clarifies the fragmented understanding of technologies, unpredictable lifecycles, and the unknown adoption for organizations to enable their business with useful technologies. More than one million contributions of features, bugs, and changes were pushed on public available code repositories to develop big data technologies with hidden understanding of the underlying data basis. Using this source could help to identify insights about technological domains as well as their adoption process of contributors to new uprising big data technologies. A knowledge discovery process provided the potential to analyze 269 big data technologies regarding their contribution behavior of over 21,000 contributors. As a result, investigations show an ecosystem of structuring big data technologies based on dynamic contributor networks that have implications on organizations adoption.

Research paper thumbnail of Shaping Data : Scaling Data Visualizations

Generating the maximum number of visual patterns by uncovering the entire space of possible visua... more Generating the maximum number of visual patterns by uncovering the entire space of possible visual designs remains a challenge within the construction process of information visualization. Users interact with different mindsets consisting of design, data analysis, application development, and hardware resource usage. Therefore, they desire a flexible and productive interface that keeps them clued into the design process without requiring knowledge of the underlying technical system. A general model was applied to a prototype to demonstrate the benefits and restrictions of this construction process and to contribute toward bringing different mindsets together. Type of presentation: Research contribution – Visual analytics

Research paper thumbnail of Früherkennung von Erfolgsfaktoren im unternehmerischen Alltag

Research paper thumbnail of Shaping data: Visualization under construction

2015 IEEE International Conference on Big Data (Big Data), 2015

Generating the maximum number of visual patterns by uncovering the entire space of possible visua... more Generating the maximum number of visual patterns by uncovering the entire space of possible visual designs remains a challenge within the construction process of information visualization. Users interact with different mindsets consisting of design, data analysis, application development, and hardware resource usage. Therefore, they desire a flexible and productive interface that keeps them clued into the design process without requiring knowledge of the underlying technical system. This was summarized under the no developing, more modeling paradigm, which is described through a visual theory as the basis of the shaping process. A general model was applied to a prototype to demonstrate the benefits and restrictions of this construction process and to contribute toward bringing different mindsets together.

Research paper thumbnail of Die Vitalität des Unternehmens – Entwicklung eines taktischen Frühwarnsystems

Wertschöpfungsmanagement im Mittelstand, 2010

Research paper thumbnail of Shaping Unlimited Patterns

Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems, 2014

Scaling data visualizations to represent large data sets remains one of the top challenges from t... more Scaling data visualizations to represent large data sets remains one of the top challenges from the last decade. The fast growing amount of data is facing limitations of human perception and of technology as bottlenecks to exploring hidden patterns. This literature review summarizes the boundaries of visual scalability and their influencing factors, addressing state-of-the-art challenges. In a semantic differential approach, contrary characteristics were studied to support the classification of low and highly scalable data visualizations. As a result, the literature supports a vision of a shift toward adapting the taxonomy of visual scalability facing emergent technologies.

Research paper thumbnail of Representing Multidimensional Cancer Registry Data

Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies, 2013

ABSTRACT Epidemiology requires the analysis and visualization of massive data sets. The field of ... more ABSTRACT Epidemiology requires the analysis and visualization of massive data sets. The field of cancer statistics in particular is facing the challenging task of visualizing a large data set that contains a wide range of available dimensions. The existing work of epidemiologists has been time-consuming because of visualization techniques that could not be scaled to support an unguided exploration process. This limitation has led to the inefficient use of data representations that are mainly used for detailed analysis. Our goal was to find a scalable visualization technique that focused on covering a wide range of categorical information. For this purpose, a task by data type taxonomy is used to analyze the existing data visualization techniques. The chosen representation was based on the implemented flow visualization and provided an overview for exploring the data by epidemiologists. In this way, a more scalable visualization delivered the ability to support the creation of hypotheses by finding relationships of interest.