Conference Paper

Involving the Expert in the Delivery of Environmental Information from the Web

To read the full-text of this research, you can request a copy directly from the authors.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

The often cited information explosion is not limited to volatile network traffic and massive multimedia capture data. Structured and high quality data from diverse fields of study become easily and freely available, too. This is due to crowd sourced data collections, better sharing infrastructure, or more generally speaking user generated content of the Web 2.0 and the popular transparency and open data movements. At the same time as data generation is shifting to everyday casual users, data analysis is often still reserved to large companies specialized in content analysis and distribution such as today's internet giants Amazon, Google, and Facebook. Here, fully automatic algorithms analyze metadata and content to infer interests and believes of their users and present only matching navigation suggestions and advertisements. Besides the problem of creating a filter bubble, in which users never see conflicting information due to the reinforcement nature of history based navigation suggestions, the use of fully automatic approaches has inherent problems, e.g. being unable to find the unexpected and adopt to changes, which lead to the introduction of the Visual Analytics (VA) agenda. If users intend to perform their own analysis on the available data, they are often faced with either generic toolkits that cover a broad range of applicable domains and features or specialized VA systems that focus on one domain. Both are not suited to support casual users in their analysis as they don't match the users' goals and capabilities. The former tend to be complex and targeted to analysis professionals due to the large range of supported features and programmable visualization techniques. The latter trade general flexibility for improved ease of use and optimized interaction for a specific domain requirement. This work describes two approaches building on interactive visualization to reduce this gap between generic toolkits and domain-specific systems. The first one builds upon the idea that most data relevant for casual users are collections of entities with attributes. This least common denominator is commonly employed in faceted browsing scenarios and filter/flow environments. Thinking in sets of entities is natural and allows for a very direct visual interaction with the analysis subject and it stands for a common ground for adding analysis functionality to domain-specific visualization software. Encapsulating the interaction with sets of entities into a filter/flow graph component can be used to record analysis steps and intermediate results into an explicit structure to support collaboration, reporting, and reuse of filters and result sets. This generic analysis functionality is provided as a plugin-in component and was integrated into several domain-specific data visualization and analysis prototypes. This way, the plug-in benefits from the implicit domain knowledge of the host system (e.g. selection semantics and domain-specific visualization) while being used to structure and record the user's analysis process. The second approach directly exploits encoded domain knowledge in order to help casual users interacting with very specific domain data. By observing the interrelations in the ontology, the user interface can automatically be adjusted to indicate problems with invalid user input and transform the system's output to explain its relation to the user. Here, the domain related visualizations are personalized and orchestrated for each user based on user profiles and ontology information. In conclusion, this thesis introduces novel approaches at the boundary of generic analysis tools and their domain-specific context to extend the usage of visual analytics to casual users by exploiting domain knowledge for supporting analysis tasks, input validation, and personalized information visualization.
ResearchGate has not been able to resolve any references for this publication.