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The Web provides access to a mass of heterogeneous information such as: websites, news, articles, statistics, numbers, facts and so on. Accessing this information through search engines and browsing is nowadays a standard procedure for everyone. Interactive visualizations are not yet an integral part of this search process due to a long-time lack of standards for native graphics on the Web and a lack of models for their connectivity. However, interactive visualizations provide a lot of benefits like (1) a variety of different representations for big, heterogeneous and complex information and (2) their interactivity that supports the cognition process of the user. In this article, a model for the integration of interactive visualizations into the process of information searching and linking on the Web is developed. This enables interactive visualizations to be an integral part of the web search process. The model has been used as basis for the implementation of the Vizgr toolkit and has been applied and tested in different application scenarios. This article is a shortened and revised summary of the relevant chapter in the dissertation of Hienert (2013).

This article gives an overview of the foundations in the areas of Information Search, Information Visualization and Information Processing. They form the basis for developing the model in the previous article (Hienert 2014). The field of Information Search provides various models which describe how users search for information. Several characteristics and methods are presented which are part of the search process. A major challenge is the heterogenous information basis on the Web. The section Information Visualization describes the goals, benefits, processes, models and techniques of interactive visualizations. It can be shown that interactive visualizations are beneficial for the representation of large and complex information collections. The following section Information Processing shows how users process information. For this purpose, basic mechanisms of cognitive processing and properties are presented. Based on this, the process of cognitive processing of visualizations is described. Interactive Visualizations can expand the cognitive process in which an ongoing exchange between external and internal representation takes place. This article is a shortened and revised summary of the chapter in the dissertation of Hienert (2013).

Author affiliations provide key information when attributing academic performance like publication counts. So far, such measures have been aggregated either manually or only to top-level institutions, such as universities. Supervised affiliation resolution requires a large number of annotated alignments between affiliation strings and known institutions, which are not readily available. We introduce the task of unsupervised hierarchical affiliation resolution, which assigns affiliations to institutions on all hierarchy levels (e.g. departments), discovering the institutions as well as their hierarchical ordering on the fly. From the corresponding requirements, we derive a simple conceptual framework based on the subset partial order that can be extended to account for the discrepancies evident in realistic affiliations from the Web of Science. We implement initial baselines and provide datasets and evaluation metrics for experimentation. Results show that mapping affiliations to known institutions and discovering lower-level institutions works well with simple baselines, whereas unsupervised top-level- and hierarchical resolution is more challenging. Our work provides structured guidance for further in-depth studies and improved methodology by identifying and discussing a number of observed difficulties and important challenges that future work needs to address.

Part 1: Cross-Domain Conference and Workshop on Multidisciplinary Research and Practice for Information Systems (CD-ARES 2013) ; International audience ; Today, more and more open data statistics are published by governments, statistical offices and organizations like the United Nations, The World Bank or Eurostat. This data is freely available and can be consumed by end users in interactive visualizations. However, additional information is needed to enable laymen to interpret these statistics in order to make sense of the raw data. In this paper, we present an approach to combine open data statistics with historical events. In a user interface we have integrated interactive visualizations of open data statistics with a timeline of thematically appropriate historical events from Wikipedia. This can help users to explore statistical data in several views and to get related events for certain trends in the timeline. Events include links to Wikipedia articles, where details can be found and the search process can be continued. We have conducted a user study to evaluate if users can use the interface intuitively, if relations between trends in statistics and historical events can be found and if users like this approach for their exploration process.