Information Retrieval for Multivariate Research Data Repositories

In this dissertation, I tackle the challenge of information retrieval for multivariate research data by providing novel means of content-based access. Large amounts of multivariate data are produced and collected in different areas of scientific research and industrial applications, including the human or natural sciences, the social or economical sciences and applications like quality control, security and machine monitoring. Archival and re-use of this kind of data has been identified as an important factor in the supply of information to support research and industrial production. Due to increasing efforts in the digital library community, such multivariate data are collected, archived and often made publicly available by specialized research data repositories. A multivariate research data document consists of tabular data with $m$ columns (measurement parameters, e.g., temperature, pressure, humidity, etc.) and $n$ rows (observations). To render such data-sets accessible, they are annotated with meta-data according to well-defined meta-data standard when being archived. These annotations include time, location, parameters, title, author (and potentially many more) of the document under concern. In particular for multivariate data, each column is annotated with the parameter name and unit of its data (e.g., water depth [m]). The task of retrieving and ranking the documents an information seeker is looking for is an important and difficult challenge. To date, access to this data is primarily provided by means of annotated, textual meta-data as described above. An information seeker can search for documents of interest, by querying for the annotated meta-data. For example, an information seeker can retrieve all documents that were obtained in a specific region or within a certain period of time. Similarly, she can search for data-sets that contain a particular measurement via its parameter name or search for data-sets that were produced by a specific scientist. However, retrieval via textual annotations is limited and does not allow for content-based search, e.g., retrieving data which contains a particular measurement pattern like a linear relationship between water depth and water pressure, or which is similar to example data the information seeker provides. In this thesis, I deal with this challenge and develop novel indexing and retrieval schemes, to extend the established, meta-data based access to multivariate research data. By analyzing and indexing the data patterns occurring in multivariate data, one can support new techniques for content-based retrieval and exploration, well beyond meta-data based query methods. This allows information seekers to query for multivariate data-sets that exhibit patterns similar to an example data-set they provide. Furthermore, information seekers can specify one or more particular patterns they are looking for, to retrieve multivariate data-sets that contain similar patterns. To this end, I also develop visual-interactive techniques to support information seekers in formulating such queries, which inherently are more complex than textual search strings. These techniques include providing an over-view of potentially interesting patterns to search for, that interactively adapt to the user's query as it is being entered. Furthermore, based on the pattern description of each multivariate data document, I introduce a similarity measure for multivariate data. This allows scientists to quickly discover similar (or contradictory) data to their own measurements

Graph-based combinations of fragment descriptors for improved 3D object retrieval

3D Object Retrieval is an important field of research with many application possibilities. One of the main goals in this research is the development of discriminative methods for similarity search. The descriptor-based approach to date has seen a lot of research attention, with many different extraction algorithms proposed. In previous work, we have introduced a simple but effective scheme for 3D model retrieval based on a spatially fixed combination of 3D object fragment descriptors. In this work, we propose a novel flexible combination scheme based on finding the best matching fragment descriptors to use in the combination. By an exhaustive experimental evaluation on established benchmark data we show the capability of the new combination scheme to provide improved retrieval effectiveness. The method is proposed as a versatile and inexpensive method to enhance the effectiveness of a given global 3D descriptor approach