Hans Hinterberger

The Grid File: An Adaptable, Symmetric Multikey File Structure

Traditional file structures that provide multikey access to records, for example, inverted files, are extensions of file structures originally designed for single-key access. They manifest various deficiencies in particular for multikey access to highly dynamic files. We study the dynamic aspects of file structures that treat all keys symmetrically, that is, file structures which avoid the distinction between primary and secondary keys. We start from a bitmap approach and treat the problem of file design as one of data compression of a large sparse matrix. This leads to the notions of a grid partition of the search space and of a grid directory, which are the keys to a dynamic file structure called the grid file. This file system adapts gracefully to its contents under insertions and deletions, and thus achieves an upper bound of two disk accesses for single record retrieval; it also handles range queries and partially specified queries efficiently. We discuss in detail the design decisions that led to the grid file, present simulation results of its behavior, and compare it to other multikey access file structures.

Comparative multivariate visualization across conceptually different graphic displays

To extend the scope of multivariate data visualization, the notion of comparative visualization is introduced: it allows the comparison of visualization methods by interconnecting several different graphic displays. This linking of visualizations, together with the possibility to interactively manipulate data, enable an analyst to display the same data set with a number of conceptually different visualization methods simultaneously and to carry out graphical operations across them. Graphical effects in different displays not only reveal information about the data themselves, they also provide the basis to investigate how the different visualization methods relate to each other. With the VisuLab, we developed a software tool for personal computers to investigate comparative multivariate data visualization.<<ETX>>

Content-Based Image Retrieval in Astronomy

Content-based image retrieval in astronomy needs methods that can deal with an image content made of noisy and diffuse structures. This motivates investigations on how information should be summarized and indexed for this specific kind of images. The method we present first summarizes the image information content by partitioning the image in regions with same texture. We call this process texture summarization. Second, indexing features are generated by examining the distribution of parameters describing image regions. Indexing features can be associated with global or local image characteristics. Both kinds of indexing features are evaluated on the retrieval system of the Zurich archive of solar radio spectrograms. The evaluation shows that generating local indexing features using self-organizing maps yields the best effectiveness of all tested methods.

The Grid File: An Adaptable, Symmetric Multi-Key File Structure

Traditional file structures that provide multi-key access to records, for example inverted files, are extensions of file structures originally designed for single-key access. They manifest various deficiencies, in particular for multi-key access to highly dynamic files. We study the dynamic aspects of file structures that treat all keys symmetrically, that is, avoid the distinction between primary key and secondary keys. We start from a bitmap approach and treat the problem of file design as one of data compression of a large sparse matrix. This leads to the notion of a grid directory, which is the key to a dynamic file structure called the grid file. The file system described adapts gracefully to its contents under insertions and deletions (remains balanced). It achieves an upper bound of two disk accesses on the average. First results of a simulation of the behavior of one implementation of the grid file are presented.

Spatial data reallocation based on multidimensional range queries. A contribution to data management for the earth sciences

Earth scientists, by definition, work in an interdisciplinary environment and therefore collect and disseminate data using distinct methods, depending on whether the associated information arises during field measurements, arrives via remote sensing, or represents simulation results. When calculating the global radiation over one of the oceans, for instance, it is essential that cloud data from at least three different sources can be accessed based on one and the same geographic grid. Differently gridded source data must therefore be reorganized through interpolation for example. This can be done in different ways and we investigate a method whose practicality depends on fast spatial range queries, but in turn provides a flexibility that makes it particularly easy to accommodate differently organized data sets-a valuable feature when unanticipated grid organizations show up.<<ETX>>

Discrete object detection and motion registration based on a data management approach

When scientific data sets can be interpreted visually they are typically managed as pictures and consequently stored as large collections of bitmaps. Valuable information contained in images is often not exploited, however simply because the data is not processed further. Common reasons for this are that access to information in image collections is notoriously difficult and that interesting applications often depend on supplementary data with incompatible formats. If such data sets are treated as higher-dimensional point data instead of byte streams and managed with a suitable multidimensional file structure, then it is possible to transform fuzzy objects into n-dimensional solids. Several benefits result: content based access becomes possible, the potential for data compression without loss of relevant information exists and additional information can readily be incorporated simply by increasing the file structures dimensionality. This paper describes how this approach has been successfully applied to detect and track storm cells in weather radar and various satellite images. The key is to parametrize the data for efficient access based on several different image attributes.

A scope classification of data quality requirements for food composition data.

Data quality is an important issue when managing food composition data since the usage of the data can have a significant influence on policy making and further research. Although several frameworks for data quality have been proposed, general tools and measures are still lacking. As a first step in this direction, we investigated data quality requirements for an information system to manage food composition data, called FoodCASE. The objective of our investigation was to find out if different requirements have different impacts on the intrinsic data quality that must be regarded during data quality assessment and how these impacts can be described. We refer to the resulting classification with its categories as the scope classification of data quality requirements. As proof of feasibility, the scope classification has been implemented in the FoodCASE system.