Udo W. Lipeck

Efficient index structures for spatio-temporal objects

We present a family of four tree-based access structures for indexing spatio-temporal objects. Our indexing methods support spatio-temporal, as well as purely spatial and purely temporal queries. In order to handle sets of extended spatio-temporal objects we propose to specialize generalized search trees by combining the advantages of the well-known spatial structures R*-tree (Beckmann et al., 1990) and SS-tree (White and Jain, 1996). We consider size-based (R*-tree like) and distance-based (SS-tree like) penalty metrics for insertions, and we view the temporal dimension either as a regular third or as a special dimension. We evaluate the four access methods on different real-life datasets and identify one of them to be the most efficient access structure for the case of general spatio-temporal data with known extents in every dimension. This method continues the R*-tree split policy with penalty metric and insertion policy from the SS-tree and treats the temporal dimension as a special dimension.

A Scalable Approach for Generalization of Land Cover Data

The paper presents a scalable approach for generalization of large land-cover data sets using partitioning in a spatial database and fast generalization algorithms. In the partitioning step, the data set is split into rectangular overlapping tiles. These are processed independently and then composed into one result. For each tile, semantic and geometric generalization operations are performed to remove features that are too small from the data set. The generalization approach is composed of several steps consisting of topologic cleaning, aggregation, feature partitioning, identification of mixed feature classes to form heterogeneous classes, and simplification of feature outlines.

Web-Enabling Geographic Data with Object-Relational Databases

We show how geographic data sets that are available in a proprietary file format only, can be web-enabled by using an object-relational database system. In particular we propose a database schema for official cartographic-topographic (ATKIS) base data implemented in Oracle8i to store and manage such data sets in an object-structured way. We have implemented an import routine for such data into the database, and for inspection and selection purposes we have developed a visualization tool for arbitrary spatial data residing in an object-relational database. In order to exchange cartographic-topographic data sets over the internet, we introduce an XML-based document type definition along with some general rules on how to design DTDs for data strongly structured according to a database schema. Finally we describe how to generate files for data exchange according to the DTD. We claim that our techniques can be used as a reference pattern for similar tasks on other data formats or in other domains as well.

SimMatching: adaptable road network matching for efficient and scalable spatial data integration

Spatial data integration is a challenging task due to the high degree of diversity between different geodata sources, the inherent complexity of objects, and the large size of datasets. To avoid duplicates in an integrated dataset, input sources have to be linked on the instance level. By matching spatial objects, multiple representations of the same real-world entity shall be identified based on similarity computation. In this paper, we present an approach for similarity-based spatial matching of road networks. Our SimMatching algorithm adapts to a variety of input data characteristics by using weighted similarity measures. Geometric and semantic attributes are considered as well as the dataset topology to enhance similarity computations with relational measures. We use a greedy approach and optimizations to keep the number of match candidates minimal all the time. This allows very low runtimes while giving high quality matching results. Supported by a partitioning framework and parallel processing, it also guarantees scalability to large datasets.

Performance of querying temporal attributes in object-relational databases

We evaluate a model for temporal data utilizing the benefits of object-relational database systems (ORDBS). In particular we show how attribute timestamping can be efficiently implemented in state-of-the-art ORDBS. The attribute timestamping concept is based on introducing user-specific types for temporal versions of datatypes. Moreover on the physical level we make use of user-defined index structures; in particular adapted spatial indexes based on generalized search trees are applied. These index structures greatly improve performance of important operators on both valid and bitemporal datatypes and show the effectiveness of this approach.

Specifying with defaults: Compositional semantics

We present an abstract specification theory that formalizes non-monotonic composition constructs from specification languages providing explicit non-monotonic mechanisms as a specification facility. This theory generalizes the institutional framework from Goguen and Burstall by adding defeasibility mechanisms to a given institution. The denotation of a specification module consists of defaults (formulas organized by priority) that are assumed to be true in the absence of explicit information to the contrary. In other words defaults are assumed to be true unless they are overridden by other defaults of higher priority. Formulas that cannot be overriden are called axioms. Such structures of axioms and prioritized defaults are called hierarchic specifications. The abstract specification theory of hierarchic specifications consists in formalizing, independently of the underlying logic, the structuring operations of hierarchic specifications. These operations are defined both on the syntactical and semantical levels by canonical constructions on corresponding syntactical and semantical categories, and account for the modular construction of hierarchic specifications by combining, reusing and modifying (with overriding) previously specified modules.