The Impact of Global Clustering on Spatial Database Systems

Global clustering has rarely been investigated in the area of spatial database systems although dramatic performance improvements can be achieved by using suitable techniques. In this paper, we propose a simple approach to global clustering called cluster organization. We will demonstrate that this cluster organization leads to considerable performance improvements without any algorithmic overhead. Based on real geographic data, we perform a detailed empirical performance evaluation and compare the cluster organization to other organization models not using global clustering. We will show that global clustering speeds up the processing of window queries as well as spatial joins without decreasing the performance of the insertion of new objects and of selective queries such as point queries. The spatial join is sped up by a factor of about 4, whereas non-selective window queries are accelerated by even higher speed up factors

Multi-Step Processing of Spatial Joins

Spatial joins are one of the most important operations for combining spatial objects of several relations. In this paper, spatial join processing is studied in detail for extended spatial objects in twodimensional data space. We present an approach for spatial join processing that is based on three steps. First, a spatial join is performed on the minimum bounding rectangles of the objects returning a set of candidates. Various approaches for accelerating this step of join processing have been examined at the last year’s conference [BKS 93a]. In this paper, we focus on the problem how to compute the answers from the set of candidates which is handled by the following two steps. First of all, sophisticated approximations are used to identify answers as well as to filter out false hits from the set of candidates. For this purpose, we investigate various types of conservative and progressive approximations. In the last step, the exact geometry of the remaining candidates has to be tested against the join predicate. The time required for computing spatial join predicates can essentially be reduced when objects are adequately organized in main memory. In our approach, objects are first decomposed into simple components which are exclusively organized by a main-memory resident spatial data structure. Overall, we present a complete approach of spatial join processing on complex spatial objects. The performance of the individual steps of our approach is evaluated with data sets from real cartographic applications. The results show that our approach reduces the total execution time of the spatial join by factors

The combination of spatial access methods and computational geometry in geographic database systems

Geographic database systems, known as geographic information systems (GISs) particularly among non-computer scientists, are one of the most important applications of the very active research area named spatial database systems. Consequently following the database approach, a GIS hag to be seamless, i.e. store the complete area of interest (e.g. the whole world) in one database map. For exhibiting acceptable performance a seamless GIS hag to use spatial access methods. Due to the complexity of query and analysis operations on geographic objects, state-of-the-art computational geomeny concepts have to be used in implementing these operations. In this paper, we present GIS operations based on the compuational geomeny technique plane sweep. Specifically, we show how the two ingredients spatial access methods and computational geomeny concepts can be combined für improving the performance of GIS operations. The fruitfulness of this combination is based on the fact that spatial access methods efficiently provide the data at the time when computational geomeny algorithms need it für processing. Additionally, this combination avoids page faults and facilitates the parallelization of the algorithms.

Noot bij: EHRM 1 juni 2017, EHRC, 2017, 179 (Recht op een eerlijk proces)

Contains fulltext : 180828pre.pdf (preprint version ) (Open Access)1 juni 201

Absatzprognosen: Eine empirische Bestandsaufnahme der unternehmerischen Praxis

Die möglichst präzise Vorhersage der zukünftigen Absatzentwicklung als Informationsgrundlage für strategische und operative Entscheidungen zur Steuerung des Geschäftes stellt für Unternehmen eine permanente Herausforderung dar. Mit Hilfe von Absatzprognosen sollen Trendentwicklungen und drohende Strukturbrüche in den bearbeiteten Marktsegmenten frühzeitig erkannt und daraus wirksame unternehmerische Maßnahmen abgeleitet werden. Unsere empirische Studie untersucht Absatzprognosen in der unternehmerischen Praxis der 500 größten deutschen Unternehmen mit dem Ziel, einen Überblick zur Relevanz von Absatzprognosen, zur Einschätzung der Prognosegenauigkeit und zur organisatorischen Verantwortlichkeit für Prognosen im Unternehmen zu gewinnen. Darüber hinaus sollen die mit Absatzprognosen verbundenen Herausforderungen jeweils unter kurz-, mittel- und langfristigem Aspekt kritisch bewertet und gleichzeitig die eingesetzten Methoden skizziert werden. Ein weiterer Aspekt der Studie befasst sich mit der Frage, wie Unternehmen auf stark veränderte Absatzprognosen reagieren. Abschließend soll unsere Studie gerade aufgrund der inzwischen vorliegenden recht positiven Forschungsergebnisse zu Prognosemärkten Informationen liefern, in wieweit diese Methodik in der Praxis bekannt ist und eingesetzt wird. Ferner interessiert uns, wie die Stärken und Schwächen sowie die Einsatzpotentiale von Prognosemärkten bewertet werden. Die Ergebnisse der Studie zeigen deutlich, dass treffsichere Prognosen für zukünftige Absatzmärkte sehr bedeutsam sind und hier ein erheblicher Optimierungsbedarf besteht.Precise predictions about future sales as a foundation for strategic and operative decisions are a permanent management challenge. Our empirical study surveys the use of sales forecasts in the 500 biggest German companies. The study gives an overview concerning the relevance of sales forecasts, an evaluation of the forecast reliability and the responsibility for forecasts within the corporation. Long-, medium- and short-term challenges and methods associated with sales forecasting will be examined. We further want to figure out how companies react on strong sales forecast deviations. Finally, our study provides information on how these methods are accepted and used in practice, and we will provide information about strengths and weaknesses of predictive markets. The results show clearly that distinct forecasts for future markets are relevant and that there is room for improvement in the corporate forecasting practice

Co-Clustering Network-Constrained Trajectory Data

Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajectories of vehicles whose movement is restricted by the underlying road network. We model relations between these trajectories and road segments as a bipartite graph and we try to cluster its vertices. We demonstrate our approaches on synthetic data and show how it could be useful in inferring knowledge about the flow dynamics and the behavior of the drivers using the road network