Non-zero probability of nearest neighbor searching

Nearest Neighbor (NN) searching is a challenging problem in data management and has been widely studied in data mining, pattern recognition and computational geometry. The goal of NN searching is efficiently reporting the nearest data to a given object as a query. In most of the studies both the data and query are assumed to be precise, however, due to the real applications of NN searching, such as tracking and locating services, GIS and data mining, it is possible both of them are imprecise. So, in this situation, a natural way to handle the issue is to report the data have a nonzero probability —called nonzero nearest neighbor— to be the nearest neighbor of a given query. Formally, let P be a set of n uncertain points modeled by some regions. We first consider the following variation of NN searching problem under uncertainty. If both the query and the data are uncertain points modeled by distinct unit segments parallel to the x-axis, we propose an efficient algorithm that reports nonzero nearest neighbors under Manhattan metric in O(n^2 α(n^2 )) preprocessing and O(log⁡n+k) query time, where α(.) is the extremely slowly growing functional inverse of Ackermann’s function. Finally, for the arbitrarily length segments parallel to the x-axis, we propose an approximation algorithm that reports nonzero nearest neighbor with maximum error L in O(n^2 α(n^2 )) preprocessing and O(log⁡n+k) query time, where L is the length of the query

Efficient SUM Query Processing over Uncertain Data

Selected as one of the best papersNational audienceSUM queries are crucial for many applications that need to deal with probabilistic data. In this paper, we are interested in the queries, called ALL_SUM, that return all possible sum values and their probabilities. In general, there is no efficient solution for the problem of evaluating ALL_SUM queries. But, for many practical applications, where aggregate values are small integers or real numbers with small precision, it is possible to develop efficient solutions. In this paper, based on a recursive approach, we propose a new solution for this problem. We implemented our solution and conducted an extensive experimental evaluation over synthetic and real-world data sets; the results show its effectiveness

Graph-Based Approaches to Protein StructureComparison - From Local to Global Similarity

The comparative analysis of protein structure data is a central aspect of structural bioinformatics. Drawing upon structural information allows the inference of function for unknown proteins even in cases where no apparent homology can be found on the sequence level. Regarding the function of an enzyme, the overall fold topology might less important than the specific structural conformation of the catalytic site or the surface region of a protein, where the interaction with other molecules, such as binding partners, substrates and ligands occurs. Thus, a comparison of these regions is especially interesting for functional inference, since structural constraints imposed by the demands of the catalyzed biochemical function make them more likely to exhibit structural similarity. Moreover, the comparative analysis of protein binding sites is of special interest in pharmaceutical chemistry, in order to predict cross-reactivities and gain a deeper understanding of the catalysis mechanism. From an algorithmic point of view, the comparison of structured data, or, more generally, complex objects, can be attempted based on different methodological principles. Global methods aim at comparing structures as a whole, while local methods transfer the problem to multiple comparisons of local substructures. In the context of protein structure analysis, it is not a priori clear, which strategy is more suitable. In this thesis, several conceptually different algorithmic approaches have been developed, based on local, global and semi-global strategies, for the task of comparing protein structure data, more specifically protein binding pockets. The use of graphs for the modeling of protein structure data has a long standing tradition in structural bioinformatics. Recently, graphs have been used to model the geometric constraints of protein binding sites. The algorithms developed in this thesis are based on this modeling concept, hence, from a computer scientist's point of view, they can also be regarded as global, local and semi-global approaches to graph comparison. The developed algorithms were mainly designed on the premise to allow for a more approximate comparison of protein binding sites, in order to account for the molecular flexibility of the protein structures. A main motivation was to allow for the detection of more remote similarities, which are not apparent by using more rigid methods. Subsequently, the developed approaches were applied to different problems typically encountered in the field of structural bioinformatics in order to assess and compare their performance and suitability for different problems. Each of the approaches developed during this work was capable of improving upon the performance of existing methods in the field. Another major aspect in the experiments was the question, which methodological concept, local, global or a combination of both, offers the most benefits for the specific task of protein binding site comparison, a question that is addressed throughout this thesis

Cross-Border Collaboration in Disaster Management

Wenn sich eine Katastrophe ereignet, ist eine schnelle und koordinierte Reaktion der verschiedenen Krisenmanagementakteure unerlässlich, um die vorhandenen Ressourcen bestmöglich einzusetzen und somit ihre Auswirkungen zu begrenzen. Dieses Zusammenspiel wird erschwert, wenn die Katastrophe mehrere Länder betrifft. Neben den unterschiedlichen Regelungen und Systemen spielen dann auch kulturelle Einflüsse wie Sprachbarrieren oder mangelndes Vertrauen eine entscheidende Rolle. Obwohl die Resilienz von Grenzgebieten von fundamentaler Bedeutung ist, wird diese in der wissenschaftlichen Literatur immer noch unterschätzt. Im ersten Teil dieser Arbeit wird ein agentenbasiertes Modell zur Untersuchung der organisationsübergreifenden Zusammenarbeit bei Katastropheneinsätzen in einer Grenzregion vorgestellt. Indem Kommunikationsprotokolle aus der Literatur auf den Kontext der grenzüberschreitenden Kooperation erweitert werden, analysiert das Modell die globale Dynamik, die aus lokalen Entscheidungen resultiert. Ein szenariobasierter Ansatz zeigt, dass höheres Vertrauen zwar zu signifikant besseren Versorgungsraten führt, der Abbau von Sprachbarrieren aber noch effizienter ist. Insbesondere gilt dies, wenn die Akteure die Sprache des Nachbarlandes direkt sprechen, anstatt sich auf eine allgemeine Lingua franca zu verlassen. Die Untersuchung der Koordination zeigt, dass Informationsflüsse entlang der hierarchischen Organisationsstruktur am erfolgreichsten sind, während spontane Zusammenarbeit durch ein etabliertes informelles Netzwerk privater Kontakte den Informationsaustausch ergänzen und in dynamischen Umgebungen einen Vorteil darstellen kann. Darüber hinaus verdoppelt die Einbindung von Spontanfreiwilligen den Koordinationsaufwand. Die Koordination über beide Dimensionen, zum einen die Einbindung in den Katastrophenschutz und zum anderen über Grenzen hinweg, führt jedoch zu einer optimalen Versorgung der betroffenen Bevölkerung. In einem zweiten Teil stellt diese Arbeit ein innovatives empirisches Studiendesign vor, das auf transnationalem Sozialkapital und Weiners Motivationstheorie basiert, um prosoziale Beziehungen der Menschen über nationale Grenzen hinweg zu quantifizieren. Regionale Beziehungen innerhalb der Länder werden dabei als Vergleichsbasis genommen. Die mittels repräsentativer Telefoninterviews in Deutschland, Frankreich und der deutsch-französischen Grenzregion erhobenen Daten belegen die Hypothese, dass das Sozialkapital und die Hilfsbereitschaft über die deutsch-französische Grenze hinweg mindestens so hoch ist wie das regionale Sozialkapital und die Hilfsbereitschaft innerhalb der jeweiligen Länder. Folglich liefert die Arbeit wertvolle Erkenntnisse für Entscheidungsträger, um wesentliche Barrieren in der grenzüberschreitenden Kooperation abzubauen und damit die grenzüberschreitende Resilienz bei zukünftigen Katastrophen zu verbessern. Implikationen für die heutige Zeit in Bezug auf Globalisierung versus aufkommendem Nationalismus sowie Auswirkungen von (Natur-) Katastrophen werden diskutiert

Cross-Border Collaboration in Disaster Management

In recent years, disaster events spreading across national borders have increased, which requires improved collaboration between countries. By means of an agent-based simulation and an empirical study, this thesis provides valuable insights for decision-makers in order to overcome barriers in cross-border cooperation and thus, enhance borderland resilience for future events. Finally, implications for today's world in terms of globalization versus emerging nationalism are discussed

MEASURING & MONITORING Plant Populations

The root of the word monitoring means to warn, and an essential purpose of monitoring is to raise a warning flag that the current course of action is not working. Monitoring is a powerful tool for identifying problems in the early stages, before they become dramatically obvious or crises. If identified early, problems can be addressed while cost-effective solutions are still available. For example, an invasive species that threatens a rare plant population is much easier to control at the initial stages of invasion, compared to eradicating it once it is well established at a site. Monitoring is also critical for measuring management success. Good monitoring can demonstrate that the current management approach is working and provide evidence supporting the continuation of current management