Spatial Database Support for Virtual Engineering

The development, design, manufacturing and maintenance of modern engineering products is a very expensive and complex task. Shorter product cycles and a greater diversity of models are becoming decisive competitive factors in the hard-fought automobile and plane market. In order to support engineers to create complex products when being pressed for time, systems are required which answer collision and similarity queries effectively and efficiently. In order to achieve industrial strength, the required specialized functionality has to be integrated into fully-fledged database systems, so that fundamental services of these systems can be fully reused, including transactions, concurrency control and recovery. This thesis aims at the development of theoretical sound and practical realizable algorithms which effectively and efficiently detect colliding and similar complex spatial objects. After a short introductory Part I, we look in Part II at different spatial index structures and discuss their integrability into object-relational database systems. Based on this discussion, we present two generic approaches for accelerating collision queries. The first approach exploits available statistical information in order to accelerate the query process. The second approach is based on a cost-based decompositioning of complex spatial objects. In a broad experimental evaluation based on real-world test data sets, we demonstrate the usefulness of the presented techniques which allow interactive query response times even for large data sets of complex objects. In Part III of the thesis, we discuss several similarity models for spatial objects. We show by means of a new evaluation method that data-partitioning similarity models yield more meaningful results than space-partitioning similarity models. We introduce a very effective similarity model which is based on a new paradigm in similarity search, namely the use of vector set represented objects. In order to guarantee efficient query processing, suitable filters are introduced for accelerating similarity queries on complex spatial objects. Based on clustering and the introduced similarity models we present an industrial prototype which helps the user to navigate through massive data sets.Ein schneller und reibungsloser Entwicklungsprozess neuer Produkte ist ein wichtiger Faktor für den wirtschaftlichen Erfolg vieler Unternehmen insbesondere aus der Luft- und Raumfahrttechnik und der Automobilindustrie. Damit Ingenieure in immer kürzerer Zeit immer anspruchsvollere Produkte entwickeln können, werden effektive und effiziente Kollisions- und Ähnlichkeitsanfragen auf komplexen räumlichen Objekten benötigt. Um den hohen Anforderungen eines produktiven Einsatzes zu genügen, müssen entsprechend spezialisierte Zugriffsmethoden in vollwertige Datenbanksysteme integriert werden, so dass zentrale Datenbankdienste wie Trans-aktionen, kontrollierte Nebenläufigkeit und Wiederanlauf sichergestellt sind. Ziel dieser Doktorarbeit ist es deshalb, effektive und effiziente Algorithmen für Kollisions- und Ähnlichkeitsanfragen auf komplexen räumlichen Objekten zu ent-wickeln und diese in kommerzielle Objekt-Relationale Datenbanksysteme zu integrieren. Im ersten Teil der Arbeit werden verschiedene räumliche Indexstrukturen zur effizienten Bearbeitung von Kollisionsanfragen diskutiert und auf ihre Integrationsfähigkeit in Objekt-Relationale Datenbanksysteme hin untersucht. Daran an-knüpfend werden zwei generische Verfahren zur Beschleunigung von Kollisionsanfragen vorgestellt. Das erste Verfahren benutzt statistische Informationen räumlicher Indexstrukturen, um eine gegebene Anfrage zu beschleunigen. Das zweite Verfahren beruht auf einer kostenbasierten Zerlegung komplexer räumlicher Datenbank- Objekte. Diese beiden Verfahren ergänzen sich gegenseitig und können unabhängig voneinander oder zusammen eingesetzt werden. In einer ausführlichen experimentellen Evaluation wird gezeigt, dass die beiden vorgestellten Verfahren interaktive Kollisionsanfragen auf umfangreichen Datenmengen und komplexen Objekten ermöglichen. Im zweiten Teil der Arbeit werden verschiedene Ähnlichkeitsmodelle für räum-liche Objekte vorgestellt. Es wird experimentell aufgezeigt, dass datenpartitionierende Modelle effektiver sind als raumpartitionierende Verfahren. Weiterhin werden geeignete Filtertechniken zur Beschleunigung des Anfrageprozesses entwickelt und experimentell untersucht. Basierend auf Clustering und den entwickelten Ähnlichkeitsmodellen wird ein industrietauglicher Prototyp vorgestellt, der Benutzern hilft, durch große Datenmengen zu navigieren

Microresonator solitons for massively parallel coherent optical communications

Optical solitons are waveforms that preserve their shape while propagating, relying on a balance of dispersion and nonlinearity. Soliton-based data transmission schemes were investigated in the 1980s, promising to overcome the limitations imposed by dispersion of optical fibers. These approaches, however, were eventually abandoned in favor of wavelength-division multiplexing (WDM) schemes that are easier to implement and offer improved scalability to higher data rates. Here, we show that solitons may experience a comeback in optical communications, this time not as a competitor, but as a key element of massively parallel WDM. Instead of encoding data on the soliton itself, we exploit continuously circulating dissipative Kerr solitons (DKS) in a microresonator. DKS are generated in an integrated silicon nitride microresonator by four-photon interactions mediated by Kerr nonlinearity, leading to low-noise, spectrally smooth and broadband optical frequency combs. In our experiments, we use two interleaved soliton Kerr combs to transmit a data stream of more than 50Tbit/s on a total of 179 individual optical carriers that span the entire telecommunication C and L bands. Equally important, we demonstrate coherent detection of a WDM data stream by using a pair of microresonator Kerr soliton combs - one as a multi-wavelength light source at the transmitter, and another one as a corresponding local oscillator (LO) at the receiver. This approach exploits the scalability advantages of microresonator soliton comb sources for massively parallel optical communications both at the transmitter and receiver side. Taken together, the results prove the significant potential of these sources to replace arrays of continuous-wave lasers in high-speed communications.Comment: 10 pages, 3 figure

Tertiary Lymphoid Structures:Autoimmunity Goes Local

Tertiary lymphoid structures (TLS) are frequently observed in target organs of autoimmune diseases. TLS present features of secondary lymphoid organs such as segregated T and B cell zones, presence of follicular dendritic cell networks, high endothelial venules and specialized lymphoid fibroblasts and display the mechanisms to support local adaptive immune responses toward locally displayed antigens. TLS detection in the tissue is often associated with poor prognosis of disease, auto-antibody production and malignancy development. This review focuses on the contribution of TLS toward the persistence of the inflammatory drive, the survival of autoreactive lymphocyte clones and post-translational modifications, responsible for the pathogenicity of locally formed autoantibodies, during autoimmune disease development

11. GI-Fachtagung Datenbanksysteme für Business, Technologie und Web (BTW), Springer Lecture Notes in Informatics (LNI), 2005. Measuring the Quality of Approximated Clusterings

Abstract. Clustering has become an increasingly important task in modern application domains. In many areas, e.g. when clustering complex objects, in distributed clustering, or when clustering mobile objects, due to technical, security, or efficiency reasons it is not possible to compute an “optimal ” clustering. Recently a lot of research has been done on efficiently computing approximated clusterings. Here, the crucial question is, how much quality has to be sacrificed for the achieved gain in efficiency. In this paper, we present suitable quality measures allowing us to compare approximated clusterings with reference clusterings. We first introduce a quality measure for clusters based on the symmetric set difference. Using this distance function between single clusters, we introduce a quality measure based on the minimum weight perfect matching of sets for comparing partitioning clusterings, as well as a quality measure based on the degree-2 edit distance for comparing hierarchical clusterings.

Efficient Similarity Search on Vector Sets

Abstract: Similarity search in database systems is becoming an increasingly important task in modern application domains such as multimedia, molecular biology, medical imaging, computer aided design and many others. Whereas most of the existing similarity models are based on feature vectors, there exist some models which use very complex object representations such as trees and graphs. A promising way between too simple and too complex object representations in similarity search are sets of feature vectors. In this paper, we first motivate the use of this modeling approach for complete object similarity search as well as for partial object similarity search. After introducing a distance measure between vector sets, suitable for many different application ranges, we present and discuss different filters which are indispensable for efficient query processing. In a broad experimental evaluation based on artificial and real-world test datasets, we show that our approach considerably outperforms both the sequential scan and metric index structures.

IASTED Int. Conf. on Databases and Applications (DBA'04) A Quality Measure for Distributed Clustering

Clustering has become an increasingly important task in modern application domains. Mostly, the data are originally collected at different sites. In order to extract information from these data, they are merged at a central site and then clustered. Another approach is to cluster the data locally and extract suitable representatives from these clusters. Based on these representatives a global server tries to reconstruct the complete clustering. In this paper, we discuss the complex problem of finding a suitable quality measure for evaluating the quality of such a distributed clustering. We introduce a discrete and continuous quality criterion which we empirically compare to each other