Output constraints in multimedia database systems

Zusammenfassung Semantische Fehler treten bei jeder Art von Datenverwaltung auf. Herkömmliche Datenbanksysteme verwenden eine Integritätskontrolle, um semantische Fehler zu vermeiden. Um die Integrität der Daten zu gewährleisten werden Integritätsregeln benutzt. Diese Regeln können allerdings nur die Konsistenz einfach strukturierter Daten überprüfen. Multimedia Datenbanksystem verwalten neben einfachen alphanumerischen Daten auch komplexe Mediendaten wie Videos. Um die Konsistenz dieser Daten zu sichern, bedarf es einer erheblichen Erweiterung des bestehenden Integritätskonzeptes. Dabei muss besonders auf die konsistente Datenausgabe geachtet werden. Im Gegensatz zu alphanumerischen Daten können Mediendaten während der Ausgabe verfälscht werden. Dieser Fall kann eintreten, wenn eine geforderte Datenqualität bei der Ausgabe nicht erreicht werden kann oder wenn Synchronisationsbedingungen zwischen Medienobjekten nicht eingehalten werden können. Es besteht daher die Notwendigkeit, Ouptut Constraints einzuführen. Mit ihrer Hilfe kann definiert werden, wann die Ausgabe von Mediendaten semantisch korrekt ist. Das Datenbanksystem kann diese Bedingungen überprüfen und so gewährleisten, dass der Nutzer semantisch einwandfreie Daten erhält. In dieser Arbeit werden alle Aspekte betrachtet, die notwendig sind, um Ausgabebedingungen in ein Multimedia Datenbanksystem zu integrieren. Im einzelnen werden die Modellierung der Bedingungen, deren datenbankinterne Repräsentation sowie die Bedingungsüberprüfung betrachtet. Für die Bedingungsmodellierung wird eine Constraint Language auf Basis der Prädikatenlogik eingeführt. Um die Definition von zeitlichen und räumlichen Synchronisationen zu ermöglichen, verwenden wir Allen-Relationen. Für die effiziente Überprüfung der Ausgabebedingungen müssen diese aus der Spezifikationssprache in eine datenbankinterne Darstellung überführt werden. Für die datenbankinterne Darstellung werden Difference Constraints verwendet. Diese erlauben eine sehr effiziente Bedingungsüberprüfung. Wir haben Algorithmen entwickelt, die eine effiziente Überprüfung von Ausgabebedingungen erlauben und dies anhand von Experimenten nachgewiesen. Neben der Überprüfung der Bedingungen müssen Mediendaten so synchronisiert werden, dass dies den Ausgabebedingungen entspricht. Wir haben dazu das Konzept des Output Schedules entwickelt. Dieser wird aufgrund der definierten Ausgabebedingungen generiert. Durch die Ausgabebedingungen, die in dieser Arbeit eingeführt werden, werden semantische Fehler bei der Verwaltung von Mediendaten erheblich reduziert. Die Arbeit stellt daher einen Beitrag zur qualitativen Verbesserung der Verwaltung von Mediendaten dar.Semantic errors exist as long as data are managed. Traditional database systems try to prevent this errors by proposing integrity concepts for stored data. Integrity constraints are used to implement these integrity concepts. However, integrity constraints can only detect semantic errors in elementary data. Multimedia database systems manage elementary data as well as complex media data, like videos. Considering these media data we need a much wider consistency concept as traditional database systems provide. Especially, data output of media data must be taken into account. In contrast to alphanumeric data the semantics of media data can be falsified during data output if data quality or synchronization of data are not suitable. Thus, we need a concept for output constraints that allow for preventing semantic errors in case of data output. For integrating output constraints into a multimedia database system we have to consider modelling, representation and checking of output constraints. For modelling output constraints we have introduced a constraint language which uses the same principles as traditional constraint languages. Our constraint specification language must support temporal and spatial synchronization constraints. However, it is desired to support both kinds of synchronization in almost the same manner. Therefore, we use Allen-Relations for defining temporal synchronization constraints as well as for defining spatial synchronization constraints. We need a database internal representation of output constraints that makes efficient constraint checking possible. The Allen-Relations used in the constraint language cannot be checked efficiently. However, difference constraints are a class of constraints that allows an very efficient checking. Therefore, we use difference constraints as database internal representation of output constraints. As methods for checking consistency of output constraints we use an approach based on graph theory as well as an analytical approach. Both approaches require a constraint graph as data structure. For data output we need an output order that is adequate to the defined output constraints. This output schedule can be produced based on the output constraints. With output constraints, proposed in this thesis, semantical correctness of media data considering the data output can be supported.Thus, the contribution of this work is an qualitative improvement of managing media data by database systems

Transformation of an uncertain video search pipeline to a sketch-based visual analytics loop

Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatiotemporal attributes from sports video to identify key instances of the team and player performance. © 1995-2012 IEEE

Dagstuhl News January - December 2008

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

Sensor-Driven, Spatially Explicit Agent-Based Models

Conventionally, agent-based models (ABMs) are specified from well-established theory about the systems under investigation. For such models, data is only introduced to ensure the validity of the specified models. In cases where the underlying mechanisms of the system of interest are unknown, rich datasets about the system can reveal patterns and processes of the systems. Sensors have become ubiquitous allowing researchers to capture precise characteristics of entities in both time and space. The combination of data from in situ sensors to geospatial outputs provides a rich resource for characterising geospatial environments and entities on earth. More importantly, the sensor data can capture behaviours and interactions of entities allowing us to visualise emerging patterns from the interactions. However, there is a paucity of standardised methods for the integration of dynamic sensor data streams into ABMs. Further, only few models have attempted to incorporate spatial and temporal data dynamically from sensors for model specification, calibration and validation. This chapter documents the state of the art of methods for bridging the gap between sensor data observations and specification of accurate spatially explicit agent-based models. In addition, this work proposes a conceptual framework for dynamic validation of sensor-driven spatial ABMs to address the risk of model overfitting

INRISCO: INcident monitoRing in Smart COmmunities

Major advances in information and communication technologies (ICTs) make citizens to be considered as sensors in motion. Carrying their mobile devices, moving in their connected vehicles or actively participating in social networks, citizens provide a wealth of information that, after properly processing, can support numerous applications for the benefit of the community. In the context of smart communities, the INRISCO [1] proposal intends for (i) the early detection of abnormal situations in cities (i.e., incidents), (ii) the analysis of whether, according to their impact, those incidents are really adverse for the community; and (iii) the automatic actuation by dissemination of appropriate information to citizens and authorities. Thus, INRISCO will identify and report on incidents in traffic (jam, accident) or public infrastructure (e.g., works, street cut), the occurrence of specific events that affect other citizens' life (e.g., demonstrations, concerts), or environmental problems (e.g., pollution, bad weather). It is of particular interest to this proposal the identification of incidents with a social and economic impact, which affects the quality of life of citizens.This work was supported in part by the Spanish Government through the projects INRISCO under Grant TEC2014-54335-C4-1-R, Grant TEC2014-54335-C4-2-R, Grant TEC2014-54335-C4-3-R, and Grant TEC2014-54335-C4-4-R, in part by the MAGOS under Grant TEC2017-84197-C4-1-R, Grant TEC2017-84197-C4-2-R, and Grant TEC2017-84197-C4-3-R, in part by the European Regional Development Fund (ERDF), and in part by the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC)