5,077 research outputs found
Efficient querying and learning in probabilistic and temporal databases
Probabilistic databases store, query, and manage large amounts of uncertain information. This thesis advances the state-of-the-art in probabilistic databases in three different ways:
1. We present a closed and complete data model for temporal probabilistic databases and analyze its complexity. Queries are posed via temporal deduction rules which induce lineage formulas capturing both time and uncertainty.
2. We devise a methodology for computing the top-k most probable query answers. It is based on first-order lineage formulas representing sets of answer candidates. Theoretically derived probability bounds on these formulas enable pruning low-probability answers.
3. We introduce the problem of learning tuple probabilities which allows updating and cleaning of probabilistic databases. We study its complexity, characterize its solutions, cast it into an optimization problem, and devise an approximation algorithm based on stochastic gradient descent.
All of the above contributions support consistency constraints and are evaluated experimentally.Probabilistische Datenbanken können große Mengen an ungewissen Informationen speichern, anfragen und verwalten. Diese Doktorarbeit treibt den Stand der Technik in diesem Gebiet auf drei Arten vorran:
1. Ein abgeschlossenes und vollständiges Datenmodell für temporale, probabilistische Datenbanken wird präsentiert. Anfragen werden mittels Deduktionsregeln gestellt, welche logische Formeln induzieren, die sowohl Zeit als auch Ungewissheit erfassen.
2. Ein Methode zur Berechnung der k Anworten höchster Wahrscheinlichkeit wird entwickelt. Sie basiert auf logischen Formeln erster Stufe, die Mengen an Antwortkandidaten repräsentieren. Beschränkungen der Wahrscheinlichkeit dieser Formeln ermöglichen das Kürzen von Antworten mit niedriger Wahrscheinlichkeit.
3. Das Problem des Lernens von Tupelwahrscheinlichkeiten für das Aktualisieren und Bereiningen von probabilistischen Datenbanken wird eingeführt, auf Komplexität und Lösungen untersucht, als Optimierungsproblem dargestellt und von einem stochastischem Gradientenverfahren approximiert. All diese Beiträge unterstützen Konsistenzbedingungen und wurden experimentell analysiert
Time-Aware Probabilistic Knowledge Graphs
The emergence of open information extraction as a tool for constructing and expanding knowledge graphs has aided the growth of temporal data, for instance, YAGO, NELL and Wikidata. While YAGO and Wikidata maintain the valid time of facts, NELL records the time point at which a fact is retrieved from some Web corpora. Collectively, these knowledge graphs (KG) store facts extracted from Wikipedia and other sources. Due to the imprecise nature of the extraction tools that are used to build and expand KG, such as NELL, the facts in the KG are weighted (a confidence value representing the correctness of a fact). Additionally, NELL can be considered as a transaction time KG because every fact is associated with extraction date. On the other hand, YAGO and Wikidata use the valid time model because they maintain facts together with their validity time (temporal scope). In this paper, we propose a bitemporal model (that combines transaction and valid time models) for maintaining and querying bitemporal probabilistic knowledge graphs. We study coalescing and scalability of marginal and MAP inference. Moreover, we show that complexity of reasoning tasks in atemporal probabilistic KG carry over to the bitemporal setting. Finally, we report our evaluation results of the proposed model
The relationship between IR and multimedia databases
Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud
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Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud
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Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud
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First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud
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Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud
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Third, we add the functionality to process the users' relevance feedback.\ud
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We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud
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We conclude with an outline for implementation of miRRor on top of the Monet extensible database system
Learning Tuple Probabilities
Learning the parameters of complex probabilistic-relational models from
labeled training data is a standard technique in machine learning, which has
been intensively studied in the subfield of Statistical Relational Learning
(SRL), but---so far---this is still an under-investigated topic in the context
of Probabilistic Databases (PDBs). In this paper, we focus on learning the
probability values of base tuples in a PDB from labeled lineage formulas. The
resulting learning problem can be viewed as the inverse problem to confidence
computations in PDBs: given a set of labeled query answers, learn the
probability values of the base tuples, such that the marginal probabilities of
the query answers again yield in the assigned probability labels. We analyze
the learning problem from a theoretical perspective, cast it into an
optimization problem, and provide an algorithm based on stochastic gradient
descent. Finally, we conclude by an experimental evaluation on three real-world
and one synthetic dataset, thus comparing our approach to various techniques
from SRL, reasoning in information extraction, and optimization
Graph Summarization
The continuous and rapid growth of highly interconnected datasets, which are
both voluminous and complex, calls for the development of adequate processing
and analytical techniques. One method for condensing and simplifying such
datasets is graph summarization. It denotes a series of application-specific
algorithms designed to transform graphs into more compact representations while
preserving structural patterns, query answers, or specific property
distributions. As this problem is common to several areas studying graph
topologies, different approaches, such as clustering, compression, sampling, or
influence detection, have been proposed, primarily based on statistical and
optimization methods. The focus of our chapter is to pinpoint the main graph
summarization methods, but especially to focus on the most recent approaches
and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie
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