This article provides a detailed explanation of the BBQ-Tree, a unified logic-based model that integrates both classical Decision Trees and Quantum-Logic Decision Trees into a generalized framework for classification and regression. As it combines these paradigms, the BBQ-Tree effectively addresses problems with both linear and curved decision boundaries while prioritizing interpretability. We provide a detailed description of the underlying concepts, a possible training algorithm, experimental evaluations and the incorporation of regression functionality, broadening its applicability beyond classification tasks. Strategies for efficient training and model optimization are also presented. Experimental results demonstrate that the BBQ-Tree produces compact, interpretable models capable of revealing data trends, while achieving accuracy comparable to Decision Trees. Furthermore, its new regression capabilities highlight its versatility and performance across a wider range of tasks.

Besides a good prediction a classifier is to give an explanation how the input data is related to the classification result. There is a general agreement that logic expressions provide a better explanation than other methods like SVM, logistic regression, and neural networks. However, a classifier based on Boolean logic needs to map continuous data to Boolean values which can cause a loss of information. In contrast, we design a quantum-logic-inspired classifier where continuous data are directly processed and the laws of the Boolean algebra are maintained. As a result from our approach we obtain a CQQL condition which provides good insights into the relation of input features to the class decision. Furthermore, our experiment shows a good prediction accuracy.

Experts and crowds can work together to generate high-quality datasets, but such collaboration is limited to a large-scale pool of data. In other words, training on a large-scale dataset depends more on crowdsourced datasets with aggregated labels than expert intensively checked labels. However, the limited amount of high-quality dataset can be used as an objective test dataset to build a connection between disagreement and aggregated labels. In this paper, we claim that the disagreement behind an aggregated label indicates more semantics (e.g. ambiguity or difficulty) of an instance than just spam or error assessment. We attempt to take advantage of the informativeness of disagreement to assist learning neural networks by computing a series of disagreement measurements and incorporating disagreement with distinct mechanisms. Experiments on two datasets demonstrate that the consideration of disagreement, treating training instances differently, can promisingly result in improved performance.

Mobile cyber-physical systems (MCPSs) such as cars, trains, aeroplanes or spaceships are widely disseminated in our today's life. MCPSs are embedded into a physical environment which is usually harsh and uncertain. Hence, monitoring MCPSs is essential to ensure reliability and to avoid critical damage. Monitoring MCPSs is a very interdisciplinary research area. The monitoring process presupposes knowledge about the MCPS, the belonging physical environment and the related system behaviour. Such knowledge has to be discovered continuously during runtime due to continuous changes like wear and tear. Our contribution is the definition of an abstract process cycle for monitoring MCPSs which is called the Knowledge Discovery Cycle (KDC). The KDC is based on key challenges for monitoring MCPSs and common characteristics of MCPSs. We particularly discuss key challenges and common characteristics by means of the International Space Station (ISS). Based on this, we examine five overall considerations for monitoring MCPSs. Further on, we identify processing steps for the KDC and associate existing concepts and technologies with the identified processing steps. This involves knowledge discovery in databases, knowledge discovery from data streams, as well as information flow processing. As a result, our work outlines open problems for monitoring MCPSs.

PythiaSearch is a multimedia information retrieval system supporting multiple search strategies. Based on the promising results of the underlying query model in 2011's Image-CLEF Wikipedia task, we have implemented an interactive retrieval system which supports multimodal data such as images, (multilingual) texts, and various metadata formats that can be used to query or browse a collection. The support of multiple search strategies is crucial, because it is subject to change during the user's interaction with the retrieval system. The directed search and browsing mechanisms rely both on the same formal query model providing a seamless adaption to the user's search strategy. Additionally, it features a relevance feedback process that can be used to adjust or even learn specific queries based on the user's interaction with the system alone which can be saved for later usage.

ProQua is a new probabilistic database system which combines information retrieval concepts with database technologies. In contrast to other state-of-the-art probabilistic database systems ProQua supports logic-based and weighted similarity conditions within its query language by a generic similarity operator. In this work we introduce the ProQua query language QSQL2 by presenting its syntax, semantics and query capabilities. Furthermore, we briefly discuss the main components of ProQua.

The utility of preferences within the database domain is widely accepted. Preferences provide an effective means for query personalization and information filtering. Nevertheless, two preference approaches - qualitative and quantitative ones - do still compete. In this paper, we contribute to the bridging of both approaches and compare their expressive power and different usage scenarios. In order to combine qualitative and quantitative preferences, mappings are introduced and discussed, which transform a query from one approach into its counter-part. We consider Chomicki's preference formulas and as a quantitative approach our CQQL approach that extends the relational calculus with proximity predicates. In order to facilitate query formulation for the user, we extend the CQQL approach to condition learning. That is, user-defined preferences amongst database objects serve as input to learn logical conditions within a CQQL query. Hereby, we can support the user in the cognitively demanding task of query formulation.

Most of the currently existing image retrieval systems make use of either low-level features or semantic (textual) annotations. A combined usage during annotation and retrieval is rarely attempted. In this paper, we present a query language that makes use of both, low and high level annotations, that are provided in the MPEG-7 standard. The query language is based on results from quantum logic and improves existing fuzzy approaches. Furthermore, it is integrated in the SAFIRE system, a framework for semi-automatic image annotation and retrieval.

The visual database query language QBE (query by example) is a classical, declarative query language based on the relational domain calculus. However, due to insufficient support of vagueness QBE is not an appropriate query language for formulating similarity queries required in the context of multimedia databases. In this work we propose the query language WS-QBE which combines a schema to weight query terms as well as concepts from fuzzy logic and QBE into one language. WS-QBE enables a visual, declarative formulation of complex similarity queries. The semantics of WS-QBE is defined by a mapping of WS-QBE queries onto the similarity domain calculus SDC which is proposed here, too.

Article An incremental approach to schema integration by refining extensional relationships Share on Authors: Ingo Schmitt Otto-von-Guericke-Universität Magdeburg, Institut für Technische und Betriebliche Informationssysteme, Postfach 4120, D-39016 Magdeburg, Germany Otto-von-Guericke-Universität Magdeburg, Institut für Technische und Betriebliche Informationssysteme, Postfach 4120, D-39016 Magdeburg, GermanyView Profile , Can Türker Otto-von-Guericke-Universität Magdeburg, Institut für Technische und Betriebliche Informationssysteme, Postfach 4120, D-39016 Magdeburg, Germany Otto-von-Guericke-Universität Magdeburg, Institut für Technische und Betriebliche Informationssysteme, Postfach 4120, D-39016 Magdeburg, GermanyView Profile Authors Info & Claims CIKM '98: Proceedings of the seventh international conference on Information and knowledge managementNovember 1998 Pages 322–330https://doi.org/10.1145/288627.288673Online:01 November 1998Publication History 3citation356DownloadsMetricsTotal Citations3Total Downloads356Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Merging inheritance hierarchies with overlapping class extensions and types is an essential task in database design. In the context of view integration and schema integration for federated databases and multidatabases conflicting inheritance hierarchies have to be merged. Inheritance hierarchies often occur explicitly in object-oriented databases as well as implicitly in relational databases. Since a concept lattice can be regarded as an inheritance hierarchy we propose to apply the theory of concept analysis to the problem of merging inheritance hierarchies. After investigating the power and complexity of concept analysis algorithms we provide a new algorithm tailored to our problem. The new algorithm has polynomial complexity and helps to optimize the resulting hierarchy with respect to certain quality criteria, e.g. number of classes and null values. An example demonstrates the practicability of our approach to merge conflicting inheritance hierarchies.