Logic Programming Applications: What Are the Abstractions and Implementations?

This article presents an overview of applications of logic programming, classifying them based on the abstractions and implementations of logic languages that support the applications. The three key abstractions are join, recursion, and constraint. Their essential implementations are for-loops, fixed points, and backtracking, respectively. The corresponding kinds of applications are database queries, inductive analysis, and combinatorial search, respectively. We also discuss language extensions and programming paradigms, summarize example application problems by application areas, and touch on example systems that support variants of the abstractions with different implementations

Provenance, Incremental Evaluation, and Debugging in Datalog

The Datalog programming language has recently found increasing traction in research and industry. Driven by its clean declarative semantics, along with its conciseness and ease of use, Datalog has been adopted for a wide range of important applications, such as program analysis, graph problems, and networking. To enable this adoption, modern Datalog engines have implemented advanced language features and high-performance evaluation of Datalog programs. Unfortunately, critical infrastructure and tooling to support Datalog users and developers are still missing. For example, there are only limited tools addressing the crucial debugging problem, where developers can spend up to 30% of their time finding and fixing bugs. This thesis addresses Datalog’s tooling gaps, with the ultimate goal of improving the productivity of Datalog programmers. The first contribution is centered around the critical problem of debugging: we develop a new debugging approach that explains the execution steps taken to produce a faulty output. Crucially, our debugging method can be applied for large-scale applications without substantially sacrificing performance. The second contribution addresses the problem of incremental evaluation, which is necessary when program inputs change slightly, and results need to be recomputed. Incremental evaluation allows this recomputation to happen more efficiently, without discarding the previous results and recomputing from scratch. Finally, the last contribution provides a new incremental debugging approach that identifies the root causes of faulty outputs that occur after an incremental evaluation. Incremental debugging focuses on the relationship between input and output and can provide debugging suggestions to amend the inputs so that faults no longer occur. These techniques, in combination, form a corpus of critical infrastructure and tooling developments for Datalog, allowing developers and users to use Datalog more productively

Efficient Provenance-Aware Querying of Graph Databases with Datalog

International audienceWe establish a translation between a formalism for dynamic programming over hypergraphs and the computation of semiringbased provenance for Datalog programs. The benefit of this translation is a new method for computing the provenance of Datalog programs for specific classes of semirings, which we apply to provenance-aware querying of graph databases. Theoretical results and practical optimizations lead to an efficient implementation using Soufflé, a state-of-the-art Datalog interpreter. Experimental results on real-world data suggest this approach to be efficient in practical contexts, competing with dedicated solutions for graphs

Provenance à base de semi-anneaux pour les bases de données graphes

The growing amount of data collected by sensors or generated by human interaction has led to an increasing use of graph databases, an efficient model for representing intricate data.Techniques to keep track of the history of computations applied to the data inside classical relational database systems are also topical because of their application to enforce Data Protection Regulations (e.g., GDPR).Our research work mixes the two by considering a semiring-based provenance model for navigational queries over graph databases.We first present a comprehensive survey on semiring theory and their applications in different fields of computer sciences, geared towards their relevance for our context. From the richness of the literature, we notably obtain a lower bound for the complexity of the full provenance computation in our setting.In a second part, we focus on the model itself by introducing a toolkit of provenance-aware algorithms, each targeting specific properties of the semiring of use.We notably introduce a new method based on lattice theory permitting an efficient provenance computation for complex graph queries.We propose an open-source implementation of the above-mentioned algorithms, and we conduct an experimental study over real transportation networks of large size, witnessing the practical efficiency of our approach in practical scenarios.We finally consider how this framework is positioned compared to other provenance models such as the semiring-based Datalog provenance model.We make explicit how the methods we applied for graph databases can be extended to Datalog queries, and we show how they can be seen as an extension of the semi-naïve evaluation strategy.To leverage this fact, we extend the capabilities of Soufflé, a state-of-the-art Datalog solver, to design an efficient provenance-aware Datalog evaluator. Experimental results based on our open-source implementation entail the fact this approach stays competitive with dedicated graph solutions, despite being more general.In a final round, we discuss on some research ideas for improving the model, and state open questions raised by our work.L'augmentation du volume de données collectées par des capteurs et générées par des interactions humaines a mené à l'utilisation des bases de données orientées graphes en tant que modèle de représentation efficace pour les données complexes.Les techniques permettant de tracer les calculs qui ont été appliqués aux données au sein d'une base de données relationnelle classique sont sur le devant de la scène, notamment grâce à leur utilité pourfaire respecter les régulations sur les données privées telles que le RGPD en Union Européenne.Notre travail de recherche croise ces deux problématiques en s'intéressant à un modèle de provenance à base de semi-anneaux pour les requêtes navigationnelles.Nous commençons par présenter une étude approfondie de la théorie des semi-anneaux et de leurs applications au sein des sciences informatiques en se concentrant sur les résultats ayant un intérêt direct pour notre travail de recherche.La richesse de la littérature sur le domaine nous a notamment permis d'obtenir une borne inférieure sur la complexité de notre modèle.Dans une seconde partie, nous étudions le modèle en lui-même et introduisons un ensemble cohérent d'algorithmes permettant d'effectuer des calculs de provenance et adaptés aux propriétés des semi-anneaux utilisés.Nous introduisons notablement une nouvelle méthode basée sur la théorie des treillis permettant de calculer la provenance pour des requêtes complexes.Nous proposons une implémentation open-source de ces algorithmes et faisons une étude expérimentale sur de larges réseaux de transport issus de la vie réelle pour attester de l'efficacité pratique de notre approche.On s'intéresse finalement au positionnement de ce cadre de travail par rapport à d'autres modèles de provenance à base de semi-anneaux. Nous nous intéressons à Datalog en particulier.Nous démontrons que les méthodes que nous avons développées pour les bases de données orientées graphes peuvent se généraliser sur des requêtes Datalog. Nous montrons de plus qu'elles peuvent être vues comme des généralisations de la méthode semi-naïve.En se basant sur ce fait-là, nous étendons les capacités de Soufflé, un évaluateur Datalog appartenant à l'état de l'art, afin d'effectuer des calculs de provenance pour des requêtes Datalog.Les études expérimentales basées sur cette implémentation open-source confirment que cette approche reste compétitive avec les solutions spécifiques pour les graphes, mais tout en étant plus générale.Nous terminons par une discussion sur les améliorations possibles du modèle et énonçons les questions ouvertes qui ont été soulevées au cours de ce travail

Scalable Query Answering Under Uncertainty to Neuroscientific Ontological Knowledge: The NeuroLang Approach

Researchers in neuroscience have a growing number of datasets available to study the brain, which is made possible by recent technological advances. Given the extent to which the brain has been studied, there is also available ontological knowledge encoding the current state of the art regarding its different areas, activation patterns, keywords associated with studies, etc. Furthermore, there is inherent uncertainty associated with brain scans arising from the mapping between voxels—3D pixels—and actual points in different individual brains. Unfortunately, there is currently no unifying framework for accessing such collections of rich heterogeneous data under uncertainty, making it necessary for researchers to rely on ad hoc tools. In particular, one major weakness of current tools that attempt to address this task is that only very limited propositional query languages have been developed. In this paper we present NeuroLang, a probabilistic language based on first-order logic with existential rules, probabilistic uncertainty, ontologies integration under the open world assumption, and built-in mechanisms to guarantee tractable query answering over very large datasets. NeuroLang’s primary objective is to provide a unified framework to seamlessly integrate heterogeneous data, such as ontologies, and map fine-grained cognitive domains to brain regions through a set of formal criteria, promoting shareable and highly reproducible research. After presenting the language and its general query answering architecture, we discuss real-world use cases showing how NeuroLang can be applied to practical scenarios.Fil: Zanitti, Gaston E.. No especifíca;Fil: Soto, Yamil Osvaldo Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Iovene, Valentin. No especifíca;Fil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Rodriguez, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Wassermann, Demian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin

On Multiple Semantics for Declarative Database Repairs

We study the problem of database repairs through a rule-based framework that we refer to as Delta Rules. Delta Rules are highly expressive and allow specifying complex, cross-relations repair logic associated with Denial Constraints, Causal Rules, and allowing to capture Database Triggers of interest. We show that there are no one-size-fits-all semantics for repairs in this inclusive setting, and we consequently introduce multiple alternative semantics, presenting the case for using each of them. We then study the relationships between the semantics in terms of their output and the complexity of computation. Our results formally establish the tradeoff between the permissiveness of the semantics and its computational complexity. We demonstrate the usefulness of the framework in capturing multiple data repair scenarios for an Academic Search database and the TPC-H databases, showing how using different semantics affects the repair in terms of size and runtime, and examining the relationships between the repairs. We also compare our approach with SQL triggers and a state-of-the-art data repair system

Semantically defined Analytics for Industrial Equipment Diagnostics

In this age of digitalization, industries everywhere accumulate massive amount of data such that it has become the lifeblood of the global economy. This data may come from various heterogeneous systems, equipment, components, sensors, systems and applications in many varieties (diversity of sources), velocities (high rate of changes) and volumes (sheer data size). Despite significant advances in the ability to collect, store, manage and filter data, the real value lies in the analytics. Raw data is meaningless, unless it is properly processed to actionable (business) insights. Those that know how to harness data effectively, have a decisive competitive advantage, through raising performance by making faster and smart decisions, improving short and long-term strategic planning, offering more user-centric products and services and fostering innovation. Two distinct paradigms in practice can be discerned within the field of analytics: semantic-driven (deductive) and data-driven (inductive). The first emphasizes logic as a way of representing the domain knowledge encoded in rules or ontologies and are often carefully curated and maintained. However, these models are often highly complex, and require intensive knowledge processing capabilities. Data-driven analytics employ machine learning (ML) to directly learn a model from the data with minimal human intervention. However, these models are tuned to trained data and context, making it difficult to adapt. Industries today that want to create value from data must master these paradigms in combination. However, there is great need in data analytics to seamlessly combine semantic-driven and data-driven processing techniques in an efficient and scalable architecture that allows extracting actionable insights from an extreme variety of data. In this thesis, we address these needs by providing: • A unified representation of domain-specific and analytical semantics, in form of ontology models called TechOnto Ontology Stack. It is highly expressive, platform-independent formalism to capture conceptual semantics of industrial systems such as technical system hierarchies, component partonomies etc and its analytical functional semantics. • A new ontology language Semantically defined Analytical Language (SAL) on top of the ontology model that extends existing DatalogMTL (a Horn fragment of Metric Temporal Logic) with analytical functions as first class citizens. • A method to generate semantic workflows using our SAL language. It helps in authoring, reusing and maintaining complex analytical tasks and workflows in an abstract fashion. • A multi-layer architecture that fuses knowledge- and data-driven analytics into a federated and distributed solution. To our knowledge, the work in this thesis is one of the first works to introduce and investigate the use of the semantically defined analytics in an ontology-based data access setting for industrial analytical applications. The reason behind focusing our work and evaluation on industrial data is due to (i) the adoption of semantic technology by the industries in general, and (ii) the common need in literature and in practice to allow domain expertise to drive the data analytics on semantically interoperable sources, while still harnessing the power of analytics to enable real-time data insights. Given the evaluation results of three use-case studies, our approach surpass state-of-the-art approaches for most application scenarios.Im Zeitalter der Digitalisierung sammeln die Industrien überall massive Daten-mengen, die zum Lebenselixier der Weltwirtschaft geworden sind. Diese Daten können aus verschiedenen heterogenen Systemen, Geräten, Komponenten, Sensoren, Systemen und Anwendungen in vielen Varianten (Vielfalt der Quellen), Geschwindigkeiten (hohe Änderungsrate) und Volumina (reine Datengröße) stammen. Trotz erheblicher Fortschritte in der Fähigkeit, Daten zu sammeln, zu speichern, zu verwalten und zu filtern, liegt der eigentliche Wert in der Analytik. Rohdaten sind bedeutungslos, es sei denn, sie werden ordnungsgemäß zu verwertbaren (Geschäfts-)Erkenntnissen verarbeitet. Wer weiß, wie man Daten effektiv nutzt, hat einen entscheidenden Wettbewerbsvorteil, indem er die Leistung steigert, indem er schnellere und intelligentere Entscheidungen trifft, die kurz- und langfristige strategische Planung verbessert, mehr benutzerorientierte Produkte und Dienstleistungen anbietet und Innovationen fördert. In der Praxis lassen sich im Bereich der Analytik zwei unterschiedliche Paradigmen unterscheiden: semantisch (deduktiv) und Daten getrieben (induktiv). Die erste betont die Logik als eine Möglichkeit, das in Regeln oder Ontologien kodierte Domänen-wissen darzustellen, und wird oft sorgfältig kuratiert und gepflegt. Diese Modelle sind jedoch oft sehr komplex und erfordern eine intensive Wissensverarbeitung. Datengesteuerte Analysen verwenden maschinelles Lernen (ML), um mit minimalem menschlichen Eingriff direkt ein Modell aus den Daten zu lernen. Diese Modelle sind jedoch auf trainierte Daten und Kontext abgestimmt, was die Anpassung erschwert. Branchen, die heute Wert aus Daten schaffen wollen, müssen diese Paradigmen in Kombination meistern. Es besteht jedoch ein großer Bedarf in der Daten-analytik, semantisch und datengesteuerte Verarbeitungstechniken nahtlos in einer effizienten und skalierbaren Architektur zu kombinieren, die es ermöglicht, aus einer extremen Datenvielfalt verwertbare Erkenntnisse zu gewinnen. In dieser Arbeit, die wir auf diese Bedürfnisse durch die Bereitstellung: • Eine einheitliche Darstellung der Domänen-spezifischen und analytischen Semantik in Form von Ontologie Modellen, genannt TechOnto Ontology Stack. Es ist ein hoch-expressiver, plattformunabhängiger Formalismus, die konzeptionelle Semantik industrieller Systeme wie technischer Systemhierarchien, Komponenten-partonomien usw. und deren analytische funktionale Semantik zu erfassen. • Eine neue Ontologie-Sprache Semantically defined Analytical Language (SAL) auf Basis des Ontologie-Modells das bestehende DatalogMTL (ein Horn fragment der metrischen temporären Logik) um analytische Funktionen als erstklassige Bürger erweitert. • Eine Methode zur Erzeugung semantischer workflows mit unserer SAL-Sprache. Es hilft bei der Erstellung, Wiederverwendung und Wartung komplexer analytischer Aufgaben und workflows auf abstrakte Weise. • Eine mehrschichtige Architektur, die Wissens- und datengesteuerte Analysen zu einer föderierten und verteilten Lösung verschmilzt. Nach unserem Wissen, die Arbeit in dieser Arbeit ist eines der ersten Werke zur Einführung und Untersuchung der Verwendung der semantisch definierten Analytik in einer Ontologie-basierten Datenzugriff Einstellung für industrielle analytische Anwendungen. Der Grund für die Fokussierung unserer Arbeit und Evaluierung auf industrielle Daten ist auf (i) die Übernahme semantischer Technologien durch die Industrie im Allgemeinen und (ii) den gemeinsamen Bedarf in der Literatur und in der Praxis zurückzuführen, der es der Fachkompetenz ermöglicht, die Datenanalyse auf semantisch inter-operablen Quellen voranzutreiben, und nutzen gleichzeitig die Leistungsfähigkeit der Analytik, um Echtzeit-Daten-einblicke zu ermöglichen. Aufgrund der Evaluierungsergebnisse von drei Anwendungsfällen Übertritt unser Ansatz für die meisten Anwendungsszenarien Modernste Ansätze