760 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Current and Future Challenges in Knowledge Representation and Reasoning

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    Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade

    Consistent Query Answering for Primary Keys on Rooted Tree Queries

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    We study the data complexity of consistent query answering (CQA) on databases that may violate the primary key constraints. A repair is a maximal subset of the database satisfying the primary key constraints. For a Boolean query q, the problem CERTAINTY(q) takes a database as input, and asks whether or not each repair satisfies q. The computational complexity of CERTAINTY(q) has been established whenever q is a self-join-free Boolean conjunctive query, or a (not necessarily self-join-free) Boolean path query. In this paper, we take one more step towards a general classification for all Boolean conjunctive queries by considering the class of rooted tree queries. In particular, we show that for every rooted tree query q, CERTAINTY(q) is in FO, NL-hard ∩\cap LFP, or coNP-complete, and it is decidable (in polynomial time), given q, which of the three cases applies. We also extend our classification to larger classes of queries with simple primary keys. Our classification criteria rely on query homomorphisms and our polynomial-time fixpoint algorithm is based on a novel use of context-free grammar (CFG).Comment: To appear in PODS'2

    Synthesizing Conjunctive Queries for Code Search

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    This paper presents Squid, a new conjunctive query synthesis algorithm for searching code with target patterns. Given positive and negative examples along with a natural language description, Squid analyzes the relations derived from the examples by a Datalog-based program analyzer and synthesizes a conjunctive query expressing the search intent. The synthesized query can be further used to search for desired grammatical constructs in the editor. To achieve high efficiency, we prune the huge search space by removing unnecessary relations and enumerating query candidates via refinement. We also introduce two quantitative metrics for query prioritization to select the queries from multiple candidates, yielding desired queries for code search. We have evaluated Squid on over thirty code search tasks. It is shown that Squid successfully synthesizes the conjunctive queries for all the tasks, taking only 2.56 seconds on average

    Temporal datalog with existential quantification

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    Existential rules, also known as tuple-generating dependencies (TGDs) or Datalog± rules, are heavily studied in the communities of Knowledge Representation and Reasoning, Semantic Web, and Databases, due to their rich modelling capabilities. In this paper we consider TGDs in the temporal setting, by introducing and studying DatalogMTL∃—an extension of metric temporal Datalog (DatalogMTL) obtained by allowing for existential rules in programs. We show that DatalogMTL∃ is undecidable even in the restricted cases of guarded and weakly-acyclic programs. To address this issue we introduce uniform semantics which, on the one hand, is well-suited for modelling temporal knowledge as it prevents from unintended value invention and, on the other hand, provides decidability of reasoning; in particular, it becomes 2-ExpSpace-complete for weakly-acyclic programs but remains undecidable for guarded programs. We provide an implementation for the decidable case and demonstrate its practical feasibility. Thus we obtain an expressive, yet decidable, rule-language and a system which is suitable for complex temporal reasoning with existential rules

    Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches

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    Traditional networking devices support only fixed features and limited configurability. Network softwarization leverages programmable software and hardware platforms to remove those limitations. In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms. This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0. P4 is the most popular technology to implement programmable data planes. However, programmable data planes, and in particular, the P4 technology, emerged only recently. Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking. The research of this thesis focuses on two open issues of programmable data planes. First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet. Second, it enables BIER in high-performance P4 data planes. BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet. The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study. Two more peer-reviewed papers contain additional content that is not directly related to the main results. They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts

    Systems and Algorithms for Dynamic Graph Processing

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    Data generated from human and systems interactions could be naturally represented as graph data. Several emerging applications rely on graph data, such as the semantic web, social networks, bioinformatics, finance, and trading among others. These applications require graph querying capabilities which are often implemented in graph database management systems (GDBMS). Many GDBMSs have capabilities to evaluate one-time versions of recursive or subgraph queries over static graphs – graphs that do not change or a single snapshot of a changing graph. They generally do not support incrementally maintaining queries as graphs change. However, most applications that employ graphs are dynamic in nature resulting in graphs that change over time, also known as dynamic graphs. This thesis investigates how to build a generic and scalable incremental computation solution that is oblivious to graph workloads. It focuses on two fundamental computations performed by many applications: recursive queries and subgraph queries. Specifically, for subgraph queries, this thesis presents the first approach that (i) performs joins with worstcase optimal computation and communication costs; and (ii) maintains a total memory footprint almost linear in the number of input edges. For recursive queries, this thesis studies optimizations for using differential computation (DC). DC is a general incremental computation that can maintain the output of a recursive dataflow computation upon changes. However, it requires a prohibitively large amount of memory because it maintains differences that track changes in queries input/output. The thesis proposes a suite of optimizations that are based on reducing the number of these differences and recomputing them when necessary. The techniques and optimizations in this thesis, for subgraph and recursive computations, represent a proposal for how to build a state-of-the-art generic and scalable GDBMS for dynamic graph data management

    Incremental algorithm for Decision Rule generation in data stream contexts

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    Actualmente, la ciencia de datos está ganando mucha atención en diferentes sectores. Concretamente en la industria, muchas aplicaciones pueden ser consideradas. Utilizar técnicas de ciencia de datos en el proceso de toma de decisiones es una de esas aplicaciones que pueden aportar valor a la industria. El incremento de la disponibilidad de los datos y de la aparición de flujos continuos en forma de data streams hace emerger nuevos retos a la hora de trabajar con datos cambiantes. Este trabajo presenta una propuesta innovadora, Incremental Decision Rules Algorithm (IDRA), un algoritmo que, de manera incremental, genera y modifica reglas de decisión para entornos de data stream para incorporar cambios que puedan aparecer a lo largo del tiempo. Este método busca proponer una nueva estructura de reglas que busca mejorar el proceso de toma de decisiones, planteando una base de conocimiento descriptiva y transparente que pueda ser integrada en una herramienta decisional. Esta tesis describe la lógica existente bajo la propuesta de IDRA, en todas sus versiones, y propone una variedad de experimentos para compararlas con un método clásico (CREA) y un método adaptativo (VFDR). Conjuntos de datos reales, juntamente con algunos escenarios simulados con diferentes tipos y ratios de error, se utilizan para comparar estos algoritmos. El estudio prueba que IDRA, específicamente la versión reactiva de IDRA (RIDRA), mejora la precisión de VFDR y CREA en todos los escenarios, tanto reales como simulados, a cambio de un incremento en el tiempo.Nowadays, data science is earning a lot of attention in many different sectors. Specifically in the industry, many applications might be considered. Using data science techniques in the decision-making process is a valuable approach among the mentioned applications. Along with this, the growth of data availability and the appearance of continuous data flows in the form of data stream arise other challenges when dealing with changing data. This work presents a novel proposal of an algorithm, Incremental Decision Rules Algorithm (IDRA), that incrementally generates and modify decision rules for data stream contexts to incorporate the changes that could appear over time. This method aims to propose new rule structures that improve the decision-making process by providing a descriptive and transparent base of knowledge that could be integrated in a decision tool. This work describes the logic underneath IDRA, in all its versions, and proposes a variety of experiments to compare them with a classical method (CREA) and an adaptive method (VFDR). Some real datasets, together with some simulated scenarios with different error types and rates are used to compare these algorithms. The study proved that IDRA, specifically the reactive version of IDRA (RIDRA), improves the accuracies of VFDR and CREA in all the studied scenarios, both real and simulated, in exchange of more time
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