386 research outputs found

    Reactive Rules for Emergency Management

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    The goal of the following survey on Event-Condition-Action (ECA) Rules is to come to a common understanding and intuition on this topic within EMILI. Thus it does not give an academic overview on Event-Condition-Action Rules which would be valuable for computer scientists only. Instead the survey tries to introduce Event-Condition-Action Rules and their use for emergency management based on real-life examples from the use-cases identified in Deliverable 3.1. In this way we hope to address both, computer scientists and security experts, by showing how the Event-Condition-Action Rule technology can help to solve security issues in emergency management. The survey incorporates information from other work packages, particularly from Deliverable D3.1 and its Annexes, D4.1, D2.1 and D6.2 wherever possible

    Automated Process Discovery: A Literature Review and a Comparative Evaluation with Domain Experts

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    Äriprotsesside kaeve meetodi vĂ”imaldavad analĂŒĂŒtikul kasutada logisid saamaks teadmisi protsessi tegeliku toimise kohta. Neist meetodist ĂŒks enim uuritud on automaatne Ă€riprotsesside avastamine. SĂŒndmuste logi vĂ”etakse kui sisend automaatse Ă€riprotsesside avastamise meetodi poolt ning vĂ€ljundina toodetakse Ă€riprotsessi mudel, mis kujutab logis talletatud sĂŒndmuste kontrollvoogu. Viimase kahe kĂŒmnendi jooksul on vĂ€ljapakutud mitmeidki automaatseid Ă€riprotsessi avastamise meetodeid balansseerides erinevalt toodetavate mudelite skaleeruvuse, tĂ€psuse ning keerukuse vahel. Siiani on automaatsed Ă€riprotsesside avastamise meetodid testitud ad-hoc kombel, kus erinevad autorid kasutavad erinevaid andmestike, seadistusi, hindamismeetrikuid ning alustĂ”desid, mis viib tihti vĂ”rdlematute tulemusteni ning mĂ”nikord ka mittetaastoodetavate tulemusteni suletud andmestike kasutamise tĂ”ttu. Eelpool toodu mĂ”istes sooritatakse antud magistritöö raames sĂŒstemaatiline kirjanduse ĂŒlevaade automaatsete Ă€riprotsesside avastamise meetoditest ja ka sĂŒstemaatiline hindav vĂ”rdlus ĂŒle nelja kvaliteedimeetriku olemasolevate automaatsete Ă€riprotsesside avastamise meetodite kohta koostöös domeeniekspertidega ning kasutades reaalset logi rahvusvahelisest tarkvara firmast. Kirjanduse ĂŒlevaate ning hindamise tulemused tĂ”stavad esile puudujÀÀke ning seni uurimata kompromisse mudelite loomiseks nelja kvaliteedimeetriku kontekstis. Antud magistritöö tulemused vĂ”imaldavad teaduritel parandada puudujÀÀgid meetodites. Samuti vastatakse kĂŒsimusele automaatsete Ă€riprotsesside avastamise meetodite kasutamise kohta vĂ€ljaspool akadeemilist maailma.Process mining methods allow analysts to use logs of historical executions of business processes in order to gain knowledge about the actual performance of these processes.One of the most widely studied process mining operations is automated process discovery.An event log is taken as input by an automated process discovery method and produces a business process model as output that captures the control-flow relations between tasks that are described by the event log.Several automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models.So far, automated process discovery methods have been evaluated in an ad hoc manner, with different authors employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of non-publicly available datasets.In this setting, this thesis provides a systematic review of automated process discovery methods and a systematic comparative evaluation of existing implementations of these methods with domain experts by using a real-life event log extracted from a international software engineering company and four quality metrics.The review and evaluation results highlight gaps and unexplored tradeoffs in the field in the context of four business process model quality metrics.The results of this master thesis allows researchers to improve the lacks in the automated process discovery methods and also answers question about the usability of process discovery techniques in industry

    Learning Kit 7 : Learning Models

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    Disponible en français dans EDUQ.info sous le titre "Trousse no 7 : Modùles d’apprentissage"

    A Semantic Framework for Declarative and Procedural Knowledge

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    In any scientic domain, the full set of data and programs has reached an-ome status, i.e. it has grown massively. The original article on the Semantic Web describes the evolution of a Web of actionable information, i.e.\ud information derived from data through a semantic theory for interpreting the symbols. In a Semantic Web, methodologies are studied for describing, managing and analyzing both resources (domain knowledge) and applications (operational knowledge) - without any restriction on what and where they\ud are respectively suitable and available in the Web - as well as for realizing automatic and semantic-driven work\ud ows of Web applications elaborating Web resources.\ud This thesis attempts to provide a synthesis among Semantic Web technologies, Ontology Research, Knowledge and Work\ud ow Management. Such a synthesis is represented by Resourceome, a Web-based framework consisting of two components which strictly interact with each other: an ontology-based and domain-independent knowledge manager system (Resourceome KMS) - relying on a knowledge model where resource and operational knowledge are contextualized in any domain - and a semantic-driven work ow editor, manager and agent-based execution system (Resourceome WMS).\ud The Resourceome KMS and the Resourceome WMS are exploited in order to realize semantic-driven formulations of work\ud ows, where activities are semantically linked to any involved resource. In the whole, combining the use of domain ontologies and work ow techniques, Resourceome provides a exible domain and operational knowledge organization, a powerful engine for semantic-driven work\ud ow composition, and a distributed, automatic and\ud transparent environment for work ow execution

    Modifiability of the psychomotor domain

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    Bibliography: p. 194-225

    A Hypothesis-Frame System for Recognition Problems

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    Work reported herein was conducted at the Artificial Intelligence Laboratory, a Massachusetts Institute of Technology research program supported in part by the Advanced Research Projects Agency of the Department of Defense and monitored by the Office of Naval Research under Contract Number N00014-70-A-0362-0005.This paper proposes a new approach to a broad class of recognition problems ranging from medical diagnosis to vision. The features of this approach include a top-down hypothesize-and-test style and the use of a great deal of high-level knowledge about the subject. This knowledge is packaged into small groups of related facts and procedures called frames.MIT Artificial Intelligence Laborator

    Expert system verification and validation study: Workshop and presentation material

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    Workshop and presentation material are included. Following an introduction, the basic concepts, techniques, and guidelines are discussed. Handouts and worksheets are included
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