14,137 research outputs found
Modeling views in the layered view model for XML using UML
In data engineering, view formalisms are used to provide flexibility to users and user applications by allowing them to extract and elaborate data from the stored data sources. Conversely, since the introduction of Extensible Markup Language (XML), it is fast emerging as the dominant standard for storing, describing, and interchanging data among various web and heterogeneous data sources. In combination with XML Schema, XML provides rich facilities for defining and constraining user-defined data semantics and properties, a feature that is unique to XML. In this context, it is interesting to investigate traditional database features, such as view models and view design techniques for XML. However, traditional view formalisms are strongly coupled to the data language and its syntax, thus it proves to be a difficult task to support views in the case of semi-structured data models. Therefore, in this paper we propose a Layered View Model (LVM) for XML with conceptual and schemata extensions. Here our work is three-fold; first we propose an approach to separate the implementation and conceptual aspects of the views that provides a clear separation of concerns, thus, allowing analysis and design of views to be separated from their implementation. Secondly, we define representations to express and construct these views at the conceptual level. Thirdly, we define a view transformation methodology for XML views in the LVM, which carries out automated transformation to a view schema and a view query expression in an appropriate query language. Also, to validate and apply the LVM concepts, methods and transformations developed, we propose a view-driven application development framework with the flexibility to develop web and database applications for XML, at varying levels of abstraction
Past, present and future of information and knowledge sharing in the construction industry: Towards semantic service-based e-construction
The paper reviews product data technology initiatives in the construction sector and provides a synthesis of related ICT industry needs. A comparison between (a) the data centric characteristics of Product Data Technology (PDT) and (b) ontology with a focus on semantics, is given, highlighting the pros and cons of each approach. The paper advocates the migration from data-centric application integration to ontology-based business process support, and proposes inter-enterprise collaboration architectures and frameworks based on semantic services, underpinned by ontology-based knowledge structures. The paper discusses the main reasons behind the low industry take up of product data technology, and proposes a preliminary roadmap for the wide industry diffusion of the proposed approach. In this respect, the paper stresses the value of adopting alliance-based modes of operation
An MPEG-7 scheme for semantic content modelling and filtering of digital video
Abstract Part 5 of the MPEG-7 standard specifies Multimedia Description Schemes (MDS); that is, the format multimedia content models should conform to in order to ensure interoperability across multiple platforms and applications. However, the standard does not specify how the content or the associated model may be filtered. This paper proposes an MPEG-7 scheme which can be deployed for digital video content modelling and filtering. The proposed scheme, COSMOS-7, produces rich and multi-faceted semantic content models and supports a content-based filtering approach that only analyses content relating directly to the preferred content requirements of the user. We present details of the scheme, front-end systems used for content modelling and filtering and experiences with a number of users
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Two-fold Semantic Web service matchmaking â applying ontology mapping for service discovery
Semantic Web Services (SWS) aim at the automated discovery and orchestration of Web services on the basis of comprehensive, machine-interpretable semantic descriptions. Since SWS annotations usually are created by distinct SWS providers, semantic-level mediation, i.e. mediation between concurrent semantic representations, is a key requirement for SWS discovery. Since semantic-level mediation aims at enabling interoperability across heterogeneous semantic representations, it can be perceived as a particular instantiation of the ontology mapping problem. While recent SWS matchmakers usually rely on manual alignments or subscription to a common ontology, we propose a two-fold SWS matchmaking approach, consisting of (a) a general-purpose semantic-level mediator and (b) comparison and matchmaking of SWS capabilities. Our semantic-level mediation approach enables the implicit representation of similarities across distinct SWS by grounding service descriptions in so-called Mediation Spaces (MS). Given a set of SWS and their respective grounding, a SWS matchmaker automatically computes instance similarities across distinct SWS ontologies and matches the request to the most suitable SWS. A prototypical application illustrates our approach
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Geospatial data integration with Semantic Web services: the eMerges approach
Geographic space still lacks the semantics allowing a unified view of spatial data. Indeed, as a unique but all encompassing domain, it presents specificities that geospatial applications are still unable to handle. Moreover, to be useful, new spatial applications need to match human cognitive abilities of spatial representation and reasoning. In this context, eMerges, an approach to geospatial data integration based on Semantic Web Services (SWS), allows the unified representation and manipulation of heterogeneous spatial data sources. eMerges provides this integration by mediating legacy spatial data sources to high-level spatial ontologies through SWS and by presenting for each object context dependent affordances. This generic approach is applied here in the context of an emergency management use case developed in collaboration with emergency planners of public agencies
Towards Prescriptive Analytics in Cyber-Physical Systems
More and more of our physical world today is being monitored and controlled by so-called cyber-physical systems (CPSs). These are compositions of networked autonomous cyber and physical agents such as sensors, actuators, computational elements, and humans in the loop. Today, CPSs are still relatively small-scale and very limited compared to CPSs to be witnessed in the future. Future CPSs are expected to be far more complex, large-scale, wide-spread, and mission-critical, and found in a variety of domains such as transportation, medicine, manufacturing, and energy, where they will bring many advantages such as the increased efficiency, sustainability, reliability, and security. To unleash their full potential, CPSs need to be equipped with, among other features, the support for automated planning and control, where computing agents collaboratively and continuously plan and control their actions in an intelligent and well-coordinated manner to secure and optimize a physical process, e.g., electricity flow in the power grid.
In todayâs CPSs, the control is typically automated, but the planning is solely performed by humans. Unfortunately, it is intractable and infeasible for humans to plan every action in a future CPS due to the complexity, scale, and volatility of a physical process. Due to these properties, the control and planning has to be continuous and automated in future CPSs. Humans may only analyse and tweak the systemâs operation using the set of tools supporting prescriptive analytics that allows them (1) to make predictions, (2) to get the suggestions of the most prominent set of actions (decisions) to be taken, and (3) to analyse the implications as if such actions were taken.
This thesis considers the planning and control in the context of a large-scale multi-agent CPS. Based on the smart-grid use-case, it presents a so-called PrescriptiveCPS â which is (the conceptual model of) a multi-agent, multi-role, and multi-level CPS automatically and continuously taking and realizing decisions in near real-time and providing (human) users prescriptive analytics tools to analyse and manage the performance of the underlying physical system (or process). Acknowledging the complexity of CPSs, this thesis provides contributions at the following three levels of scale: (1) the level of a (full) PrescriptiveCPS, (2) the level of a single PrescriptiveCPS agent, and (3) the level of a component of a CPS agent software system.
At the CPS level, the contributions include the definition of PrescriptiveCPS, according to which it is the system of interacting physical and cyber (sub-)systems. Here, the cyber system consists of hierarchically organized inter-connected agents, collectively managing instances of so-called flexibility, decision, and prescription models, which are short-lived, focus on the future, and represent a capability, an (userâs) intention, and actions to change the behaviour (state) of a physical system, respectively.
At the agent level, the contributions include the three-layer architecture of an agent software system, integrating the number of components specially designed or enhanced to support the functionality of PrescriptiveCPS.
At the component level, the most of the thesis contribution is provided. The contributions include the description, design, and experimental evaluation of (1) a unified multi-dimensional schema for storing flexibility and prescription models (and related data), (2) techniques to incrementally aggregate flexibility model instances and disaggregate prescription model instances, (3) a database management system (DBMS) with built-in optimization problem solving capability allowing to formulate optimization problems using SQL-like queries and to solve them âinside a databaseâ, (4) a real-time data management architecture for processing instances of flexibility and prescription models under (soft or hard) timing constraints, and (5) a graphical user interface (GUI) to visually analyse the flexibility and prescription model instances. Additionally, the thesis discusses and exemplifies (but provides no evaluations of) (1) domain-specific and in-DBMS generic forecasting techniques allowing to forecast instances of flexibility models based on historical data, and (2) powerful ways to analyse past, current, and future based on so-called hypothetical what-if scenarios and flexibility and prescription model instances stored in a database. Most of the contributions at this level are based on the smart-grid use-case.
In summary, the thesis provides (1) the model of a CPS with planning capabilities, (2) the design and experimental evaluation of prescriptive analytics techniques allowing to effectively forecast, aggregate, disaggregate, visualize, and analyse complex models of the physical world, and (3) the use-case from the energy domain, showing how the introduced concepts are applicable in the real world. We believe that all this contribution makes a significant step towards developing planning-capable CPSs in the future.Mehr und mehr wird heute unsere physische Welt ĂŒberwacht und durch sogenannte Cyber-Physical-Systems (CPS) geregelt. Dies sind Kombinationen von vernetzten autonomen cyber und physischen Agenten wie Sensoren, Aktoren, Rechenelementen und Menschen. Heute sind CPS noch relativ klein und im Vergleich zu CPS der Zukunft sehr begrenzt. ZukĂŒnftige CPS werden voraussichtlich weit komplexer, gröĂer, weit verbreiteter und unternehmenskritischer sein sowie in einer Vielzahl von Bereichen wie Transport, Medizin, Fertigung und Energie â in denen sie viele Vorteile wie erhöhte Effizienz, Nachhaltigkeit, ZuverlĂ€ssigkeit und Sicherheit bringen â anzutreffen sein. Um ihr volles Potenzial entfalten zu können, mĂŒssen CPS unter anderem mit der UnterstĂŒtzung automatisierter Planungs- und SteuerungsfunktionalitĂ€t ausgestattet sein, so dass Agents ihre Aktionen gemeinsam und kontinuierlich auf intelligente und gut koordinierte Weise planen und kontrollieren können, um einen physischen Prozess wie den Stromfluss im Stromnetz sicherzustellen und zu optimieren.
Zwar sind in den heutigen CPS Steuerung und Kontrolle typischerweise automatisiert, aber die Planung wird weiterhin allein von Menschen durchgefĂŒhrt. Leider ist diese Aufgabe nur schwer zu bewĂ€ltigen, und es ist fĂŒr den Menschen schlicht unmöglich, jede Aktion in einem zukĂŒnftigen CPS auf Basis der KomplexitĂ€t, des Umfangs und der VolatilitĂ€t eines physikalischen Prozesses zu planen. Aufgrund dieser Eigenschaften mĂŒssen Steuerung und Planung in CPS der Zukunft kontinuierlich und automatisiert ablaufen. Der Mensch soll sich dabei ganz auf die Analyse und Einflussnahme auf das System mit Hilfe einer Reihe von Werkzeugen konzentrieren können. Derartige Werkzeuge erlauben (1) Vorhersagen, (2) VorschlĂ€ge der wichtigsten auszufĂŒhrenden Aktionen (Entscheidungen) und (3) die Analyse und potentiellen Auswirkungen der zu fĂ€llenden Entscheidungen.
Diese Arbeit beschĂ€ftigt sich mit der Planung und Kontrolle im Rahmen groĂer Multi-Agent-CPS. Basierend auf dem Smart-Grid als Anwendungsfall wird ein sogenanntes PrescriptiveCPS vorgestellt, welches einem Multi-Agent-, Multi-Role- und Multi-Level-CPS bzw. dessen konzeptionellem Modell entspricht. Diese PrescriptiveCPS treffen und realisieren automatisch und kontinuierlich Entscheidungen in naher Echtzeit und stellen Benutzern (Menschen) Prescriptive-Analytics-Werkzeuge und Verwaltung der Leistung der zugrundeliegenden physischen Systeme bzw. Prozesse zur VerfĂŒgung. In Anbetracht der KomplexitĂ€t von CPS leistet diese Arbeit BeitrĂ€ge auf folgenden Ebenen: (1) Gesamtsystem eines PrescriptiveCPS, (2) PrescriptiveCPS-Agenten und (3) Komponenten eines CPS-Agent-Software-Systems.
Auf CPS-Ebene umfassen die BeitrÀge die Definition von PrescriptiveCPS als ein System von wechselwirkenden physischen und cyber (Sub-)Systemen. Das Cyber-System besteht hierbei aus hierarchisch organisierten verbundenen Agenten, die zusammen Instanzen sogenannter Flexibility-, Decision- und Prescription-Models verwalten, welche von kurzer Dauer sind, sich auf die Zukunft konzentrieren und FÀhigkeiten, Absichten (des Benutzers) und Aktionen darstellen, die das Verhalten des physischen Systems verÀndern.
Auf Agenten-Ebene umfassen die BeitrĂ€ge die Drei-Ebenen-Architektur eines Agentensoftwaresystems sowie die Integration von Komponenten, die insbesondere zur besseren UnterstĂŒtzung der FunktionalitĂ€t von PrescriptiveCPS entwickelt wurden.
Der Schwerpunkt dieser Arbeit bilden die BeitrĂ€ge auf der Komponenten-Ebene, diese umfassen Beschreibung, Design und experimentelle Evaluation (1) eines einheitlichen multidimensionalen Schemas fĂŒr die Speicherung von Flexibility- and Prescription-Models (und verwandten Daten), (2) der Techniken zur inkrementellen Aggregation von Instanzen eines FlexibilitĂ€tsmodells und Disaggregation von Prescription-Models, (3) eines Datenbankmanagementsystem (DBMS) mit integrierter Optimierungskomponente, die es erlaubt, Optimierungsprobleme mit Hilfe von SQL-Ă€hnlichen Anfragen zu formulieren und sie âin einer Datenbank zu lösenâ, (4) einer Echtzeit-Datenmanagementarchitektur zur Verarbeitung von Instanzen der Flexibility- and Prescription-Models unter (weichen oder harten) Zeitvorgaben und (5) einer grafische BenutzeroberflĂ€che (GUI) zur Visualisierung und Analyse von Instanzen der Flexibility- and Prescription-Models. DarĂŒber hinaus diskutiert und veranschaulicht diese Arbeit beispielhaft ohne detaillierte Evaluation (1) anwendungsspezifische und im DBMS integrierte Vorhersageverfahren, die die Vorhersage von Instanzen der Flexibility- and Prescription-Models auf Basis historischer Daten ermöglichen, und (2) leistungsfĂ€hige Möglichkeiten zur Analyse von Vergangenheit, Gegenwart und Zukunft auf Basis sogenannter hypothetischer âWhat-ifâ-Szenarien und der in der Datenbank hinterlegten Instanzen der Flexibility- and Prescription-Models. Die meisten der BeitrĂ€ge auf dieser Ebene basieren auf dem Smart-Grid-Anwendungsfall.
Zusammenfassend befasst sich diese Arbeit mit (1) dem Modell eines CPS mit Planungsfunktionen, (2) dem Design und der experimentellen Evaluierung von Prescriptive-Analytics-Techniken, die eine effektive Vorhersage, Aggregation, Disaggregation, Visualisierung und Analyse komplexer Modelle der physischen Welt ermöglichen und (3) dem Anwendungsfall der EnergiedomĂ€ne, der zeigt, wie die vorgestellten Konzepte in der Praxis Anwendung finden. Wir glauben, dass diese BeitrĂ€ge einen wesentlichen Schritt in der zukĂŒnftigen Entwicklung planender CPS darstellen.Mere og mere af vores fysiske verden bliver overvĂ„get og kontrolleret af sĂ„kaldte cyber-fysiske systemer (CPSer). Disse er sammensĂŠtninger af netvĂŠrksbaserede autonome IT (cyber) og fysiske (physical) agenter, sĂ„som sensorer, aktuatorer, beregningsenheder, og mennesker. I dag er CPSer stadig forholdsvis smĂ„ og meget begrĂŠnsede i forhold til de CPSer vi kan forvente i fremtiden. Fremtidige CPSer forventes at vĂŠre langt mere komplekse, storstilede, udbredte, og missionskritiske, og vil kunne findes i en rĂŠkke omrĂ„der sĂ„som transport, medicin, produktion og energi, hvor de vil give mange fordele, sĂ„som Ăžget effektivitet, bĂŠredygtighed, pĂ„lidelighed og sikkerhed. For at frigĂžre CPSernes fulde potentiale, skal de bl.a. udstyres med stĂžtte til automatiseret planlĂŠgning og kontrol, hvor beregningsagenter i samspil og lĂžbende planlĂŠgger og styrer deres handlinger pĂ„ en intelligent og velkoordineret mĂ„de for at sikre og optimere en fysisk proces, sĂ„som elforsyningen i elnettet.
I nuvÊrende CPSer er styringen typisk automatiseret, mens planlÊgningen udelukkende er foretaget af mennesker. Det er umuligt for mennesker at planlÊgge hver handling i et fremtidigt CPS pÄ grund af kompleksiteten, skalaen, og omskifteligheden af en fysisk proces. PÄ grund af disse egenskaber, skal kontrol og planlÊgning vÊre kontinuerlig og automatiseret i fremtidens CPSer. Mennesker kan kun analysere og justere systemets drift ved hjÊlp af det sÊt af vÊrktÞjer, der understÞtter prÊskriptive analyser (prescriptive analytics), der giver dem mulighed for (1) at lave forudsigelser, (2) at fÄ forslagene fra de mest fremtrÊdende sÊt handlinger (beslutninger), der skal tages, og (3) at analysere konsekvenserne, hvis sÄdanne handlinger blev udfÞrt.
Denne afhandling omhandler planlÊgning og kontrol i forbindelse med store multi-agent CPSer. Baseret pÄ en smart-grid use case, prÊsenterer afhandlingen det sÄkaldte PrescriptiveCPS hvilket er (den konceptuelle model af) et multi-agent, multi-rolle, og multi-level CPS, der automatisk og kontinuerligt tager beslutninger i nÊr-realtid og leverer (menneskelige) brugere prÊskriptiveanalysevÊrktÞjer til at analysere og hÄndtere det underliggende fysiske system (eller proces).
I erkendelse af kompleksiteten af CPSer, giver denne afhandling bidrag til fĂžlgende tre niveauer: (1) niveauet for et (fuldt) PrescriptiveCPS,
(2) niveauet for en enkelt PrescriptiveCPS agent, og (3) niveauet for en komponent af et CPS agent software system.
PÄ CPS-niveau, omfatter bidragene definitionen af PrescriptiveCPS, i henhold til hvilken det er det system med interagerende fysiske- og IT- (under-) systemer. Her bestÄr IT-systemet af hierarkisk organiserede forbundne agenter der sammen styrer instanser af sÄkaldte fleksibilitet (flexibility), beslutning (decision) og prÊskriptive (prescription) modeller, som henholdsvis er kortvarige, fokuserer pÄ fremtiden, og reprÊsenterer en kapacitet, en (brugers) intention, og mÄder til at Êndre adfÊrd (tilstand) af et fysisk system.
PĂ„ agentniveau omfatter bidragene en tre-lags arkitektur af et agent software system, der integrerer antallet af komponenter, der er specielt konstrueret eller udbygges til at understĂžtte funktionaliteten af PrescriptiveCPS.
Komponentniveauet er hvor afhandlingen har sit hovedbidrag. Bidragene omfatter beskrivelse, design og eksperimentel evaluering af (1) et samlet multi- dimensionelt skema til at opbevare fleksibilitet og prÊskriptive modeller (og data), (2) teknikker til trinvis aggregering af fleksibilitet modelinstanser og disaggregering af prÊskriptive modelinstanser (3) et database management system (DBMS) med indbygget optimeringsproblemlÞsning (optimization problem solving) der gÞr det muligt at formulere optimeringsproblemer ved hjÊlp af SQL-lignende forespÞrgsler og at lÞse dem "inde i en database", (4) en realtids data management arkitektur til at behandle instanser af fleksibilitet og prÊskriptive modeller under (blÞde eller hÄrde) tidsbegrÊnsninger, og (5) en grafisk brugergrÊnseflade (GUI) til visuelt at analysere fleksibilitet og prÊskriptive modelinstanser. Derudover diskuterer og eksemplificerer afhandlingen (men giver ingen evalueringer af) (1) domÊne-specifikke og in-DBMS generiske prognosemetoder der gÞr det muligt at forudsige instanser af fleksibilitet modeller baseret pÄ historiske data, og (2) kraftfulde mÄder at analysere tidligere-, nutids- og fremtidsbaserede sÄkaldte hypotetiske hvad-hvis scenarier og fleksibilitet og prÊskriptive modelinstanser gemt i en database. De fleste af bidragene pÄ dette niveau er baseret pÄ et smart-grid brugsscenarie.
Sammenfattende giver afhandlingen (1) modellen for et CPS med planlÊgningsmulighed, (2) design og eksperimentel evaluering af prÊskriptive analyse teknikker der gÞr det muligt effektivt at forudsige, aggregere, disaggregere, visualisere og analysere komplekse modeller af den fysiske verden, og (3) brugsscenariet fra energiomrÄdet, der viser, hvordan de indfÞrte begreber kan anvendes i den virkelige verden. Vi mener, at dette bidrag udgÞr et betydeligt skridt i retning af at udvikle CPSer til planlÊgningsbrug i fremtiden
Early aspects: aspect-oriented requirements engineering and architecture design
This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications
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