Global ETD Search

1	A framework for event classification in Tweets based on hybrid semantic enrichment / Um framework para classificação de eventos em tweets baseado em enriquecimento semântico híbrido Romero, Simone Aparecida Pinto January 2017 (has links) As plataformas de Mídias Sociais se tornaram um meio essencial para a disponibilização de informações. Dentre elas, o Twitter tem se destacado, devido ao grande volume de mensagens que são compartilhadas todos os dias, principalmente mencionando eventos ao redor do mundo. Tais mensagens são uma importante fonte de informação e podem ser utilizadas em diversas aplicações. Contudo, a classificação de texto em tweets é uma tarefa não trivial. Além disso, não há um consenso quanto à quais tarefas devem ser executadas para Identificação e Classificação de Eventos em tweets, uma vez que as abordagens existentes trabalham com tipos específicos de eventos e determinadas suposições, que dificultam a reprodução e a comparação dessas abordagens em eventos de natureza distinta. Neste trabalho, nós elaboramos um framework para a classificação de eventos de natureza distinta. O framework possui os seguintes elementos chave: a) enriquecimento externo a partir da exploração de páginas web relacionadas, como uma forma de complementar a extração de features conceituais do conteúdo dos tweets; b) enriquecimento semântico utilizando recursos da Linked Open Data cloud para acrescentar features semânticas relacionadas; e c) técnica de poda para selecionar as features semânticas mais discriminativas Nós avaliamos o framework proposto através de um vasto conjunto de experimentos, que incluem: a) sete eventos alvos de natureza distinta; b) diferentes combinações das features conceituais propostas (i.e. entidades, vocabulário, e a combinação de ambos); c) estratégias distintas para a extração de features (i.e. a partir do conteúdo dos tweets e das páginas web); d) diferentes métodos para a seleção das features semânticas mais relevantes de acordo com o domínio (i.e. poda, seleção de features, e a combinação de ambos); e) dois algoritmos de classificação. Nós também comparamos o desempenho do framework em relação a outro método utilização para o enriquecimento contextual, o qual tem como base word embeddings. Os resultados mostraram as vantagens da utilização do framework proposto e que a nossa solução é factível e generalizável, dando suporte a classificação de diferentes tipos de eventos. / Social Media platforms have become key as a means of spreading information, opinions or awareness about real-world events. Twitter stands out due to the huge volume of messages about all sorts of topics posted every day. Such messages are an important source of useful information about events, presenting many useful applications (e.g. the detection of breaking news, real-time awareness, updates about events). However, text classification on Twitter is by no means a trivial task that can be handled by conventional Natural Language Processing techniques. In addition, there is no consensus about the definition of which kind of tasks are executed in the Event Identification and Classification in tweets, since existing approaches often focus on specific types of events, based on specific assumptions, which makes it difficult to reproduce and compare these approaches in events of distinct natures. In this work, we aim at building a unifying framework that is suitable for the classification of events of distinct natures. The framework has as key elements: a) external enrichment using related web pages for extending the conceptual features contained within the tweets; b) semantic enrichment using the Linked Open Data cloud to add related semantic features; and c) a pruning technique that selects the semantic features with discriminative potential We evaluated our proposed framework using a broad experimental setting, that includes: a) seven target events of different natures; b) different combinations of the conceptual features proposed (i.e. entities, vocabulary and their combination); c) distinct feature extraction strategies (i.e. from tweet text and web related documents); d) different methods for selecting the discriminative semantic features (i.e. pruning, feature selection, and their combination); and e) two classification algorithms. We also compared the proposed framework against another kind of contextual enrichment based on word embeddings. The results showed the advantages of using the proposed framework, and that our solution is a feasible and generalizable method to support the classification of distinct event types. Web semântica Redes sociais Recuperacao : Informacao Semantic web DBPedia LOD Twitter Event classification
2	Unusual Patterns of Seismicity during Eruptive and Non-eruptive Periods at the Persistently Restless Telica Volcano, Nicaragua Rodgers, Melanie 01 January 2013 (has links) Telica Volcano, Nicaragua, is a persistently restless volcano with high rates of seismicity that can vary from less than ten events to over a thousand events per day. Low-frequency (LF) events dominate the seismic catalogue and seismicity rates at Telica show little clear correlation with periods of eruption. As such, traditional methods of forecasting of volcanic activity based on increases in seismicity and recognition of LF activity are not applicable. A single seismic station has been operating at Telica since 1993, and in 2010 we installed a broadband seismic and continuous GPS network (TESAND network) at Telica. In this study we investigate the seismic characteristics surrounding a nine-month period of phreatic to phreatomagmatic explosions in 1999, and also from the initial three-and-a-half year deployment of the TESAND network, including a three-month phreatic vulcanian eruptive period in 2011. We demonstrate that pertinent information can be obtained from analysis of single-station data, and while large seismic networks are preferable when possible, we note that for many volcanoes this is not possible. We find unusual patterns of seismicity before both eruptive periods; rather than a precursory increase in seismicity as is observed prior to many volcanic eruptions, we observe a decrease in seismicity many months prior to eruption. We developed a new program for cross-correlation of large seismic data catalogues and analysed multiplet activity surrounding both eruptive periods. We observed that the formation of new multiplets corresponds to periods of high event rates (during inter-eruptive periods) and high percentages of daily events that belong to a multiplet. We propose a model for the seismicity patterns observed at Telica, where changes in seismicity are related to a cyclic transition between open-system degassing and closed-system degassing. Periods of open-system degassing occur during non-eruptive episodes and are characterised by high event rates, a broad range of frequency content of events and high degrees of waveform correlation. A transition to closed-system degassing could be due to sealing of fluid pathways in the magmatic and/or hydrothermal system, or due to magma withdrawal. Periods of closed-system degassing are characterised by low event rates, higher frequency contents and low degrees of waveform correlation. Eruptive periods may then represent a transition from closed-system degassing to open-system degassing, however the system must also be capable of transitioning to open-system degassing without eruption. These observations have important implications for volcano monitoring and eruption forecasting at persistently restless volcanoes. Rather than a precursory increase in seismicity as is often observed prior to eruption at other volcanoes, our observations indicate that phreatic eruptions at Telica occur after a decrease in seismicity, a corresponding change in the frequency content of events, and a decrease in waveform correlation. These changes may represent a period of closed-system degassing that could culminate in phreatic eruptions. The inclusion of real-time analysis of variations in frequency content and multiplet activity provides critical information for volcano monitoring institutions. Low-frequency earthquakes Multiplet analysis Phreatic eruption Seismic event classification Volcano monitoring Geophysics and Seismology
3	A framework for event classification in Tweets based on hybrid semantic enrichment / Um framework para classificação de eventos em tweets baseado em enriquecimento semântico híbrido Romero, Simone Aparecida Pinto January 2017 (has links) As plataformas de Mídias Sociais se tornaram um meio essencial para a disponibilização de informações. Dentre elas, o Twitter tem se destacado, devido ao grande volume de mensagens que são compartilhadas todos os dias, principalmente mencionando eventos ao redor do mundo. Tais mensagens são uma importante fonte de informação e podem ser utilizadas em diversas aplicações. Contudo, a classificação de texto em tweets é uma tarefa não trivial. Além disso, não há um consenso quanto à quais tarefas devem ser executadas para Identificação e Classificação de Eventos em tweets, uma vez que as abordagens existentes trabalham com tipos específicos de eventos e determinadas suposições, que dificultam a reprodução e a comparação dessas abordagens em eventos de natureza distinta. Neste trabalho, nós elaboramos um framework para a classificação de eventos de natureza distinta. O framework possui os seguintes elementos chave: a) enriquecimento externo a partir da exploração de páginas web relacionadas, como uma forma de complementar a extração de features conceituais do conteúdo dos tweets; b) enriquecimento semântico utilizando recursos da Linked Open Data cloud para acrescentar features semânticas relacionadas; e c) técnica de poda para selecionar as features semânticas mais discriminativas Nós avaliamos o framework proposto através de um vasto conjunto de experimentos, que incluem: a) sete eventos alvos de natureza distinta; b) diferentes combinações das features conceituais propostas (i.e. entidades, vocabulário, e a combinação de ambos); c) estratégias distintas para a extração de features (i.e. a partir do conteúdo dos tweets e das páginas web); d) diferentes métodos para a seleção das features semânticas mais relevantes de acordo com o domínio (i.e. poda, seleção de features, e a combinação de ambos); e) dois algoritmos de classificação. Nós também comparamos o desempenho do framework em relação a outro método utilização para o enriquecimento contextual, o qual tem como base word embeddings. Os resultados mostraram as vantagens da utilização do framework proposto e que a nossa solução é factível e generalizável, dando suporte a classificação de diferentes tipos de eventos. / Social Media platforms have become key as a means of spreading information, opinions or awareness about real-world events. Twitter stands out due to the huge volume of messages about all sorts of topics posted every day. Such messages are an important source of useful information about events, presenting many useful applications (e.g. the detection of breaking news, real-time awareness, updates about events). However, text classification on Twitter is by no means a trivial task that can be handled by conventional Natural Language Processing techniques. In addition, there is no consensus about the definition of which kind of tasks are executed in the Event Identification and Classification in tweets, since existing approaches often focus on specific types of events, based on specific assumptions, which makes it difficult to reproduce and compare these approaches in events of distinct natures. In this work, we aim at building a unifying framework that is suitable for the classification of events of distinct natures. The framework has as key elements: a) external enrichment using related web pages for extending the conceptual features contained within the tweets; b) semantic enrichment using the Linked Open Data cloud to add related semantic features; and c) a pruning technique that selects the semantic features with discriminative potential We evaluated our proposed framework using a broad experimental setting, that includes: a) seven target events of different natures; b) different combinations of the conceptual features proposed (i.e. entities, vocabulary and their combination); c) distinct feature extraction strategies (i.e. from tweet text and web related documents); d) different methods for selecting the discriminative semantic features (i.e. pruning, feature selection, and their combination); and e) two classification algorithms. We also compared the proposed framework against another kind of contextual enrichment based on word embeddings. The results showed the advantages of using the proposed framework, and that our solution is a feasible and generalizable method to support the classification of distinct event types. Web semântica Redes sociais Recuperacao : Informacao Semantic web DBPedia LOD Twitter Event classification
4	A framework for event classification in Tweets based on hybrid semantic enrichment / Um framework para classificação de eventos em tweets baseado em enriquecimento semântico híbrido Romero, Simone Aparecida Pinto January 2017 (has links) As plataformas de Mídias Sociais se tornaram um meio essencial para a disponibilização de informações. Dentre elas, o Twitter tem se destacado, devido ao grande volume de mensagens que são compartilhadas todos os dias, principalmente mencionando eventos ao redor do mundo. Tais mensagens são uma importante fonte de informação e podem ser utilizadas em diversas aplicações. Contudo, a classificação de texto em tweets é uma tarefa não trivial. Além disso, não há um consenso quanto à quais tarefas devem ser executadas para Identificação e Classificação de Eventos em tweets, uma vez que as abordagens existentes trabalham com tipos específicos de eventos e determinadas suposições, que dificultam a reprodução e a comparação dessas abordagens em eventos de natureza distinta. Neste trabalho, nós elaboramos um framework para a classificação de eventos de natureza distinta. O framework possui os seguintes elementos chave: a) enriquecimento externo a partir da exploração de páginas web relacionadas, como uma forma de complementar a extração de features conceituais do conteúdo dos tweets; b) enriquecimento semântico utilizando recursos da Linked Open Data cloud para acrescentar features semânticas relacionadas; e c) técnica de poda para selecionar as features semânticas mais discriminativas Nós avaliamos o framework proposto através de um vasto conjunto de experimentos, que incluem: a) sete eventos alvos de natureza distinta; b) diferentes combinações das features conceituais propostas (i.e. entidades, vocabulário, e a combinação de ambos); c) estratégias distintas para a extração de features (i.e. a partir do conteúdo dos tweets e das páginas web); d) diferentes métodos para a seleção das features semânticas mais relevantes de acordo com o domínio (i.e. poda, seleção de features, e a combinação de ambos); e) dois algoritmos de classificação. Nós também comparamos o desempenho do framework em relação a outro método utilização para o enriquecimento contextual, o qual tem como base word embeddings. Os resultados mostraram as vantagens da utilização do framework proposto e que a nossa solução é factível e generalizável, dando suporte a classificação de diferentes tipos de eventos. / Social Media platforms have become key as a means of spreading information, opinions or awareness about real-world events. Twitter stands out due to the huge volume of messages about all sorts of topics posted every day. Such messages are an important source of useful information about events, presenting many useful applications (e.g. the detection of breaking news, real-time awareness, updates about events). However, text classification on Twitter is by no means a trivial task that can be handled by conventional Natural Language Processing techniques. In addition, there is no consensus about the definition of which kind of tasks are executed in the Event Identification and Classification in tweets, since existing approaches often focus on specific types of events, based on specific assumptions, which makes it difficult to reproduce and compare these approaches in events of distinct natures. In this work, we aim at building a unifying framework that is suitable for the classification of events of distinct natures. The framework has as key elements: a) external enrichment using related web pages for extending the conceptual features contained within the tweets; b) semantic enrichment using the Linked Open Data cloud to add related semantic features; and c) a pruning technique that selects the semantic features with discriminative potential We evaluated our proposed framework using a broad experimental setting, that includes: a) seven target events of different natures; b) different combinations of the conceptual features proposed (i.e. entities, vocabulary and their combination); c) distinct feature extraction strategies (i.e. from tweet text and web related documents); d) different methods for selecting the discriminative semantic features (i.e. pruning, feature selection, and their combination); and e) two classification algorithms. We also compared the proposed framework against another kind of contextual enrichment based on word embeddings. The results showed the advantages of using the proposed framework, and that our solution is a feasible and generalizable method to support the classification of distinct event types. Web semântica Redes sociais Recuperacao : Informacao Semantic web DBPedia LOD Twitter Event classification
5	Real-time Audio Classification onan Edge Device : Using YAMNet and TensorFlow Lite Malmberg, Christoffer January 2021 (has links) Edge computing is the idea of moving computations away from the cloud andinstead perform them at the edge of the network. The benefits of edge computing arereduced latency, increased integrity, and less strain on networks. Edge AI is the practiceof deploying machine learning algorithms to perform computations on the edge.In this project, a pre-trained model YAMNet is retrained and used to perform audioclassification in real-time to detect gunshots, glass shattering, and speech. The modelis deployed onto the edge device both as a full TensorFlow model and as TensorFlowLite models. Comparing results of accuracy, inference time, and memory allocationfor full TensorFlow and TensorFlow Lite models with and without optimization. Resultsfrom this research were that it was a valid option to use both TensorFlow andTensorFlow Lite but there was a lot of performance to gain by using TensorFlow Litewith little downside. Audio Event classification edge device audio classification YAMNet TensorFlow Lite comparison Computer Sciences Datavetenskap (datalogi)
6	Studies on Fundamental Problems in Event-Level Language Analysis / イベントレベルの言語解析における基礎的課題に関する研究 Kiyomaru, Hirokazu 23 March 2022 (has links) 京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24029号 / 情博第785号 / 新制\|\|情\|\|133(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授黒橋禎夫, 教授河原達也, 教授鹿島久嗣 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM natural language processing event classification event-to-event relation analysis event prediction 007
7	Vibration Event Detection and Classification in an Instrumented Building Hupfeldt, William George 23 February 2022 (has links) Accelerometers deployed within smart structures produce a wealth of vibration data that can be analyzed to infer information about the types of acceleration events that are occurring within the structure. In the case of monitored smart buildings, some of these acceleration events are linked to occupant behavior, such as walking, operating machinery, closing doors, etc. The identification and classification of such events has many potential applications within a smart structure or city. Understanding occupant patterns could be beneficial for operations, retail, or HVAC management, as it could be used to monitor occupancy flow with a relatively sparse sensor network. It may also have detrimental implications in terms of cybersecurity, where such information could be mined for malicious practices if unauthorized access to the data was obtained. This work presents methods for the detection and classification of vibration events in an experimental smart building, Goodwin Hall at Virginia Tech. Goodwin Hall's 200+ accelerometer network is used to gather acceleration data, from which vibration events are automatically detected and clustered. The presence of a vibration event is detected from a raw acceleration signal with an adaptive RMS threshold method. A feature vector is then created for each extracted event as areas under regions of the FFT of the event's acceleration signal. The feature vectors are then mapped into a low-dimensional space using principal component analysis, where they are clustered with various unsupervised algorithms. These processes have shown to be successful when gathering vibration events from a single-sensor setup, but pose challenges when expanded to a multi-sensor network. Because of this, expanded applications such as a semi-supervised classifier for events detected anywhere in the building are currently still under development. This semi-supervised process, combined with the known location of each sensor would allow inferences to be drawn about the frequency of different activity types in regions of the building not captured in the labeled data. Future work intends to address these multi-sensor challenges with adjustments to the algorithm process. / Master of Science / All objects experience vibrations when they are disturbed by some force. In the case of this work, the object is complex, a classroom building, but the principle still stands. When the building is disturbed by a force it will vibrate, even if the force is small, such as a person walking down a hallway or closing a door. The vibrations caused by these 'events' are unique to the type of event, that is, footstep vibrations will be different from door vibrations. These vibrations are observed with accelerometers, and the corresponding signal is used to determine what type of event caused the vibration. First, an event is automatically detected within the signal and separated from it. Second, characteristics unique to the signal are identified, a process known as 'feature extraction.' Finally, those features are used to distinguish the event from others and to identify what had caused it based on previous experimental data. The ability to detect these events and classify them introduces many interesting applications, including any that would stem from occupant detection, including improved security or operations, retail, or HVAC management. The methods here may also be applicable to other applications, such as monitoring bridges and machinery, or for developing cutting-edge smartphone applications with the accelerometer that is built in. Unsupervised Classification Event Detection Event Classification Occupancy Monitoring Distributed Sensor Systems Smart Buildings
8	Table tennis event detection and classification Oldham, Kevin M. January 2015 (has links) It is well understood that multiple video cameras and computer vision (CV) technology can be used in sport for match officiating, statistics and player performance analysis. A review of the literature reveals a number of existing solutions, both commercial and theoretical, within this domain. However, these solutions are expensive and often complex in their installation. The hypothesis for this research states that by considering only changes in ball motion, automatic event classification is achievable with low-cost monocular video recording devices, without the need for 3-dimensional (3D) positional ball data and representation. The focus of this research is a rigorous empirical study of low cost single consumer-grade video camera solutions applied to table tennis, confirming that monocular CV based detected ball location data contains sufficient information to enable key match-play events to be recognised and measured. In total a library of 276 event-based video sequences, using a range of recording hardware, were produced for this research. The research has four key considerations: i) an investigation into an effective recording environment with minimum configuration and calibration, ii) the selection and optimisation of a CV algorithm to detect the ball from the resulting single source video data, iii) validation of the accuracy of the 2-dimensional (2D) CV data for motion change detection, and iv) the data requirements and processing techniques necessary to automatically detect changes in ball motion and match those to match-play events. Throughout the thesis, table tennis has been chosen as the example sport for observational and experimental analysis since it offers a number of specific CV challenges due to the relatively high ball speed (in excess of 100kph) and small ball size (40mm in diameter). Furthermore, the inherent rules of table tennis show potential for a monocular based event classification vision system. As the initial stage, a proposed optimum location and configuration of the single camera is defined. Next, the selection of a CV algorithm is critical in obtaining usable ball motion data. It is shown in this research that segmentation processes vary in their ball detection capabilities and location out-puts, which ultimately affects the ability of automated event detection and decision making solutions. Therefore, a comparison of CV algorithms is necessary to establish confidence in the accuracy of the derived location of the ball. As part of the research, a CV software environment has been developed to allow robust, repeatable and direct comparisons between different CV algorithms. An event based method of evaluating the success of a CV algorithm is proposed. Comparison of CV algorithms is made against the novel Efficacy Metric Set (EMS), producing a measurable Relative Efficacy Index (REI). Within the context of this low cost, single camera ball trajectory and event investigation, experimental results provided show that the Horn-Schunck Optical Flow algorithm, with a REI of 163.5 is the most successful method when compared to a discrete selection of CV detection and extraction techniques gathered from the literature review. Furthermore, evidence based data from the REI also suggests switching to the Canny edge detector (a REI of 186.4) for segmentation of the ball when in close proximity to the net. In addition to and in support of the data generated from the CV software environment, a novel method is presented for producing simultaneous data from 3D marker based recordings, reduced to 2D and compared directly to the CV output to establish comparative time-resolved data for the ball location. It is proposed here that a continuous scale factor, based on the known dimensions of the ball, is incorporated at every frame. Using this method, comparison results show a mean accuracy of 3.01mm when applied to a selection of nineteen video sequences and events. This tolerance is within 10% of the diameter of the ball and accountable by the limits of image resolution. Further experimental results demonstrate the ability to identify a number of match-play events from a monocular image sequence using a combination of the suggested optimum algorithm and ball motion analysis methods. The results show a promising application of 2D based CV processing to match-play event classification with an overall success rate of 95.9%. The majority of failures occur when the ball, during returns and services, is partially occluded by either the player or racket, due to the inherent problem of using a monocular recording device. Finally, the thesis proposes further research and extensions for developing and implementing monocular based CV processing of motion based event analysis and classification in a wider range of applications. 621.36
9	Supply Chain Event Management – Bedarf, Systemarchitektur und Nutzen aus Perspektive fokaler Unternehmen der Modeindustrie Tröger, Ralph 10 November 2014 (has links) (PDF) Supply Chain Event Management (SCEM) bezeichnet eine Teildisziplin des Supply Chain Management und ist für Unternehmen ein Ansatzpunkt, durch frühzeitige Reaktion auf kritische Ausnahmeereignisse in der Wertschöpfungskette Logistikleistung und -kosten zu optimieren. Durch Rahmenbedingungen wie bspw. globale Logistikstrukturen, eine hohe Artikelvielfalt und volatile Geschäftsbeziehungen zählt die Modeindustrie zu den Branchen, die für kritische Störereignisse besonders anfällig ist. In diesem Sinne untersucht die vorliegende Dissertation nach einer Beleuchtung der wesentlichen Grundlagen zunächst, inwiefern es in der Modeindustrie tatsächlich einen Bedarf an SCEM-Systemen gibt. Anknüpfend daran zeigt sie nach einer Darstellung bisheriger SCEM-Architekturkonzepte Gestaltungsmöglichkeiten für eine Systemarchitektur auf, die auf den Designprinzipien der Serviceorientierung beruht. In diesem Rahmen erfolgt u. a. auch die Identifikation SCEM-relevanter Business Services. Die Vorzüge einer serviceorientierten Gestaltung werden detailliert anhand der EPCIS (EPC Information Services)-Spezifikation illustriert. Abgerundet wird die Arbeit durch eine Betrachtung der Nutzenpotenziale von SCEM-Systemen. Nach einer Darstellung von Ansätzen, welche zur Nutzenbestimmung infrage kommen, wird der Nutzen anhand eines Praxisbeispiels aufgezeigt und fließt zusammen mit den Ergebnissen einer Literaturrecherche in eine Konsolidierung von SCEM-Nutzeffekten. Hierbei wird auch beleuchtet, welche zusätzlichen Vorteile sich für Unternehmen durch eine serviceorientierte Architekturgestaltung bieten. In der Schlussbetrachtung werden die wesentlichen Erkenntnisse der Arbeit zusammengefasst und in einem Ausblick sowohl beleuchtet, welche Relevanz die Ergebnisse der Arbeit für die Bewältigung künftiger Herausforderungen innehaben als auch welche Anknüpfungspunkte sich für anschließende Forschungsarbeiten ergeben. SCEM Supply Chain Event Management SOA Serviceorientierte Architektur EPCIS EPC Information Services Modeindustrie Bedarf Architektur Systemarchitektur Nutzen SCEM-System Business Services Geschäftsorientierte Services Supply Chain Management SCM Logistik Fokale Unternehmen Bekleidungsindustrie Textilindustrie Logistikleistung Logistikkosten Simulation Service Science Service Design Event Kritisches Ausnahmeereignis Störung Störereignis Lieferkette Wertschöpfungskette Fallstudie EPC Electronic Product Code Cloud Computing Cloud-Infrastruktur Cloud-ERP Service-Identifikation Service-Beschreibung Gerry Weber van Laack Gardeur Seidensticker ThyssenKrupp Deutsche Post DHL fTRACE GS1 GS1 Germany René Lezard März München Kybernetischer Regelkreis Management by Exception Supply Chain Risk Management Supply Network Event Management SNEM Service-Repository Service-Registry Original Brand Manufacturer OBM SaaS Software as a Service RFID Radio Frequency Identification Komplexität Störungsanfälligkeit Nutzenpotenzial SCEM-Lösung Value Chain Integrator Transaktionskostentheorie Ressourcenbasierter Ansatz Theorie der informatorischen Kaskaden Systemtheorie Prinzipal-Agent-Theorie Theorien zur Informationsverarbeitung Nutzenquantifizierung Auto-ID Automatische Identifikation EDI Electronic Data Interchange Event-Typisierung SCEM-Funktionen SCEM-Funktionalitäten Geschäftsprozesse Servicekatalog Servicekandidat Servicespezifikation Standard Schnittstelle Überwachen Melden Steuern Messen Simulieren FMEA Fehlermöglichkeits- und Einflussanalyse SCEM-FMEA Ereignisorientierte Simulation Konfiguration SCEM Supply chain event management SOA Service-oriented architecture EPCIS EPC information services Fashion industry Need Business need Architecture System architecture Benefit SCEM system Business services Business-oriented services Supply chain management SCM Logistics Apparel industry Textile industry Logistics performance Logistics costs Simulation Service science Service design Event Critical exception Deviation Disruption Disruptive event Supply chain Value chain Case study EPC Electronic product code Cloud computing Cloud infrastructure Cloud ERP Service identification Service description Gerry Weber van Laack Gardeur Seidensticker ThyssenKrupp Deutsche Post DHL fTRACE GS1 GS1 Germany René Lezard März München Control system Management by exception Supply chain risk management Supply network event management SNEM Service repository Service registry Original brand manufacturer OBM SaaS Software as a Service RFID Radio Frequency Identification Complexity Vulnerability to disruptions Potential SCEM solution Value chain integrator Transaction cost theory Resource-based view Information cascades System theory Principal-agent theory Information processing AIDC EDI Electronic data interchange Event classification SCEM functions SCEM functionalities Business processes Service catalogue Service candidate Service specification Standard Interface Monitor Notify Simulate Control Measure FMEA Failure mode and effects analysis SCEM FMEA Discrete event simulation Configuration ddc:330 ddc:000 ddc:004 ddc:650
10	Supply Chain Event Management – Bedarf, Systemarchitektur und Nutzen aus Perspektive fokaler Unternehmen der Modeindustrie Tröger, Ralph 17 October 2014 (has links) Supply Chain Event Management (SCEM) bezeichnet eine Teildisziplin des Supply Chain Management und ist für Unternehmen ein Ansatzpunkt, durch frühzeitige Reaktion auf kritische Ausnahmeereignisse in der Wertschöpfungskette Logistikleistung und -kosten zu optimieren. Durch Rahmenbedingungen wie bspw. globale Logistikstrukturen, eine hohe Artikelvielfalt und volatile Geschäftsbeziehungen zählt die Modeindustrie zu den Branchen, die für kritische Störereignisse besonders anfällig ist. In diesem Sinne untersucht die vorliegende Dissertation nach einer Beleuchtung der wesentlichen Grundlagen zunächst, inwiefern es in der Modeindustrie tatsächlich einen Bedarf an SCEM-Systemen gibt. Anknüpfend daran zeigt sie nach einer Darstellung bisheriger SCEM-Architekturkonzepte Gestaltungsmöglichkeiten für eine Systemarchitektur auf, die auf den Designprinzipien der Serviceorientierung beruht. In diesem Rahmen erfolgt u. a. auch die Identifikation SCEM-relevanter Business Services. Die Vorzüge einer serviceorientierten Gestaltung werden detailliert anhand der EPCIS (EPC Information Services)-Spezifikation illustriert. Abgerundet wird die Arbeit durch eine Betrachtung der Nutzenpotenziale von SCEM-Systemen. Nach einer Darstellung von Ansätzen, welche zur Nutzenbestimmung infrage kommen, wird der Nutzen anhand eines Praxisbeispiels aufgezeigt und fließt zusammen mit den Ergebnissen einer Literaturrecherche in eine Konsolidierung von SCEM-Nutzeffekten. Hierbei wird auch beleuchtet, welche zusätzlichen Vorteile sich für Unternehmen durch eine serviceorientierte Architekturgestaltung bieten. In der Schlussbetrachtung werden die wesentlichen Erkenntnisse der Arbeit zusammengefasst und in einem Ausblick sowohl beleuchtet, welche Relevanz die Ergebnisse der Arbeit für die Bewältigung künftiger Herausforderungen innehaben als auch welche Anknüpfungspunkte sich für anschließende Forschungsarbeiten ergeben. info:eu-repo/classification/ddc/330 ddc:330 info:eu-repo/classification/ddc/000 ddc:000 info:eu-repo/classification/ddc/004 ddc:004 info:eu-repo/classification/ddc/650 ddc:650

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