The Emergency Care Clinical Decision Support System (EC-CDSS) has proven to improve the quality of the Emergency Care System (ECS), which is crucial for providing timely life-saving care. The literature lacks a data p...
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ISBN:
(纸本)9780998133171
The Emergency Care Clinical Decision Support System (EC-CDSS) has proven to improve the quality of the Emergency Care System (ECS), which is crucial for providing timely life-saving care. The literature lacks a data processing architecture for an integrated EC-CDSS that can fulfill all quality attributes while satisfying all stakeholders' information needs. To address this literature gap, this study designs a new data processing architecture, called PICT-DPA. The PICT-DPA was evaluated by its instantiation of a PICT-enabled EDSS and user interviews. Results demonstrate that the PICT-DPA improves quality attributes and meets stakeholders' information needs. The design process of the PICT-DPA shows the importance of understanding the research domain, integrating the theoretical foundations, and iterative design. Furthermore, the PICT-DPA can enhance the capabilities of dataprocessing tasks in any domain with similar quality attribute requirements.
The development of industry 4.0 has spurred the transformation of traditional manufacturing into modern industrial Internet-of-Things. The most notable feature during this transition is the improvement of digitization...
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The development of industry 4.0 has spurred the transformation of traditional manufacturing into modern industrial Internet-of-Things. The most notable feature during this transition is the improvement of digitization and intelligence based on the massive data drives. In such a data-driven environment, the processing, storage, and utilization of the industry data get more and more important. Usually, the traditional data processing architecture runs as a one-way streamline, which cannot adapt to the different requirements of the multi-scenario application. This paper proposed a new industrial big data processing architecture called Phi architecture, which can realize many functions such as batch dataprocessing and stream dataprocessing, distributed storage and access, and real-time control. Compared with other data processing architecture, the Phi architecture combined with edge computing and feedback control has the ability to deal with the different demands in aviation manufacturing. Next, the new architecture is designed for microservices pattern, which improves the flexibility and stability of the architecture, and makes it independent operated in multi-scenarios, such as state monitoring of workshop, adaptive data acquisition, feedback control, and user-oriented information classification. As a proof of concept, the architecture has been tested in a simulation digital manufacturing workshop. The results verify the improved effectiveness of the Phi architecture on the data feedback control and real-time processing. And, the development of microservices architecture greatly improves the efficiency, adaptability, and extensibility of the manufacturing process.
With the rapid deployment of a number of sensors, it is crucial to efficiently manage their data streams with heterogeneous properties. To achieve various sensor applications such as discovery and mashup, a method of ...
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ISBN:
(纸本)9781467366564
With the rapid deployment of a number of sensors, it is crucial to efficiently manage their data streams with heterogeneous properties. To achieve various sensor applications such as discovery and mashup, a method of retrieving meaningful information from raw sensor data is required. However, it is hard to analyze and represent the sensor data since sensors generate streaming data of different patterns and continuously transmit the observations to servers in real-time. In this paper, we propose a sensor data processing architecture to retrieve meaningful information from raw sensor data. In particular, we adopt a machine leaning strategy for sensor data analysis. Semantic sensor data are modeled based on ontologies. The processed semantic data construct a semantic knowledgebase, which allows a user to make the best use of sensor information. We present an evaluation of our approach by using real-world datasets and experimental results.
This paper addresses the prediction of mobility and emphasizes the main steps of our context-based mobility prediction approach that are trajectory modelling, dataprocessing and prediction process. First, we present ...
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ISBN:
(纸本)9798350381993;9798350382006
This paper addresses the prediction of mobility and emphasizes the main steps of our context-based mobility prediction approach that are trajectory modelling, dataprocessing and prediction process. First, we present our context-based and prediction-oriented trajectory model, which relies on the grid technique for trajectory description. Second, we describe a data processing architecture for data coming from Wifi networks. Third, we focus on our data mining-based prediction process used for the first experimentations. Evaluation of our contributions is performed on a real dataset. First results showed the positive impact of our trajectory model on our mobility prediction process.
Social media provide continuous data streams that contain information with different level of sensitivity, validity and accuracy. Therefore, this type of information has to be properly filtered, extracted and processe...
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ISBN:
(纸本)9781450372411
Social media provide continuous data streams that contain information with different level of sensitivity, validity and accuracy. Therefore, this type of information has to be properly filtered, extracted and processed to avoid noisy and inaccurate results. The main goal of this work is to propose architecture and workflow able to process Twitter social network data in near real-time. The primary design of the introduced modern architecture covers all processing aspects from data ingestion and storing to dataprocessing and analysing. This paper presents Apache Spark and Hadoop implementation. The secondary objective is to analyse tweets with the defined topic - floods. The word frequency method (Word Clouds) is shown as a major tool to analyse the content of the input dataset. The experimental architecture confirmed the usefulness of many well-known functions of Spark and Hadoop in the social data domain. The platforms which were used provided effective tools for optimal data ingesting, storing as well as processing and analysing. Based on the analytical part, it was observed that the word frequency method (n-grams) can effectively reveal the tweets content. According to the results of this study, the tweets proved their high informative potential regarding data quality and quantity.
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