The Experts below are selected from a list of 36360 Experts worldwide ranked by ideXlab platform
Philipp Vorst - One of the best experts on this subject based on the ideXlab platform.
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energy cloud real Time cloud native energy management system to monitor and analyze energy consumption in multiple industrial sites
IEEE ACM International Conference Utility and Cloud Computing, 2014Co-Authors: Hugo Sequeira, Paulo Carreira, Thomas Goldschmidt, Philipp VorstAbstract:Industrial organizations use Energy Management Systems (EMS) to monitor, control, and optimize their energy consumption. Industrial EMS are complex and expensive systems due to the unique requirements of performance, reliability, and interoperability. Moreover, industry is facing challenges with current EMS implementations such as cross-site monitoring of energy consumption and CO2 emissions, integration between energy and production data, and meaningful energy efficiency benchmarking. Additionally, big data has emerged because of recent advances in field instrumentation that led to the generation of large quantities of machine data, with much more detail and higher sampling rates. This created a challenge for real-Time Analytics. In order to address all these needs and challenges, we propose a cloud-native industrial EMS solution with cloud computing capabilities. Through this innovative approach we expect to generate useful knowledge in a shorter Time period, enabling organizations to react quicker to changes of events and detect hidden patterns that compromise efficiency.
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energy cloud real Time cloud native energy management system to monitor and analyze energy consumption in multiple industrial sites
IEEE ACM International Conference Utility and Cloud Computing, 2014Co-Authors: Hugo Sequeira, Paulo Carreira, Thomas Goldschmidt, Philipp VorstAbstract:Industrial organizations use Energy Management Systems (EMS) to monitor, control, and optimize their energy consumption. Industrial EMS are complex and expensive systems due to the unique requirements of performance, reliability, and interoperability. Moreover, industry is facing challenges with current EMS implementations such as cross-site monitoring of energy consumption and CO2 emissions, integration between energy and production data, and meaningful energy efficiency benchmarking. Additionally, big data has emerged because of recent advances in field instrumentation that led to the generation of large quantities of machine data, with much more detail and higher sampling rates. This created a challenge for real-Time Analytics. In order to address all these needs and challenges, we propose a cloud-native industrial EMS solution with cloud computing capabilities. Through this innovative approach we expect to generate useful knowledge in a shorter Time period, enabling organizations to react quicker to changes of events and detect hidden patterns that compromise efficiency.
Hugo Sequeira - One of the best experts on this subject based on the ideXlab platform.
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energy cloud real Time cloud native energy management system to monitor and analyze energy consumption in multiple industrial sites
IEEE ACM International Conference Utility and Cloud Computing, 2014Co-Authors: Hugo Sequeira, Paulo Carreira, Thomas Goldschmidt, Philipp VorstAbstract:Industrial organizations use Energy Management Systems (EMS) to monitor, control, and optimize their energy consumption. Industrial EMS are complex and expensive systems due to the unique requirements of performance, reliability, and interoperability. Moreover, industry is facing challenges with current EMS implementations such as cross-site monitoring of energy consumption and CO2 emissions, integration between energy and production data, and meaningful energy efficiency benchmarking. Additionally, big data has emerged because of recent advances in field instrumentation that led to the generation of large quantities of machine data, with much more detail and higher sampling rates. This created a challenge for real-Time Analytics. In order to address all these needs and challenges, we propose a cloud-native industrial EMS solution with cloud computing capabilities. Through this innovative approach we expect to generate useful knowledge in a shorter Time period, enabling organizations to react quicker to changes of events and detect hidden patterns that compromise efficiency.
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energy cloud real Time cloud native energy management system to monitor and analyze energy consumption in multiple industrial sites
IEEE ACM International Conference Utility and Cloud Computing, 2014Co-Authors: Hugo Sequeira, Paulo Carreira, Thomas Goldschmidt, Philipp VorstAbstract:Industrial organizations use Energy Management Systems (EMS) to monitor, control, and optimize their energy consumption. Industrial EMS are complex and expensive systems due to the unique requirements of performance, reliability, and interoperability. Moreover, industry is facing challenges with current EMS implementations such as cross-site monitoring of energy consumption and CO2 emissions, integration between energy and production data, and meaningful energy efficiency benchmarking. Additionally, big data has emerged because of recent advances in field instrumentation that led to the generation of large quantities of machine data, with much more detail and higher sampling rates. This created a challenge for real-Time Analytics. In order to address all these needs and challenges, we propose a cloud-native industrial EMS solution with cloud computing capabilities. Through this innovative approach we expect to generate useful knowledge in a shorter Time period, enabling organizations to react quicker to changes of events and detect hidden patterns that compromise efficiency.
Thomas Goldschmidt - One of the best experts on this subject based on the ideXlab platform.
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energy cloud real Time cloud native energy management system to monitor and analyze energy consumption in multiple industrial sites
IEEE ACM International Conference Utility and Cloud Computing, 2014Co-Authors: Hugo Sequeira, Paulo Carreira, Thomas Goldschmidt, Philipp VorstAbstract:Industrial organizations use Energy Management Systems (EMS) to monitor, control, and optimize their energy consumption. Industrial EMS are complex and expensive systems due to the unique requirements of performance, reliability, and interoperability. Moreover, industry is facing challenges with current EMS implementations such as cross-site monitoring of energy consumption and CO2 emissions, integration between energy and production data, and meaningful energy efficiency benchmarking. Additionally, big data has emerged because of recent advances in field instrumentation that led to the generation of large quantities of machine data, with much more detail and higher sampling rates. This created a challenge for real-Time Analytics. In order to address all these needs and challenges, we propose a cloud-native industrial EMS solution with cloud computing capabilities. Through this innovative approach we expect to generate useful knowledge in a shorter Time period, enabling organizations to react quicker to changes of events and detect hidden patterns that compromise efficiency.
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energy cloud real Time cloud native energy management system to monitor and analyze energy consumption in multiple industrial sites
IEEE ACM International Conference Utility and Cloud Computing, 2014Co-Authors: Hugo Sequeira, Paulo Carreira, Thomas Goldschmidt, Philipp VorstAbstract:Industrial organizations use Energy Management Systems (EMS) to monitor, control, and optimize their energy consumption. Industrial EMS are complex and expensive systems due to the unique requirements of performance, reliability, and interoperability. Moreover, industry is facing challenges with current EMS implementations such as cross-site monitoring of energy consumption and CO2 emissions, integration between energy and production data, and meaningful energy efficiency benchmarking. Additionally, big data has emerged because of recent advances in field instrumentation that led to the generation of large quantities of machine data, with much more detail and higher sampling rates. This created a challenge for real-Time Analytics. In order to address all these needs and challenges, we propose a cloud-native industrial EMS solution with cloud computing capabilities. Through this innovative approach we expect to generate useful knowledge in a shorter Time period, enabling organizations to react quicker to changes of events and detect hidden patterns that compromise efficiency.
Paulo Carreira - One of the best experts on this subject based on the ideXlab platform.
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energy cloud real Time cloud native energy management system to monitor and analyze energy consumption in multiple industrial sites
IEEE ACM International Conference Utility and Cloud Computing, 2014Co-Authors: Hugo Sequeira, Paulo Carreira, Thomas Goldschmidt, Philipp VorstAbstract:Industrial organizations use Energy Management Systems (EMS) to monitor, control, and optimize their energy consumption. Industrial EMS are complex and expensive systems due to the unique requirements of performance, reliability, and interoperability. Moreover, industry is facing challenges with current EMS implementations such as cross-site monitoring of energy consumption and CO2 emissions, integration between energy and production data, and meaningful energy efficiency benchmarking. Additionally, big data has emerged because of recent advances in field instrumentation that led to the generation of large quantities of machine data, with much more detail and higher sampling rates. This created a challenge for real-Time Analytics. In order to address all these needs and challenges, we propose a cloud-native industrial EMS solution with cloud computing capabilities. Through this innovative approach we expect to generate useful knowledge in a shorter Time period, enabling organizations to react quicker to changes of events and detect hidden patterns that compromise efficiency.
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energy cloud real Time cloud native energy management system to monitor and analyze energy consumption in multiple industrial sites
IEEE ACM International Conference Utility and Cloud Computing, 2014Co-Authors: Hugo Sequeira, Paulo Carreira, Thomas Goldschmidt, Philipp VorstAbstract:Industrial organizations use Energy Management Systems (EMS) to monitor, control, and optimize their energy consumption. Industrial EMS are complex and expensive systems due to the unique requirements of performance, reliability, and interoperability. Moreover, industry is facing challenges with current EMS implementations such as cross-site monitoring of energy consumption and CO2 emissions, integration between energy and production data, and meaningful energy efficiency benchmarking. Additionally, big data has emerged because of recent advances in field instrumentation that led to the generation of large quantities of machine data, with much more detail and higher sampling rates. This created a challenge for real-Time Analytics. In order to address all these needs and challenges, we propose a cloud-native industrial EMS solution with cloud computing capabilities. Through this innovative approach we expect to generate useful knowledge in a shorter Time period, enabling organizations to react quicker to changes of events and detect hidden patterns that compromise efficiency.
H. Chang - One of the best experts on this subject based on the ideXlab platform.
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An agent-based architecture for analyzing business processes of real-Time enterprises
Seventh IEEE International Enterprise Distributed Object Computing Conference, 2003. Proceedings., 2003Co-Authors: Jun-jang Jeng, Josef Schiefer, H. ChangAbstract:As the desire for business intelligence capabilities for e-business processes expands, existing workflow management systems and decision support systems are not able to provide continuous, real-Time Analytics for decision makers. Business intelligence requirements may appear to be different across the various industries, but the underlying requirements are similar nformation that is integrated, current, detailed, and immediately accessible. In this paper we introduce an agent-based architecture that supports a complete business intelligence process to sense, interpret, predict, automate and respond to business processes and aims to decrease the Time it takes to make business decisions. In fact, there should be almost zero-latency between the cause and effect of a business decision. Our architecture enables analysis across corporate business processes notifies the business of auctionable recommendations or automatically triggers business operations, effectively closing the gap between business intelligence systems and business processes.
