The Experts below are selected from a list of 2823 Experts worldwide ranked by ideXlab platform
Luis Lafuente - One of the best experts on this subject based on the ideXlab platform.
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Data Stream Processing on Real-Time Mobile Advertisement: Ericsson Research Approach
2011 IEEE 12th International Conference on Mobile Data Management, 2011Co-Authors: Manuel Couceiro, David Suarez, David Manzano, Luis LafuenteAbstract:This paper describes the use of the Data Stream Processing technology on a telecommunications network to create a Real-time Mobile Advertisement System. Ads are based on the user's context, interests and profile. First we will describe the Stream Data Processing technology, its main key features, application scenarios and how it fits perfectly with the requirements posed by a proactive advertising system which seeks to provide answers to Who, When, What and How at the time of sending the ads. The When is an essential component as it provides the real-time aspect so we can take advantage of the environmental context in which the user operates. Finally we will introduce the proof of concept implemented at Ericsson Research to develop the Real-time mobile advertisement concept and its adaptation to the existing nodes in a telecommunications network. We will also describe how this implementation enables setting triggers based on location for any kind of terminal and with a sufficient positioning precision, and how Data Stream Processing handles the high Data throughput involved.
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Mobile Data Management (1) - Data Stream Processing on Real-Time Mobile Advertisement: Ericsson Research Approach
2011 IEEE 12th International Conference on Mobile Data Management, 2011Co-Authors: Manuel Couceiro, David Suarez, David Manzano, Luis LafuenteAbstract:This paper describes the use of the Data Stream Processing technology on a telecommunications network to create a Real-time Mobile Advertisement System. Ads are based on the user's context, interests and profile. First we will describe the Stream Data Processing technology, its main key features, application scenarios and how it fits perfectly with the requirements posed by a proactive advertising system which seeks to provide answers to Who, When, What and How at the time of sending the ads. The When is an essential component as it provides the real-time aspect so we can take advantage of the environmental context in which the user operates. Finally we will introduce the proof of concept implemented at Ericsson Research to develop the Real-time mobile advertisement concept and its adaptation to the existing nodes in a telecommunications network. We will also describe how this implementation enables setting triggers based on location for any kind of terminal and with a sufficient positioning precision, and how Data Stream Processing handles the high Data throughput involved.
Schahram Dustdar - One of the best experts on this subject based on the ideXlab platform.
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VTDL: A Notation for Data Stream Processing Applications
2018 IEEE Symposium on Service-Oriented System Engineering (SOSE), 2018Co-Authors: Christoph Hochreiner, Stefan Schulte, Matteo Nardelli, Bernhard Knasmueller, Schahram DustdarAbstract:The continuing growth of the Internet of Things (IoT) requires established Stream Processing engines (SPEs) to cope with new challenges, like the geographic distribution of IoT sensors and clouds hosting the SPEs. These challenges obligate SPEs to support distributed Stream Processing across different geographic locations which also require a new approach on how Data Stream Processing topologies are defined. In this paper, we identify required features for next-generation SPEs and introduce the Vienna Topology Description Language (VTDL). This language is specifically designed to address challenges for next-generation SPEs and proposes several novel aspects compared to existing topology description concepts. To assess not only the feasibility but also the reduced management overhead due to the VTDL, we evaluate the VTDL within the VISP Stream Processing ecosystem and show that the usage of the VTDL approach results in a management time reduction of up to 18 times.
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SOSE - VTDL: A Notation for Data Stream Processing Applications
2018 IEEE Symposium on Service-Oriented System Engineering (SOSE), 2018Co-Authors: Christoph Hochreiner, Stefan Schulte, Matteo Nardelli, Bernhard Knasmueller, Schahram DustdarAbstract:The continuing growth of the Internet of Things (IoT) requires established Stream Processing engines (SPEs) to cope with new challenges, like the geographic distribution of IoT sensors and clouds hosting the SPEs. These challenges obligate SPEs to support distributed Stream Processing across different geographic locations which also require a new approach on how Data Stream Processing topologies are defined. In this paper, we identify required features for next-generation SPEs and introduce the Vienna Topology Description Language (VTDL). This language is specifically designed to address challenges for next-generation SPEs and proposes several novel aspects compared to existing topology description concepts. To assess not only the feasibility but also the reduced management overhead due to the VTDL, we evaluate the VTDL within the VISP Stream Processing ecosystem and show that the usage of the VTDL approach results in a management time reduction of up to 18 times.
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EDOC - VISP: An Ecosystem for Elastic Data Stream Processing for the Internet of Things
2016 IEEE 20th International Enterprise Distributed Object Computing Conference (EDOC), 2016Co-Authors: Christoph Hochreiner, Michael Vogler, Philipp Waibel, Schahram DustdarAbstract:The Internet of Things is getting more and more traction, nevertheless, state-of-the-art approaches only focus on specific aspects, like the integration of heterogeneous devices or the Processing of sensor Data emitted by these devices. However, such domain-specific approaches slow the adoption rate of the Internet of Things, because users need to select and integrate different approaches in order to build a solution that fits all their requirements. To resolve this shortcoming, we have designed and implemented the VISP ecosystem, which provides a holistic approach for elastic Data Stream Processing in Internet of Things scenarios by supporting the complete lifecycle of designing, deploying, and executing such scenarios. VISP further tackles the challenges of Data privacy as well as software reuse, including monetization aspects in today's service landscapes. This paper analyzes challenges for creating solutions for the Internet of Things, presents the VISP ecosystem, and discusses its applicability for use case specific Data Stream Processing topologies.
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VISP: An Ecosystem for Elastic Data Stream Processing for the Internet of Things
2016 IEEE 20th International Enterprise Distributed Object Computing Conference (EDOC), 2016Co-Authors: Christoph Hochreiner, Michael Vogler, Philipp Waibel, Schahram DustdarAbstract:The Internet of Things is getting more and more traction, nevertheless, state-of-the-art approaches only focus on specific aspects, like the integration of heterogeneous devices or the Processing of sensor Data emitted by these devices. However, such domain-specific approaches slow the adoption rate of the Internet of Things, because users need to select and integrate different approaches in order to build a solution that fits all their requirements. To resolve this shortcoming, we have designed and implemented the VISP ecosystem, which provides a holistic approach for elastic Data Stream Processing in Internet of Things scenarios by supporting the complete lifecycle of designing, deploying, and executing such scenarios. VISP further tackles the challenges of Data privacy as well as software reuse, including monetization aspects in today's service landscapes. This paper analyzes challenges for creating solutions for the Internet of Things, presents the VISP ecosystem, and discusses its applicability for use case specific Data Stream Processing topologies.
Shahzad Khan - One of the best experts on this subject based on the ideXlab platform.
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A Survey of Distributed Data Stream Processing Frameworks
IEEE Access, 2019Co-Authors: Haruna Isah, Tariq Abughofa, Sazia Mahfuz, Dharmitha Ajerla, Farhana Zulkernine, Shahzad KhanAbstract:Big Data Processing systems are evolving to be more Stream oriented where each Data record is processed as it arrives by distributed and low-latency computational frameworks on a continuous basis. As the Stream Processing technology matures and more organizations invest in digital transformations, new applications of Stream analytics will be identified and implemented across a wide spectrum of industries. One of the challenges in developing a Streaming analytics infrastructure is the difficulty in selecting the right Stream Processing framework for the different use cases. With a view to addressing this issue, in this paper we present a taxonomy, a comparative study of distributed Data Stream Processing and analytics frameworks, and a critical review of representative open source (Storm, Spark Streaming, Flink, Kafka Streams) and commercial (IBM Streams) distributed Data Stream Processing frameworks. The study also reports our ongoing study on a multilevel Streaming analytics architecture that can serve as a guide for organizations and individuals planning to implement a real-time Data Stream Processing and analytics framework.
Manuel Couceiro - One of the best experts on this subject based on the ideXlab platform.
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Data Stream Processing on Real-Time Mobile Advertisement: Ericsson Research Approach
2011 IEEE 12th International Conference on Mobile Data Management, 2011Co-Authors: Manuel Couceiro, David Suarez, David Manzano, Luis LafuenteAbstract:This paper describes the use of the Data Stream Processing technology on a telecommunications network to create a Real-time Mobile Advertisement System. Ads are based on the user's context, interests and profile. First we will describe the Stream Data Processing technology, its main key features, application scenarios and how it fits perfectly with the requirements posed by a proactive advertising system which seeks to provide answers to Who, When, What and How at the time of sending the ads. The When is an essential component as it provides the real-time aspect so we can take advantage of the environmental context in which the user operates. Finally we will introduce the proof of concept implemented at Ericsson Research to develop the Real-time mobile advertisement concept and its adaptation to the existing nodes in a telecommunications network. We will also describe how this implementation enables setting triggers based on location for any kind of terminal and with a sufficient positioning precision, and how Data Stream Processing handles the high Data throughput involved.
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Mobile Data Management (1) - Data Stream Processing on Real-Time Mobile Advertisement: Ericsson Research Approach
2011 IEEE 12th International Conference on Mobile Data Management, 2011Co-Authors: Manuel Couceiro, David Suarez, David Manzano, Luis LafuenteAbstract:This paper describes the use of the Data Stream Processing technology on a telecommunications network to create a Real-time Mobile Advertisement System. Ads are based on the user's context, interests and profile. First we will describe the Stream Data Processing technology, its main key features, application scenarios and how it fits perfectly with the requirements posed by a proactive advertising system which seeks to provide answers to Who, When, What and How at the time of sending the ads. The When is an essential component as it provides the real-time aspect so we can take advantage of the environmental context in which the user operates. Finally we will introduce the proof of concept implemented at Ericsson Research to develop the Real-time mobile advertisement concept and its adaptation to the existing nodes in a telecommunications network. We will also describe how this implementation enables setting triggers based on location for any kind of terminal and with a sufficient positioning precision, and how Data Stream Processing handles the high Data throughput involved.
Christoph Hochreiner - One of the best experts on this subject based on the ideXlab platform.
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Towards a Framework for Data Stream Processing in the Fog
Informatik Spektrum, 2019Co-Authors: Thomas Hießl, Christoph Hochreiner, Stefan SchulteAbstract:In volatile Data Streams as encountered in the Internet of Things (IoT), the Data volume to be processed changes permanently. Hence, to ensure timely Data Processing, there is a need to reconfigure the computational resources used for Processing Data Streams. Up to now, mostly cloud-based computational resources have been utilized for this. However, cloud Data centers are usually located far away from IoT Data sources, which leads to an increase in latency since Data needs to be sent from the Data sources to the cloud and back. With the advent of fog computing, it is possible to perform Data Processing in the cloud as well as at the edge of the network, i. e., by exploiting the computational resources offered by networked devices. This leads to decreased latency and a lower communication overhead. Despite this, there is currently a lack of approaches to Data Stream Processing which explicitly exploit the computational resources available in the fog. Within this paper, we consider the usage of fog-based computational resources for the purposes of Data Stream Processing in the IoT. For this, we introduce a representative application scenario in the field of Industry 4.0 and present a framework for Stream Processing in the fog.
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VTDL: A Notation for Data Stream Processing Applications
2018 IEEE Symposium on Service-Oriented System Engineering (SOSE), 2018Co-Authors: Christoph Hochreiner, Stefan Schulte, Matteo Nardelli, Bernhard Knasmueller, Schahram DustdarAbstract:The continuing growth of the Internet of Things (IoT) requires established Stream Processing engines (SPEs) to cope with new challenges, like the geographic distribution of IoT sensors and clouds hosting the SPEs. These challenges obligate SPEs to support distributed Stream Processing across different geographic locations which also require a new approach on how Data Stream Processing topologies are defined. In this paper, we identify required features for next-generation SPEs and introduce the Vienna Topology Description Language (VTDL). This language is specifically designed to address challenges for next-generation SPEs and proposes several novel aspects compared to existing topology description concepts. To assess not only the feasibility but also the reduced management overhead due to the VTDL, we evaluate the VTDL within the VISP Stream Processing ecosystem and show that the usage of the VTDL approach results in a management time reduction of up to 18 times.
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SOSE - VTDL: A Notation for Data Stream Processing Applications
2018 IEEE Symposium on Service-Oriented System Engineering (SOSE), 2018Co-Authors: Christoph Hochreiner, Stefan Schulte, Matteo Nardelli, Bernhard Knasmueller, Schahram DustdarAbstract:The continuing growth of the Internet of Things (IoT) requires established Stream Processing engines (SPEs) to cope with new challenges, like the geographic distribution of IoT sensors and clouds hosting the SPEs. These challenges obligate SPEs to support distributed Stream Processing across different geographic locations which also require a new approach on how Data Stream Processing topologies are defined. In this paper, we identify required features for next-generation SPEs and introduce the Vienna Topology Description Language (VTDL). This language is specifically designed to address challenges for next-generation SPEs and proposes several novel aspects compared to existing topology description concepts. To assess not only the feasibility but also the reduced management overhead due to the VTDL, we evaluate the VTDL within the VISP Stream Processing ecosystem and show that the usage of the VTDL approach results in a management time reduction of up to 18 times.
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EDOC - VISP: An Ecosystem for Elastic Data Stream Processing for the Internet of Things
2016 IEEE 20th International Enterprise Distributed Object Computing Conference (EDOC), 2016Co-Authors: Christoph Hochreiner, Michael Vogler, Philipp Waibel, Schahram DustdarAbstract:The Internet of Things is getting more and more traction, nevertheless, state-of-the-art approaches only focus on specific aspects, like the integration of heterogeneous devices or the Processing of sensor Data emitted by these devices. However, such domain-specific approaches slow the adoption rate of the Internet of Things, because users need to select and integrate different approaches in order to build a solution that fits all their requirements. To resolve this shortcoming, we have designed and implemented the VISP ecosystem, which provides a holistic approach for elastic Data Stream Processing in Internet of Things scenarios by supporting the complete lifecycle of designing, deploying, and executing such scenarios. VISP further tackles the challenges of Data privacy as well as software reuse, including monetization aspects in today's service landscapes. This paper analyzes challenges for creating solutions for the Internet of Things, presents the VISP ecosystem, and discusses its applicability for use case specific Data Stream Processing topologies.
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VISP: An Ecosystem for Elastic Data Stream Processing for the Internet of Things
2016 IEEE 20th International Enterprise Distributed Object Computing Conference (EDOC), 2016Co-Authors: Christoph Hochreiner, Michael Vogler, Philipp Waibel, Schahram DustdarAbstract:The Internet of Things is getting more and more traction, nevertheless, state-of-the-art approaches only focus on specific aspects, like the integration of heterogeneous devices or the Processing of sensor Data emitted by these devices. However, such domain-specific approaches slow the adoption rate of the Internet of Things, because users need to select and integrate different approaches in order to build a solution that fits all their requirements. To resolve this shortcoming, we have designed and implemented the VISP ecosystem, which provides a holistic approach for elastic Data Stream Processing in Internet of Things scenarios by supporting the complete lifecycle of designing, deploying, and executing such scenarios. VISP further tackles the challenges of Data privacy as well as software reuse, including monetization aspects in today's service landscapes. This paper analyzes challenges for creating solutions for the Internet of Things, presents the VISP ecosystem, and discusses its applicability for use case specific Data Stream Processing topologies.
