�ݺ�ߣshows by User: mirkokaempf

�ݺ�ߣshows by User: mirkokaempf / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: mirkokaempf / Wed, 09 Sep 2020 11:33:59 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: mirkokaempf Time Series Analysis Using an Event Streaming Platform /slideshow/time-series-analysis-using-an-event-streaming-platform/238431122 opentsx-meetup-part1-200909113359
Advanced time series analysis (TSA) requires very special data preparation procedures to convert raw data into useful and compatible formats. In this presentation you will see some typical processing patterns for time series based research, from simple statistics to reconstruction of correlation networks. The first case is relevant for anomaly detection and to protect safety. Reconstruction of graphs from time series data is a very useful technique to better understand complex systems like supply chains, material flows in factories, information flows within organizations, and especially in medical research. With this motivation we will look at typical data aggregation patterns. We investigate how to apply analysis algorithms in the cloud. Finally we discuss a simple reference architecture for TSA on top of the Confluent Platform or Confluent cloud.]]>
Advanced time series analysis (TSA) requires very special data preparation procedures to convert raw data into useful and compatible formats. In this presentation you will see some typical processing patterns for time series based research, from simple statistics to reconstruction of correlation networks. The first case is relevant for anomaly detection and to protect safety. Reconstruction of graphs from time series data is a very useful technique to better understand complex systems like supply chains, material flows in factories, information flows within organizations, and especially in medical research. With this motivation we will look at typical data aggregation patterns. We investigate how to apply analysis algorithms in the cloud. Finally we discuss a simple reference architecture for TSA on top of the Confluent Platform or Confluent cloud.]]> Wed, 09 Sep 2020 11:33:59 GMT /slideshow/time-series-analysis-using-an-event-streaming-platform/238431122 mirkokaempf@slideshare.net(mirkokaempf) Time Series Analysis Using an Event Streaming Platform mirkokaempf Advanced time series analysis (TSA) requires very special data preparation procedures to convert raw data into useful and compatible formats. In this presentation you will see some typical processing patterns for time series based research, from simple statistics to reconstruction of correlation networks. The first case is relevant for anomaly detection and to protect safety. Reconstruction of graphs from time series data is a very useful technique to better understand complex systems like supply chains, material flows in factories, information flows within organizations, and especially in medical research. With this motivation we will look at typical data aggregation patterns. We investigate how to apply analysis algorithms in the cloud. Finally we discuss a simple reference architecture for TSA on top of the Confluent Platform or Confluent cloud. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/opentsx-meetup-part1-200909113359-thumbnail.jpg?width=120&height=120&fit=bounds" /> Advanced time series analysis (TSA) requires very special data preparation procedures to convert raw data into useful and compatible formats. In this presentation you will see some typical processing patterns for time series based research, from simple statistics to reconstruction of correlation networks. The first case is relevant for anomaly detection and to protect safety. Reconstruction of graphs from time series data is a very useful technique to better understand complex systems like supply chains, material flows in factories, information flows within organizations, and especially in medical research. With this motivation we will look at typical data aggregation patterns. We investigate how to apply analysis algorithms in the cloud. Finally we discuss a simple reference architecture for TSA on top of the Confluent Platform or Confluent cloud.

Time Series Analysis Using an Event Streaming Platform from Dr. Mirko K辰mpf

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0 IoT meets AI in the Clouds /mirkokaempf/iot-meets-ai-in-the-clouds iotmeetsaiintheclouds-v2-cleankopie-180917181624
In this presentation I do a review of the architecture of an AI application for IoT environments. Since specific modeling and training aspects also have an impact on the final implementation of an enterprise ready solution, such solutions become very complex pretty soon. The complexity of AI system for IoT is a big challenge – thus, I want to break this complexity down into particular views, which emphasize the individual but still interconnected aspects more clearly.]]>
In this presentation I do a review of the architecture of an AI application for IoT environments. Since specific modeling and training aspects also have an impact on the final implementation of an enterprise ready solution, such solutions become very complex pretty soon. The complexity of AI system for IoT is a big challenge – thus, I want to break this complexity down into particular views, which emphasize the individual but still interconnected aspects more clearly.]]> Mon, 17 Sep 2018 18:16:24 GMT /mirkokaempf/iot-meets-ai-in-the-clouds mirkokaempf@slideshare.net(mirkokaempf) IoT meets AI in the Clouds mirkokaempf In this presentation I do a review of the architecture of an AI application for IoT environments. Since specific modeling and training aspects also have an impact on the final implementation of an enterprise ready solution, such solutions become very complex pretty soon. The complexity of AI system for IoT is a big challenge – thus, I want to break this complexity down into particular views, which emphasize the individual but still interconnected aspects more clearly. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iotmeetsaiintheclouds-v2-cleankopie-180917181624-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this presentation I do a review of the architecture of an AI application for IoT environments. Since specific modeling and training aspects also have an impact on the final implementation of an enterprise ready solution, such solutions become very complex pretty soon. The complexity of AI system for IoT is a big challenge – thus, I want to break this complexity down into particular views, which emphasize the individual but still interconnected aspects more clearly.

IoT meets AI in the Clouds from Dr. Mirko K辰mpf

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0 Improving computer vision models at scale (Strata Data NYC) /slideshow/improving-computer-vision-models-at-scale-strata-data-nyc/114145980 improvingcomputervisionmodelsatscale-stratadatanyc-v6-180912212159
Rigorous improvement of an image recognition model often requires multiple iterations of eyeballing outliers, inspecting statistics of the output labels, then modifying and retraining the model. When testing data is present at the petabyte scale, the ability to seamlessly access all the images that have been assigned specific labels poses a technical challenge by itself. We share a solution that automates the process of running the model on the testing data and populating an index of the labels so they become searchable. Images and labels are stored in HBase. The model is encapsulated in a (Py)Spark program, while the images are indexed with Solr and can be accessed from a Hue dashboard. Triplification of facts, detected inside images contributes to a large knowledge graph, queryable via SPARQL. ]]>
Rigorous improvement of an image recognition model often requires multiple iterations of eyeballing outliers, inspecting statistics of the output labels, then modifying and retraining the model. When testing data is present at the petabyte scale, the ability to seamlessly access all the images that have been assigned specific labels poses a technical challenge by itself. We share a solution that automates the process of running the model on the testing data and populating an index of the labels so they become searchable. Images and labels are stored in HBase. The model is encapsulated in a (Py)Spark program, while the images are indexed with Solr and can be accessed from a Hue dashboard. Triplification of facts, detected inside images contributes to a large knowledge graph, queryable via SPARQL. ]]> Wed, 12 Sep 2018 21:21:59 GMT /slideshow/improving-computer-vision-models-at-scale-strata-data-nyc/114145980 mirkokaempf@slideshare.net(mirkokaempf) Improving computer vision models at scale (Strata Data NYC) mirkokaempf Rigorous improvement of an image recognition model often requires multiple iterations of eyeballing outliers, inspecting statistics of the output labels, then modifying and retraining the model. When testing data is present at the petabyte scale, the ability to seamlessly access all the images that have been assigned specific labels poses a technical challenge by itself. We share a solution that automates the process of running the model on the testing data and populating an index of the labels so they become searchable. Images and labels are stored in HBase. The model is encapsulated in a (Py)Spark program, while the images are indexed with Solr and can be accessed from a Hue dashboard. Triplification of facts, detected inside images contributes to a large knowledge graph, queryable via SPARQL. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/improvingcomputervisionmodelsatscale-stratadatanyc-v6-180912212159-thumbnail.jpg?width=120&height=120&fit=bounds" /> Rigorous improvement of an image recognition model often requires multiple iterations of eyeballing outliers, inspecting statistics of the output labels, then modifying and retraining the model. When testing data is present at the petabyte scale, the ability to seamlessly access all the images that have been assigned specific labels poses a technical challenge by itself. We share a solution that automates the process of running the model on the testing data and populating an index of the labels so they become searchable. Images and labels are stored in HBase. The model is encapsulated in a (Py)Spark program, while the images are indexed with Solr and can be accessed from a Hue dashboard. Triplification of facts, detected inside images contributes to a large knowledge graph, queryable via SPARQL.

Improving computer vision models at scale (Strata Data NYC) from Dr. Mirko K辰mpf

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0 Improving computer vision models at scale presentation /slideshow/improving-computer-vision-models-at-scale-presentation/111279684 improvingcomputervisionmodelsatscalepresentation-180824064148
Rigorous improvement of an image recognition model often requires multiple iterations of eyeballing outliers, inspecting statistics of the output labels, then modifying and retraining the model. When testing data is present at the petabyte scale, the ability to seamlessly access all the images that have been assigned specific labels poses a technical challenge by itself. Marton Balassi, Mirko Kämpf, and Jan Kunigk share a solution that automates the process of running the model on the testing data and populating an index of the labels so they become searchable. Images and labels are stored in HBase. The model is encapsulated in a PySpark program, while the images are indexed with Solr and can be accessed from a Hue dashboard.]]>
Rigorous improvement of an image recognition model often requires multiple iterations of eyeballing outliers, inspecting statistics of the output labels, then modifying and retraining the model. When testing data is present at the petabyte scale, the ability to seamlessly access all the images that have been assigned specific labels poses a technical challenge by itself. Marton Balassi, Mirko Kämpf, and Jan Kunigk share a solution that automates the process of running the model on the testing data and populating an index of the labels so they become searchable. Images and labels are stored in HBase. The model is encapsulated in a PySpark program, while the images are indexed with Solr and can be accessed from a Hue dashboard.]]> Fri, 24 Aug 2018 06:41:48 GMT /slideshow/improving-computer-vision-models-at-scale-presentation/111279684 mirkokaempf@slideshare.net(mirkokaempf) Improving computer vision models at scale presentation mirkokaempf Rigorous improvement of an image recognition model often requires multiple iterations of eyeballing outliers, inspecting statistics of the output labels, then modifying and retraining the model. When testing data is present at the petabyte scale, the ability to seamlessly access all the images that have been assigned specific labels poses a technical challenge by itself. Marton Balassi, Mirko Kämpf, and Jan Kunigk share a solution that automates the process of running the model on the testing data and populating an index of the labels so they become searchable. Images and labels are stored in HBase. The model is encapsulated in a PySpark program, while the images are indexed with Solr and can be accessed from a Hue dashboard. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/improvingcomputervisionmodelsatscalepresentation-180824064148-thumbnail.jpg?width=120&height=120&fit=bounds" /> Rigorous improvement of an image recognition model often requires multiple iterations of eyeballing outliers, inspecting statistics of the output labels, then modifying and retraining the model. When testing data is present at the petabyte scale, the ability to seamlessly access all the images that have been assigned specific labels poses a technical challenge by itself. Marton Balassi, Mirko Kämpf, and Jan Kunigk share a solution that automates the process of running the model on the testing data and populating an index of the labels so they become searchable. Images and labels are stored in HBase. The model is encapsulated in a PySpark program, while the images are indexed with Solr and can be accessed from a Hue dashboard.

Improving computer vision models at scale presentation from Dr. Mirko K辰mpf

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0 Enterprise Metadata Integration /slideshow/enterprise-metadata-integration/75940777 graphconnect-2017-mirko-kampf-clouderafinal-v2-170513082752
EMI supports knowledge management for better Data Science. Presented @GraphConnect 2017 (QEII Centre London)]]>
EMI supports knowledge management for better Data Science. Presented @GraphConnect 2017 (QEII Centre London)]]> Sat, 13 May 2017 08:27:52 GMT /slideshow/enterprise-metadata-integration/75940777 mirkokaempf@slideshare.net(mirkokaempf) Enterprise Metadata Integration mirkokaempf EMI supports knowledge management for better Data Science. Presented @GraphConnect 2017 (QEII Centre London) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/graphconnect-2017-mirko-kampf-clouderafinal-v2-170513082752-thumbnail.jpg?width=120&height=120&fit=bounds" /> EMI supports knowledge management for better Data Science. Presented @GraphConnect 2017 (QEII Centre London)

Enterprise Metadata Integration from Dr. Mirko K辰mpf

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0 PCAP Graphs for Cybersecurity and System Tuning /slideshow/pcap-graphs-for-cybersecurity-an-system-tuning/71772662 pcapgraphsfosdemfinal-170205080229
Cybersecurity is a broad topic and many commercial products are related to it. We demonstrate a fundamental concept in network analysis: re-construction and visualization of temporal networks. Furthermore, we apply the method to describe operational conditions of a Hadoop cluster. Our experiments provide first results and allow a classification of the cluster state related to current workloads. The temporal networks show significant differences for different operation modes. In reallity we would expect mixed workloads. If such workload parameters are known, we are able to handle a-typical events accordingly - which means, we are able to create alerts based on context information, rather than only the package content. We show an end-to-end example: (1) Data collection is done via python, using the sniffer script; (2) using Apache Hive and Apache Spark we analyze the network traffic data and create the temporary network. Finally, we are able to visualize the results using Gephi in step (3). In a next step, we plan to contribute to the Apache Spot project.]]>
Cybersecurity is a broad topic and many commercial products are related to it. We demonstrate a fundamental concept in network analysis: re-construction and visualization of temporal networks. Furthermore, we apply the method to describe operational conditions of a Hadoop cluster. Our experiments provide first results and allow a classification of the cluster state related to current workloads. The temporal networks show significant differences for different operation modes. In reallity we would expect mixed workloads. If such workload parameters are known, we are able to handle a-typical events accordingly - which means, we are able to create alerts based on context information, rather than only the package content. We show an end-to-end example: (1) Data collection is done via python, using the sniffer script; (2) using Apache Hive and Apache Spark we analyze the network traffic data and create the temporary network. Finally, we are able to visualize the results using Gephi in step (3). In a next step, we plan to contribute to the Apache Spot project.]]> Sun, 05 Feb 2017 08:02:29 GMT /slideshow/pcap-graphs-for-cybersecurity-an-system-tuning/71772662 mirkokaempf@slideshare.net(mirkokaempf) PCAP Graphs for Cybersecurity and System Tuning mirkokaempf Cybersecurity is a broad topic and many commercial products are related to it. We demonstrate a fundamental concept in network analysis: re-construction and visualization of temporal networks. Furthermore, we apply the method to describe operational conditions of a Hadoop cluster. Our experiments provide first results and allow a classification of the cluster state related to current workloads. The temporal networks show significant differences for different operation modes. In reallity we would expect mixed workloads. If such workload parameters are known, we are able to handle a-typical events accordingly - which means, we are able to create alerts based on context information, rather than only the package content. We show an end-to-end example: (1) Data collection is done via python, using the sniffer script; (2) using Apache Hive and Apache Spark we analyze the network traffic data and create the temporary network. Finally, we are able to visualize the results using Gephi in step (3). In a next step, we plan to contribute to the Apache Spot project. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pcapgraphsfosdemfinal-170205080229-thumbnail.jpg?width=120&height=120&fit=bounds" /> Cybersecurity is a broad topic and many commercial products are related to it. We demonstrate a fundamental concept in network analysis: re-construction and visualization of temporal networks. Furthermore, we apply the method to describe operational conditions of a Hadoop cluster. Our experiments provide first results and allow a classification of the cluster state related to current workloads. The temporal networks show significant differences for different operation modes. In reallity we would expect mixed workloads. If such workload parameters are known, we are able to handle a-typical events accordingly - which means, we are able to create alerts based on context information, rather than only the package content. We show an end-to-end example: (1) Data collection is done via python, using the sniffer script; (2) using Apache Hive and Apache Spark we analyze the network traffic data and create the temporary network. Finally, we are able to visualize the results using Gephi in step (3). In a next step, we plan to contribute to the Apache Spot project.

PCAP Graphs for Cybersecurity and System Tuning from Dr. Mirko K辰mpf

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0 Etosha - Data Asset Manager : Status and road map /slideshow/etosha-data-asset-manager-status-and-road-map/65201652 etoshaarchitecture-statusandroadmaptov1-160821085101
Etosha is an enterprise focused collaborative graph database with facts about data sets, analysis procedures, and research methods. People from multiple organizations can be connected while every owner retains full control about its own data.]]>
Etosha is an enterprise focused collaborative graph database with facts about data sets, analysis procedures, and research methods. People from multiple organizations can be connected while every owner retains full control about its own data.]]> Sun, 21 Aug 2016 08:51:01 GMT /slideshow/etosha-data-asset-manager-status-and-road-map/65201652 mirkokaempf@slideshare.net(mirkokaempf) Etosha - Data Asset Manager : Status and road map mirkokaempf Etosha is an enterprise focused collaborative graph database with facts about data sets, analysis procedures, and research methods. People from multiple organizations can be connected while every owner retains full control about its own data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/etoshaarchitecture-statusandroadmaptov1-160821085101-thumbnail.jpg?width=120&height=120&fit=bounds" /> Etosha is an enterprise focused collaborative graph database with facts about data sets, analysis procedures, and research methods. People from multiple organizations can be connected while every owner retains full control about its own data.

Etosha - Data Asset Manager : Status and road map from Dr. Mirko K辰mpf

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0 From Events to Networks: Time Series Analysis on Scale /slideshow/from-events-to-networks-time-series-analysis-on-scale/64977678 followupmodification-ap4-fromeventstonetworks-timeseriesanalysis-clouderafcesummitlv2016-08-02v9-160814090624
Event processing, time series aggregation and analysis, and finally analysis of structural patterns between those data snippets can all be done on Hadoop clusters on huge data volumes. In order to find hidden relations and invisible structures one has to combine three disciplines using a variety of tools. Luckily, the Hadoop ecosystem offers many of such tools. In this session you can see practical examples and a demonstration of the "Hadoop-Oscilloscope". Generic analysis patterns and recommendations towards selection of appropriate algorithms will also provide additional background. ]]>
Event processing, time series aggregation and analysis, and finally analysis of structural patterns between those data snippets can all be done on Hadoop clusters on huge data volumes. In order to find hidden relations and invisible structures one has to combine three disciplines using a variety of tools. Luckily, the Hadoop ecosystem offers many of such tools. In this session you can see practical examples and a demonstration of the "Hadoop-Oscilloscope". Generic analysis patterns and recommendations towards selection of appropriate algorithms will also provide additional background. ]]> Sun, 14 Aug 2016 09:06:24 GMT /slideshow/from-events-to-networks-time-series-analysis-on-scale/64977678 mirkokaempf@slideshare.net(mirkokaempf) From Events to Networks: Time Series Analysis on Scale mirkokaempf Event processing, time series aggregation and analysis, and finally analysis of structural patterns between those data snippets can all be done on Hadoop clusters on huge data volumes. In order to find hidden relations and invisible structures one has to combine three disciplines using a variety of tools. Luckily, the Hadoop ecosystem offers many of such tools. In this session you can see practical examples and a demonstration of the "Hadoop-Oscilloscope". Generic analysis patterns and recommendations towards selection of appropriate algorithms will also provide additional background. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/followupmodification-ap4-fromeventstonetworks-timeseriesanalysis-clouderafcesummitlv2016-08-02v9-160814090624-thumbnail.jpg?width=120&height=120&fit=bounds" /> Event processing, time series aggregation and analysis, and finally analysis of structural patterns between those data snippets can all be done on Hadoop clusters on huge data volumes. In order to find hidden relations and invisible structures one has to combine three disciplines using a variety of tools. Luckily, the Hadoop ecosystem offers many of such tools. In this session you can see practical examples and a demonstration of the "Hadoop-Oscilloscope". Generic analysis patterns and recommendations towards selection of appropriate algorithms will also provide additional background.

From Events to Networks: Time Series Analysis on Scale from Dr. Mirko K辰mpf

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0 Apache Spark in Scientific Applications /slideshow/apache-spark-in-scientific-applications/58220346 mkapache-spark-in-scientific-applciations-v3-160213110958
For your Quick Start into Apache Spark! ]]>
For your Quick Start into Apache Spark! ]]> Sat, 13 Feb 2016 11:09:58 GMT /slideshow/apache-spark-in-scientific-applications/58220346 mirkokaempf@slideshare.net(mirkokaempf) Apache Spark in Scientific Applications mirkokaempf For your Quick Start into Apache Spark! <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mkapache-spark-in-scientific-applciations-v3-160213110958-thumbnail.jpg?width=120&height=120&fit=bounds" /> For your Quick Start into Apache Spark!

Apache Spark in Scientific Applications from Dr. Mirko K辰mpf

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0 Apache Spark in Scientific Applciations /slideshow/apache-spark-in-scientific-applciations/58220304 mkapache-spark-in-scientific-applciations-v3-160213110714
Quick start into Apache Spark for Scientists on their way to "Data Science"]]>
Quick start into Apache Spark for Scientists on their way to "Data Science"]]> Sat, 13 Feb 2016 11:07:13 GMT /slideshow/apache-spark-in-scientific-applciations/58220304 mirkokaempf@slideshare.net(mirkokaempf) Apache Spark in Scientific Applciations mirkokaempf Quick start into Apache Spark for Scientists on their way to "Data Science" <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mkapache-spark-in-scientific-applciations-v3-160213110714-thumbnail.jpg?width=120&height=120&fit=bounds" /> Quick start into Apache Spark for Scientists on their way to "Data Science"

Apache Spark in Scientific Applciations from Dr. Mirko K辰mpf

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0 DPG Berlin - SOE 18 - talk v1.2.4 /slideshow/dpg-berlin-soe-18-talk-v124/57991712 dpgtalkv1-160208061530
Simulation of information flow on dynamic interlinked networks.]]>
Simulation of information flow on dynamic interlinked networks.]]> Mon, 08 Feb 2016 06:15:30 GMT /slideshow/dpg-berlin-soe-18-talk-v124/57991712 mirkokaempf@slideshare.net(mirkokaempf) DPG Berlin - SOE 18 - talk v1.2.4 mirkokaempf Simulation of information flow on dynamic interlinked networks. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dpgtalkv1-160208061530-thumbnail.jpg?width=120&height=120&fit=bounds" /> Simulation of information flow on dynamic interlinked networks.

DPG Berlin - SOE 18 - talk v1.2.4 from Dr. Mirko K辰mpf

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0 Information Spread in the Context of Evacuation Optimization /mirkokaempf/information-spread-in-the-context-of-evacuation-optimization dpg2011-dresden-poster-m-151216144443
Abstract: Our evacuation simulation tool utilizes established algorithms for the emotional and intelligence driven motion of human beings in addition to a simple lattice gas simulation. We analyze the spread of information inside a restricted geometry of a real building and compare these results with the data from a simulation in the free space. We apply the DFA and the RIS statistic to our simulation dataset to detect phases or phase transitions of the whole system. We study the impact of communication technology by comparison of different update algorithms and exit strategies. These results help us to define basic functional requirements to the underlying communication technology and network topology as well as to the needed sensors.]]>
Abstract: Our evacuation simulation tool utilizes established algorithms for the emotional and intelligence driven motion of human beings in addition to a simple lattice gas simulation. We analyze the spread of information inside a restricted geometry of a real building and compare these results with the data from a simulation in the free space. We apply the DFA and the RIS statistic to our simulation dataset to detect phases or phase transitions of the whole system. We study the impact of communication technology by comparison of different update algorithms and exit strategies. These results help us to define basic functional requirements to the underlying communication technology and network topology as well as to the needed sensors.]]> Wed, 16 Dec 2015 14:44:43 GMT /mirkokaempf/information-spread-in-the-context-of-evacuation-optimization mirkokaempf@slideshare.net(mirkokaempf) Information Spread in the Context of Evacuation Optimization mirkokaempf Abstract: Our evacuation simulation tool utilizes established algorithms for the emotional and intelligence driven motion of human beings in addition to a simple lattice gas simulation. We analyze the spread of information inside a restricted geometry of a real building and compare these results with the data from a simulation in the free space. We apply the DFA and the RIS statistic to our simulation dataset to detect phases or phase transitions of the whole system. We study the impact of communication technology by comparison of different update algorithms and exit strategies. These results help us to define basic functional requirements to the underlying communication technology and network topology as well as to the needed sensors. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dpg2011-dresden-poster-m-151216144443-thumbnail.jpg?width=120&height=120&fit=bounds" /> Abstract: Our evacuation simulation tool utilizes established algorithms for the emotional and intelligence driven motion of human beings in addition to a simple lattice gas simulation. We analyze the spread of information inside a restricted geometry of a real building and compare these results with the data from a simulation in the free space. We apply the DFA and the RIS statistic to our simulation dataset to detect phases or phase transitions of the whole system. We study the impact of communication technology by comparison of different update algorithms and exit strategies. These results help us to define basic functional requirements to the underlying communication technology and network topology as well as to the needed sensors.

Information Spread in the Context of Evacuation Optimization from Dr. Mirko K辰mpf

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0 Hadoop & Complex Systems Research /slideshow/hadoop-complex-systems-research/56205944 mirkokaempfkarlsruhegridka2014-09-01v11-151216144044
Presentation during GridKa Summer School in 2014]]>
Presentation during GridKa Summer School in 2014]]> Wed, 16 Dec 2015 14:40:44 GMT /slideshow/hadoop-complex-systems-research/56205944 mirkokaempf@slideshare.net(mirkokaempf) Hadoop & Complex Systems Research mirkokaempf Presentation during GridKa Summer School in 2014 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mirkokaempfkarlsruhegridka2014-09-01v11-151216144044-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation during GridKa Summer School in 2014

Hadoop & Complex Systems Research from Dr. Mirko K辰mpf

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0 DPG 2014: "Context Sensitive and Time Dependent Relevance of Wikipedia Articles" /slideshow/dpg-2014-time-05-1/33213066 dpg2014-dresden-poster-e-140407042703-phpapp01
Since the numbers of hypertext pages and hyperlinks in the WWW have been continuously growing for more than 20 years, the problem of finding relevant content has become increasingly important. We have developed and evaluated techniques for a time-dependent characterization of the global and local relevance of WWW pages based on document length, number of links, and cross-correlations in user-access time series. We focus on content and user activity in selected groups of Wikipedia articles as a first application mainly because of data availability. Our goal is the assignment of ranking values to a hypertext page (node). The values shall cover static properties of the node and its neighbourhood (context) as well as dynamic properties derived from its page-view rates that depend on underlying communication processes. We show in several examples how this goal can be achieved.]]>
Since the numbers of hypertext pages and hyperlinks in the WWW have been continuously growing for more than 20 years, the problem of finding relevant content has become increasingly important. We have developed and evaluated techniques for a time-dependent characterization of the global and local relevance of WWW pages based on document length, number of links, and cross-correlations in user-access time series. We focus on content and user activity in selected groups of Wikipedia articles as a first application mainly because of data availability. Our goal is the assignment of ranking values to a hypertext page (node). The values shall cover static properties of the node and its neighbourhood (context) as well as dynamic properties derived from its page-view rates that depend on underlying communication processes. We show in several examples how this goal can be achieved.]]> Mon, 07 Apr 2014 04:27:03 GMT /slideshow/dpg-2014-time-05-1/33213066 mirkokaempf@slideshare.net(mirkokaempf) DPG 2014: "Context Sensitive and Time Dependent Relevance of Wikipedia Articles" mirkokaempf Since the numbers of hypertext pages and hyperlinks in the WWW have been continuously growing for more than 20 years, the problem of finding relevant content has become increasingly important. We have developed and evaluated techniques for a time-dependent characterization of the global and local relevance of WWW pages based on document length, number of links, and cross-correlations in user-access time series. We focus on content and user activity in selected groups of Wikipedia articles as a first application mainly because of data availability. Our goal is the assignment of ranking values to a hypertext page (node). The values shall cover static properties of the node and its neighbourhood (context) as well as dynamic properties derived from its page-view rates that depend on underlying communication processes. We show in several examples how this goal can be achieved. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dpg2014-dresden-poster-e-140407042703-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Since the numbers of hypertext pages and hyperlinks in the WWW have been continuously growing for more than 20 years, the problem of finding relevant content has become increasingly important. We have developed and evaluated techniques for a time-dependent characterization of the global and local relevance of WWW pages based on document length, number of links, and cross-correlations in user-access time series. We focus on content and user activity in selected groups of Wikipedia articles as a first application mainly because of data availability. Our goal is the assignment of ranking values to a hypertext page (node). The values shall cover static properties of the node and its neighbourhood (context) as well as dynamic properties derived from its page-view rates that depend on underlying communication processes. We show in several examples how this goal can be achieved.

DPG 2014: "Context Sensitive and Time Dependent Relevance of Wikipedia Articles" from Dr. Mirko K辰mpf

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0 https://cdn.slidesharecdn.com/profile-photo-mirkokaempf-48x48.jpg?cb=1599651066 I love to work on new ways to cope with data streams and solve problems using distributed software solution. www.linkedin.com/in/kamir/ https://cdn.slidesharecdn.com/ss_thumbnails/opentsx-meetup-part1-200909113359-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/time-series-analysis-using-an-event-streaming-platform/238431122 Time Series Analysis ... https://cdn.slidesharecdn.com/ss_thumbnails/iotmeetsaiintheclouds-v2-cleankopie-180917181624-thumbnail.jpg?width=320&height=320&fit=bounds mirkokaempf/iot-meets-ai-in-the-clouds IoT meets AI in the Cl... https://cdn.slidesharecdn.com/ss_thumbnails/improvingcomputervisionmodelsatscale-stratadatanyc-v6-180912212159-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/improving-computer-vision-models-at-scale-strata-data-nyc/114145980 Improving computer vis...