ºÝºÝߣshows by User: hpccsystems / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: hpccsystems / Fri, 24 Jul 2020 12:38:47 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: hpccsystems Natural Language to SQL Query conversion using Machine Learning Techniques on HPCC Systems /slideshow/natural-language-to-sql-query-conversion-using-machine-learning-techniques-on-hpcc-systems/237213407 nlp-200724123847
Presented at the RV College of Engineering Multidisciplinary Trends in Information Technology, July 21 - 25, 2020.]]>
Presented at the RV College of Engineering Multidisciplinary Trends in Information Technology, July 21 - 25, 2020.]]> Fri, 24 Jul 2020 12:38:47 GMT /slideshow/natural-language-to-sql-query-conversion-using-machine-learning-techniques-on-hpcc-systems/237213407 hpccsystems@slideshare.net(hpccsystems) Natural Language to SQL Query conversion using Machine Learning Techniques on HPCC Systems hpccsystems Presented at the RV College of Engineering Multidisciplinary Trends in Information Technology, July 21 - 25, 2020. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nlp-200724123847-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presented at the RV College of Engineering Multidisciplinary Trends in Information Technology, July 21 - 25, 2020.
Natural Language to SQL Query conversion using Machine Learning Techniques on HPCC Systems from HPCC Systems
]]> 443 0 https://cdn.slidesharecdn.com/ss_thumbnails/nlp-200724123847-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Improving Efficiency of Machine Learning Algorithms using HPCC Systems /slideshow/improving-efficiency-of-machine-learning-algorithms-using-hpcc-systems/236427025 ieee-200630174201
Presented at the IEEE-RVCE Computer Society Webinar Series]]>
Presented at the IEEE-RVCE Computer Society Webinar Series]]> Tue, 30 Jun 2020 17:42:01 GMT /slideshow/improving-efficiency-of-machine-learning-algorithms-using-hpcc-systems/236427025 hpccsystems@slideshare.net(hpccsystems) Improving Efficiency of Machine Learning Algorithms using HPCC Systems hpccsystems Presented at the IEEE-RVCE Computer Society Webinar Series <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ieee-200630174201-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presented at the IEEE-RVCE Computer Society Webinar Series
Improving Efficiency of Machine Learning Algorithms using HPCC Systems from HPCC Systems
]]> 166 0 https://cdn.slidesharecdn.com/ss_thumbnails/ieee-200630174201-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Towards Trustable AI for Complex Systems /slideshow/towards-trustable-ai-for-complex-systems/201978775 odsceurope2019pptxy-191205184351
Xian Yang's presentation to ODSC Europe 2019. Yang is a research fellow in the Data Science Institute at Imperial College London.]]>
Xian Yang's presentation to ODSC Europe 2019. Yang is a research fellow in the Data Science Institute at Imperial College London.]]> Thu, 05 Dec 2019 18:43:51 GMT /slideshow/towards-trustable-ai-for-complex-systems/201978775 hpccsystems@slideshare.net(hpccsystems) Towards Trustable AI for Complex Systems hpccsystems Xian Yang's presentation to ODSC Europe 2019. Yang is a research fellow in the Data Science Institute at Imperial College London. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/odsceurope2019pptxy-191205184351-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Xian Yang&#39;s presentation to ODSC Europe 2019. Yang is a research fellow in the Data Science Institute at Imperial College London.
Towards Trustable AI for Complex Systems from HPCC Systems
]]> 214 6 https://cdn.slidesharecdn.com/ss_thumbnails/odsceurope2019pptxy-191205184351-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Welcome /slideshow/welcome-185880216/185880216 001welcome-191023172555
2019 HPCC Systems Community Day]]>
2019 HPCC Systems Community Day]]> Wed, 23 Oct 2019 17:25:55 GMT /slideshow/welcome-185880216/185880216 hpccsystems@slideshare.net(hpccsystems) Welcome hpccsystems 2019 HPCC Systems Community Day <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/001welcome-191023172555-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 2019 HPCC Systems Community Day
Welcome from HPCC Systems
]]> 162 0 https://cdn.slidesharecdn.com/ss_thumbnails/001welcome-191023172555-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Closing / Adjourn /slideshow/closing-adjourn/185879662 024aclosing-191023172439
Closing / Adjourn ]]>
Closing / Adjourn ]]> Wed, 23 Oct 2019 17:24:39 GMT /slideshow/closing-adjourn/185879662 hpccsystems@slideshare.net(hpccsystems) Closing / Adjourn hpccsystems Closing / Adjourn <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/024aclosing-191023172439-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Closing / Adjourn
Closing / Adjourn from HPCC Systems
]]> 137 0 https://cdn.slidesharecdn.com/ss_thumbnails/024aclosing-191023172439-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Community Website: Virtual Ribbon Cutting /slideshow/community-website-virtual-ribbon-cutting/185804290 023jessicalorti-191023142449
The reveal of how HPCC Systems is improving and expanding around the world for building a global community. ]]>
The reveal of how HPCC Systems is improving and expanding around the world for building a global community. ]]> Wed, 23 Oct 2019 14:24:49 GMT /slideshow/community-website-virtual-ribbon-cutting/185804290 hpccsystems@slideshare.net(hpccsystems) Community Website: Virtual Ribbon Cutting hpccsystems The reveal of how HPCC Systems is improving and expanding around the world for building a global community. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/023jessicalorti-191023142449-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The reveal of how HPCC Systems is improving and expanding around the world for building a global community.
Community Website: Virtual Ribbon Cutting from HPCC Systems
]]> 145 2 https://cdn.slidesharecdn.com/ss_thumbnails/023jessicalorti-191023142449-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Path to 8.0 /slideshow/path-to-80-185803656/185803656 022gavinhalliday-191023142321
Come hear a brief overview on the direction the HPCC Systems platform is heading, and get a glimpse into some of the likely highlights included in the next minor and major versions. ]]>
Come hear a brief overview on the direction the HPCC Systems platform is heading, and get a glimpse into some of the likely highlights included in the next minor and major versions. ]]> Wed, 23 Oct 2019 14:23:21 GMT /slideshow/path-to-80-185803656/185803656 hpccsystems@slideshare.net(hpccsystems) Path to 8.0 hpccsystems Come hear a brief overview on the direction the HPCC Systems platform is heading, and get a glimpse into some of the likely highlights included in the next minor and major versions. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/022gavinhalliday-191023142321-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Come hear a brief overview on the direction the HPCC Systems platform is heading, and get a glimpse into some of the likely highlights included in the next minor and major versions.
Path to 8.0 from HPCC Systems
]]> 126 1 https://cdn.slidesharecdn.com/ss_thumbnails/022gavinhalliday-191023142321-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Release Cycle Changes /slideshow/release-cycle-changes/185799616 021attilavamos-191023141348
This talk will explain the reasoning behind the release cycle changes, and how overcoming the challenges faced in the previous practice of automated testing has introduced new benefits and wider acceptance from the wider community.]]>
This talk will explain the reasoning behind the release cycle changes, and how overcoming the challenges faced in the previous practice of automated testing has introduced new benefits and wider acceptance from the wider community.]]> Wed, 23 Oct 2019 14:13:48 GMT /slideshow/release-cycle-changes/185799616 hpccsystems@slideshare.net(hpccsystems) Release Cycle Changes hpccsystems This talk will explain the reasoning behind the release cycle changes, and how overcoming the challenges faced in the previous practice of automated testing has introduced new benefits and wider acceptance from the wider community. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/021attilavamos-191023141348-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This talk will explain the reasoning behind the release cycle changes, and how overcoming the challenges faced in the previous practice of automated testing has introduced new benefits and wider acceptance from the wider community.
Release Cycle Changes from HPCC Systems
]]> 148 1 https://cdn.slidesharecdn.com/ss_thumbnails/021attilavamos-191023141348-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Geohashing with Uber’s H3 Geospatial Index /hpccsystems/geohashing-with-ubers-h3-geospatial-index 020gordonsmith-191023140728
An introductory look at the ECL H3 Plugin (available since v7.2.0) - a journey from lat/long to ROXIE Service driven visualizations]]>
An introductory look at the ECL H3 Plugin (available since v7.2.0) - a journey from lat/long to ROXIE Service driven visualizations]]> Wed, 23 Oct 2019 14:07:28 GMT /hpccsystems/geohashing-with-ubers-h3-geospatial-index hpccsystems@slideshare.net(hpccsystems) Geohashing with Uber’s H3 Geospatial Index hpccsystems An introductory look at the ECL H3 Plugin (available since v7.2.0) - a journey from lat/long to ROXIE Service driven visualizations <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/020gordonsmith-191023140728-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An introductory look at the ECL H3 Plugin (available since v7.2.0) - a journey from lat/long to ROXIE Service driven visualizations
Geohashing with Uber’s H3 Geospatial Index from HPCC Systems
]]> 2254 2 https://cdn.slidesharecdn.com/ss_thumbnails/020gordonsmith-191023140728-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Advancements in HPCC Systems Machine Learning /slideshow/advancements-in-hpcc-systems-machine-learning/185795718 019rogerdevlilixu-191023140423
This presentation will provide an overview of the latest advancements in Machine Learning modules over the past year, including Clustering, Natural Language Processing, Deep Learning, and the Expanded Model Evaluation Metrics. Clustering Methods of the HPCC Systems Machine Learning Library The clustering method is an important part of unsupervised learning. To gain the unsupervised learning capability, two widely applied clustering methods, KMeans and DBSCAN are adopted to the current Machine Learning library. This presentation will introduce the newly developed clustering algorithms and the evaluation methods. ]]>
This presentation will provide an overview of the latest advancements in Machine Learning modules over the past year, including Clustering, Natural Language Processing, Deep Learning, and the Expanded Model Evaluation Metrics. Clustering Methods of the HPCC Systems Machine Learning Library The clustering method is an important part of unsupervised learning. To gain the unsupervised learning capability, two widely applied clustering methods, KMeans and DBSCAN are adopted to the current Machine Learning library. This presentation will introduce the newly developed clustering algorithms and the evaluation methods. ]]> Wed, 23 Oct 2019 14:04:23 GMT /slideshow/advancements-in-hpcc-systems-machine-learning/185795718 hpccsystems@slideshare.net(hpccsystems) Advancements in HPCC Systems Machine Learning hpccsystems This presentation will provide an overview of the latest advancements in Machine Learning modules over the past year, including Clustering, Natural Language Processing, Deep Learning, and the Expanded Model Evaluation Metrics. Clustering Methods of the HPCC Systems Machine Learning Library The clustering method is an important part of unsupervised learning. To gain the unsupervised learning capability, two widely applied clustering methods, KMeans and DBSCAN are adopted to the current Machine Learning library. This presentation will introduce the newly developed clustering algorithms and the evaluation methods. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/019rogerdevlilixu-191023140423-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This presentation will provide an overview of the latest advancements in Machine Learning modules over the past year, including Clustering, Natural Language Processing, Deep Learning, and the Expanded Model Evaluation Metrics. Clustering Methods of the HPCC Systems Machine Learning Library The clustering method is an important part of unsupervised learning. To gain the unsupervised learning capability, two widely applied clustering methods, KMeans and DBSCAN are adopted to the current Machine Learning library. This presentation will introduce the newly developed clustering algorithms and the evaluation methods.
Advancements in HPCC Systems Machine Learning from HPCC Systems
]]> 214 4 https://cdn.slidesharecdn.com/ss_thumbnails/019rogerdevlilixu-191023140423-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Docker Support /slideshow/docker-support/185793442 017xiaomingwanggodsonfortil-191023135823
Learn how to package the HPCC Systems Platform in a Docker container and deploy it locally, and build an HPCC Systems Platform AMI followed by an AWS deployment.]]>
Learn how to package the HPCC Systems Platform in a Docker container and deploy it locally, and build an HPCC Systems Platform AMI followed by an AWS deployment.]]> Wed, 23 Oct 2019 13:58:22 GMT /slideshow/docker-support/185793442 hpccsystems@slideshare.net(hpccsystems) Docker Support hpccsystems Learn how to package the HPCC Systems Platform in a Docker container and deploy it locally, and build an HPCC Systems Platform AMI followed by an AWS deployment. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/017xiaomingwanggodsonfortil-191023135823-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Learn how to package the HPCC Systems Platform in a Docker container and deploy it locally, and build an HPCC Systems Platform AMI followed by an AWS deployment.
Docker Support from HPCC Systems
]]> 163 0 https://cdn.slidesharecdn.com/ss_thumbnails/017xiaomingwanggodsonfortil-191023135823-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Expanding HPCC Systems Deep Neural Network Capabilities /slideshow/expanding-hpcc-systems-deep-neural-network-capabilities/185792227 016robertkennedy-191023135505
The training process for modern deep neural networks requires big data and large computational power. Though HPCC Systems excels at both of these, HPCC Systems is limited to utilizing the CPU only. It has been shown that GPU acceleration vastly improves Deep Learning training time. In this talk, Robert will explain how HPCC Systems became the first GPU accelerated library while also greatly expanding its deep neural network capabilities.]]>
The training process for modern deep neural networks requires big data and large computational power. Though HPCC Systems excels at both of these, HPCC Systems is limited to utilizing the CPU only. It has been shown that GPU acceleration vastly improves Deep Learning training time. In this talk, Robert will explain how HPCC Systems became the first GPU accelerated library while also greatly expanding its deep neural network capabilities.]]> Wed, 23 Oct 2019 13:55:05 GMT /slideshow/expanding-hpcc-systems-deep-neural-network-capabilities/185792227 hpccsystems@slideshare.net(hpccsystems) Expanding HPCC Systems Deep Neural Network Capabilities hpccsystems The training process for modern deep neural networks requires big data and large computational power. Though HPCC Systems excels at both of these, HPCC Systems is limited to utilizing the CPU only. It has been shown that GPU acceleration vastly improves Deep Learning training time. In this talk, Robert will explain how HPCC Systems became the first GPU accelerated library while also greatly expanding its deep neural network capabilities. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/016robertkennedy-191023135505-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The training process for modern deep neural networks requires big data and large computational power. Though HPCC Systems excels at both of these, HPCC Systems is limited to utilizing the CPU only. It has been shown that GPU acceleration vastly improves Deep Learning training time. In this talk, Robert will explain how HPCC Systems became the first GPU accelerated library while also greatly expanding its deep neural network capabilities.
Expanding HPCC Systems Deep Neural Network Capabilities from HPCC Systems
]]> 165 3 https://cdn.slidesharecdn.com/ss_thumbnails/016robertkennedy-191023135505-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Leveraging Intra-Node Parallelization in HPCC Systems /hpccsystems/leveraging-intranode-parallelization-in-hpcc-systems 015fabianfier-191023135104
HPCC Systems offers parallelization by assuming data-independent tasks. For operations having complex predicates, such as the set similarity join (SSJ) predicates, this assumption might create a tradeoff. On the one hand, one may choose a data replication strategy with large intermediate data groups assuring that all intermediate results fit into main memory. However, this can lead to an underutilization of today's massively parallel CPUs. On the other hand, you may choose a higher degree of replication for better CPU utilization. Such a choice may lead to an overutilization of main memory on the compute nodes. Our research focuses on the parallel implementation of the set similarity join (SSJ) operator. This operator finds all pairs of records which have a similarity above a defined threshold using a similarity measure such as Jaccard. Our goal is a robust approach for executing SSJ that does not over-utilize memory and exploits CPU parallelization as much as possible. This approach requires data sharing between tasks/threads which is not foreseen in HPCC Systems so far. In this talk, we describe how we implemented multithreaded user-defined functions for the SSJ operator. To be able to control NUMAspecific parallelization conditions, we implemented a C++ plugin for HPCC Systems. Furthermore, we show how we visualized the relevant system parameters (CPU and memory usage). This talk is intended for anyone interested in extending HPCC Systems by plugins and monitoring distributed program execution. ]]>
HPCC Systems offers parallelization by assuming data-independent tasks. For operations having complex predicates, such as the set similarity join (SSJ) predicates, this assumption might create a tradeoff. On the one hand, one may choose a data replication strategy with large intermediate data groups assuring that all intermediate results fit into main memory. However, this can lead to an underutilization of today's massively parallel CPUs. On the other hand, you may choose a higher degree of replication for better CPU utilization. Such a choice may lead to an overutilization of main memory on the compute nodes. Our research focuses on the parallel implementation of the set similarity join (SSJ) operator. This operator finds all pairs of records which have a similarity above a defined threshold using a similarity measure such as Jaccard. Our goal is a robust approach for executing SSJ that does not over-utilize memory and exploits CPU parallelization as much as possible. This approach requires data sharing between tasks/threads which is not foreseen in HPCC Systems so far. In this talk, we describe how we implemented multithreaded user-defined functions for the SSJ operator. To be able to control NUMAspecific parallelization conditions, we implemented a C++ plugin for HPCC Systems. Furthermore, we show how we visualized the relevant system parameters (CPU and memory usage). This talk is intended for anyone interested in extending HPCC Systems by plugins and monitoring distributed program execution. ]]> Wed, 23 Oct 2019 13:51:04 GMT /hpccsystems/leveraging-intranode-parallelization-in-hpcc-systems hpccsystems@slideshare.net(hpccsystems) Leveraging Intra-Node Parallelization in HPCC Systems hpccsystems HPCC Systems offers parallelization by assuming data-independent tasks. For operations having complex predicates, such as the set similarity join (SSJ) predicates, this assumption might create a tradeoff. On the one hand, one may choose a data replication strategy with large intermediate data groups assuring that all intermediate results fit into main memory. However, this can lead to an underutilization of today's massively parallel CPUs. On the other hand, you may choose a higher degree of replication for better CPU utilization. Such a choice may lead to an overutilization of main memory on the compute nodes. Our research focuses on the parallel implementation of the set similarity join (SSJ) operator. This operator finds all pairs of records which have a similarity above a defined threshold using a similarity measure such as Jaccard. Our goal is a robust approach for executing SSJ that does not over-utilize memory and exploits CPU parallelization as much as possible. This approach requires data sharing between tasks/threads which is not foreseen in HPCC Systems so far. In this talk, we describe how we implemented multithreaded user-defined functions for the SSJ operator. To be able to control NUMAspecific parallelization conditions, we implemented a C++ plugin for HPCC Systems. Furthermore, we show how we visualized the relevant system parameters (CPU and memory usage). This talk is intended for anyone interested in extending HPCC Systems by plugins and monitoring distributed program execution. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/015fabianfier-191023135104-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> HPCC Systems offers parallelization by assuming data-independent tasks. For operations having complex predicates, such as the set similarity join (SSJ) predicates, this assumption might create a tradeoff. On the one hand, one may choose a data replication strategy with large intermediate data groups assuring that all intermediate results fit into main memory. However, this can lead to an underutilization of today&#39;s massively parallel CPUs. On the other hand, you may choose a higher degree of replication for better CPU utilization. Such a choice may lead to an overutilization of main memory on the compute nodes. Our research focuses on the parallel implementation of the set similarity join (SSJ) operator. This operator finds all pairs of records which have a similarity above a defined threshold using a similarity measure such as Jaccard. Our goal is a robust approach for executing SSJ that does not over-utilize memory and exploits CPU parallelization as much as possible. This approach requires data sharing between tasks/threads which is not foreseen in HPCC Systems so far. In this talk, we describe how we implemented multithreaded user-defined functions for the SSJ operator. To be able to control NUMAspecific parallelization conditions, we implemented a C++ plugin for HPCC Systems. Furthermore, we show how we visualized the relevant system parameters (CPU and memory usage). This talk is intended for anyone interested in extending HPCC Systems by plugins and monitoring distributed program execution.
Leveraging Intra-Node Parallelization in HPCC Systems from HPCC Systems
]]> 75 2 https://cdn.slidesharecdn.com/ss_thumbnails/015fabianfier-191023135104-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 DataPatterns - Profiling in ECL Watch /slideshow/datapatterns-profiling-in-ecl-watch/185788758 014dancamper-191023134524
DataPatterns.Profile() has been evolving since the last time you may have seen it. It does more. It looks better. It has been integrated into the ECL standard library and into ECL Watch. Learn what this data profiler can do for you and how its built-in visualization easily summarizes the results. ]]>
DataPatterns.Profile() has been evolving since the last time you may have seen it. It does more. It looks better. It has been integrated into the ECL standard library and into ECL Watch. Learn what this data profiler can do for you and how its built-in visualization easily summarizes the results. ]]> Wed, 23 Oct 2019 13:45:24 GMT /slideshow/datapatterns-profiling-in-ecl-watch/185788758 hpccsystems@slideshare.net(hpccsystems) DataPatterns - Profiling in ECL Watch hpccsystems DataPatterns.Profile() has been evolving since the last time you may have seen it. It does more. It looks better. It has been integrated into the ECL standard library and into ECL Watch. Learn what this data profiler can do for you and how its built-in visualization easily summarizes the results. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/014dancamper-191023134524-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> DataPatterns.Profile() has been evolving since the last time you may have seen it. It does more. It looks better. It has been integrated into the ECL standard library and into ECL Watch. Learn what this data profiler can do for you and how its built-in visualization easily summarizes the results.
DataPatterns - Profiling in ECL Watch from HPCC Systems
]]> 153 3 https://cdn.slidesharecdn.com/ss_thumbnails/014dancamper-191023134524-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Leveraging the Spark-HPCC Ecosystem /slideshow/leveraging-the-sparkhpcc-ecosystem/185460108 012jamesmcmullan-191022202007
Join us for an introductory walk-through of using the Spark-HPCC Systems ecosystem to analyze your HPCC Systems data using a collaborative Apache Zeppelin notebook environment. ]]>
Join us for an introductory walk-through of using the Spark-HPCC Systems ecosystem to analyze your HPCC Systems data using a collaborative Apache Zeppelin notebook environment. ]]> Tue, 22 Oct 2019 20:20:07 GMT /slideshow/leveraging-the-sparkhpcc-ecosystem/185460108 hpccsystems@slideshare.net(hpccsystems) Leveraging the Spark-HPCC Ecosystem hpccsystems Join us for an introductory walk-through of using the Spark-HPCC Systems ecosystem to analyze your HPCC Systems data using a collaborative Apache Zeppelin notebook environment. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/012jamesmcmullan-191022202007-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Join us for an introductory walk-through of using the Spark-HPCC Systems ecosystem to analyze your HPCC Systems data using a collaborative Apache Zeppelin notebook environment.
Leveraging the Spark-HPCC Ecosystem from HPCC Systems
]]> 73 2 https://cdn.slidesharecdn.com/ss_thumbnails/012jamesmcmullan-191022202007-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Work Unit Analysis Tool /slideshow/work-unit-analysis-tool/185459016 011shamserahmed-191022201552
The Workunit Analyser examines the entire workunit to produce advice that both novices and experienced ECL developers should find useful. The Workunit Analyser is a post-execution analyser that identifies potential issues and assists users in writing better ECL. ]]>
The Workunit Analyser examines the entire workunit to produce advice that both novices and experienced ECL developers should find useful. The Workunit Analyser is a post-execution analyser that identifies potential issues and assists users in writing better ECL. ]]> Tue, 22 Oct 2019 20:15:52 GMT /slideshow/work-unit-analysis-tool/185459016 hpccsystems@slideshare.net(hpccsystems) Work Unit Analysis Tool hpccsystems The Workunit Analyser examines the entire workunit to produce advice that both novices and experienced ECL developers should find useful. The Workunit Analyser is a post-execution analyser that identifies potential issues and assists users in writing better ECL. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/011shamserahmed-191022201552-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The Workunit Analyser examines the entire workunit to produce advice that both novices and experienced ECL developers should find useful. The Workunit Analyser is a post-execution analyser that identifies potential issues and assists users in writing better ECL.
Work Unit Analysis Tool from HPCC Systems
]]> 95 0 https://cdn.slidesharecdn.com/ss_thumbnails/011shamserahmed-191022201552-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Community Award Ceremony /slideshow/community-award-ceremony/185458053 010communityawards-191022201230
Let's congratulate the winners of the 2019 HPCC Systems Poster Competition and other award recipients. ]]>
Let's congratulate the winners of the 2019 HPCC Systems Poster Competition and other award recipients. ]]> Tue, 22 Oct 2019 20:12:30 GMT /slideshow/community-award-ceremony/185458053 hpccsystems@slideshare.net(hpccsystems) Community Award Ceremony hpccsystems Let's congratulate the winners of the 2019 HPCC Systems Poster Competition and other award recipients. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/010communityawards-191022201230-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Let&#39;s congratulate the winners of the 2019 HPCC Systems Poster Competition and other award recipients.
Community Award Ceremony from HPCC Systems
]]> 161 2 https://cdn.slidesharecdn.com/ss_thumbnails/010communityawards-191022201230-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Dapper Tool - A Bundle to Make your ECL Neater /slideshow/dapper-tool-a-bundle-to-make-your-ecl-neater/185456700 013robmansfield-191022200806
Have you ever written a long project for a simple column rename and thought, this should be easier? What about nicely named output statements? Yeah they bother me too. Oh, and DEDUP(SORT(DISTINCT()))? There is a better way! Learn how Dapper can help!]]>
Have you ever written a long project for a simple column rename and thought, this should be easier? What about nicely named output statements? Yeah they bother me too. Oh, and DEDUP(SORT(DISTINCT()))? There is a better way! Learn how Dapper can help!]]> Tue, 22 Oct 2019 20:08:06 GMT /slideshow/dapper-tool-a-bundle-to-make-your-ecl-neater/185456700 hpccsystems@slideshare.net(hpccsystems) Dapper Tool - A Bundle to Make your ECL Neater hpccsystems Have you ever written a long project for a simple column rename and thought, this should be easier? What about nicely named output statements? Yeah they bother me too. Oh, and DEDUP(SORT(DISTINCT()))? There is a better way! Learn how Dapper can help! <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/013robmansfield-191022200806-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Have you ever written a long project for a simple column rename and thought, this should be easier? What about nicely named output statements? Yeah they bother me too. Oh, and DEDUP(SORT(DISTINCT()))? There is a better way! Learn how Dapper can help!
Dapper Tool - A Bundle to Make your ECL Neater from HPCC Systems
]]> 76 3 https://cdn.slidesharecdn.com/ss_thumbnails/013robmansfield-191022200806-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 A Success Story of Challenging the Status Quo: Gadget Girls and the Inclusion of Women in STEM Teaching /slideshow/a-success-story-of-challenging-the-status-quo-gadget-girls-and-the-inclusion-of-women-in-stem-teaching/185456656 009ronnieshashoua-191022200755
Join NSU University School student and program leader for girls in robotics, Ronnie Shashoua, as she talks about Gadget Girls - a project in collaboration with the NSU Alvin Sherman Library, NSU University School, the South Florida Girl Scouts, and sponsorship from the HPCC Systems Academic Program. Gadget Girls is a program aimed at encouraging girls in fourth and fifth grade to explore their interests in and love for STEM, especially robotics and engineering. Shashoua will discuss the underrepresentation of girls in the Florida Vex Robotics circuit, such as how it demonstrates a larger trend of low numbers of women undertaking STEM educational and career paths and the role it played in inspiring the creation of Gadget Girls.]]>
Join NSU University School student and program leader for girls in robotics, Ronnie Shashoua, as she talks about Gadget Girls - a project in collaboration with the NSU Alvin Sherman Library, NSU University School, the South Florida Girl Scouts, and sponsorship from the HPCC Systems Academic Program. Gadget Girls is a program aimed at encouraging girls in fourth and fifth grade to explore their interests in and love for STEM, especially robotics and engineering. Shashoua will discuss the underrepresentation of girls in the Florida Vex Robotics circuit, such as how it demonstrates a larger trend of low numbers of women undertaking STEM educational and career paths and the role it played in inspiring the creation of Gadget Girls.]]> Tue, 22 Oct 2019 20:07:55 GMT /slideshow/a-success-story-of-challenging-the-status-quo-gadget-girls-and-the-inclusion-of-women-in-stem-teaching/185456656 hpccsystems@slideshare.net(hpccsystems) A Success Story of Challenging the Status Quo: Gadget Girls and the Inclusion of Women in STEM Teaching hpccsystems Join NSU University School student and program leader for girls in robotics, Ronnie Shashoua, as she talks about Gadget Girls - a project in collaboration with the NSU Alvin Sherman Library, NSU University School, the South Florida Girl Scouts, and sponsorship from the HPCC Systems Academic Program. Gadget Girls is a program aimed at encouraging girls in fourth and fifth grade to explore their interests in and love for STEM, especially robotics and engineering. Shashoua will discuss the underrepresentation of girls in the Florida Vex Robotics circuit, such as how it demonstrates a larger trend of low numbers of women undertaking STEM educational and career paths and the role it played in inspiring the creation of Gadget Girls. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/009ronnieshashoua-191022200755-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Join NSU University School student and program leader for girls in robotics, Ronnie Shashoua, as she talks about Gadget Girls - a project in collaboration with the NSU Alvin Sherman Library, NSU University School, the South Florida Girl Scouts, and sponsorship from the HPCC Systems Academic Program. Gadget Girls is a program aimed at encouraging girls in fourth and fifth grade to explore their interests in and love for STEM, especially robotics and engineering. Shashoua will discuss the underrepresentation of girls in the Florida Vex Robotics circuit, such as how it demonstrates a larger trend of low numbers of women undertaking STEM educational and career paths and the role it played in inspiring the creation of Gadget Girls.
A Success Story of Challenging the Status Quo: Gadget Girls and the Inclusion of Women in STEM Teaching from HPCC Systems
]]> 90 3 https://cdn.slidesharecdn.com/ss_thumbnails/009ronnieshashoua-191022200755-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Beyond the Spectrum – Creating an Environment of Diversity and Empowerment with HPCC Systems /slideshow/beyond-the-spectrum-creating-an-environment-of-diversity-and-empowerment-with-hpcc-systems/185455451 008dariusmurray-191022200351
Hear how the Florida Atlantic University Center for Autism and Related Disabilities has partnered with the HPCC Systems community to provide young people with autism both the technology and professional skills needed to compete in today’s workplace. Mentoring and hands-on coding through ECL workshops have positively impacted students, opening doors to new opportunities for both students and employers. ]]>
Hear how the Florida Atlantic University Center for Autism and Related Disabilities has partnered with the HPCC Systems community to provide young people with autism both the technology and professional skills needed to compete in today’s workplace. Mentoring and hands-on coding through ECL workshops have positively impacted students, opening doors to new opportunities for both students and employers. ]]> Tue, 22 Oct 2019 20:03:51 GMT /slideshow/beyond-the-spectrum-creating-an-environment-of-diversity-and-empowerment-with-hpcc-systems/185455451 hpccsystems@slideshare.net(hpccsystems) Beyond the Spectrum – Creating an Environment of Diversity and Empowerment with HPCC Systems hpccsystems Hear how the Florida Atlantic University Center for Autism and Related Disabilities has partnered with the HPCC Systems community to provide young people with autism both the technology and professional skills needed to compete in today’s workplace. Mentoring and hands-on coding through ECL workshops have positively impacted students, opening doors to new opportunities for both students and employers. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/008dariusmurray-191022200351-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Hear how the Florida Atlantic University Center for Autism and Related Disabilities has partnered with the HPCC Systems community to provide young people with autism both the technology and professional skills needed to compete in today’s workplace. Mentoring and hands-on coding through ECL workshops have positively impacted students, opening doors to new opportunities for both students and employers.
Beyond the Spectrum – Creating an Environment of Diversity and Empowerment with HPCC Systems from HPCC Systems
]]> 56 3 https://cdn.slidesharecdn.com/ss_thumbnails/008dariusmurray-191022200351-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-hpccsystems-48x48.jpg?cb=1634578284 HPCC Systems® (www.hpccsystems.com) from LexisNexis® Risk Solutions offers a proven, data-intensive supercomputing platform designed for the enterprise to process and solve Big Data analytical problems. As an alternative to legacy technology, HPCC Systems offers a consistent data-centric programming language, two processing platforms and a single, complete end-to-end architecture for efficient processing. www.hpccsystems.com https://cdn.slidesharecdn.com/ss_thumbnails/nlp-200724123847-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/natural-language-to-sql-query-conversion-using-machine-learning-techniques-on-hpcc-systems/237213407 Natural Language to SQ... https://cdn.slidesharecdn.com/ss_thumbnails/ieee-200630174201-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/improving-efficiency-of-machine-learning-algorithms-using-hpcc-systems/236427025 Improving Efficiency o... https://cdn.slidesharecdn.com/ss_thumbnails/odsceurope2019pptxy-191205184351-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/towards-trustable-ai-for-complex-systems/201978775 Towards Trustable AI f...