The document discusses big data visualization and visual analysis, focusing on the challenges and opportunities. It begins with an overview of visualization and then discusses several challenges in big data visualization, including integrating heterogeneous data from different sources and scales, dealing with data and task complexity, limited interaction capabilities for large data, scalability for both data and users, and the need for domain and development libraries/tools. It then provides examples of visualizing taxi GPS data and traffic patterns in Beijing to identify traffic jams.
詹剑锋:Big databench—benchmarking big data systemshdhappy001
This document discusses BigDataBench, an open source project for big data benchmarking. BigDataBench includes six real-world data sets and 19 workloads that cover common big data applications and preserve the four V's of big data. The workloads were chosen to represent typical application domains like search engines, social networks, and e-commerce. BigDataBench aims to provide a standardized benchmark for evaluating big data systems, architectures, and software stacks. It has been used in several case studies for workload characterization and evaluating the performance and energy efficiency of different hardware platforms for big data workloads.
Best pratices at BGI for the Challenges in the Era of Big Genomics DataXing Xu
BGI is the world's largest genome sequencing center, with over 150 sequencers and a sequencing throughput of 6 TB per day. It also has the largest computing and storage center for genomics in China, with over 20,000 CPU cores, 19 GPUs, 220+ teraflops of peak performance, and 17 petabytes of data storage. BGI faces challenges from the exponential growth of genomic data, complex data analysis processes, and widely distributed data. It addresses these challenges through solutions like high-speed data transfer, cloud computing platforms like EasyGenomics, and distributed algorithms and infrastructure using Hadoop and GPU acceleration.
This tutorial was held at IEEE BigData '14 on October 29, 2014 in Bethesda, ML, USA.
Presenters: Chaitan Baru and Tilmann Rabl
More information available at:
http://msrg.org/papers/BigData14-Rabl
Summary:
This tutorial will introduce the audience to the broad set of issues involved in defining big data benchmarks, for creating auditable industry-standard benchmarks that consider performance as well as price/performance. Big data benchmarks must capture the essential characteristics of big data applications and systems, including heterogeneous data, e.g. structured, semi- structured, unstructured, graphs, and streams; large-scale and evolving system configurations; varying system loads; processing pipelines that progressively transform data; workloads that include queries as well as data mining and machine learning operations and algorithms. Different benchmarking approaches will be introduced, from micro-benchmarks to application- level benchmarking.
Since May 2012, five workshops have been held on Big Data Benchmarking including participation from industry and academia. One of the outcomes of these meetings has been the creation of industry’s first big data benchmark, viz., TPCx-HS, the Transaction Processing Performance Council’s benchmark for Hadoop Systems. During these workshops, a number of other proposals have been put forward for more comprehensive big data benchmarking. The tutorial will present and discuss salient points and essential features of such benchmarks that have been identified in these meetings, by experts in big data as well as benchmarking. Two key approaches are now being pursued—one, called BigBench, is based on extending the TPC- Decision Support (TPC-DS) benchmark with big data applications characteristics. The other called Deep Analytics Pipeline, is based on modeling processing that is routinely encountered in real-life big data applications. Both will be discussed.
We conclude with a discussion of a number of future directions for big data benchmarking
Lessons Learned on Benchmarking Big Data Platformst_ivanov
The document discusses benchmarking different big data platforms and SQL-on-Hadoop engines. It evaluates the performance of Hadoop using the TPCx-HS benchmark with different network configurations. It also compares the performance of SQL query engines like Hive, Spark SQL, Impala, and file formats like ORC and Parquet using the TPC-H benchmark on a 1TB dataset. The results show that a dedicated 1Gb network is 5 times faster than a shared network. For SQL query engines, Hive with ORC format is on average 1.44 times faster than with Parquet. Spark SQL could only run 12 queries and was faster on 5 queries compared to Hive.
Big Data Ecosystem at LinkedIn. Keynote talk at Big Data Innovators Gathering...Mitul Tiwari
LinkedIn has a large professional network with 360M members. They build data-driven products using members' rich profile data. To do this, they ingest online data into offline systems using Apache Kafka. The data is then processed using Hadoop, Spark, Samza and Cubert to compute features and train models. Results are moved back online using Voldemort and Kafka. For example, People You May Know recommendations are generated by triangle closing in Hadoop and Cubert to count common connections faster. Site speed is monitored in real-time using Samza to join logs from different services.
WBDB 2015 Performance Evaluation of Spark SQL using BigBencht_ivanov
In this paper we present the initial results of our work to run BigBench on Spark. First, we evaluated the data scalability behavior of the existing MapReduce implementation of BigBench. Next, we executed the group of 14 pure HiveQL queries on Spark SQL and compared the results with the respective Hive results. Our experiments show that: (1) for both MapReduce and Spark SQL, BigBench queries perform with the increase of the data size on average better than the linear scaling behavior and (2) pure HiveQL queries perform faster on Spark SQL than on Hive.
http://clds.sdsc.edu/wbdb2015.ca/program
This document provides an overview of streaming analytics, including definitions, common use cases, and key concepts like streaming engines, processing models, and guarantees. It also provides examples of analyzing data streams using Apache Spark Structured Streaming, Apache Flink, and Kafka Streams APIs. Code snippets demonstrate windowing, triggers, and working with event-time.
BDSE 2015 Evaluation of Big Data Platforms with HiBencht_ivanov
The document evaluates and compares the performance of DataStax Enterprise (DSE) and Cloudera Hadoop Distribution (CDH) using the HiBench benchmark suite. It finds that CDH outperforms DSE for CPU-intensive, read-intensive, and mixed workloads, while DSE has better performance for write-intensive workloads. The evaluation was conducted on an 8-node cluster using data sizes from 240GB to 440GB. Ongoing work includes analyzing availability, evaluating different file formats, and comparing graph processing engines.
詹剑锋:Big databench—benchmarking big data systemshdhappy001
This document discusses BigDataBench, an open source project for big data benchmarking. BigDataBench includes six real-world data sets and 19 workloads that cover common big data applications and preserve the four V's of big data. The workloads were chosen to represent typical application domains like search engines, social networks, and e-commerce. BigDataBench aims to provide a standardized benchmark for evaluating big data systems, architectures, and software stacks. It has been used in several case studies for workload characterization and evaluating the performance and energy efficiency of different hardware platforms for big data workloads.
Best pratices at BGI for the Challenges in the Era of Big Genomics DataXing Xu
BGI is the world's largest genome sequencing center, with over 150 sequencers and a sequencing throughput of 6 TB per day. It also has the largest computing and storage center for genomics in China, with over 20,000 CPU cores, 19 GPUs, 220+ teraflops of peak performance, and 17 petabytes of data storage. BGI faces challenges from the exponential growth of genomic data, complex data analysis processes, and widely distributed data. It addresses these challenges through solutions like high-speed data transfer, cloud computing platforms like EasyGenomics, and distributed algorithms and infrastructure using Hadoop and GPU acceleration.
This tutorial was held at IEEE BigData '14 on October 29, 2014 in Bethesda, ML, USA.
Presenters: Chaitan Baru and Tilmann Rabl
More information available at:
http://msrg.org/papers/BigData14-Rabl
Summary:
This tutorial will introduce the audience to the broad set of issues involved in defining big data benchmarks, for creating auditable industry-standard benchmarks that consider performance as well as price/performance. Big data benchmarks must capture the essential characteristics of big data applications and systems, including heterogeneous data, e.g. structured, semi- structured, unstructured, graphs, and streams; large-scale and evolving system configurations; varying system loads; processing pipelines that progressively transform data; workloads that include queries as well as data mining and machine learning operations and algorithms. Different benchmarking approaches will be introduced, from micro-benchmarks to application- level benchmarking.
Since May 2012, five workshops have been held on Big Data Benchmarking including participation from industry and academia. One of the outcomes of these meetings has been the creation of industry’s first big data benchmark, viz., TPCx-HS, the Transaction Processing Performance Council’s benchmark for Hadoop Systems. During these workshops, a number of other proposals have been put forward for more comprehensive big data benchmarking. The tutorial will present and discuss salient points and essential features of such benchmarks that have been identified in these meetings, by experts in big data as well as benchmarking. Two key approaches are now being pursued—one, called BigBench, is based on extending the TPC- Decision Support (TPC-DS) benchmark with big data applications characteristics. The other called Deep Analytics Pipeline, is based on modeling processing that is routinely encountered in real-life big data applications. Both will be discussed.
We conclude with a discussion of a number of future directions for big data benchmarking
Lessons Learned on Benchmarking Big Data Platformst_ivanov
The document discusses benchmarking different big data platforms and SQL-on-Hadoop engines. It evaluates the performance of Hadoop using the TPCx-HS benchmark with different network configurations. It also compares the performance of SQL query engines like Hive, Spark SQL, Impala, and file formats like ORC and Parquet using the TPC-H benchmark on a 1TB dataset. The results show that a dedicated 1Gb network is 5 times faster than a shared network. For SQL query engines, Hive with ORC format is on average 1.44 times faster than with Parquet. Spark SQL could only run 12 queries and was faster on 5 queries compared to Hive.
Big Data Ecosystem at LinkedIn. Keynote talk at Big Data Innovators Gathering...Mitul Tiwari
LinkedIn has a large professional network with 360M members. They build data-driven products using members' rich profile data. To do this, they ingest online data into offline systems using Apache Kafka. The data is then processed using Hadoop, Spark, Samza and Cubert to compute features and train models. Results are moved back online using Voldemort and Kafka. For example, People You May Know recommendations are generated by triangle closing in Hadoop and Cubert to count common connections faster. Site speed is monitored in real-time using Samza to join logs from different services.
WBDB 2015 Performance Evaluation of Spark SQL using BigBencht_ivanov
In this paper we present the initial results of our work to run BigBench on Spark. First, we evaluated the data scalability behavior of the existing MapReduce implementation of BigBench. Next, we executed the group of 14 pure HiveQL queries on Spark SQL and compared the results with the respective Hive results. Our experiments show that: (1) for both MapReduce and Spark SQL, BigBench queries perform with the increase of the data size on average better than the linear scaling behavior and (2) pure HiveQL queries perform faster on Spark SQL than on Hive.
http://clds.sdsc.edu/wbdb2015.ca/program
This document provides an overview of streaming analytics, including definitions, common use cases, and key concepts like streaming engines, processing models, and guarantees. It also provides examples of analyzing data streams using Apache Spark Structured Streaming, Apache Flink, and Kafka Streams APIs. Code snippets demonstrate windowing, triggers, and working with event-time.
BDSE 2015 Evaluation of Big Data Platforms with HiBencht_ivanov
The document evaluates and compares the performance of DataStax Enterprise (DSE) and Cloudera Hadoop Distribution (CDH) using the HiBench benchmark suite. It finds that CDH outperforms DSE for CPU-intensive, read-intensive, and mixed workloads, while DSE has better performance for write-intensive workloads. The evaluation was conducted on an 8-node cluster using data sizes from 240GB to 440GB. Ongoing work includes analyzing availability, evaluating different file formats, and comparing graph processing engines.
TPCx-HS is the first vendor-neutral benchmark focused on big data systems – which have become a critical part of the enterprise IT ecosystem.
Watch the video presentation: http://wp.me/p3RLHQ-cLY
Learn more: http://www.tpc.org/tpcx-hs
The document describes scientific workflows for big data and the challenges they present. It discusses Prof. Shiyong Lu's work on developing the VIEW system for designing, executing, and analyzing scientific workflows. The VIEW system provides a runtime environment for workflows, supports their execution on servers or clouds, and enables efficient storage, querying and visualization of workflow provenance data.
This document discusses HiBench, a benchmark suite for Hadoop. It provides an overview of HiBench and how it can be used to characterize and evaluate Hadoop deployments. Evaluation results using HiBench show that a newer Intel Xeon server platform provides up to 86% more throughput and is up to 56% faster than an older platform. Evaluations between Hadoop versions 0.19.1 and 0.20.0 show that improvements in the newer version help reduce job completion times. The document concludes by providing suggestions for optimizing Hadoop deployments through hardware and software configurations.
Covers different types of big data benchmarking, different suites, details into terasort, demo with TPCx-HS
Meetup Details of presentation:
http://www.meetup.com/lspe-in/events/203918952/
Big Graph Analytics on Neo4j with Apache SparkKenny Bastani
In this talk I will introduce you to a Docker container that provides you an easy way to do distributed graph processing using Apache Spark GraphX and a Neo4j graph database. You'll learn how to analyze big data graphs that are exported from Neo4j and consequently updated from the results of a Spark GraphX analysis. The types of analysis I will be talking about are PageRank, connected components, triangle counting, and community detection.
Database technologies have evolved to be able to store big data, but are largely inflexible. For complex graph data models stored in a relational database there may be tedious transformations and shuffling around of data to perform large scale analysis.
Fast and scalable analysis of big data has become a critical competitive advantage for companies. There are open source tools like Apache Hadoop and Apache Spark that are providing opportunities for companies to solve these big data problems in a scalable way. Platforms like these have become the foundation of the big data analysis movement.
Speakers
Graph analytics can be used to analyze a social graph constructed from email messages on the Spark user mailing list. Key metrics like PageRank, in-degrees, and strongly connected components can be computed using the GraphX API in Spark. For example, PageRank was computed on the 4Q2014 email graph, identifying the top contributors to the mailing list.
In 2001, as early high-speed networks were deployed, George Gilder observed that “when the network is as fast as the computer's internal links, the machine disintegrates across the net into a set of special purpose appliances.” Two decades later, our networks are 1,000 times faster, our appliances are increasingly specialized, and our computer systems are indeed disintegrating. As hardware acceleration overcomes speed-of-light delays, time and space merge into a computing continuum. Familiar questions like “where should I compute,” “for what workloads should I design computers,” and "where should I place my computers” seem to allow for a myriad of new answers that are exhilarating but also daunting. Are there concepts that can help guide us as we design applications and computer systems in a world that is untethered from familiar landmarks like center, cloud, edge? I propose some ideas and report on experiments in coding the continuum.
Social Media World News Impact on Stock Index Values - Investment Fund Analyt...Bernardo Najlis
Presentation for project on Social Media World News Impact on Stock Index Values (DJIA) for Investment Fund Analytics. Group project done in course DS8004 - Data Mining at Ryerson University for Masters in Data Science and Analytics.
Graph Data: a New Data Management FrontierDemai Ni
Graph Data: a New Data Management Frontier -- Huawei’s view and Call for Collaboration by Demai Ni:
Huawei provides Enterprise Databases, and are actively exploring the latest technology to provide end-to-end Data Management Solution on Cloud. We are looking at to bridge classic RDMS to Graph Database on a distributed platform.
Big Data is changing abruptly, and where it is likely headingPaco Nathan
Big Data technologies are changing rapidly due to shifts in hardware, data types, and software frameworks. Incumbent Big Data technologies do not fully leverage newer hardware like multicore processors and large memory spaces, while newer open source projects like Spark have emerged to better utilize these resources. Containers, clouds, functional programming, databases, approximations, and notebooks represent significant trends in how Big Data is managed and analyzed at large scale.
Big data appliance ecosystem - in memory db, hadoop, analytics, data mining, business intelligence with multiple data source charts, twitter support and analysis.
Gao cong geospatial social media data management and context-aware recommenda...jins0618
The document discusses geospatial social media data management and context-aware recommendation. It introduces technologies for geo-positioning users and content, and how user generated content from social media is increasingly associated with geo-locations. The document then outlines queries for static geo-textual data, publish/subscribe queries on geo-textual data streams, and personalized, context-aware point-of-interest recommendation based on modeling user behavior from geo-textual data.
Fully Automated QA System For Large Scale Search And Recommendation Engines U...Spark Summit
1) The document describes a fully automated QA system for large scale search and recommendation engines using Spark.
2) It discusses key concepts in information retrieval like precision, recall, and learning to rank as well as challenges in building machine learning models for ranking like obtaining labeled training data.
3) The system architecture involves extracting features from query logs, calculating relevance scores from user click signals, and training machine learning models to improve ranking.
This document provides an overview of Amundsen, an open source data discovery and metadata platform developed by Lyft. It begins with an introduction to the challenges of data discovery and outlines Amundsen's architecture, which uses a graph database and search engine to provide metadata about data resources. The document discusses how Amundsen impacts users at Lyft by reducing time spent searching for data and discusses the project's community and future roadmap.
Exploring Neo4j Graph Database as a Fast Data Access LayerSambit Banerjee
This article describes the findings of an extensive investigative work conducted to explore the feasibility of using a Neo4j Graph Database to build a Fast Data Access Layer with near-real time data ingestion from the underlying source systems.
Data analysis using hive ql & tableaupkale1708
This document describes a project analyzing crime data from Chicago to determine safe and unsafe areas of the city. The analysis uses big data tools like HiveQL on a Hadoop cluster to query a 1.3GB crime dataset. Queries find the most common crime types, crimes by location and month, and rank areas by crime counts. The results are visualized in graphs and maps. The goal is to help users identify safe residences using large-scale public crime data.
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
This document discusses benchmarking Hadoop and big data systems. It provides an overview of common Hadoop benchmarks including microbenchmarks like TestDFSIO, TeraSort, and NNBench which test individual Hadoop components. It also describes BigBench, a benchmark modeled after TPC-DS that aims to test a more complete big data analytics workload using techniques like MapReduce, Hive, and Mahout across structured, semi-structured, and unstructured data. The document emphasizes using Hadoop distributions for administration and both microbenchmarks and full benchmarks like BigBench for evaluation.
詹剑锋:Big databench—benchmarking big data systemshdhappy001
This document discusses BigDataBench, an open source project for big data benchmarking. BigDataBench includes six real-world data sets and 19 workloads that cover common big data applications and preserve the four V's of big data. The workloads were chosen to represent typical application domains like search engines, social networks, and e-commerce. BigDataBench aims to provide a standardized benchmark for evaluating big data systems, architectures, and software stacks. It has been used in several case studies for workload characterization and performance evaluation of different hardware platforms for big data workloads.
TPCx-HS is the first vendor-neutral benchmark focused on big data systems – which have become a critical part of the enterprise IT ecosystem.
Watch the video presentation: http://wp.me/p3RLHQ-cLY
Learn more: http://www.tpc.org/tpcx-hs
The document describes scientific workflows for big data and the challenges they present. It discusses Prof. Shiyong Lu's work on developing the VIEW system for designing, executing, and analyzing scientific workflows. The VIEW system provides a runtime environment for workflows, supports their execution on servers or clouds, and enables efficient storage, querying and visualization of workflow provenance data.
This document discusses HiBench, a benchmark suite for Hadoop. It provides an overview of HiBench and how it can be used to characterize and evaluate Hadoop deployments. Evaluation results using HiBench show that a newer Intel Xeon server platform provides up to 86% more throughput and is up to 56% faster than an older platform. Evaluations between Hadoop versions 0.19.1 and 0.20.0 show that improvements in the newer version help reduce job completion times. The document concludes by providing suggestions for optimizing Hadoop deployments through hardware and software configurations.
Covers different types of big data benchmarking, different suites, details into terasort, demo with TPCx-HS
Meetup Details of presentation:
http://www.meetup.com/lspe-in/events/203918952/
Big Graph Analytics on Neo4j with Apache SparkKenny Bastani
In this talk I will introduce you to a Docker container that provides you an easy way to do distributed graph processing using Apache Spark GraphX and a Neo4j graph database. You'll learn how to analyze big data graphs that are exported from Neo4j and consequently updated from the results of a Spark GraphX analysis. The types of analysis I will be talking about are PageRank, connected components, triangle counting, and community detection.
Database technologies have evolved to be able to store big data, but are largely inflexible. For complex graph data models stored in a relational database there may be tedious transformations and shuffling around of data to perform large scale analysis.
Fast and scalable analysis of big data has become a critical competitive advantage for companies. There are open source tools like Apache Hadoop and Apache Spark that are providing opportunities for companies to solve these big data problems in a scalable way. Platforms like these have become the foundation of the big data analysis movement.
Speakers
Graph analytics can be used to analyze a social graph constructed from email messages on the Spark user mailing list. Key metrics like PageRank, in-degrees, and strongly connected components can be computed using the GraphX API in Spark. For example, PageRank was computed on the 4Q2014 email graph, identifying the top contributors to the mailing list.
In 2001, as early high-speed networks were deployed, George Gilder observed that “when the network is as fast as the computer's internal links, the machine disintegrates across the net into a set of special purpose appliances.” Two decades later, our networks are 1,000 times faster, our appliances are increasingly specialized, and our computer systems are indeed disintegrating. As hardware acceleration overcomes speed-of-light delays, time and space merge into a computing continuum. Familiar questions like “where should I compute,” “for what workloads should I design computers,” and "where should I place my computers” seem to allow for a myriad of new answers that are exhilarating but also daunting. Are there concepts that can help guide us as we design applications and computer systems in a world that is untethered from familiar landmarks like center, cloud, edge? I propose some ideas and report on experiments in coding the continuum.
Social Media World News Impact on Stock Index Values - Investment Fund Analyt...Bernardo Najlis
Presentation for project on Social Media World News Impact on Stock Index Values (DJIA) for Investment Fund Analytics. Group project done in course DS8004 - Data Mining at Ryerson University for Masters in Data Science and Analytics.
Graph Data: a New Data Management FrontierDemai Ni
Graph Data: a New Data Management Frontier -- Huawei’s view and Call for Collaboration by Demai Ni:
Huawei provides Enterprise Databases, and are actively exploring the latest technology to provide end-to-end Data Management Solution on Cloud. We are looking at to bridge classic RDMS to Graph Database on a distributed platform.
Big Data is changing abruptly, and where it is likely headingPaco Nathan
Big Data technologies are changing rapidly due to shifts in hardware, data types, and software frameworks. Incumbent Big Data technologies do not fully leverage newer hardware like multicore processors and large memory spaces, while newer open source projects like Spark have emerged to better utilize these resources. Containers, clouds, functional programming, databases, approximations, and notebooks represent significant trends in how Big Data is managed and analyzed at large scale.
Big data appliance ecosystem - in memory db, hadoop, analytics, data mining, business intelligence with multiple data source charts, twitter support and analysis.
Gao cong geospatial social media data management and context-aware recommenda...jins0618
The document discusses geospatial social media data management and context-aware recommendation. It introduces technologies for geo-positioning users and content, and how user generated content from social media is increasingly associated with geo-locations. The document then outlines queries for static geo-textual data, publish/subscribe queries on geo-textual data streams, and personalized, context-aware point-of-interest recommendation based on modeling user behavior from geo-textual data.
Fully Automated QA System For Large Scale Search And Recommendation Engines U...Spark Summit
1) The document describes a fully automated QA system for large scale search and recommendation engines using Spark.
2) It discusses key concepts in information retrieval like precision, recall, and learning to rank as well as challenges in building machine learning models for ranking like obtaining labeled training data.
3) The system architecture involves extracting features from query logs, calculating relevance scores from user click signals, and training machine learning models to improve ranking.
This document provides an overview of Amundsen, an open source data discovery and metadata platform developed by Lyft. It begins with an introduction to the challenges of data discovery and outlines Amundsen's architecture, which uses a graph database and search engine to provide metadata about data resources. The document discusses how Amundsen impacts users at Lyft by reducing time spent searching for data and discusses the project's community and future roadmap.
Exploring Neo4j Graph Database as a Fast Data Access LayerSambit Banerjee
This article describes the findings of an extensive investigative work conducted to explore the feasibility of using a Neo4j Graph Database to build a Fast Data Access Layer with near-real time data ingestion from the underlying source systems.
Data analysis using hive ql & tableaupkale1708
This document describes a project analyzing crime data from Chicago to determine safe and unsafe areas of the city. The analysis uses big data tools like HiveQL on a Hadoop cluster to query a 1.3GB crime dataset. Queries find the most common crime types, crimes by location and month, and rank areas by crime counts. The results are visualized in graphs and maps. The goal is to help users identify safe residences using large-scale public crime data.
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
This document discusses benchmarking Hadoop and big data systems. It provides an overview of common Hadoop benchmarks including microbenchmarks like TestDFSIO, TeraSort, and NNBench which test individual Hadoop components. It also describes BigBench, a benchmark modeled after TPC-DS that aims to test a more complete big data analytics workload using techniques like MapReduce, Hive, and Mahout across structured, semi-structured, and unstructured data. The document emphasizes using Hadoop distributions for administration and both microbenchmarks and full benchmarks like BigBench for evaluation.
詹剑锋:Big databench—benchmarking big data systemshdhappy001
This document discusses BigDataBench, an open source project for big data benchmarking. BigDataBench includes six real-world data sets and 19 workloads that cover common big data applications and preserve the four V's of big data. The workloads were chosen to represent typical application domains like search engines, social networks, and e-commerce. BigDataBench aims to provide a standardized benchmark for evaluating big data systems, architectures, and software stacks. It has been used in several case studies for workload characterization and performance evaluation of different hardware platforms for big data workloads.
The document provides an overview of new features in HDFS in Hadoop 2, including:
- A new appendable write pipeline that allows files to be reopened for append and provides primitives like hflush and hsync.
- Support for multiple namenode federation to improve scalability and isolate namespaces.
- Namenode high availability using techniques like ZooKeeper and a quorum journal manager to avoid single points of failure.
- A new file system snapshots feature that allows point-in-time recovery through copy-on-write snapshots without data copying.
Cloud computing, big data, and mobile are three major trends that will change the world. Cloud computing provides scalable and elastic IT resources as services over the internet. Big data involves large amounts of both structured and unstructured data that can generate business insights when analyzed. The hadoop ecosystem, including components like HDFS, mapreduce, pig, and hive, provides an architecture for distributed storage and processing of big data across commodity hardware.
This document provides an overview of Capital One's plans to introduce Hadoop and discusses several proof of concepts (POCs) that could be developed. It summarizes the history and practices of using Hadoop at other companies like LinkedIn, Netflix, and Yahoo. It then outlines possible POCs for Hadoop distributions, ETL/analytics frameworks, performance testing, and developing a scaling layer. The goal is to contribute open source code and help with Capital One's transition to using Hadoop in production.
Cloud computing, big data, and mobile technologies are driving major changes in the IT world. Cloud computing provides scalable computing resources over the internet. Big data involves extremely large data sets that are analyzed to reveal business insights. Hadoop is an open-source software framework that allows distributed processing of big data across commodity hardware. It includes tools like HDFS for storage and MapReduce for distributed computing. The Hadoop ecosystem also includes additional tools for tasks like data integration, analytics, workflow management, and more. These emerging technologies are changing how businesses use and analyze data.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It consists of Hadoop Distributed File System (HDFS) for storage, and MapReduce for distributed processing. HDFS stores large files across multiple machines, with automatic replication of data for fault tolerance. It has a master/slave architecture with a NameNode managing the file system namespace and DataNodes storing file data blocks.
This document summarizes a seminar presentation on big data analytics. It reviews 25 research papers published between 2011-2014 on issues related to big data analysis, real-time big data analysis using Hadoop in cloud computing, and classification of big data using tools and frameworks. The review process involved a 5-stage analysis of the papers. Key issues identified include big data analysis, real-time analysis using Hadoop in clouds, and classification using tools like Hadoop, MapReduce, HDFS. Promising solutions discussed are MapReduce Agent Mobility framework, PuntStore with pLSM index, IOT-StatisticDB statistical database mechanism, and visual clustering analysis.
Data fusion for city live event detectionAlket Cecaj
Event detection in urban context by using aggregated mobile activity as for example CDR data and social network data in this case geo-referenced Twitter data. The experiments show that the two datasets - CDR and social data - used, complement each other by providing better event detection results and event detscription.
PL-4089, Accelerating and Evaluating OpenCL Graph Applications, by Shuai Che,...AMD Developer Central
PL-4089, Accelerating and Evaluating OpenCL Graph Applications, by Shuai Che, Bradford Bechmann, Steve Reinhardt and Kevin Skadron at the AMD Developer Summit (APU13) November 11-13, 2014.
COBWEB A quality assurance workflow authoring tool for citizen science and cr...COBWEB Project
This document describes a quality assurance workflow authoring tool for citizen science and crowd-sourced data. The tool aims to integrate authoritative and crowd-sourced data by bringing together a structured, standards-based institutional approach with a citizen-focused, timely crowd-sourced approach. The tool uses a BPMN-based workflow to chain OGC Web Processing Services for quality control processes. This allows stakeholders to design customizable QA workflows by selecting from a repository of generic quality control processes.
The “Local Ranking Problem” (LRP) is related to the computation of a centrality-like rank on a local graph, where the scores of the nodes could significantly differ from the ones computed on the global graph. Previous work has studied LRP on the hyperlink graph but never on the BrowseGraph, namely a graph where nodes are webpages and edges are browsing transitions. Recently, this graph has received more and more attention in many different tasks such as ranking, prediction and recommendation. However, a webserver has only the browsing traffic performed on its pages (local BrowseGraph) and, as a consequence, the local computation can lead to estimation errors, which hinders the increasing number of applications in the state of the art. Also, although the divergence between the local and global ranks has been measured, the possibility of estimating such divergence using only local knowledge has been mainly overlooked. These aspects are of great interest for online service providers who want to: (i) gauge their ability to correctly assess the importance of their resources only based on their local knowledge, and (ii) take into account real user browsing fluxes that better capture the actual user interest than the static hyperlink network. We study the LRP problem on a BrowseGraph from a large news provider, considering as subgraphs the aggregations of browsing traces of users coming from different domains. We show that the distance between rankings can be accurately predicted based only on structural information of the local graph, being able to achieve an average rank correlation as high as 0.8.
Drill can query JSON data stored in various data sources like HDFS, HBase, and Hive. It allows running SQL queries over JSON data without requiring a fixed schema. The document describes how Drill enables ad-hoc querying of JSON-formatted Yelp business review data using SQL, providing insights faster than traditional approaches.
A Literature Review on Vehicle Detection and Tracking in Aerial Image Sequenc...IRJET Journal
This document provides a literature review of 12 research papers related to vehicle detection and tracking in aerial image sequences using deep learning techniques. The papers cover a range of topics including performance metrics for multi-object tracking, fully-convolutional Siamese networks for object tracking, simple online and real-time tracking approaches, high-speed tracking without using image information, cascade R-CNN for high quality object detection, actor-critic tracking frameworks, hybrid task cascades for instance segmentation, open source detection toolboxes and benchmarks, low-cost tracking systems for small UAVs, adaptive combination kernel approaches for visual object tracking, dual-channel CNNs for image super-resolution, and enhanced hierarchical principal component analysis for saliency detection. The document
The document discusses implementing deep learning algorithms for object detection and scene perception in self-driving cars. It compares the YOLO and Faster R-CNN models, finding that Faster R-CNN has higher accuracy (mAP of 41.8) but lower speed (17.1 FPS), while YOLO has lower accuracy (mAP of 18.6) but higher speed (212.4 FPS). The authors conclude that achieving both high accuracy and high speed remains a goal for future work, which could explore using newer versions of YOLO or other models.
With the increasing needs of intelligent and autonomous systems to sense, move and react with the surroundings, it is a clear necessity to train such systems with as much relevant data as can be obtained. However, there are many challenges in obtaining real world data, particularly in a 3D environment. In this talk, I will cover some of the recent advances in Graphics and Computing techniques in 3D processing and their possible application in dynamic settings for autonomous systems. A vision of how synthetic data could be relevant in the future of intelligent systems is presented, along with the challenges. Backup material covers latest papers on the subject
This document provides an introduction to H2O, an open source machine learning platform, and discusses potential Internet of Things (IoT) use cases for predictive maintenance and outlier detection. The document outlines Joe Chow's background and experience, provides an overview of H2O's capabilities including algorithms, interfaces, and exporting models for production. It then demonstrates how to use H2O for predictive maintenance on a dataset of sensor readings to predict equipment failures, and for outlier detection on the MNIST handwritten digits dataset to identify anomalous images.
Graphalytics: A big data benchmark for graph-processing platformsGraph-TA
Graphalytics is a benchmark for evaluating graph processing platforms. It includes a diverse set of algorithms and synthetic and real-world datasets. The benchmark harness collects performance metrics across platforms and enables in-depth bottleneck analysis through Granula. Graphalytics aims to enable fair comparison of different graph systems and help identify areas for improvement through a modern software development process.
The document discusses several visualization tools created by Claudio Squarcella to help understand and analyze Internet data. Caidagram uses geographic maps to visualize the locations of Internet measurement data collected from nodes like RIPE Atlas probes. VisualK monitors the performance of the K-root anycast network in real-time, showing traffic patterns between its instances. BGPlay animates routing graphs to visualize interdomain routing activity for a given prefix over time based on data from sources like RIPE RIS. The tools use technologies like JavaScript, SVG, and Google Web Toolkit to create interactive web applications for exploring the data.
The document discusses several visualization tools created by Claudio Squarcella to help understand and analyze Internet data. Caidagram uses geographic maps to visualize the locations of Internet measurement data collected from nodes like RIPE Atlas probes. VisualK monitors the performance of the K-root anycast network in real-time, showing traffic patterns between its instances. BGPlay animates routing graphs to visualize interdomain routing activity for a given prefix over time based on data from sources like RIPE RIS. The tools use technologies like JavaScript, SVG, and Google Web Toolkit to create interactive web applications for exploring the data. Future work may include integrating Atlas data into the visualizations and adding new features to BGPlay.
Real-Time Anomaly Detection with Spark MLlib, Akka and CassandraNatalino Busa
We present a solution for streaming anomaly detection, named “Coral”, based on Spark, Akka and Cassandra. In the system presented, we run Spark to run the data analytics pipeline for anomaly detection. By running Spark on the latest events and data, we make sure that the model is always up-to-date and that the amount of false positives is kept low, even under changing trends and conditions. Our machine learning pipeline uses Spark decision tree ensembles and k-means clustering. Once the model is trained by Spark, the model’s parameters are pushed to the Streaming Event Processing Layer, implemented in Akka. The Akka layer will then score 1000s of event per seconds according to the last model provided by Spark. Spark and Akka communicate which each other using Cassandra as a low-latency data store. By doing so, we make sure that every element of this solution is resilient and distributed. Spark performs micro-batches to keep the model up-to-date while Akka detects the new anomalies by using the latest Spark-generated data model. The project is currently hosted on Github. Have a look at : http://coral-streaming.github.io
Bayesian Network Modeling using Python and RPyData
This document discusses Bayesian network modeling using Python and R. It begins with an introduction to Bayesian networks and their applications. It then outlines the main Bayesian network packages available in Python like scikit-learn, BayesPy, Bayes Blocks, and PyMC, and in R like bnlearn and RStan. It covers the basics of Bayes' theorem and how Bayesian networks represent probabilistic relationships between variables as a directed acyclic graph. The talk concludes with discussing algorithms for learning Bayesian networks from data and evaluating model performance.
This document discusses Bayesian network modeling using Python and R. It begins with an introduction to Bayesian networks and their applications. It then outlines the main Bayesian network packages available in Python like scikit-learn, BayesPy, Bayes Blocks, and PyMC, and in R like bnlearn and RStan. It covers the basics of Bayes' theorem and how Bayesian networks represent probabilistic relationships between variables as a directed acyclic graph. The talk concludes with discussing algorithms for learning Bayesian networks from data and evaluating model performance.
حلقة تكنولوجية 11 بحث علمى بعنوان A Systematic Mapping Study for Big Data Str...Adel Sabour
The document summarizes the results of a systematic mapping study on big data stream processing frameworks. It examines 91 studies published between 2010-2015. The study addressed 9 research questions, including the types of contributions made by the papers, research methods used, experimentation types for different frameworks, most used data ingestion tools, and preferred number of nodes in experiments. The results provided breakdowns of findings for various frameworks like Spark, Storm, Flink, and InfoSphere across the different research questions.
Enhancing Traffic Prediction with Historical Data and Estimated Time of ArrivalIRJET Journal
This document proposes a methodology to enhance traffic prediction accuracy by combining historical traffic data, real-time traffic updates, and estimated time of arrival (ETA) information. The methodology utilizes machine learning techniques, ARIMA modeling, nonparametric methods, and deep neural networks to analyze the data. While the methodology lays out a framework for collecting raw traffic congestion data from online maps and transportation departments, the research focuses on establishing a theoretical model rather than conducting empirical experiments. The goal is to develop a comprehensive solution for traffic prediction by leveraging different data sources and analytical techniques.
Spark is an open source cluster computing framework originally developed at UC Berkeley. Intel has made many contributions to Spark's development through code commits, patches, and collaborating with the Spark community. Spark is widely used by companies like Alibaba, Baidu, and Youku for large-scale data analytics and machine learning tasks. It allows for faster iterative jobs than Hadoop through its in-memory computing model and supports multiple workloads including streaming, SQL, and graph processing.
This document describes an interactive batch query system for game analytics based on Apache Drill. It addresses the problem of answering common ad-hoc queries over large volumes of log data by using a columnar data model and optimizing query plans. The system utilizes Drill's schema-free data model and vectorized query processing. It further improves performance by merging similar queries, reusing intermediate results, and pushing execution downwards to utilize multi-core CPUs. This provides a unified solution for both ad-hoc and scheduled batch analytics workloads at large scale.
刘诚忠:Running cloudera impala on postgre sqlhdhappy001
This document summarizes a presentation about running Cloudera Impala on PostgreSQL to enable SQL queries on large datasets. Key points:
- The company processes 3 billion daily ad impressions and 20TB of daily report data, requiring a scalable SQL solution.
- Impala was chosen for its fast performance from in-memory processing and code generation. The architecture runs Impala coordinators and executors across clusters.
- The author hacked Impala to also scan data from PostgreSQL for mixed workloads. This involved adding new scan node types and metadata.
- Tests on a 150 million row dataset showed Impala with PostgreSQL achieving 20 million rows scanned per second per core.
This document discusses big data in the cloud and provides an overview of YARN. It begins with introducing the speaker and their experience with VMware and Apache Hadoop. The rest of the document covers: 1) trends in big data like the rise of YARN, faster query engines, and focus on enterprise capabilities, 2) how YARN addresses limitations of MapReduce by splitting responsibilities, 3) how YARN serves as a hub for various big data applications, and 4) how YARN can integrate with cloud infrastructure for elastic resource management between the two frameworks. The document advocates for open source contribution to help advance big data technologies.
Raghu nambiar:industry standard benchmarkshdhappy001
Industry standard benchmarks have played a crucial role in advancing the computing industry by enabling healthy competition that drives product improvements and new technologies. Major benchmarking organizations like TPC, SPEC, and SPC have developed numerous benchmarks over time to keep up with industry needs. Looking ahead, new benchmarks are needed to address emerging technologies like cloud, big data, and the internet of things. International conferences and workshops bring together experts to collaborate on developing these new, relevant benchmarks.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
CAKE: Sharing Slices of Confidential Data on BlockchainClaudio Di Ciccio
Presented at the CAiSE 2024 Forum, Intelligent Information Systems, June 6th, Limassol, Cyprus.
Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdfTechgropse Pvt.Ltd.
In this blog post, we'll delve into the intersection of AI and app development in Saudi Arabia, focusing on the food delivery sector. We'll explore how AI is revolutionizing the way Saudi consumers order food, how restaurants manage their operations, and how delivery partners navigate the bustling streets of cities like Riyadh, Jeddah, and Dammam. Through real-world case studies, we'll showcase how leading Saudi food delivery apps are leveraging AI to redefine convenience, personalization, and efficiency.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
9. BDTC - Beijing, 2013-12-6
Large Volume Visualization
! Level of Details
! Out of Core
! Parallel Visualization
9
10. BDTC - Beijing, 2013-12-6
10
Top 10 Challenges in Extreme-Scale Data
Visual Analytics
Pak Chung Wong (PNNL)
Han-Wei Shen (OSU)
Chris Johnson (Utah)
Chaomei Chen (Drexel)
Robert Ross (Argonne)
11. BDTC - Beijing, 2013-12-6
Top 10 Challenges in ExtremeScale Data Visual Analytics
11
! In Situ Analysis
! Perform as much analysis as possible while the data are still in
memory
! Interaction and User Interfaces
! Machine-based automated systems vs. Human Cognition
! Large-Data Visualization
! Data projection and dimension Reduction, display technology
! Databases and Storage
! A cloud-based solution might not meet the needs
! Algorithms
! Address both data-size and visual-efficiency issues
12. BDTC - Beijing, 2013-12-6
Top 10 Challenges in ExtremeScale Data Visual Analytics
12
! Data Movement/Transport, & Network Infrastructure
! Efficiently use networking resources and provide convenient
abstractions
! Uncertainty Quantification
! Cope with incomplete data
! Parallelism
! Domain and Development Libraries, Frameworks, and Tools
! Affordable resource libraries, frameworks, and tools
! Social, Community, and Government Engagements
13. BDTC - Beijing, 2013-12-6
Challenges in Big Data
Visualization/Visual Analytics - 1
! Integrating heterogeneous Data from different resources and
scales
13
20. BDTC - Beijing, 2013-12-6
Preprocessing: Map
Matching
Raw taxi
GPS
Data
Raw Road
Network
Cleane
d GPS
Data
Processed
Road
Network
Map Matching
GPS Trajectories
Matched
to the Road Network
20
22. BDTC - Beijing, 2013-12-6
Visual Interface: Single Road
Level
22
! Pixel based visualization
Time of a day: 144 columns (each for a 10min)
Days: 24 rows
(each for one day)
Each cell represents one time bin
Color encode speed
23. BDTC - Beijing, 2013-12-6
Case Study: Road Level
Exploration and Analysis
! Different road congestion patterns
23
24. BDTC - Beijing, 2013-12-6
Case Study: Road Level
Exploration and Analysis
24
25. BDTC - Beijing, 2013-12-6
25
Propagation Graph Analysis
! Spatial Temporal information of one propagation
Large delay
Spatial path
Temporal delay
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Propagation Pattern
Exploration
! Propagation graphs for one region in the morning of different
days
26
40. BDTC - Beijing, 2013-12-6
Challenges in Big Data
Visualization/Visual Analytics - 2
! Integrating heterogeneous Data from different resources and
scales
! Scalability in Data/Task complexity
! Data inherent properties impose more computational challenges
methods for visualization and visual analysis on big data
40
43. BDTC - Beijing, 2013-12-6
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Multivariate to Multi-Run
Visual Analysis
QVAPOR
QVAPOR
QCLOUD
Pressure
Speed
Run 1
QCLOUD
QVAPOR
QCLOUD
Pressure
Speed
QVAPOR
Run 2
Pressure
Speed
(Multivariate)
QVAPOR
QCLOUD
Pressure
Speed
(Ensemble Runs)
Run 3
44. BDTC - Beijing, 2013-12-6
Eulerian and Lagriangian
Specifications
! Eulerian:
! Lagriangian:
! Relationships between two specifications (flow map):
44
45. BDTC - Beijing, 2013-12-6
Eulerian-based Attribute
Space Projection
! Samples on data grid !
Samples in attribute space !
Eulerian-based Attribute Space Projection
(EASP)
45
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Lagrangian-based Attribute
Space Projection
! Pathlines on data grid !
Pathlines in attribute space !
Lagrangian-based Attribute Space Projection (LASP)
! Both multivariate scalar fields and vector field are considered
46
48. BDTC - Beijing, 2013-12-6
48
Couple Ensemble Flow Line Advection
and Analysis (eFLAA)-Concept
! Ensemble data (large)
! Field line data (much larger than ensemble data)
! Variation field (small)
! Filtered lines (even smaller)
[Guo, Yuan, Huang and Zhu TVCG 2013 (SCIVis ‘13)]
53. BDTC - Beijing, 2013-12-6
GEOS-5 Simulation: CO2based Metric
53
! The metric: the differences of locations / CO2
concentration along the pathline
! Findings
! The variation of the wind field is high in the north hemisphere
! However, The CO2 difference is higher in south hemisphere and
some places in the north
! CO2 concentration is not sensitive to wind in above regions
54. BDTC - Beijing, 2013-12-6
Challenges in Big Data
Visualization/Visual Analytics - 3
! Integrating heterogeneous Data from different resources and
scales
! Scalability in Data/Task complexity
! Data inherent properties impose more computational challenges
methods for visualization and visual analysis on big data
! Limited access in Interaction for Large Data
54
57. BDTC - Beijing, 2013-12-6
Real-time Visual Querying of
Big Data
!
imMens
57
58. BDTC - Beijing, 2013-12-6
Real-time Visual Querying of
Big Data
!
!
58
59. BDTC - Beijing, 2013-12-6
Nanocubes for Real-Time Exploration
of Spatiotemporal Datasets
!
59
60. BDTC - Beijing, 2013-12-6
Challenges in Big Data
Visualization/Visual Analytics - 4
! Integrating heterogeneous Data from different resources and
scales
! Scalability in Data/Task complexity
! Data inherent properties impose more computational challenges
methods for visualization and visual analysis on big data
! Limited access in Interaction for Large Data
! Scalability in User
! Collaborative Visualization and Analysis on large data
! Can scientist create novel visualization without programming
60
61. BDTC - Beijing, 2013-12-6
61
Double Gulf
Visualization
Designer
Visualization
User
Representation
Evaluation
Data
Visualization
Conceptual
Model
Execution
Manipulation
62. BDTC - Beijing, 2013-12-6
62
Double Gulf
Visualization
Designer
Visualization
User
Representation
Evaluation
Data
Visualization
Conceptual
Model
Execution
Manipulation
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63
From Data to User
Visualization
User
Evaluation
Execution
Visualization
Designer
Representation
Manipulation
64. BDTC - Beijing, 2013-12-6
64
Scalability In Users
Visualization
Designer
Visualization
User
Representation
Evaluation
Data
Visualization
Conceptual
Model
Execution
Manipulation
74. BDTC - Beijing, 2013-12-6
Challenges in Big Data
Visualization/Visual Analytics - 5
! Integrating heterogeneous Data from different resources and
scales
! Scalability in Data/Task complexity
! Limited access in Interaction for Large Data
! Scalability in User
! System Development
! Domain and Development Libraries, Frameworks, and Tools
! Social, Community, and Government Engagements
74
75. BDTC - Beijing, 2013-12-6
75
SCIVIS Visualization Systems
! VisIt - LLNL
https://wci.llnl.gov/codes/visit
! ParaView- Kitware/SNL/LANL
http://www.paraview.org
! IceT (Image Composition Engine for Tiles) - Sandia
http://icet.sandia.gov
! Daxtoolkit - Data Analysis at Extreme
http://www.daxtoolkit.org
! PISTON - Portable Data-Parallel Visualization and Analysis Library LANL
http://viz.lanl.gov/projects/PISTON.html
76. BDTC - Beijing, 2013-12-6
VisIt
! Production end-user tool supporting
scientific and engineering
applications.
! Parallel post-processing that scales
from desktops to massive HPC
clusters.
76
77. BDTC - Beijing, 2013-12-6
77
Development of VisIt
! The VisIt project started in 2000 to support LLNL’s large scale ASC
physics codes.
! Supported by multiple organizations: LLNL, LBNL, ORNL, UC Davis,
Univ. of Utah, …
! Over 75 person years effort.
! 1.5+ million lines of code.
Based on SC’11 Tutorial
79. BDTC - Beijing, 2013-12-6
79
VTK
W.J. Schroeder, K. Martin, and W. Lorensen, The
Visualization Toolkit: An Object Oriented Approach to
Computer Graphics, Third Edition, Kitware, Inc.,
ISBN-1-930934-12-2 (2004).
S. E. Rogers, D. Kwak, and U. K. Kaul, A numerical study of
three-dimensional incompressible flow around multiple
post. In Proceedings of AIAA Aerospace Sciences
Conference. AIAA Paper 86-0353. Reno, Nevada, 1986.
80. BDTC - Beijing, 2013-12-6
ParaView
! 2000 Los Alamos National Laboratories and Kitware Inc.
! 2005 Sandia National Laboratories and Kitware Inc.
! Used by academic, government, and commercial institutions
worldwide.
! Downloaded ~100K times per year.
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Starlight Information
Visualization System
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Build a successful vis system
! System Design
! Domain User – Visualization Scientist “Co-design”
! Stable Development Team
! Funding Mechanism
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Build a successful vis system
! System Design
! Domain User – Visualization Scientist “Co-design”
! Stable Development Team
! Funding Mechanism
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Challenges in Big Data
Visualization/Visual Analytics - 6
! Integrating heterogeneous Data from different resources and
scales
! Scalability in Data/Task complexity
! Limited access in Interaction for Large Data
! Scalability in User
! System Development
! Visualization Experts
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Social, Community, and
Government Engagements
! Universities
!
!
!
!
!
!
!
!
University of Tennessee in Knoxville
Ohio State University
SCI Institute, University of Utah
University of California, Davis
University of California, San Diego
University of Nebraska-Lincoln
Michigan Technological University
Drexel University
! Supercomputer centers
! San Diego Supercomputer Center (SDSC)
! Texas Advanced Computing Center
(TACC)
! National Center for Supercomputing
Applications at the University of Illinois
(NCSA)
! DoE Labs
! Argonne National Laboratory (ANL)
! Lawrence Berkeley National Laboratory
(LBNL)
! Lawrence Livermore National Laboratory
(LLNL)
! Los Alamos National Laboratory (LANL)
! Pacific Northwest National Laboratory
(PNNL)
! Oak Ridge National Laboratory (ORNL)
! Sandia National Laboratories (SNL)
! National Renewable Energy Laboratory
(NREL)
! Companies
! Kitware
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Good News
! More and more universities started visualization research
program
! Many Companies are aware of the importance of visualization
! Still, lack of national infrastructure