At the StampedeCon 2015 Big Data Conference: YARN enables Hadoop to move beyond just pure batch processing. With that multiple workloads and tenants now must be able to share a single infrastructure for data processing. Features of the Capacity Scheduler enable resource sharing among multiple tenants in a fair manner with elastic queues to maximize utilization. This talk will focus on the features of the Capacity Scheduler that enable Multi-Tenancy and how resource sharing can be rebalanced using features like Preemption.
At the StampedeCon 2015 Big Data Conference: YARN enables Hadoop to move beyond just pure batch processing. With that multiple workloads and tenants now must be able to share a single infrastructure for data processing. Features of the Capacity Scheduler enable resource sharing among multiple tenants in a fair manner with elastic queues to maximize utilization. This talk will focus on the features of the Capacity Scheduler that enable Multi-Tenancy and how resource sharing can be rebalanced using features like Preemption.
Scale 12 x Efficient Multi-tenant Hadoop 2 Workloads with YarnDavid Kaiser
Hadoop is about so much more than batch processing. With the recent release of Hadoop 2, there have been significant changes to how a Hadoop cluster uses resources. YARN, the new resource management component, allows for a more efficient mix of workloads across hardware resources, and enables new applications and new processing paradigms such as stream-processing. This talk will discuss the new design and components of Hadoop 2, and examples of Modern Data Architectures that leverage Hadoop for maximum business efficiency.
3 Things to Learn About:
-How Kudu is able to fill the analytic gap between HDFS and Apache HBase
-The trade-offs between real-time transactional access and fast analytic performance
-How Kudu provides an option to achieve fast scans and random access from a single API
Securing the Data Hub--Protecting your Customer IP (Technical Workshop)Cloudera, Inc.
Your data is your IP and its security is paramount. The last thing you want is for your data to become a target for threats. This workshop will focus on the realities of protecting your customer’s IP from external and internal threats with battle hardened technologies and methodologies. Another key concept that will be examined is the connection of people, processes and technology. In addition, the session will take a look at authentication and authorisation, auditing and data lineage as well as the different groups required to play a part in the modern data hub. We will also look at how to produce high impact operation reports from Cloudera’s RecordService a new core security layer that centrally enforces fine-grained access control policy, which helps close the feedback loop to ensure awareness of security as a living entity within your organisation.
The Vortex of Change - Digital Transformation (Presented by Intel)Cloudera, Inc.
The vortex of change continues all around us – inside the company, with our customers and partners. A new norm is upon us. Business models are being turned upside down – the hunters now the hunted, global equalization – size is no longer a guarantee of success. The innovative survive and thrive…the nervous and slow go under...what does all this change means for you? Find out how does Intel’s strengths help our customers in this world of change.
Using Big Data to Transform Your Customer’s Experience - Part 1 Cloudera, Inc.
3 Things to Learn About:
-How the Customer Insights Solution helped
- How customer insights can improve customer loyalty, reduce customer churn, and increase upsell opportunities
- Which real-world use cases are ideal for using big data analytics on customer data
3 Things to Learn About:
* How Sparklyr supports a complete backend for dplyr, a popular tool for working with data frame objects both in memory and out of memory
* How Sparklyr llows data scientists to use dplyr to translate R code into Spark SQL
* How Sparklyr supports MLlib so data scientists can run classifiers, regressions, and many other machine learning algorithms in Spark
Part 1: Cloudera’s Analytic Database: BI & SQL Analytics in a Hybrid Cloud WorldCloudera, Inc.
3 Things to Learn About:
* On-premises versus the cloud: What’s the same and what’s different?
* Design and benefits of analytics in the cloud
* Best practices and architectural considerations
At Spotify we collect huge volumes of data for many purposes. Reporting to labels, powering our product features, and analyzing user growth are some of our most common ones. Additionally, we collect many operational metrics related to the responsiveness, utilization and capacity of our servers. To store and process this data, we use scalable and fault-tolerant multi-system infrastructure, and Apache Hadoop is a key part of it. Surprisingly or not, Apache Hadoop generates large amounts of data in the form of logs and metrics that describe its behaviour and performance. To process this data in a scalable and performant manner we use … also Hadoop! During this presentation, I will talk about how we analyze various logs generated by Apache Hadoop using custom scripts (written in Pig or Java/Python MapReduce) and available open-source tools to get data-driven answers to many questions related to the behaviour of our 690-node Hadoop cluster. At Spotify we frequently leverage these tools to learn how fast we are growing, when to buy new nodes, how to calculate the empirical retention policy for each dataset, optimize the scheduler, benchmark the cluster, find its biggest offenders (both people and datasets) and more.
3 Things to Learn About:
*The IoT ecosystem and data management considerations for IoT
*Top IoT use cases and data architecture strategies for managing the sheer volume and variety of IoT data
*Real-life case studies on how our customers are using Cloudera Enterprise to drive insights and analytics from all of their IoT data
Multi-Tenant Operations with Cloudera 5.7 & BTCloudera, Inc.
One benefit of Apache Hadoop is the ability to power multiple workloads, across many different users and departments, all within a single, shared cluster. Hear how BT is doing this today and learn about new features in Cloudera Manager to provide better visibility for multi-tenant operations.
Data Engineering: Elastic, Low-Cost Data Processing in the CloudCloudera, Inc.
3 Things to Learn About:
*On-premises versus the cloud: What’s the same and what’s different?
*Benefits of data processing in the cloud
*Best practices and architectural considerations
Part 1: Lambda Architectures: Simplified by Apache KuduCloudera, Inc.
3 Things to Learn About:
* The concept of lambda architectures
* The Hadoop ecosystem components involved in lambda architectures
* The advantages and disadvantages of lambda architectures
Gartner Data and Analytics Summit: Bringing Self-Service BI & SQL Analytics ...Cloudera, Inc.
For self-service BI and exploratory analytic workloads, the cloud can provide a number of key benefits, but the move to the cloud isn’t all-or-nothing. Gartner predicts nearly 80 percent of businesses will adopt a hybrid strategy. Learn how a modern analytic database can power your business-critical workloads across multi-cloud and hybrid environments, while maintaining data portability. We'll also discuss how to best leverage the increased agility cloud provides, while maintaining peak performance.
Things to Learn About:
*Customer success stories from Navistar and other leading auto manufacturers and insurers
*Key use cases and data architecture strategies for managing data from connected vehicles
Scale 12 x Efficient Multi-tenant Hadoop 2 Workloads with YarnDavid Kaiser
Hadoop is about so much more than batch processing. With the recent release of Hadoop 2, there have been significant changes to how a Hadoop cluster uses resources. YARN, the new resource management component, allows for a more efficient mix of workloads across hardware resources, and enables new applications and new processing paradigms such as stream-processing. This talk will discuss the new design and components of Hadoop 2, and examples of Modern Data Architectures that leverage Hadoop for maximum business efficiency.
3 Things to Learn About:
-How Kudu is able to fill the analytic gap between HDFS and Apache HBase
-The trade-offs between real-time transactional access and fast analytic performance
-How Kudu provides an option to achieve fast scans and random access from a single API
Securing the Data Hub--Protecting your Customer IP (Technical Workshop)Cloudera, Inc.
Your data is your IP and its security is paramount. The last thing you want is for your data to become a target for threats. This workshop will focus on the realities of protecting your customer’s IP from external and internal threats with battle hardened technologies and methodologies. Another key concept that will be examined is the connection of people, processes and technology. In addition, the session will take a look at authentication and authorisation, auditing and data lineage as well as the different groups required to play a part in the modern data hub. We will also look at how to produce high impact operation reports from Cloudera’s RecordService a new core security layer that centrally enforces fine-grained access control policy, which helps close the feedback loop to ensure awareness of security as a living entity within your organisation.
The Vortex of Change - Digital Transformation (Presented by Intel)Cloudera, Inc.
The vortex of change continues all around us – inside the company, with our customers and partners. A new norm is upon us. Business models are being turned upside down – the hunters now the hunted, global equalization – size is no longer a guarantee of success. The innovative survive and thrive…the nervous and slow go under...what does all this change means for you? Find out how does Intel’s strengths help our customers in this world of change.
Using Big Data to Transform Your Customer’s Experience - Part 1 Cloudera, Inc.
3 Things to Learn About:
-How the Customer Insights Solution helped
- How customer insights can improve customer loyalty, reduce customer churn, and increase upsell opportunities
- Which real-world use cases are ideal for using big data analytics on customer data
3 Things to Learn About:
* How Sparklyr supports a complete backend for dplyr, a popular tool for working with data frame objects both in memory and out of memory
* How Sparklyr llows data scientists to use dplyr to translate R code into Spark SQL
* How Sparklyr supports MLlib so data scientists can run classifiers, regressions, and many other machine learning algorithms in Spark
Part 1: Cloudera’s Analytic Database: BI & SQL Analytics in a Hybrid Cloud WorldCloudera, Inc.
3 Things to Learn About:
* On-premises versus the cloud: What’s the same and what’s different?
* Design and benefits of analytics in the cloud
* Best practices and architectural considerations
At Spotify we collect huge volumes of data for many purposes. Reporting to labels, powering our product features, and analyzing user growth are some of our most common ones. Additionally, we collect many operational metrics related to the responsiveness, utilization and capacity of our servers. To store and process this data, we use scalable and fault-tolerant multi-system infrastructure, and Apache Hadoop is a key part of it. Surprisingly or not, Apache Hadoop generates large amounts of data in the form of logs and metrics that describe its behaviour and performance. To process this data in a scalable and performant manner we use … also Hadoop! During this presentation, I will talk about how we analyze various logs generated by Apache Hadoop using custom scripts (written in Pig or Java/Python MapReduce) and available open-source tools to get data-driven answers to many questions related to the behaviour of our 690-node Hadoop cluster. At Spotify we frequently leverage these tools to learn how fast we are growing, when to buy new nodes, how to calculate the empirical retention policy for each dataset, optimize the scheduler, benchmark the cluster, find its biggest offenders (both people and datasets) and more.
3 Things to Learn About:
*The IoT ecosystem and data management considerations for IoT
*Top IoT use cases and data architecture strategies for managing the sheer volume and variety of IoT data
*Real-life case studies on how our customers are using Cloudera Enterprise to drive insights and analytics from all of their IoT data
Multi-Tenant Operations with Cloudera 5.7 & BTCloudera, Inc.
One benefit of Apache Hadoop is the ability to power multiple workloads, across many different users and departments, all within a single, shared cluster. Hear how BT is doing this today and learn about new features in Cloudera Manager to provide better visibility for multi-tenant operations.
Data Engineering: Elastic, Low-Cost Data Processing in the CloudCloudera, Inc.
3 Things to Learn About:
*On-premises versus the cloud: What’s the same and what’s different?
*Benefits of data processing in the cloud
*Best practices and architectural considerations
Part 1: Lambda Architectures: Simplified by Apache KuduCloudera, Inc.
3 Things to Learn About:
* The concept of lambda architectures
* The Hadoop ecosystem components involved in lambda architectures
* The advantages and disadvantages of lambda architectures
Gartner Data and Analytics Summit: Bringing Self-Service BI & SQL Analytics ...Cloudera, Inc.
For self-service BI and exploratory analytic workloads, the cloud can provide a number of key benefits, but the move to the cloud isn’t all-or-nothing. Gartner predicts nearly 80 percent of businesses will adopt a hybrid strategy. Learn how a modern analytic database can power your business-critical workloads across multi-cloud and hybrid environments, while maintaining data portability. We'll also discuss how to best leverage the increased agility cloud provides, while maintaining peak performance.
Things to Learn About:
*Customer success stories from Navistar and other leading auto manufacturers and insurers
*Key use cases and data architecture strategies for managing data from connected vehicles
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
9. Queues
● A short queue name
● A full queue path name
● A list of child queues and applications associated with them
● Guaranteed capacity of the queue
● Maximum capacity to which a queue can grow
● A list of active users and the corresponding limits of sharing between users
● State of the queue
● ACLs governing the access to the queue
いろいろあるけど全部後の節で詳しく語られる
20. Queue Access Control
e.g. “sherlock,pacioli cfo-group”
sherlock,pacioliユーザとcfo-groupグループがアクセス可能
e.g. “ cfo-group”
cfo-groupグループがアクセス可能
e.g. “*”
全員アクセス可能