Building Apps with Distributed In-Memory Computing Using Apache GeodePivotalOpenSourceHub
Slides from the Meetup Monday March 7, 2016 just before the beginning of #GeodeSummit, where we cover an introduction of the technology and community that is Apache Geode, the in-memory data grid.
Latest (storage IO) patterns for cloud-native applications OpenEBS
Applying micro service patterns to storage giving each workload its own Container Attached Storage (CAS) system. This puts the DevOps persona within full control of the storage requirements and brings data agility to k8s persistent workloads. We will go over the concept and the implementation of CAS, as well as its orchestration.
Building Apps with Distributed In-Memory Computing Using Apache GeodePivotalOpenSourceHub
Slides from the Meetup Monday March 7, 2016 just before the beginning of #GeodeSummit, where we cover an introduction of the technology and community that is Apache Geode, the in-memory data grid.
Latest (storage IO) patterns for cloud-native applications OpenEBS
Applying micro service patterns to storage giving each workload its own Container Attached Storage (CAS) system. This puts the DevOps persona within full control of the storage requirements and brings data agility to k8s persistent workloads. We will go over the concept and the implementation of CAS, as well as its orchestration.
Simulating Heterogeneous Resources in CloudLightningCloudLightning
In this presentation, Dr Christos Papadopoulos-Filelis (Democritus University of Thrace, Greece) discusses resource characterisation, simulation tools and the elements of simulation used in CloudLightning.
This presentation was given at the National Conference on Cloud Computing in Dublin City University on 12th April 2016.
Running Non-MapReduce Big Data Applications on Apache Hadoophitesh1892
Apache Hadoop has become popular from its specialization in the execution of MapReduce programs. However, it has been hard to leverage existing Hadoop infrastructure for various other processing paradigms such as real-time streaming, graph processing and message-passing. That was true until the introduction of Apache Hadoop YARN in Apache Hadoop 2.0. YARN supports running arbitrary processing paradigms on the same Hadoop cluster. This allows for development of newer frameworks as well as more efficient implementations of existing frameworks that can all run on and share the resources of a single multi-tenant YARN cluster. This talk gives a brief introduction to YARN. We will illustrate how to create applications and how to best make use of YARN. We will show examples of different applications such as Apache Tez and Apache Samza that can leverage YARN and present best practices/guidelines on building applications on top of Apache Hadoop YARN.
How to get the maximum performance from your AEP server. This will discuss ways to improve execution time of short running jobs and how to properly configure the server depending on the expected number of users as well as the average size and duration of individual jobs. Included will be examples of making use of job pooling, Database connection sharing, and parallel subprotocol tuning. Determining when to make use of cluster, grid, or load balanced configurations along with memory and CPU sizing guidelines will also be discussed.
Hadoop Summit Europe Talk 2014: Apache Hadoop YARN: Present and FutureVinod Kumar Vavilapalli
Title: Apache Hadoop YARN: Present and Future
Abstract: Apache Hadoop YARN evolves the Hadoop compute platform from being centered only around MapReduce to being a generic data processing platform that can take advantage of a multitude of programming paradigms all on the same data. In this talk, we'll talk about the journey of YARN from a concept to being the cornerstone of Hadoop 2 GA releases. We'll cover the current status of YARN, how it is faring today and how it stands apart from the monochromatic world that is Hadoop 1.0. We`ll then move on to the exciting future of YARN - features that are making YARN a first class resource-management platform for enterprise Hadoop, rolling upgrades, high availability, support for long running services alongside applications, fine-grain isolation for multi-tenancy, preemption, application SLAs, application-history to name a few.
Operational systems manage our finances, shopping, devices and much more. Adding real-time analytics to these systems enables them to instantly respond to changing conditions and provide immediate, targeted feedback. This use of analytics is called "operational intelligence," and the need for it is widespread.
This talk will explain how in-memory computing techniques can be used to implement operational intelligence. It will show how an in-memory data grid integrated with a data-parallel compute engine can track events generated by a live system, analyze them in real time, and create alerts that help steer the system’s behavior. Code samples will demonstrate how an in-memory data grid employs object-oriented techniques to simplify the correlation and analysis of incoming events by maintaining an in-memory model of a live system.
The talk also will examine simplifications offered by this approach over directly analyzing incoming event streams from a live system using complex event processing or Storm. Lastly, it will explain key requirements of the in-memory computing platform for operational intelligence, in particular real-time updating of individual objects and high availability using data replication, and contrast these requirements to the design goals for stream processing in Spark.
One key feature that differentiates HBase from other distributed databases is its support of coprocessors. Bloomberg develops and manages some very low-latency systems that service real-time requests. In order to achieve real-time speeds, it was necessary to utilize coprocessors, which are similar to traditional stored procedures. As a result, we were able to match the average latency of an HBase cluster with that of a traditional database. This was done by using coprocessors to parallelize a lot of data computation and reduce the number of round-trips to the cluster by a factor of 5, thereby lowering the amount of data sent over the wire by 5. However, there are also significant challenges to managing coprocessors in a production environment. In this talk, I will to review the use case for HBase coprocessors and some practical tips on how to properly develop and deploy them. Some of the key topics covered in this talk are:
Type of coprocessors
Development challenges
Deployment challenges
Speakers
Amit Anand, Senior Software Developer, Bloomberg LP
Esther Kundin, Senior Software Engineer, Bloomberg LP
Achieving scale and performance using cloud native environmentRakuten Group, Inc.
ID Platform Product can be used by every Rakuten Group Companies and can easily serve millions of users. Multi-Region product challenges are many, example:
- Ensure 4 9’s availability
- Management across each region
- Alerting and Monitoring across each region
- Auto scaling (Scale up and Scale down) across each region
- Performance (vertical scale up)
- Cost
- DB Consistency Across Multiple Regions
- Resiliency
At Ecosystem Platform Layer for Rakuten, we handle each of these and this presentation is about how we handle these challenging scenarios.
PostgreSQL continuous backup and PITR with BarmanEDB
How can I achieve an RPO of 5 minutes for the backups of my PostgreSQL databases? And what about RPO=0 for zero data loss backups? This talk will give you answers to those questions, by guiding you through an overview of Disaster Recovery of PostgreSQL databases with Barman, covering its key concepts and providing useful patterns and tips.
Building large scale, job processing systems with Scala Akka Actor frameworkVignesh Sukumar
The Akka Actor framework is designed to be a fast message processing system. In this talk, we will explain how, at Box, we have used this framework to develop a large scale job processing system that works on billions of data files and achieves a high degree of throughput and fault tolerance. Over the course of the talk, we will explore the usage of Akka framework’s Supervisor functionality to provide a more controllable fault-tolerance strategy, and how we can use Futures to manage asynchronous jobs.
Apache Tez : Accelerating Hadoop Query ProcessingBikas Saha
Apache Tez is the new data processing framework in the Hadoop ecosystem. It runs on top of YARN - the new compute platform for Hadoop 2. Learn how Tez is built from the ground up to tackle a broad spectrum of data processing scenarios in Hadoop/BigData - ranging from interactive query processing to complex batch processing. With a high degree of automation built-in, and support for extensive customization, Tez aims to work out of the box for good performance and efficiency. Apache Hive and Pig are already adopting Tez as their platform of choice for query execution.
Edge 2016 SCL-2484: a software defined scalable and flexible container manage...Yong Feng
The material for IBM Edge 2016 session for Spectrum Container Management Solution.
https://www-01.ibm.com/events/global/edge/sessions/.
Please refer to http://ibm.biz/ConductorForContainers for more details about Spectrum Conductor for Containers.
Please refer to https://www.youtube.com/watch?v=7YMjP6EypqA and https://www.youtube.com/watch?v=d9oVPU3rwhE for the demo of Spectrum Conductor for Containers.
Simulating Heterogeneous Resources in CloudLightningCloudLightning
In this presentation, Dr Christos Papadopoulos-Filelis (Democritus University of Thrace, Greece) discusses resource characterisation, simulation tools and the elements of simulation used in CloudLightning.
This presentation was given at the National Conference on Cloud Computing in Dublin City University on 12th April 2016.
Running Non-MapReduce Big Data Applications on Apache Hadoophitesh1892
Apache Hadoop has become popular from its specialization in the execution of MapReduce programs. However, it has been hard to leverage existing Hadoop infrastructure for various other processing paradigms such as real-time streaming, graph processing and message-passing. That was true until the introduction of Apache Hadoop YARN in Apache Hadoop 2.0. YARN supports running arbitrary processing paradigms on the same Hadoop cluster. This allows for development of newer frameworks as well as more efficient implementations of existing frameworks that can all run on and share the resources of a single multi-tenant YARN cluster. This talk gives a brief introduction to YARN. We will illustrate how to create applications and how to best make use of YARN. We will show examples of different applications such as Apache Tez and Apache Samza that can leverage YARN and present best practices/guidelines on building applications on top of Apache Hadoop YARN.
How to get the maximum performance from your AEP server. This will discuss ways to improve execution time of short running jobs and how to properly configure the server depending on the expected number of users as well as the average size and duration of individual jobs. Included will be examples of making use of job pooling, Database connection sharing, and parallel subprotocol tuning. Determining when to make use of cluster, grid, or load balanced configurations along with memory and CPU sizing guidelines will also be discussed.
Hadoop Summit Europe Talk 2014: Apache Hadoop YARN: Present and FutureVinod Kumar Vavilapalli
Title: Apache Hadoop YARN: Present and Future
Abstract: Apache Hadoop YARN evolves the Hadoop compute platform from being centered only around MapReduce to being a generic data processing platform that can take advantage of a multitude of programming paradigms all on the same data. In this talk, we'll talk about the journey of YARN from a concept to being the cornerstone of Hadoop 2 GA releases. We'll cover the current status of YARN, how it is faring today and how it stands apart from the monochromatic world that is Hadoop 1.0. We`ll then move on to the exciting future of YARN - features that are making YARN a first class resource-management platform for enterprise Hadoop, rolling upgrades, high availability, support for long running services alongside applications, fine-grain isolation for multi-tenancy, preemption, application SLAs, application-history to name a few.
Operational systems manage our finances, shopping, devices and much more. Adding real-time analytics to these systems enables them to instantly respond to changing conditions and provide immediate, targeted feedback. This use of analytics is called "operational intelligence," and the need for it is widespread.
This talk will explain how in-memory computing techniques can be used to implement operational intelligence. It will show how an in-memory data grid integrated with a data-parallel compute engine can track events generated by a live system, analyze them in real time, and create alerts that help steer the system’s behavior. Code samples will demonstrate how an in-memory data grid employs object-oriented techniques to simplify the correlation and analysis of incoming events by maintaining an in-memory model of a live system.
The talk also will examine simplifications offered by this approach over directly analyzing incoming event streams from a live system using complex event processing or Storm. Lastly, it will explain key requirements of the in-memory computing platform for operational intelligence, in particular real-time updating of individual objects and high availability using data replication, and contrast these requirements to the design goals for stream processing in Spark.
One key feature that differentiates HBase from other distributed databases is its support of coprocessors. Bloomberg develops and manages some very low-latency systems that service real-time requests. In order to achieve real-time speeds, it was necessary to utilize coprocessors, which are similar to traditional stored procedures. As a result, we were able to match the average latency of an HBase cluster with that of a traditional database. This was done by using coprocessors to parallelize a lot of data computation and reduce the number of round-trips to the cluster by a factor of 5, thereby lowering the amount of data sent over the wire by 5. However, there are also significant challenges to managing coprocessors in a production environment. In this talk, I will to review the use case for HBase coprocessors and some practical tips on how to properly develop and deploy them. Some of the key topics covered in this talk are:
Type of coprocessors
Development challenges
Deployment challenges
Speakers
Amit Anand, Senior Software Developer, Bloomberg LP
Esther Kundin, Senior Software Engineer, Bloomberg LP
Achieving scale and performance using cloud native environmentRakuten Group, Inc.
ID Platform Product can be used by every Rakuten Group Companies and can easily serve millions of users. Multi-Region product challenges are many, example:
- Ensure 4 9’s availability
- Management across each region
- Alerting and Monitoring across each region
- Auto scaling (Scale up and Scale down) across each region
- Performance (vertical scale up)
- Cost
- DB Consistency Across Multiple Regions
- Resiliency
At Ecosystem Platform Layer for Rakuten, we handle each of these and this presentation is about how we handle these challenging scenarios.
PostgreSQL continuous backup and PITR with BarmanEDB
How can I achieve an RPO of 5 minutes for the backups of my PostgreSQL databases? And what about RPO=0 for zero data loss backups? This talk will give you answers to those questions, by guiding you through an overview of Disaster Recovery of PostgreSQL databases with Barman, covering its key concepts and providing useful patterns and tips.
Building large scale, job processing systems with Scala Akka Actor frameworkVignesh Sukumar
The Akka Actor framework is designed to be a fast message processing system. In this talk, we will explain how, at Box, we have used this framework to develop a large scale job processing system that works on billions of data files and achieves a high degree of throughput and fault tolerance. Over the course of the talk, we will explore the usage of Akka framework’s Supervisor functionality to provide a more controllable fault-tolerance strategy, and how we can use Futures to manage asynchronous jobs.
Apache Tez : Accelerating Hadoop Query ProcessingBikas Saha
Apache Tez is the new data processing framework in the Hadoop ecosystem. It runs on top of YARN - the new compute platform for Hadoop 2. Learn how Tez is built from the ground up to tackle a broad spectrum of data processing scenarios in Hadoop/BigData - ranging from interactive query processing to complex batch processing. With a high degree of automation built-in, and support for extensive customization, Tez aims to work out of the box for good performance and efficiency. Apache Hive and Pig are already adopting Tez as their platform of choice for query execution.
Edge 2016 SCL-2484: a software defined scalable and flexible container manage...Yong Feng
The material for IBM Edge 2016 session for Spectrum Container Management Solution.
https://www-01.ibm.com/events/global/edge/sessions/.
Please refer to http://ibm.biz/ConductorForContainers for more details about Spectrum Conductor for Containers.
Please refer to https://www.youtube.com/watch?v=7YMjP6EypqA and https://www.youtube.com/watch?v=d9oVPU3rwhE for the demo of Spectrum Conductor for Containers.
InfoSphere BigInsights - Analytics power for Hadoop - field experienceWilfried Hoge
How to analyze binary data as a technical business user. Use InfoSphere BigInsights to bring analytics on Hadoop closer to a user.
Presented at the OOP conference in Munich, 27.01.2015
Bigdata Hadoop project payment gateway domainKamal A
Live Hadoop project in payment gateway domain for people seeking real time work experience in bigdata domain. Email: Onlinetraining2011@gmail.com ,
Skypeid: onlinetraining2011
My profile: www.linkedin.com/pub/kamal-a/65/2b2/2b5
Opal: Simple Web Services Wrappers for Scientific ApplicationsSriram Krishnan
The grid-based infrastructure enables large-scale scientific applications to be run on distributed resources and coupled in innovative ways. However, in practice, grid resources are not very easy to use for the end-users who have to learn how to generate security credentials, stage inputs and outputs, access grid-based schedulers, and install complex client software. There is an imminent need to provide transparent access to these resources so that the end-users are shielded from the complicated details, and free to concentrate on their domain science. Scientific applications wrapped as Web services alleviate some of these problems by hiding the complexities of the back-end security and computational infrastructure, only exposing a simple SOAP API that can be accessed programmatically by application-specific user interfaces. However, writing the application services that access grid resources can be quite complicated, especially if it has to be replicated for every application. In this presentation, we present Opal which is a toolkit for wrapping scientific applications as Web services in a matter of hours, providing features such as scheduling, standards-based grid security and data management in an easy-to-use and configurable manner
DEVNET-1169 CI/CT/CD on a Micro Services Applications using Docker, Salt & Ni...Cisco DevNet
Nowadays, we heard a lot regarding micro services and DevOps but then, what are the impacts for an application development and how to really achieve this? The demo will demonstrate the benefits of using Docker (and related tools / technologies) for a micro services application and then having a continuous integration / tests / deployment workflow on CCS/Nimbus.
Adding Support for Networking and Web Technologies to an Embedded SystemJohn Efstathiades
These are the slides for a presentation we gave at Device Developer Conference 2014 in the UK. The presentation discusses the work done, experiences, and lessons learnt from adding an open source TCP/IP network stack and web server to an existing industrial control system running on an ARM Cortex M3-based processor from TI.
The presentation covers the following:
· Integrating the network stack into the existing software base
· Configuring and using the network stack and web server
· Adding support for HTTP basic authentication to restrict user access
· Using HTTP to remotely access the target system and retrieve operational data
· Debugging hints and tips
· Pitfalls to avoid and other lessons learnt
2013 Enterprise Track, Using Spatial ETL in a Multi-vendor Enterprise GIS Env...GIS in the Rockies
Large, infrastructure-based organizations such as utility companies often find that they need to operate with multiple GIS and CAD systems as part of their computing environments. This can create numerous challenges with data management and sharing of information within the organization. These issues can be addressed in part by the use of Spatial Extract-Transform-Load (ETL) technologies working in an enterprise environment along with a common data repository.
This presentation highlights experiences SBS has had in using Spatial ETL technologies in organizations to provide an advanced architecture for sharing spatial data. There are a number of technical considerations that must be addressed in these environments. These include:
• Support for multiple GIS platforms that all access a common data base
• A common, canonical data model that supports existing data models in the enterprise
• Cross platform conflict resolution
• A common data validation framework
• Global ID management
• A comprehensive network model to support business application requirements
• Scalability to support a very large user community
• Integration with other systems to support business workflows and advanced analytics
Supporting Research through "Desktop as a Service" models of e-infrastructure...David Wallom
Keynote presentation given 13/9/16 @ ESA Earth Observation Open Science workshop 2016.
"The rise in cloud computing as an e-infrastructure model is one that has the power to democratise access to computational and data resources throughout the research communities. We have seen the difference that Infrastructure as a Service (IaaS) has made for different communities and are now only beginning to understand what different models further up the stack can make. It is also becoming clear that with the increase in research data volumes, the number of sources and the possibility of utilising data from different regulatory regimes that a different model of how analysis is performed on the data is possible. Utilising a "Desktop as a Service" model, with community focused applications installed on a common and well understood virtual system image that is directly connected to community relevant data allows the researcher to no longer have to consider moving data but only the final analysed results. This massively simplifies both the user model and the data and resource owner model. We will consider the specific example of the Environmental Ecomics Synthesis Cloud and how it could easily be generalised to other areas."
Similar to PEARC17: Live Integrated Visualization Environment: An Experiment in Generalized Structured Frameworks for Visualization and Analysis (20)
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
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.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
PEARC17: Live Integrated Visualization Environment: An Experiment in Generalized Structured Frameworks for Visualization and Analysis
1. Live Integrated Visualization Environment:
An Experiment in Generalized Structured
Frameworks for Visualization and Analysis
James H. Money, Ph.D.
Idaho National Laboratory
PEARC 17
2. Contents
• Background
– Problem Environment
– Past Approaches/Goals
• Live Integrate Visualization Environment
– Data Feeds/Input Connectors
– Output Connectors
– Technical & System Details
– Initial Application
– Driver Details
• Output Connector Process
– Modes of Use
– Dynamic Model Processing
• Shortcomings
• SIEVAS
• Accomplishments
• Examples
3. Background
• Joint Intelligence Laboratory (JIL) – built in 2006 as a rapid test bed for end-to-end
intelligence solutions for Department of Defense (DoD)
• Initially based on the Joint Intelligence Operations Center Experimental (JIOC-X)
developed early 2000s
• Built to test, experiment and train using real operational data
• Supported a number of advanced visualization systems including:
– Knowledge Wall
– Tabletop Touch Displays
– Stereo Wall
– Knowledge Advanced Visualization Environment (KAVE) – A CAVE by another
name
• Joined the JIL in late 2007
• During 2008 installed a new CAVE that is 18’x10’x10’ – largest in DoD at the time
4. Problem Environment
• DoD has invested in over 18 CAVEs now, mostly used for modeling and simulation and
intelligence work
• Software products used by these groups included:
– Presagis Vega Prime
– Mechdyne vGeo
– Mechdyne Conduit
– GeoTime
– Delta3D
– Google Earth Professional
– And several more…
• Strategy included having the DoD pay for modifications to CAVE-enable the proprietary
tools (high supports costs!)
5. Problem Environment
• Various groups were attempting to process and analyze data in real
time with these and other production systems
• Approaches varied but all contained some major flaws in execution
• The desire was to ingest real time feeds into all the environments
seamlessly
• Data feeds included Distributed Common Ground System (DCGS),
Global Command and Control System (GCCS), as well as other lessor
know systems
• Showed first prototype of real-time/in-situ visualization with GOTS
systems using DCGS-A and a Force Directed Layout in 2008 – this led
to the development of a framework for more general purpose use
6. Past Approaches
• Vendor Specific Extensions
– Works out of the box
– Breaks down on larger datasets/Addition cost
• Direct Vendor Modifications
– Costly to install
– Frequently, do not meet 100% of user requirements
• Custom Coding/Toolkits
– Costly to build and maintain
– Custom build for each CAVE
• OpenGL Interceptors
– Usage of desktop applications
– Requires desktop to use, features do not work in immersive
environment
7. Goals
1. Multi-application and domain area aware
2. Data/Model Abstraction using standard techniques
3. Simultaneous access to same data streams
4. Real-time access with DVR capability
5. Merging of simulated and live data streams
6. Utilization of off-the-shelf products
8. Live Integrated Visualization Environment
• Live Integrated Visualization Environment (LIVE) is the end-to-end
solution for handling the live feeds while allowing a myriad of software
and tools to visualize the results
• Supported geospatial data as well as non-geospatial data at the design
phase
• Allowed for advanced analytics in the system with verification in the
CAVE (Now called “big data”)
• Allowed for live viewing, recording, playback and manipulation of data
• Permits remote viewing to phones, tablets and other types of displays
9. LIVE
• Built on idea of “connectors”
• Utilized input connectors for importing and storing data
• Utilized output connectors for visualization of results
• All the components were loosely coupled and connected by a
data/message bus
• Everything was distributed out of the box
• This allows products such as Google Earth to have local data sources
without changes
11. LIVE Data Feeds
• DCGS-A (ESRI Map Server)
• GCCS Tactical Management Server (TMS)
• Link 16
• Distributed Interactive Simulation (DIS)/High Level Architecture (HLA)
• Cursor-on-Target (CoT)
• System Toolkit (STK)
12. Live Output Connectors
• Vega Prime Modules (display, control, and loading/saving)
• Google Earth KML feed
• Force Directed Layout (FDL)
• GeoJSON for GeoTime
13. Technical Details
• Built initial on Microsoft .NET 2.0 -> Later migrate to 3.5
• Data storage used Microsoft SQL Server
• MessageBus custom developed using .NET Remoting
• Combination of reflection, C#, and managed and unmanaged C++ to
connect components
• Contained system information on sessions, data sources, drivers, and
configuration options
• Session
– List of Data Sources -> Associated Driver
• DVRService will load these sessions
• Also possible to use in distributed mode without centralized drivers
• Output connectors required to choose session at startup or pass by
configuration option
14. System Details
• First developed demo application using System Toolkit for UAV
applications to aid in planning tasks
• Developed Google Earth connector
• DVR Controls developed as desktop application
• Later DVR moved into Vega Prime as billboard controls
• Message bus used a publish-subscribe paradigm for messaging
• Message bus sent both control (for example DVR play, stop, goto) and
messages
18. Output Connector Process
• Process
– Session would load drivers for each data source at startup
– Connect to web service for information about session
– Connect to message bus
– Subscribe to messages of interest – in this case DVR controls and
Platform type messages
– Handling dynamic loading of models in threads
– Show model and changes after model load and thereafter
• Google Earth (using Stand-alone middleware)
– Connect and listen for message
– Keep log of messages
– Generate KML when requested from Google Earth
– KMZ file would request periodic refresh of KML data
19. Modes of Use
• Two primary modes of use for input: Drivers (Input Connectors) and
Standalone applications
– Drivers allowed automated processing but not real user interaction
at run time
– Standalone – allowed user to change items on the fly; used by the
UAV Tool
• Output similarly two ways to obtain data
– Plugin using native SDK (Vega Prime)
– Standalone application that acted as middleware between the
message bus and the data (Google Earth)
• This allowed swapping of simulated feeds in place of real-time data
connections when network connectivity was limited. (For example,
static vs. live full motion video (FMV) feeds)
• Chaining with multiple instances for data analytics
21. Dynamic Model Processing
LIVE
Web Service
Request for
Model
Information
Model
Information
Returned
Request for
Model
Model ZIP
Returned Model
extracted and
dynamically
added
Vega
Prime
22. Shortcomings
• Single point of failure for message bus “server”
• No partitioning
• No multiple session support
• No user authentication support
• Not multi-platform
• Hard to integrate some SDKs with C#
• Direct connections to databases for certain tasks
• Needs to be open source
23. Scientific & Intelligence Exascale Visualization Analysis System –
aka LIVE 2.0
• Fixed the above shortcomings by integrating users, multiple sessions
support, and distributed “servers”
• Enabled using Java primarily with ActiveMQ
• Supports a range of clients from Java, C#, C++, Python, R, etc. – just
need a Http Client, ActiveMQ Client, and JSON Mapper to connect
• Acts much like a microservice architecture but with web services
driving longer term activities
• Server side uses component such as Spring Framework, Hibernate,
Jackson JSON Mapper
• Clients use Apache HttpClient, Jackson, and ActiveMQ client
• Web services are RESTful with JSON data exchange
• Native web interface for administration
24. SIEVAS
• Integrated:
– Unity 3D
– Aspen Data
– Imagery (position + orientation from EXIF data)
– CT Data
– N-Body particle physics
– Initial Dashboard
• Release on Github in ~30 days on INL’s page
https://github.com/idaholab
25. Accomplishments
• Added in-situ/real-time capability to immersive environments
• Permitted code re-use and interoperability among display systems
using disparate datasets
• Decreased time to completion from months and years to days and
weeks.
• Utilized in production use cases for mission planning, rehearsal, and
after action reviews across a range of domains
• Permitted discovery of new insights and analyses for ISR based
missions not before seen using traditional methods
26. Development Status
Completed In-Progress
(FY2017)
Multiple Sessions
Users, Groups, Authentication
Non-Java Clients (Unity)
DVR (Java & Unity)
Configurable data sources
Multiple data source integration
Auto-Partitioning (Driver level)
One-time session keys
HPC Connection/C++ Client
Larger volume datasets
Dynamic Model Loading
Dynamic isosurfaces