Genomics Deployments - How to Get Right with Software Defined StorageSandeep Patil
This document discusses genomics workloads and the requirements for storage infrastructure to support them. It begins with an introduction to genomics and the growth of the field. It then examines the characteristics of genomic sequencing workloads, including the multi-step process and file-based nature. Key requirements for storage are outlined, such as high throughput, large ingestion of files, and support for POSIX and other access protocols. The document proposes a solution using a software-defined, clustered file system like IBM Spectrum Scale to provide scalable, high performance file storage as a building block of a composable infrastructure for genomics applications. It provides an example architecture and performance results for GATK-based analysis.
This document presents a technique called VM-aware adaptive storage cache prefetching that uses information from virtual machines to improve storage caching performance in hybrid storage arrays. It exploits access locality based on file layouts obtained from the guest file system and adaptively tunes prefetching by adjusting the prefetch window size based on application performance statistics. An evaluation using TPCx-V benchmarks showed the technique improved performance over 32% by using file layout information and 6.7% by using adaptive prefetch window tuning compared to other caching approaches.
IBM Cloud Object Storage: How it works and typical use casesTony Pearson
This session covers the general concepts of object storage and in particular the IBM Cloud Object Storage offerings. Presented at IBM TechU in Johannesburg, South Africa September 2019
This document provides an overview of IBM's reference architecture for deep learning clusters. It discusses the hardware and software components, including POWER-based servers with NVIDIA GPUs connected by Mellanox InfiniBand switches. It describes the storage architecture using IBM Spectrum Scale for a shared filesystem. The software stack is based on Red Hat Enterprise Linux, CUDA, Nvidia-Docker, IBM PowerAI, and container orchestration with either Kubernetes or IBM Spectrum LSF. Operational models and workflows are shown to support experimentation, scaling, and production phases of deep learning.
This document discusses bringing artificial intelligence capabilities directly to storage devices through computational storage. It describes how traditional approaches of moving large amounts of data between storage, memory and CPUs for AI tasks like image similarity search do not scale well. Computational storage aims to perform AI tasks like indexing and searching directly on the storage device by embedding processing capabilities. This reduces data movement and enables scaling to larger datasets. The document outlines an SSD platform with dedicated processing resources and software that allows common AI workloads to run directly on the storage device using standard interfaces and without impacting traditional storage functions. It provides examples showing significantly better performance and scalability compared to traditional approaches.
ACIC: Automatic Cloud I/O Configurator for HPC ApplicationsMingliang Liu
ACIC is a system which automatically searches for optimized I/O system configurations from many candidates for each individual HPC application running on a given cloud platform.
This work was published in SuperComputing 2013, Denver. See event http://sc13.supercomputing.org/schedule/event_detail.php-evid=pap127.html
IBM Spectrum Scale is software-defined storage that provides file storage for cloud, big data, and analytics solutions. It offers data security through native encryption and secure erase, scalability via snapshots, and high performance using flash acceleration. Spectrum Scale is proven at over 3,000 customers handling large datasets for applications such as weather modeling, digital media, and healthcare. It scales to over a billion petabytes and supports file sharing in on-premises, private, and public cloud deployments.
Genomics Deployments - How to Get Right with Software Defined StorageSandeep Patil
This document discusses genomics workloads and the requirements for storage infrastructure to support them. It begins with an introduction to genomics and the growth of the field. It then examines the characteristics of genomic sequencing workloads, including the multi-step process and file-based nature. Key requirements for storage are outlined, such as high throughput, large ingestion of files, and support for POSIX and other access protocols. The document proposes a solution using a software-defined, clustered file system like IBM Spectrum Scale to provide scalable, high performance file storage as a building block of a composable infrastructure for genomics applications. It provides an example architecture and performance results for GATK-based analysis.
This document presents a technique called VM-aware adaptive storage cache prefetching that uses information from virtual machines to improve storage caching performance in hybrid storage arrays. It exploits access locality based on file layouts obtained from the guest file system and adaptively tunes prefetching by adjusting the prefetch window size based on application performance statistics. An evaluation using TPCx-V benchmarks showed the technique improved performance over 32% by using file layout information and 6.7% by using adaptive prefetch window tuning compared to other caching approaches.
IBM Cloud Object Storage: How it works and typical use casesTony Pearson
This session covers the general concepts of object storage and in particular the IBM Cloud Object Storage offerings. Presented at IBM TechU in Johannesburg, South Africa September 2019
This document provides an overview of IBM's reference architecture for deep learning clusters. It discusses the hardware and software components, including POWER-based servers with NVIDIA GPUs connected by Mellanox InfiniBand switches. It describes the storage architecture using IBM Spectrum Scale for a shared filesystem. The software stack is based on Red Hat Enterprise Linux, CUDA, Nvidia-Docker, IBM PowerAI, and container orchestration with either Kubernetes or IBM Spectrum LSF. Operational models and workflows are shown to support experimentation, scaling, and production phases of deep learning.
This document discusses bringing artificial intelligence capabilities directly to storage devices through computational storage. It describes how traditional approaches of moving large amounts of data between storage, memory and CPUs for AI tasks like image similarity search do not scale well. Computational storage aims to perform AI tasks like indexing and searching directly on the storage device by embedding processing capabilities. This reduces data movement and enables scaling to larger datasets. The document outlines an SSD platform with dedicated processing resources and software that allows common AI workloads to run directly on the storage device using standard interfaces and without impacting traditional storage functions. It provides examples showing significantly better performance and scalability compared to traditional approaches.
ACIC: Automatic Cloud I/O Configurator for HPC ApplicationsMingliang Liu
ACIC is a system which automatically searches for optimized I/O system configurations from many candidates for each individual HPC application running on a given cloud platform.
This work was published in SuperComputing 2013, Denver. See event http://sc13.supercomputing.org/schedule/event_detail.php-evid=pap127.html
IBM Spectrum Scale is software-defined storage that provides file storage for cloud, big data, and analytics solutions. It offers data security through native encryption and secure erase, scalability via snapshots, and high performance using flash acceleration. Spectrum Scale is proven at over 3,000 customers handling large datasets for applications such as weather modeling, digital media, and healthcare. It scales to over a billion petabytes and supports file sharing in on-premises, private, and public cloud deployments.
Beyond Moore's Law: The Challenge of Heterogeneous Compute & Memory Systemsinside-BigData.com
This document summarizes a presentation given by Mike Ignatowski from AMD Research on heterogeneous computing and memory systems. Some key points include:
- Heterogeneous systems with specialized accelerators are dominating the top of the Green500 supercomputer list.
- Standards like HSA, Gen-Z, CCIX and OpenCAPI are helping to better integrate accelerators.
- AMD is developing heterogeneous computing technologies like the ROCm programming model and machine learning optimized Radeon graphics cards.
- Future systems will utilize more specialized cores and accelerators alongside general-purpose CPUs to improve performance and efficiency.
Windows 10 Upgrade: The best bad idea you never had…Arik Fletcher
This document discusses upgrading to Windows 10 from older Windows versions. It notes that while Windows 10 will no longer receive updates or security patches, compatibility for new hardware and software may be limited on older systems. Windows 10 is positioned as more secure and compatible with newer technology. The document provides guidance on checking system requirements like CPU, memory, and storage to see if a device can be upgraded. It also discusses options for recycling older equipment that cannot be upgraded.
This document discusses optimizing Apache Spark machine learning workloads on OpenPOWER platforms. It provides an overview of Spark, machine learning, and deep learning. It then discusses how OpenPOWER systems are well-suited for these workloads due to features like high memory bandwidth, large caches, and GPU support. The document outlines various techniques for tuning Spark performance on OpenPOWER, such as configuration of executors, cores, memory, and storage levels. It also presents examples analyzing the performance of a matrix factorization machine learning application under different Spark configurations.
Much industry focus is on All-Flash Arrays with traditional databases, but new databases using native direct-attached Flash have proven reliable, performant, and popular for operational use cases. Today, these operational databases store account information for banking and retail applications, real-time routing information for telecoms, and user profiles for advertising; they also support machine learning for applications in the financial industry, such as fraud detection. While proprietary PCIe and “wide SATA” had previously been popular, NVMe has finally come into operational use. Aerospike will discuss the benefits of NVMe for these use cases (including specific configurations and performance numbers), as well as the architectural implications of low-latency Flash and Storage Class Memory.
The document discusses the components inside a computer system unit. It describes how computers represent and store data, the components on the motherboard like the CPU and memory, and how the CPU processes instructions. It also outlines various connectors and ports on the exterior of the system unit that allow connection of peripheral devices.
This document summarizes a presentation on introducing the Persistent Memory Development Kit (PMDK) into PostgreSQL to utilize persistent memory (PMEM). The presentation covers: (1) hacking the PostgreSQL write-ahead log (WAL) and relation files to directly memory copy to PMEM, (2) evaluating the hacks which showed a 3% improvement to transactions and 30% reduction to checkpoint time, and (3) tips for PMEM programming like cache flushing and avoiding volatile layers.
This 3-sentence summary provides the key details from the document:
The document discusses an upcoming technical conference on IBM Systems that will cover IBM Cloud Object Storage features and use cases. It provides an overview of IBM Cloud Object Storage, including how it differs from block and file storage, its erasure coding technology, deployment options, applications and typical use cases. The speaker will discuss why object storage is becoming popular for storing large amounts of unstructured data cost effectively at scale.
The document discusses hardware components used in computer systems including input, processing, storage and output devices. It describes the functions of the central processing unit and memory for processing instructions and data. Secondary storage devices like magnetic tapes, disks and optical disks are described along with their access methods and storage capacities. A variety of input devices including keyboards, mice and scanners and output devices like monitors and printers are also summarized. Different types of computer systems are classified and factors for selecting and upgrading systems are outlined.
This document discusses in-memory analytics and compares it to traditional disk-based databases. In-memory analytics stores all data in RAM rather than on disk storage, allowing for much faster data access and analytics. Key advantages of in-memory systems include speeds 50-100 times faster than disk-based databases and the ability to perform real-time analytics. The document outlines optimization aspects for in-memory data management like data layout, parallelism, and fault tolerance. It concludes with some common questions around in-memory analytics regarding adoption, performance, skills needs, and data size.
High Performance Computing for LiDAR Data ProductionMattBethel1
- 64-bit computing with multi-core/multi-threaded CPUs and large amounts of RAM is recommended to process LiDAR data efficiently. Using multiple processors in parallel processing can significantly reduce processing times, with up to 91% time savings observed.
- GPU processing provides further performance gains over CPU processing alone due to the parallel nature and larger number of cores in GPUs.
- Writing data temporarily to a fast local drive before transferring to a network drive for storage can reduce export times by up to 80% compared to direct network I/O.
- A high-performance server configuration is suggested, including 10GbE networking, an SSD for temporary files, SAS internal drives, and either distributed processing or
Lessons learned processing 70 billion data points a day using the hybrid cloudDataWorks Summit
NetApp receives 70 billion data points of telemetry information each day from its customer’s storage systems. This telemetry data contains configuration information, performance counters, and logs. All of this data is processed using multiple Hadoop clusters, and feeds a machine learning pipeline and a data serving infrastructure that produces insights for customers via an application called Active IQ. We describe the evolution of our Hadoop infrastructure from a traditional on-premises architecture to the hybrid cloud, and lessons learned.
We’ll discuss the insights we are able to produce for our customers, and the techniques used. Finally, we describe the data management challenges with our multi-petabyte Hadoop data lake. We solved these problems by building a unified data lake on-premises and using the NetApp Data Fabric to seamlessly connect to public clouds for data science and machine learning compute resources.
Architecting a truly hybrid cloud implementation allowed NetApp to free up our data scientists to use any software on any cloud, kept the customer log data safe on NetApp Private Storage in Equinix, resulted in faster ability to innovate and release new code and provided flexibility to use any public cloud at the same time with data on NetApp in Equinix.
Speaker
Pranoop Erasani, NetApp, Senior Technical Director, ONTAP
Shankar Pasupathy, NetApp, Technical Director, ACE Engineering
This document provides an overview of computers, including their introduction, generations, classifications, organization, components like registers and buses, memory and storage systems, input/output devices, software, operating systems, and applications. It defines computers and their functions, limitations, and features. It describes the evolution of computers through five generations based on the underlying technology. It also classifies computers based on their application (analog, digital, hybrid) and size/capability (micro, mini, mainframe, super). The document outlines the central components that make up a computer's organization.
CH02-Computer Organization and Architecture 10e.pptxHafizSaifullah4
This document discusses computer performance and benchmarking. It covers several topics related to improving computer performance, including designing for performance, microprocessor speed techniques, improvements in chip organization and architecture like multicore processors, and issues that limit further increases in clock speed. It also discusses Amdahl's Law, Little's Law, and ways to measure computer performance, including various types of means to calculate benchmark results. SPEC benchmarks are mentioned as examples of widely used benchmark programs.
Backup Options for IBM PureData for Analytics powered by NetezzaTony Pearson
This document discusses backup options for IBM PureData System for Analytics powered by Netezza. It describes using either a filesystem approach backing up metadata and databases to external storage, or using external backup software. When using external backup software, the document recommends IBM Tivoli Storage Manager and describes backup architectures using a TSM proxy node or LAN-free configuration. It also provides best practices like running multiple backup streams in parallel.
This document discusses the challenges of maintaining performance SLAs for data infrastructure as application requirements change daily. It introduces the concept of an auto-scaling data infrastructure that can autonomously manage performance through real-time modeling of application workloads and cache performance. Such a system would learn performance models, predict the impact of resizing caches and other resources, and auto-scale resources just enough to maximize efficiency while meeting performance targets like latency thresholds. This avoids costly performance issues and revenue disruption from unpredictable infrastructure.
Aerospike meetup july 2019 | Big Data DemystifiedOmid Vahdaty
Building a low latency (sub millisecond), high throughput database that can handle big data AND linearly scale is not easy - but we did it anyway...
In this session we will get to know Aerospike, an enterprise distributed primary key database solution.
- We will do an introduction to Aerospike - basic terms, how it works and why is it widely used in mission critical systems deployments.
- We will understand the 'magic' behind Aerospike ability to handle small, medium and even Petabyte scale data, and still guarantee predictable performance of sub-millisecond latency
- We will learn how Aerospike devops is different than other solutions in the market, and see how easy it is to run it on cloud environments as well as on premise.
We will also run a demo - showing a live example of the performance and self-healing technologies the database have to offer.
Datalagring för AI
Vad bör man att tänka på, hur bygger man och vilken skillnad kan IBM's infrastruktur göra.
Talare: Christofer Jensen, Storage Technical Specialist, IBM
Presentationen hölls på Watson Kista Summit 2018
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
📕 Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
💻 Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
Control Flow in Studio
inQuba Webinar Mastering Customer Journey Management with Dr Graham HillLizaNolte
HERE IS YOUR WEBINAR CONTENT! 'Mastering Customer Journey Management with Dr. Graham Hill'. We hope you find the webinar recording both insightful and enjoyable.
In this webinar, we explored essential aspects of Customer Journey Management and personalization. Here’s a summary of the key insights and topics discussed:
Key Takeaways:
Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement.
Personalization Strategies: We discussed how to leverage data and insights to create personalized experiences that resonate with customers.
Technology Integration: Insights were shared on how inQuba’s advanced technology can streamline customer interactions and drive operational efficiency.
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This document summarizes a presentation given by Mike Ignatowski from AMD Research on heterogeneous computing and memory systems. Some key points include:
- Heterogeneous systems with specialized accelerators are dominating the top of the Green500 supercomputer list.
- Standards like HSA, Gen-Z, CCIX and OpenCAPI are helping to better integrate accelerators.
- AMD is developing heterogeneous computing technologies like the ROCm programming model and machine learning optimized Radeon graphics cards.
- Future systems will utilize more specialized cores and accelerators alongside general-purpose CPUs to improve performance and efficiency.
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This document discusses upgrading to Windows 10 from older Windows versions. It notes that while Windows 10 will no longer receive updates or security patches, compatibility for new hardware and software may be limited on older systems. Windows 10 is positioned as more secure and compatible with newer technology. The document provides guidance on checking system requirements like CPU, memory, and storage to see if a device can be upgraded. It also discusses options for recycling older equipment that cannot be upgraded.
This document discusses optimizing Apache Spark machine learning workloads on OpenPOWER platforms. It provides an overview of Spark, machine learning, and deep learning. It then discusses how OpenPOWER systems are well-suited for these workloads due to features like high memory bandwidth, large caches, and GPU support. The document outlines various techniques for tuning Spark performance on OpenPOWER, such as configuration of executors, cores, memory, and storage levels. It also presents examples analyzing the performance of a matrix factorization machine learning application under different Spark configurations.
Much industry focus is on All-Flash Arrays with traditional databases, but new databases using native direct-attached Flash have proven reliable, performant, and popular for operational use cases. Today, these operational databases store account information for banking and retail applications, real-time routing information for telecoms, and user profiles for advertising; they also support machine learning for applications in the financial industry, such as fraud detection. While proprietary PCIe and “wide SATA” had previously been popular, NVMe has finally come into operational use. Aerospike will discuss the benefits of NVMe for these use cases (including specific configurations and performance numbers), as well as the architectural implications of low-latency Flash and Storage Class Memory.
The document discusses the components inside a computer system unit. It describes how computers represent and store data, the components on the motherboard like the CPU and memory, and how the CPU processes instructions. It also outlines various connectors and ports on the exterior of the system unit that allow connection of peripheral devices.
This document summarizes a presentation on introducing the Persistent Memory Development Kit (PMDK) into PostgreSQL to utilize persistent memory (PMEM). The presentation covers: (1) hacking the PostgreSQL write-ahead log (WAL) and relation files to directly memory copy to PMEM, (2) evaluating the hacks which showed a 3% improvement to transactions and 30% reduction to checkpoint time, and (3) tips for PMEM programming like cache flushing and avoiding volatile layers.
This 3-sentence summary provides the key details from the document:
The document discusses an upcoming technical conference on IBM Systems that will cover IBM Cloud Object Storage features and use cases. It provides an overview of IBM Cloud Object Storage, including how it differs from block and file storage, its erasure coding technology, deployment options, applications and typical use cases. The speaker will discuss why object storage is becoming popular for storing large amounts of unstructured data cost effectively at scale.
The document discusses hardware components used in computer systems including input, processing, storage and output devices. It describes the functions of the central processing unit and memory for processing instructions and data. Secondary storage devices like magnetic tapes, disks and optical disks are described along with their access methods and storage capacities. A variety of input devices including keyboards, mice and scanners and output devices like monitors and printers are also summarized. Different types of computer systems are classified and factors for selecting and upgrading systems are outlined.
This document discusses in-memory analytics and compares it to traditional disk-based databases. In-memory analytics stores all data in RAM rather than on disk storage, allowing for much faster data access and analytics. Key advantages of in-memory systems include speeds 50-100 times faster than disk-based databases and the ability to perform real-time analytics. The document outlines optimization aspects for in-memory data management like data layout, parallelism, and fault tolerance. It concludes with some common questions around in-memory analytics regarding adoption, performance, skills needs, and data size.
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- GPU processing provides further performance gains over CPU processing alone due to the parallel nature and larger number of cores in GPUs.
- Writing data temporarily to a fast local drive before transferring to a network drive for storage can reduce export times by up to 80% compared to direct network I/O.
- A high-performance server configuration is suggested, including 10GbE networking, an SSD for temporary files, SAS internal drives, and either distributed processing or
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NetApp receives 70 billion data points of telemetry information each day from its customer’s storage systems. This telemetry data contains configuration information, performance counters, and logs. All of this data is processed using multiple Hadoop clusters, and feeds a machine learning pipeline and a data serving infrastructure that produces insights for customers via an application called Active IQ. We describe the evolution of our Hadoop infrastructure from a traditional on-premises architecture to the hybrid cloud, and lessons learned.
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Architecting a truly hybrid cloud implementation allowed NetApp to free up our data scientists to use any software on any cloud, kept the customer log data safe on NetApp Private Storage in Equinix, resulted in faster ability to innovate and release new code and provided flexibility to use any public cloud at the same time with data on NetApp in Equinix.
Speaker
Pranoop Erasani, NetApp, Senior Technical Director, ONTAP
Shankar Pasupathy, NetApp, Technical Director, ACE Engineering
This document provides an overview of computers, including their introduction, generations, classifications, organization, components like registers and buses, memory and storage systems, input/output devices, software, operating systems, and applications. It defines computers and their functions, limitations, and features. It describes the evolution of computers through five generations based on the underlying technology. It also classifies computers based on their application (analog, digital, hybrid) and size/capability (micro, mini, mainframe, super). The document outlines the central components that make up a computer's organization.
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Building a low latency (sub millisecond), high throughput database that can handle big data AND linearly scale is not easy - but we did it anyway...
In this session we will get to know Aerospike, an enterprise distributed primary key database solution.
- We will do an introduction to Aerospike - basic terms, how it works and why is it widely used in mission critical systems deployments.
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We will also run a demo - showing a live example of the performance and self-healing technologies the database have to offer.
Datalagring för AI
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Talare: Christofer Jensen, Storage Technical Specialist, IBM
Presentationen hölls på Watson Kista Summit 2018
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
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Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
💻 Extra training through UiPath Academy:
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Control Flow in Studio
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Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement.
Personalization Strategies: We discussed how to leverage data and insights to create personalized experiences that resonate with customers.
Technology Integration: Insights were shared on how inQuba’s advanced technology can streamline customer interactions and drive operational efficiency.
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Dynamic. Modular. Productive.
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Interoperability at its Core
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Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
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UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
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👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
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Key Learnings:
• Grasp the concept of REDO logs and their significance in InnoDB's transaction management.
• Discover the advantages of dynamic REDO log configuration and how to leverage it for optimal performance.
• Understand the inner workings of instant ADD/DROP columns and their impact on database operations.
• Gain valuable insights into the row versioning mechanism that empowers instant column modifications.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.