This document provides an introduction to cloud computing and parallel/distributed processing. It discusses how web-scale problems involving large amounts of data require distributed computing across large data centers. Different models of cloud computing are described, including utility computing, platform as a service (PaaS), and software as a service (SaaS). The document also discusses how web applications are moving to highly-interactive models enabled by technologies like AJAX. Key aspects of cloud computing covered include large data centers, virtualization, MapReduce processing for large datasets, and interactive web applications.
This document provides an introduction to cloud computing and parallel/distributed processing through a lecture on the topic. It discusses web-scale problems involving large amounts of data, the trend toward large centralized data centers, and different computing models like utility computing. It also introduces concepts like virtualization, MapReduce, and designing highly interactive web applications. The lecture covers challenges in parallelization like assigning work and synchronizing workers. It provides examples of patterns for parallelism like master/slave and producer/consumer models.
This document provides an introduction to cloud computing and parallel/distributed processing. It discusses web-scale problems involving large amounts of data, the use of large data centers to process this data, and different computing models like utility computing. It also introduces concepts like virtualization, MapReduce for batch processing, and AJAX for interactive web applications. The document outlines some of the challenges in parallel and distributed systems like assigning work units and synchronizing workers. Common patterns for parallelism like master/slave and producer/consumer models are also discussed.
This document provides an introduction to cloud computing and parallel/distributed processing through a lecture on the topic. It discusses web-scale problems involving large amounts of data, the use of large data centers to process this data, and different computing models like utility computing. It also introduces concepts like virtualization, MapReduce, and designing applications around patterns for parallelism. The overall goal of the lecture is to define cloud computing and discuss how techniques like MapReduce can help manage distributed and parallel processing of large datasets in the cloud.
This document provides an introduction and overview of cloud computing and related topics. It discusses web-scale problems involving large amounts of data, the use of large data centers to process this data in parallel, and different models for cloud computing including utility computing, platform as a service, and software as a service. It also covers challenges in parallel and distributed processing like assigning work units across workers and managing shared resources and synchronization.
This document discusses energy efficiency in cloud computing. It begins by noting that the IT industry accounts for 2% of global carbon emissions and that 50-75% of this is from powering devices. The author then discusses how operational costs of data centers now exceed purchase costs due to rising energy prices. Several techniques for improving energy efficiency are proposed, including virtualization, task consolidation, load balancing, and profiling virtual machines to match them with servers based on resource requirements. The goal is to increase server utilization and turn off unused servers to reduce overall energy usage.
Gridforum David De Roure Newe Science 20080402vrij
The document discusses the evolution of e-Science and how it enables new forms of collaborative research. Key points include:
- e-Science has progressed from specialized teams doing "heroic science" to everyday researchers conducting routine research using ubiquitous digital tools and data sharing.
- Web 2.0 technologies and approaches like open data, workflows, and social networking are empowering researchers and supporting new types of collaborative, data-driven science.
- Future e-Science relies on making these technologies simple and accessible to researchers from all domains to further break down barriers to collaborative, data-centric research.
Introduction - Lecture 1 - Seminar Web Information Systems Technology (WE-DIN...Beat Signer
The document summarizes a seminar on web information systems taught by Prof. Beat Signer. It provides an overview of topics to be covered in the seminar, including the history of hypertext, the world wide web, web 2.0, semantic web, cloud computing, and more. Students are asked to select three topics they are interested in and provide feedback on the seminar organization.
This is a slightly extended version of the BioDBcore presentation I gave at Biocuration2012 in the Workshop Databases & Journals – How to have a sustainable long term plan for journals and databases?
This document provides an introduction to cloud computing and parallel/distributed processing through a lecture on the topic. It discusses web-scale problems involving large amounts of data, the trend toward large centralized data centers, and different computing models like utility computing. It also introduces concepts like virtualization, MapReduce, and designing highly interactive web applications. The lecture covers challenges in parallelization like assigning work and synchronizing workers. It provides examples of patterns for parallelism like master/slave and producer/consumer models.
This document provides an introduction to cloud computing and parallel/distributed processing. It discusses web-scale problems involving large amounts of data, the use of large data centers to process this data, and different computing models like utility computing. It also introduces concepts like virtualization, MapReduce for batch processing, and AJAX for interactive web applications. The document outlines some of the challenges in parallel and distributed systems like assigning work units and synchronizing workers. Common patterns for parallelism like master/slave and producer/consumer models are also discussed.
This document provides an introduction to cloud computing and parallel/distributed processing through a lecture on the topic. It discusses web-scale problems involving large amounts of data, the use of large data centers to process this data, and different computing models like utility computing. It also introduces concepts like virtualization, MapReduce, and designing applications around patterns for parallelism. The overall goal of the lecture is to define cloud computing and discuss how techniques like MapReduce can help manage distributed and parallel processing of large datasets in the cloud.
This document provides an introduction and overview of cloud computing and related topics. It discusses web-scale problems involving large amounts of data, the use of large data centers to process this data in parallel, and different models for cloud computing including utility computing, platform as a service, and software as a service. It also covers challenges in parallel and distributed processing like assigning work units across workers and managing shared resources and synchronization.
This document discusses energy efficiency in cloud computing. It begins by noting that the IT industry accounts for 2% of global carbon emissions and that 50-75% of this is from powering devices. The author then discusses how operational costs of data centers now exceed purchase costs due to rising energy prices. Several techniques for improving energy efficiency are proposed, including virtualization, task consolidation, load balancing, and profiling virtual machines to match them with servers based on resource requirements. The goal is to increase server utilization and turn off unused servers to reduce overall energy usage.
Gridforum David De Roure Newe Science 20080402vrij
The document discusses the evolution of e-Science and how it enables new forms of collaborative research. Key points include:
- e-Science has progressed from specialized teams doing "heroic science" to everyday researchers conducting routine research using ubiquitous digital tools and data sharing.
- Web 2.0 technologies and approaches like open data, workflows, and social networking are empowering researchers and supporting new types of collaborative, data-driven science.
- Future e-Science relies on making these technologies simple and accessible to researchers from all domains to further break down barriers to collaborative, data-centric research.
Introduction - Lecture 1 - Seminar Web Information Systems Technology (WE-DIN...Beat Signer
The document summarizes a seminar on web information systems taught by Prof. Beat Signer. It provides an overview of topics to be covered in the seminar, including the history of hypertext, the world wide web, web 2.0, semantic web, cloud computing, and more. Students are asked to select three topics they are interested in and provide feedback on the seminar organization.
This is a slightly extended version of the BioDBcore presentation I gave at Biocuration2012 in the Workshop Databases & Journals – How to have a sustainable long term plan for journals and databases?
This document describes a study using the ODIN text mining system to extract relationships between genes, drugs, and diseases from biomedical literature and validate those relationships against the PharmGKB knowledge base. The researchers developed methods to improve relationship ranking and conducted a revalidation experiment with curators from Stanford evaluating a sample of automatically extracted relationships. The curators provided feedback that led to improvements in the interactive curation interface to better suit their needs. Lessons were learned about obtaining user requirements and rapidly implementing and testing prototypes to develop usable curation tools.
This document appears to list voice file names without any additional context or information. It includes the voice file names 001.3ga, 002.3ga, 003.3ga, 005.3ga, and 006.3ga.
Viralzone: a web resource dedicated to viruses, by Patrick Masson, Chantal Hulo, Edouard De Castro, Lydie Bougueleret, Philippe Le Mercier and Ioannis Xenarios.
Presented at the 5th International Biocuration Conference, hosted by PIR in Washington, DC, April 2-4, 2012.
BioDBCore: Current Status and Next DevelopmentsPascale Gaudet
The document discusses BioDBCore, a collaborative project aimed at gathering and standardizing metadata about biological databases. It provides an overview of BioDBCore's goals of improving data integration, encouraging standards, and maximizing resources. BioDBCore is led by Pascale Gaudet and Philippe Rocca-Serra and implemented on the BioSharing website. The document outlines the BioDBCore descriptors for databases and provides an example entry for the dictyBase database. It discusses maintaining and expanding BioDBCore records with the help of database providers and journals.
Aptamer Base: A collaborative knowledge base to describe aptamers and SELEX experiments, by Cruz-Toledo, Jose; McKeague, Maureen; Zhang, Xueru; Giamberardino, Amanda; McConnell, Erin; Francis, Tariq; DeRosa, Maria; Dumontier, Michel.
Presented at the 5th International Biocuration Conference, hosted by PIR in Washington, DC, April 2-4, 2012.
This document discusses cloud computing and network traffic. It covers several topics:
1. Web-scale problems that are data-intensive and require large data centers to solve. Examples include searching the web and scientific research data.
2. Large data centers have become necessary to handle web-scale problems by centralizing computing resources. Issues include redundancy, efficiency, and management.
3. Different cloud computing models have emerged like utility computing, platform as a service, and software as a service.
For all the network traffic generated, smart network access control, traffic management, and effective security techniques are needed, and there is ongoing research in these areas.
Introduction to the Artificial Intelligence and Computer Vision revolutionDarian Frajberg
Deep learning and computer vision have revolutionized artificial intelligence. Deep learning uses artificial neural networks inspired by the human brain to learn from large amounts of data without being explicitly programmed. Computer vision gives computers the ability to understand digital images and videos. Key breakthroughs include AlexNet achieving unprecedented accuracy on ImageNet in 2012, demonstrating the power of deep convolutional neural networks for computer vision tasks. Challenges remain around ensuring AI systems are beneficial to society, avoiding data biases, and increasing transparency.
100% training accuracy without overfittingLibgirlTeam
The document discusses overfitting and underfitting in machine learning models. It argues that with enough context data and a large model, 100% training accuracy can be achieved without overfitting. Typically, overfitting occurs when a model learns noise in the data as patterns. However, the document asserts that data only appears noisy if we lack context about how it was generated. A model can fit all training data if it learns patterns according to their proper contexts. With enough contextual data dimensions and a large model, the training data can be learned without overfitting to noise.
Data-Intensive Text Processing with MapReduceGeorge Ang
The document discusses using MapReduce for graph algorithms like single source shortest path and PageRank, describing how Dijkstra's algorithm works for shortest path problems on graphs and how it can be parallelized using MapReduce by performing a breadth-first search across partitions of the graph.
Data-Intensive Text Processing with MapReduce George Ang
The document discusses using MapReduce for graph algorithms like single source shortest path, PageRank, and describes how graphs can be represented through adjacency matrices and lists. It provides an example of Dijkstra's algorithm for finding shortest paths in a graph and discusses how to implement breadth-first search for single source shortest path using MapReduce.
Full day lectures @International University, HCM City, Vietnam, May 2019. Review of AI in 2019; outlook into the future; empirical research in AI; introduction to AI research at Deakin University
Artificial intelligence in the post-deep learning eraDeakin University
Deep learning has recently reached the heights that pioneers in the field had aspired to, serving as the driving force behind recent breakthroughs in AI, which have arguably surpassed the Turing test. At present, the spotlight is on scaling Transformers and diffusion models on Internet-scale data. In this talk, I will provide an overview of the fundamental principles of deep learning, its powers, and limitations, and explore the new era of post-deep learning. This new era encompasses novel objectives, dynamic architectures, abstract reasoning, neurosymbolic hybrid systems, and LLM-based agent systems.
Thoughts on Knowledge Graphs & Deeper ProvenancePaul Groth
Thinking about the need for deeper provenance for knowledge graphs but also using knowledge graphs to enrich provenance. Presented at https://seminariomirianandres.unirioja.es/sw19/
In this video from the DDN User Group at SC17, Jake Carroll from The Queensland Brain Institute, The University of Queensland, Australia presents: MeDiCI - How to Withstand a Research Data Tsunami.
The Metropolitan Data Caching Infrastructure (MeDiCI) project is a data storage fabric developed and trialled at UQ that delivers data to researchers where needed at any time. The “magic” of MeDiCI is it offers the illusion of a single virtual data centre next door even when it is actually distributed over potentially very wide areas with varying network connectivity. MeDiCI enables a range of techniques for national and international data sharing. Recent tests between UQ, the University of California, San Diego and the National Institute of Advanced Industrial Science and Technology in Japan indicated that MeDiCI can even support seamless access at very high data rates globally.
Watch the video: https://wp.me/p3RLHQ-hNG
Learn more: http://ddn.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
This document summarizes a talk on data science for software engineering. It discusses how data science involves various fields like statistics, machine learning, and data mining. It notes that while "big data" is often discussed, software engineering data is typically small and sparse. Domain knowledge is important for data mining to avoid misinterpreting data. Data science with software engineering data requires understanding organizations and their willingness to share data given privacy concerns. The document outlines sharing data, models, and methods for learning across different organizations and discusses techniques for balancing privacy and utility when sharing data.
Cyberinfrastructure and its Role in ScienceCameron Kiddle
This presentation examines some of the challenges scientists face and describes various cyberinfrastructure technologies that help address these challenges. Example projects employing cyberinfrastructure technologies that we have worked on at the Grid Research Centre, including the GeoChronos project, are also presented. This presentation was given at the IAI International Wireless Sensor Networks Summer School held at the University of Alberta on July 6th, 2009.
Scientific instruments, environmental sensors, and large-scale simulations are creating more scientific data than ever before. By using advanced, large-scale information processing facilities, scientists are now able to analyze massive volumes of data in ways that never would have been possible just a few years ago. While a few researchers have access to these large computer systems, most are limited by the processing capacity they can access conveniently and quickly. Cloud computing solutions utilizing Microsoft Azure allow environmental science researchers to access the compute and storage resources that they need, when they need them—without the up-front financial investment required—and helps reduce the time between progress and breakthroughs. Microsoft Azure brings on-demand computing and data access to environmental scientists and researchers everywhere.
This document discusses the roles that cloud computing and virtualization can play in reproducible research. It notes that virtualization allows for capturing the full computational environment of an experiment. The cloud builds on this by providing scalable resources and services for storage, computation and managing virtual machines. Challenges include costs, handling large datasets, and cultural adoption issues. Databases in the cloud may help support exploratory analysis of large datasets. Overall, the cloud shows promise for improving reproducibility by enabling sharing of full experimental environments and resources for computationally intensive analysis.
This document describes a study using the ODIN text mining system to extract relationships between genes, drugs, and diseases from biomedical literature and validate those relationships against the PharmGKB knowledge base. The researchers developed methods to improve relationship ranking and conducted a revalidation experiment with curators from Stanford evaluating a sample of automatically extracted relationships. The curators provided feedback that led to improvements in the interactive curation interface to better suit their needs. Lessons were learned about obtaining user requirements and rapidly implementing and testing prototypes to develop usable curation tools.
This document appears to list voice file names without any additional context or information. It includes the voice file names 001.3ga, 002.3ga, 003.3ga, 005.3ga, and 006.3ga.
Viralzone: a web resource dedicated to viruses, by Patrick Masson, Chantal Hulo, Edouard De Castro, Lydie Bougueleret, Philippe Le Mercier and Ioannis Xenarios.
Presented at the 5th International Biocuration Conference, hosted by PIR in Washington, DC, April 2-4, 2012.
BioDBCore: Current Status and Next DevelopmentsPascale Gaudet
The document discusses BioDBCore, a collaborative project aimed at gathering and standardizing metadata about biological databases. It provides an overview of BioDBCore's goals of improving data integration, encouraging standards, and maximizing resources. BioDBCore is led by Pascale Gaudet and Philippe Rocca-Serra and implemented on the BioSharing website. The document outlines the BioDBCore descriptors for databases and provides an example entry for the dictyBase database. It discusses maintaining and expanding BioDBCore records with the help of database providers and journals.
Aptamer Base: A collaborative knowledge base to describe aptamers and SELEX experiments, by Cruz-Toledo, Jose; McKeague, Maureen; Zhang, Xueru; Giamberardino, Amanda; McConnell, Erin; Francis, Tariq; DeRosa, Maria; Dumontier, Michel.
Presented at the 5th International Biocuration Conference, hosted by PIR in Washington, DC, April 2-4, 2012.
This document discusses cloud computing and network traffic. It covers several topics:
1. Web-scale problems that are data-intensive and require large data centers to solve. Examples include searching the web and scientific research data.
2. Large data centers have become necessary to handle web-scale problems by centralizing computing resources. Issues include redundancy, efficiency, and management.
3. Different cloud computing models have emerged like utility computing, platform as a service, and software as a service.
For all the network traffic generated, smart network access control, traffic management, and effective security techniques are needed, and there is ongoing research in these areas.
Introduction to the Artificial Intelligence and Computer Vision revolutionDarian Frajberg
Deep learning and computer vision have revolutionized artificial intelligence. Deep learning uses artificial neural networks inspired by the human brain to learn from large amounts of data without being explicitly programmed. Computer vision gives computers the ability to understand digital images and videos. Key breakthroughs include AlexNet achieving unprecedented accuracy on ImageNet in 2012, demonstrating the power of deep convolutional neural networks for computer vision tasks. Challenges remain around ensuring AI systems are beneficial to society, avoiding data biases, and increasing transparency.
100% training accuracy without overfittingLibgirlTeam
The document discusses overfitting and underfitting in machine learning models. It argues that with enough context data and a large model, 100% training accuracy can be achieved without overfitting. Typically, overfitting occurs when a model learns noise in the data as patterns. However, the document asserts that data only appears noisy if we lack context about how it was generated. A model can fit all training data if it learns patterns according to their proper contexts. With enough contextual data dimensions and a large model, the training data can be learned without overfitting to noise.
Data-Intensive Text Processing with MapReduceGeorge Ang
The document discusses using MapReduce for graph algorithms like single source shortest path and PageRank, describing how Dijkstra's algorithm works for shortest path problems on graphs and how it can be parallelized using MapReduce by performing a breadth-first search across partitions of the graph.
Data-Intensive Text Processing with MapReduce George Ang
The document discusses using MapReduce for graph algorithms like single source shortest path, PageRank, and describes how graphs can be represented through adjacency matrices and lists. It provides an example of Dijkstra's algorithm for finding shortest paths in a graph and discusses how to implement breadth-first search for single source shortest path using MapReduce.
Full day lectures @International University, HCM City, Vietnam, May 2019. Review of AI in 2019; outlook into the future; empirical research in AI; introduction to AI research at Deakin University
Artificial intelligence in the post-deep learning eraDeakin University
Deep learning has recently reached the heights that pioneers in the field had aspired to, serving as the driving force behind recent breakthroughs in AI, which have arguably surpassed the Turing test. At present, the spotlight is on scaling Transformers and diffusion models on Internet-scale data. In this talk, I will provide an overview of the fundamental principles of deep learning, its powers, and limitations, and explore the new era of post-deep learning. This new era encompasses novel objectives, dynamic architectures, abstract reasoning, neurosymbolic hybrid systems, and LLM-based agent systems.
Thoughts on Knowledge Graphs & Deeper ProvenancePaul Groth
Thinking about the need for deeper provenance for knowledge graphs but also using knowledge graphs to enrich provenance. Presented at https://seminariomirianandres.unirioja.es/sw19/
In this video from the DDN User Group at SC17, Jake Carroll from The Queensland Brain Institute, The University of Queensland, Australia presents: MeDiCI - How to Withstand a Research Data Tsunami.
The Metropolitan Data Caching Infrastructure (MeDiCI) project is a data storage fabric developed and trialled at UQ that delivers data to researchers where needed at any time. The “magic” of MeDiCI is it offers the illusion of a single virtual data centre next door even when it is actually distributed over potentially very wide areas with varying network connectivity. MeDiCI enables a range of techniques for national and international data sharing. Recent tests between UQ, the University of California, San Diego and the National Institute of Advanced Industrial Science and Technology in Japan indicated that MeDiCI can even support seamless access at very high data rates globally.
Watch the video: https://wp.me/p3RLHQ-hNG
Learn more: http://ddn.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
This document summarizes a talk on data science for software engineering. It discusses how data science involves various fields like statistics, machine learning, and data mining. It notes that while "big data" is often discussed, software engineering data is typically small and sparse. Domain knowledge is important for data mining to avoid misinterpreting data. Data science with software engineering data requires understanding organizations and their willingness to share data given privacy concerns. The document outlines sharing data, models, and methods for learning across different organizations and discusses techniques for balancing privacy and utility when sharing data.
Cyberinfrastructure and its Role in ScienceCameron Kiddle
This presentation examines some of the challenges scientists face and describes various cyberinfrastructure technologies that help address these challenges. Example projects employing cyberinfrastructure technologies that we have worked on at the Grid Research Centre, including the GeoChronos project, are also presented. This presentation was given at the IAI International Wireless Sensor Networks Summer School held at the University of Alberta on July 6th, 2009.
Scientific instruments, environmental sensors, and large-scale simulations are creating more scientific data than ever before. By using advanced, large-scale information processing facilities, scientists are now able to analyze massive volumes of data in ways that never would have been possible just a few years ago. While a few researchers have access to these large computer systems, most are limited by the processing capacity they can access conveniently and quickly. Cloud computing solutions utilizing Microsoft Azure allow environmental science researchers to access the compute and storage resources that they need, when they need them—without the up-front financial investment required—and helps reduce the time between progress and breakthroughs. Microsoft Azure brings on-demand computing and data access to environmental scientists and researchers everywhere.
This document discusses the roles that cloud computing and virtualization can play in reproducible research. It notes that virtualization allows for capturing the full computational environment of an experiment. The cloud builds on this by providing scalable resources and services for storage, computation and managing virtual machines. Challenges include costs, handling large datasets, and cultural adoption issues. Databases in the cloud may help support exploratory analysis of large datasets. Overall, the cloud shows promise for improving reproducibility by enabling sharing of full experimental environments and resources for computationally intensive analysis.
Cloud Programming Models: eScience, Big Data, etc.Alexandru Iosup
This document discusses cloud programming models. It begins by defining programming models and noting that they provide an abstraction of a computer system through a language, libraries and runtime system. It then lists some key characteristics of a cloud programming model including efficiency, scalability, fault tolerance and data models. The document outlines an agenda to cover programming models for compute-intensive and big data workloads. It provides examples of bags of tasks and workflow programming models and their applications in fields like bioinformatics.
Andrew Lenards works on the core software team at the iPlant Collaborative at the University of Arizona. He has a computer science background and experience in software development. He is learning about requirements, software design, and computational biology. His interests include open source software, bioinformatics, and empowering future generations of biologists to solve problems related to fuel, food, and water supply through interdisciplinary collaboration and cyberinfrastructure.
The document discusses data science and distributed computing. It includes details about the author's contact information and areas of expertise including locations, programming tools, and frameworks. It then discusses perspectives on data science and how fields are now data science fields. It provides examples of using data exploration and data mining from various sources. It also includes information about genetic causes of disease research conducted on Azure cloud using many cores. Finally, it lists various data analytics and machine learning tools and frameworks.
The document discusses the evolution of online learning from early e-learning approaches to modern microlearning. It notes that early e-learning focused on transferring traditional classrooms online but that learning now occurs through small pieces of information on the web. The document proposes that standards for online learning need to be adapted for this microcontent approach and focus on informal, individual learning rather than formal, organizational training.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
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
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Cloud computing
1. Cloud Computing Lecture #1
What is Cloud Computing?
(and an intro to parallel/distributed processing)
Jimmy Lin
The iSchool
University of Maryland
Wednesday, September 3, 2008
Some material adapted from slides by Christophe Bisciglia, Aaron Kimball, & Sierra Michels-Slettvet,
Google Distributed Computing Seminar, 2007 (licensed under Creation Commons Attribution 3.0 License)
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States
See http://creativecommons.org/licenses/by-nc-sa/3.0/us/ for details
3. What is Cloud Computing?
1. Web-scale problems
2. Large data centers
3. Different models of computing
4. Highly-interactive Web applications
The iSchool
University of Maryland
4. 1. Web-Scale Problems
Characteristics:
Definitely data-intensive
May also be processing intensive
Examples:
Crawling, indexing, searching, mining the Web
“Post-genomics” life sciences research
Other scientific data (physics, astronomers, etc.)
Sensor networks
Web 2.0 applications
…
The iSchool
University of Maryland
5. How much data?
Wayback Machine has 2 PB + 20 TB/month (2006)
Google processes 20 PB a day (2008)
“all words ever spoken by human beings” ~ 5 EB
NOAA has ~1 PB climate data (2007)
CERN’s LHC will generate 15 PB a year (2008)
640K ought to be
enough for anybody.
The iSchool
University of Maryland
8. There’s nothing like more data!
s/inspiration/data/g;
The iSchool
(Banko and Brill, ACL 2001)
University of Maryland
(Brants et al., EMNLP 2007)
9. What to do with more data?
Answering factoid questions
Pattern matching on the Web
Works amazingly well
Who shot Abraham Lincoln? → X shot Abraham Lincoln
Learning relations
Start with seed instances
Search for patterns on the Web
Using patterns to find more instances
Wolfgang Amadeus Mozart (1756 - 1791)
Einstein was born in 1879
Birthday-of(Mozart, 1756)
Birthday-of(Einstein, 1879)
PERSON (DATE –
PERSON was born in DATE
The iSchool
(Brill et al., TREC 2001; Lin, ACM TOIS 2007)
(Agichtein and Gravano, DL 2000; Ravichandran and Hovy, ACL 2002; … ) University of Maryland
10. 2. Large Data Centers
Web-scale problems? Throw more machines at it!
Clear trend: centralization of computing resources in large
data centers
Necessary ingredients: fiber, juice, and space
What do Oregon, Iceland, and abandoned mines have in
common?
Important Issues:
Redundancy
Efficiency
Utilization
Management
The iSchool
University of Maryland
13. Key Technology: Virtualization
App App App
App App App OS OS OS
Operating System Hypervisor
Hardware Hardware
Traditional Stack Virtualized Stack
The iSchool
University of Maryland
14. 3. Different Computing Models
“Why do it yourself if you can pay someone to do it for you?”
Utility computing
Why buy machines when you can rent cycles?
Examples: Amazon’s EC2, GoGrid, AppNexus
Platform as a Service (PaaS)
Give me nice API and take care of the implementation
Example: Google App Engine
Software as a Service (SaaS)
Just run it for me!
Example: Gmail
The iSchool
University of Maryland
15. 4. Web Applications
A mistake on top of a hack built on sand held together by
duct tape?
What is the nature of software applications?
From the desktop to the browser
SaaS == Web-based applications
Examples: Google Maps, Facebook
How do we deliver highly-interactive Web-based
applications?
AJAX (asynchronous JavaScript and XML)
For better, or for worse…
The iSchool
University of Maryland
16. What is the course about?
MapReduce: the “back-end” of cloud computing
Batch-oriented processing of large datasets
Ajax: the “front-end” of cloud computing
Highly-interactive Web-based applications
Computing “in the clouds”
Amazon’s EC2/S3 as an example of utility computing
The iSchool
University of Maryland
17. Amazon Web Services
Elastic Compute Cloud (EC2)
Rent computing resources by the hour
Basic unit of accounting = instance-hour
Additional costs for bandwidth
Simple Storage Service (S3)
Persistent storage
Charge by the GB/month
Additional costs for bandwidth
You’ll be using EC2/S3 for course assignments!
The iSchool
University of Maryland
18. This course is not for you…
If you’re not genuinely interested in the topic
If you’re not ready to do a lot of programming
If you’re not open to thinking about computing in new ways
If you can’t cope with uncertainly, unpredictability, poor
documentation, and immature software
If you can’t put in the time
Otherwise, this will be a richly rewarding course!
The iSchool
University of Maryland
20. Cloud Computing Zen
Don’t get frustrated (take a deep breath)…
This is bleeding edge technology
Those W$*#T@F! moments
Be patient…
This is the second first time I’ve taught this course
Be flexible…
There will be unanticipated issues along the way
Be constructive…
Tell me how I can make everyone’s experience better
The iSchool
University of Maryland
25. Things to go over…
Course schedule
Assignments and deliverables
Amazon EC2/S3
The iSchool
University of Maryland
26. Web-Scale Problems?
Don’t hold your breath:
Biocomputing
Nanocomputing
Quantum computing
…
It all boils down to…
Divide-and-conquer
Throwing more hardware at the problem
Simple to understand… a lifetime to master…
The iSchool
University of Maryland
27. Divide and Conquer
“Work”
Partition
w1 w2 w3
“worker” “worker” “worker”
r1 r2 r3
“Result” Combine
The iSchool
University of Maryland
28. Different Workers
Different threads in the same core
Different cores in the same CPU
Different CPUs in a multi-processor system
Different machines in a distributed system
The iSchool
University of Maryland
29. Choices, Choices, Choices
Commodity vs. “exotic” hardware
Number of machines vs. processor vs. cores
Bandwidth of memory vs. disk vs. network
Different programming models
The iSchool
University of Maryland
30. Flynn’s Taxonomy
Instructions
Single (SI) Multiple (MI)
SISD MISD
Single (SD)
Single-threaded Pipeline
process architecture
Data
SIMD MIMD
Multiple (MD)
Vector Processing Multi-threaded
Programming
The iSchool
University of Maryland
31. SISD
Processor
D D D D D D D
Instructions
The iSchool
University of Maryland
33. MIMD
Processor
D D D D D D D
Instructions
Processor
D D D D D D D
Instructions
The iSchool
University of Maryland
34. Memory Typology: Shared
Processor Processor
Memory
Processor Processor
The iSchool
University of Maryland
35. Memory Typology: Distributed
Processor Memory Processor Memory
Network
Processor Memory Processor Memory
The iSchool
University of Maryland
36. Memory Typology: Hybrid
Processor Processor
Memory Memory
Processor Processor
Network
Processor Processor
Memory Memory
Processor Processor
The iSchool
University of Maryland
37. Parallelization Problems
How do we assign work units to workers?
What if we have more work units than workers?
What if workers need to share partial results?
How do we aggregate partial results?
How do we know all the workers have finished?
What if workers die?
What is the common theme of all of these problems?
The iSchool
University of Maryland
38. General Theme?
Parallelization problems arise from:
Communication between workers
Access to shared resources (e.g., data)
Thus, we need a synchronization system!
This is tricky:
Finding bugs is hard
Solving bugs is even harder
The iSchool
University of Maryland
39. Managing Multiple Workers
Difficult because
(Often) don’t know the order in which workers run
(Often) don’t know where the workers are running
(Often) don’t know when workers interrupt each other
Thus, we need:
Semaphores (lock, unlock)
Conditional variables (wait, notify, broadcast)
Barriers
Still, lots of problems:
Deadlock, livelock, race conditions, ...
Moral of the story: be careful!
Even trickier if the workers are on different machines
The iSchool
University of Maryland
40. Patterns for Parallelism
Parallel computing has been around for decades
Here are some “design patterns” …
The iSchool
University of Maryland
41. Master/Slaves
master
slaves
The iSchool
University of Maryland
43. Work Queues
P C
shared queue
P W W W W W C
P C
The iSchool
University of Maryland
44. Rubber Meets Road
From patterns to implementation:
pthreads, OpenMP for multi-threaded programming
MPI for clustering computing
…
The reality:
Lots of one-off solutions, custom code
Write you own dedicated library, then program with it
Burden on the programmer to explicitly manage everything
MapReduce to the rescue!
(for next time)
The iSchool
University of Maryland