The document discusses implementing task patterns to support knowledge work. It describes personal task patterns (PTP) that capture an individual's task execution strategies and collaborative task patterns (CTP) that model task delegation structures. PTPs are implemented in a personal task management system on the social semantic desktop, allowing users to retrieve, create, and use task patterns. CTPs are implemented in a collaborative task management system to extract and reuse knowledge from ad-hoc business processes. The systems are connected so CTPs can reference PTPs for specific tasks.
Time's Important - Let Task Management Save YoursJames Bundey
Presented at WordCamp Sunshine Coast 2016. The presentation covers how task runners such as Grunt & Gulp can be utilised to automate and save on development time.
The presentation covers how to get started using Gulp, useful plugins and how Gulp can be incorporated into WordPress theme development for create lean and fast websites.
Q-Track is an Enterprise Task Management solution which helps you to collaborate within organizations effectively and execute tasks on time and pro-actively. It helps managers to have track on delegated tasks and help their sub-ordinates to close them on exceptions. The solution has seamless integration with MS outlook and MS Projects.
In today's world its critical to have visibility on every task that is delegated to another employee and it more important to collaborate effectively to achieve the common goal of an organization. Q-Track task management software helps you to achieve the same.
Task management involves planning, tracking, and reporting tasks to help ensure they are completed on time. It focuses on managing individual tasks rather than full projects. Key aspects of task management include identifying the most important tasks (MITs) to focus on each day, achieving "inbox zero" by processing all emails as they come in, and using tools like Wunderlist to organize tasks, share lists with others, and mark items as complete. Wunderlist is a popular task management app that makes it easy to create and prioritize to-do lists across devices.
Celery is a really good framework for doing background task processing in Python (and other languages). While it is ridiculously easy to use celery, doing complex task flow has been a challenge in celery. (w.r.t task trees/graphs/dependecies etc.)
This talk introduces the audience to these challenges in celery and also explains how these can be fixed programmatically and by using latest features in Celery (3+)
This document provides an introduction to FreeRTOS version 6.0.5. It outlines the course objectives, which are to understand FreeRTOS services and APIs, experience different FreeRTOS features through labs, and understand the FreeRTOS porting process. The document describes key FreeRTOS concepts like tasks, task states, priorities, and provides an overview of task management APIs for creation, deletion, delaying, and querying tasks.
The Concept
Automated Task Management and Query management process
The Opportunity
Clear direction of task to employee
Increased backend operation efficiency
Less overhead on business owner/Team Leader
Automated follow-up with client on queries
The Potential
Customer Task Life Cycle
Employee Performance Report & Bonus Calculation
Time's Important - Let Task Management Save YoursJames Bundey
Presented at WordCamp Sunshine Coast 2016. The presentation covers how task runners such as Grunt & Gulp can be utilised to automate and save on development time.
The presentation covers how to get started using Gulp, useful plugins and how Gulp can be incorporated into WordPress theme development for create lean and fast websites.
Q-Track is an Enterprise Task Management solution which helps you to collaborate within organizations effectively and execute tasks on time and pro-actively. It helps managers to have track on delegated tasks and help their sub-ordinates to close them on exceptions. The solution has seamless integration with MS outlook and MS Projects.
In today's world its critical to have visibility on every task that is delegated to another employee and it more important to collaborate effectively to achieve the common goal of an organization. Q-Track task management software helps you to achieve the same.
Task management involves planning, tracking, and reporting tasks to help ensure they are completed on time. It focuses on managing individual tasks rather than full projects. Key aspects of task management include identifying the most important tasks (MITs) to focus on each day, achieving "inbox zero" by processing all emails as they come in, and using tools like Wunderlist to organize tasks, share lists with others, and mark items as complete. Wunderlist is a popular task management app that makes it easy to create and prioritize to-do lists across devices.
Celery is a really good framework for doing background task processing in Python (and other languages). While it is ridiculously easy to use celery, doing complex task flow has been a challenge in celery. (w.r.t task trees/graphs/dependecies etc.)
This talk introduces the audience to these challenges in celery and also explains how these can be fixed programmatically and by using latest features in Celery (3+)
This document provides an introduction to FreeRTOS version 6.0.5. It outlines the course objectives, which are to understand FreeRTOS services and APIs, experience different FreeRTOS features through labs, and understand the FreeRTOS porting process. The document describes key FreeRTOS concepts like tasks, task states, priorities, and provides an overview of task management APIs for creation, deletion, delaying, and querying tasks.
The Concept
Automated Task Management and Query management process
The Opportunity
Clear direction of task to employee
Increased backend operation efficiency
Less overhead on business owner/Team Leader
Automated follow-up with client on queries
The Potential
Customer Task Life Cycle
Employee Performance Report & Bonus Calculation
BigData: My Learnings from data analytics at Uber
Reference (highly recommended):
* Designing Data-Intensive Applications http://bit.ly/big_data_architecture
* Big Data and Machine Learning using Python tools http://bit.ly/big_data_machine_learning
* Uber Engineering Blog http://eng.uber.com
* Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale
http://bit.ly/hadoop_guide_bigdata
Iwsm2014 understanding functional reuse of erp (maya daneva) - public releaseNesma
The document summarizes a study on the level of functional reuse achieved by three telecom companies that implemented SAP ERP systems. The study found that:
1) Reuse is possible up to 80% for some modules, but for others the level of reuse varied widely between companies.
2) Reuse was measured according to four levels: from fully reused without changes (Level 3) to not reused at all (Level 0).
3) Results for individual ERP modules like materials management showed reuse levels ranging from 30-80% depending on the business process and company.
Sawmill - Integrating R and Large Data CloudsRobert Grossman
This document discusses using R for large-scale data analysis on distributed data clouds. It recommends splitting large datasets into segments using MapReduce or UDFs, then building separate models for each segment in R. PMML can be used to combine the separate models into an ensemble model. The Sawmill framework is proposed to preprocess data in parallel, build models for each segment using R, and combine the models into a PMML file for deployment. Running R on each segment sequentially allows scaling to large datasets, with examples showing processing times for different numbers of segments.
Itb298 Lecture Week 1 Sem 1 2007 Staff VersionDavidWang1027
This document provides an overview of business process modeling and management. It discusses what a business process is, why business process management is popular, and the value of taking a process-oriented approach. It also outlines the key concepts of business process modeling including purposes of modeling, commonly used techniques like event-driven process chains and tools like ARIS. The document describes the structure and objectives of a university course on business process modeling.
Human Factors in Product Data Management outlines a process for evaluating the usability of Product Data Management (PDM) systems using human factors methods. The process involves:
1. Identifying user profiles and scenarios based on common tasks.
2. Performing a Collaboration Usability Analysis of the tasks and subtasks to evaluate ease of use and learnability.
3. Heuristically evaluating the user interface against standards for suitability, learnability, controllability and error prevention.
4. Scoring and analyzing the results to identify low usability tasks and provide recommendations for improving the system design and user interactions.
The document provides an overview of the user interface development process, including analysis, design, prototyping, and usability principles. It discusses tasks such as defining user profiles and scenarios, wireframing, information architecture, visual design, and standards compliance. Web 1.0 is contrasted with newer collaborative and interactive aspects of Web 2.0.
Rapid prototyping (RP) involves using 3D computer-aided design (CAD) data to quickly fabricate scale models or prototypes. The first RP technique was stereolithography developed in 1986. RP techniques add and bond materials in layers to form objects, unlike traditional subtractive methods like milling. Common RP applications include visualization, design testing, and creating molds or tools. The basic RP process involves creating a CAD model, converting it to STL format, slicing the STL file into thin layers, building the model layer-by-layer, and finishing the prototype.
The document summarizes the results of benchmarking tests performed on the Blackboard Academic Suite to determine system sizing requirements. Key findings include:
- Tests showed a Unicode conversion taking minutes for small datasets, hours for moderate, and under 3 days for large datasets, meeting objectives.
- Regression performance from version 6.3 to 7.X met the objective of no more than a 5% degradation and potential for a 5% improvement.
- Benchmarking of different hardware platforms like Sun, Dell, and Windows showed performance varied based on configuration.
The document discusses how Blackboard sizes its Academic Suite software based on benchmarking. It provides details on the benchmarking methodology, including modeling user behavior, data growth, and performance objectives. The results showed how the software performed under different workload levels on various hardware configurations. The last part discusses using the benchmark results and sizing guide to determine an institution's adoption profile and appropriate hardware configuration based on factors like sessions per hour and page loads.
This document discusses different software estimation techniques. It describes what software estimation is, why it is needed, and some common difficulties in estimation. It then outlines factors to consider like product objectives, corporate assets, and project constraints. It discusses methods for estimating lines of code or function points. Function point analysis and the unadjusted and value adjustment components are explained. Models for calculating effort and cost using lines of code and function points are provided, including the COCOMO model and its organic, semi-detached, and embedded project types.
Example outcome of a worldwide IBM Notes Domino application analysis, including usage scan. Customers willing to decide on strategic options regarding the future of IBM Notes and Domino may want to take a look at our offerings: http://www.insight-notes.com
This document discusses organizing business process documentation from enterprise resource planning (ERP) projects. It proposes extracting process information from text documents and diagrams, linking the different representations, normalizing the data, and clustering the processes. This organized structure improves information reuse across projects, maintains consistency, and aids in duplicate detection. An evaluation of 240 process documents clustered into 23 groups found this approach boosted similarity for related business artifacts like requirements. Future work could utilize domain ontologies and extract standardized process models.
The document provides information about the Pega CPBA 7.1/7.2 certification exam, including the test domains and percentages, number of questions, time allotted, and passing score for each version. It also lists the topics covered in the training course, such as application design, case design, UI design, data modeling, automating business policies, and reporting. The course content includes practical exercises in requirements gathering, application express, direct capture of objectives, building reports, case management, data modeling, and UI design. The total course duration is 20-25 hours consisting of topics and hands-on exercises.
Apache SystemML Optimizer and Runtime techniques by Arvind Surve and Matthias...Arvind Surve
This deck includes Apache SystemML Runtime techniques. Those include parfor optimization, bufferpool optimization, spark specific rewrites, partitioning preserving operations, update in place, and ongoing research (Compressed Linear Algebra)
Apache SystemML Optimizer and Runtime techniques by Arvind Surve and Matthias...Arvind Surve
This session includes Apache SystemML Runtime techniques. Those include parfor optimization, bufferpool optimization, spark specific rewrites, partitioning preserving operations, update in place, and ongoing research (Compressed Linear Algebra)
RNNs for Recommendations and PersonalizationNick Pentreath
In the last few years, RNNs have achieved significant success in modeling time series and sequence data, in particular within the speech, language, and text domains. Recently, these techniques have been begun to be applied to session-based recommendation tasks, with very promising results. Nick Pentreath explores the latest research advances in this domain, as well as practical applications.
An introduction to Hadoop for large scale data analysisAbhijit Sharma
This document provides an overview of Hadoop and how it can be used for large scale data analysis. Some key points discussed include:
- Hadoop uses MapReduce, an programming model for processing large datasets in parallel across clusters of computers using a simple programming model.
- It also uses HDFS for reliable storage of very large files across clusters of commodity servers.
- Examples of how Hadoop can be used include distributed logging, search, analytics, and data mining of large datasets.
Big Data Architectures @ JAX / BigDataCon 2016Guido Schmutz
Mit der Architektur steht und fällt jedes IT-Projekt. Das gilt in noch stärkerem Maße für Big-Data-Projekte, denn hier konnten noch keine Standards über Jahrzehnte ihre Tauglichkeit beweisen. Dennoch verbreiten und etablieren sich auch hier gute und effektive Lösungen. Der Vortrag erklärt, welche Bausteine wichtig für die verschiedenen Einsatzmöglichkeiten im Big-Data-Umfeld sind, und wie sie in konkrete Lösungen gegossen werden können. Dabei beleuchtet er sowohl traditionelle Big-Data-Architekturen als auch aktuelle Ansätze, wie z. B. die Lambda- und die Kappa-Architektur. Ebenfalls ein Thema sind Stream-Processing-Infrastrukturen und ihre Kombination mit Big-Data-Technologien. Ausgehend von einer produkt- und technologieunabhängigen Referenzarchitektur stellt dieser Vortrag verschiedene Lösungsmöglichkeiten auf Basis von Open-Source-Komponenten vor.
1. The document discusses Enterprise Resource Planning (ERP) systems, including their history, components, implementation challenges, and critical views.
2. ERP systems emerged to integrate disparate business functions and processes, improve supply chain management, and provide real-time information to managers.
3. Major ERP vendors include SAP, Oracle, and BAAN. Successful implementation requires fitting the software to business needs, change management, and ongoing maintenance and upgrades.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
More Related Content
Similar to Task Patterns in Collaborative Semantic Task Management as Means of Corporate Experience Preservation
BigData: My Learnings from data analytics at Uber
Reference (highly recommended):
* Designing Data-Intensive Applications http://bit.ly/big_data_architecture
* Big Data and Machine Learning using Python tools http://bit.ly/big_data_machine_learning
* Uber Engineering Blog http://eng.uber.com
* Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale
http://bit.ly/hadoop_guide_bigdata
Iwsm2014 understanding functional reuse of erp (maya daneva) - public releaseNesma
The document summarizes a study on the level of functional reuse achieved by three telecom companies that implemented SAP ERP systems. The study found that:
1) Reuse is possible up to 80% for some modules, but for others the level of reuse varied widely between companies.
2) Reuse was measured according to four levels: from fully reused without changes (Level 3) to not reused at all (Level 0).
3) Results for individual ERP modules like materials management showed reuse levels ranging from 30-80% depending on the business process and company.
Sawmill - Integrating R and Large Data CloudsRobert Grossman
This document discusses using R for large-scale data analysis on distributed data clouds. It recommends splitting large datasets into segments using MapReduce or UDFs, then building separate models for each segment in R. PMML can be used to combine the separate models into an ensemble model. The Sawmill framework is proposed to preprocess data in parallel, build models for each segment using R, and combine the models into a PMML file for deployment. Running R on each segment sequentially allows scaling to large datasets, with examples showing processing times for different numbers of segments.
Itb298 Lecture Week 1 Sem 1 2007 Staff VersionDavidWang1027
This document provides an overview of business process modeling and management. It discusses what a business process is, why business process management is popular, and the value of taking a process-oriented approach. It also outlines the key concepts of business process modeling including purposes of modeling, commonly used techniques like event-driven process chains and tools like ARIS. The document describes the structure and objectives of a university course on business process modeling.
Human Factors in Product Data Management outlines a process for evaluating the usability of Product Data Management (PDM) systems using human factors methods. The process involves:
1. Identifying user profiles and scenarios based on common tasks.
2. Performing a Collaboration Usability Analysis of the tasks and subtasks to evaluate ease of use and learnability.
3. Heuristically evaluating the user interface against standards for suitability, learnability, controllability and error prevention.
4. Scoring and analyzing the results to identify low usability tasks and provide recommendations for improving the system design and user interactions.
The document provides an overview of the user interface development process, including analysis, design, prototyping, and usability principles. It discusses tasks such as defining user profiles and scenarios, wireframing, information architecture, visual design, and standards compliance. Web 1.0 is contrasted with newer collaborative and interactive aspects of Web 2.0.
Rapid prototyping (RP) involves using 3D computer-aided design (CAD) data to quickly fabricate scale models or prototypes. The first RP technique was stereolithography developed in 1986. RP techniques add and bond materials in layers to form objects, unlike traditional subtractive methods like milling. Common RP applications include visualization, design testing, and creating molds or tools. The basic RP process involves creating a CAD model, converting it to STL format, slicing the STL file into thin layers, building the model layer-by-layer, and finishing the prototype.
The document summarizes the results of benchmarking tests performed on the Blackboard Academic Suite to determine system sizing requirements. Key findings include:
- Tests showed a Unicode conversion taking minutes for small datasets, hours for moderate, and under 3 days for large datasets, meeting objectives.
- Regression performance from version 6.3 to 7.X met the objective of no more than a 5% degradation and potential for a 5% improvement.
- Benchmarking of different hardware platforms like Sun, Dell, and Windows showed performance varied based on configuration.
The document discusses how Blackboard sizes its Academic Suite software based on benchmarking. It provides details on the benchmarking methodology, including modeling user behavior, data growth, and performance objectives. The results showed how the software performed under different workload levels on various hardware configurations. The last part discusses using the benchmark results and sizing guide to determine an institution's adoption profile and appropriate hardware configuration based on factors like sessions per hour and page loads.
This document discusses different software estimation techniques. It describes what software estimation is, why it is needed, and some common difficulties in estimation. It then outlines factors to consider like product objectives, corporate assets, and project constraints. It discusses methods for estimating lines of code or function points. Function point analysis and the unadjusted and value adjustment components are explained. Models for calculating effort and cost using lines of code and function points are provided, including the COCOMO model and its organic, semi-detached, and embedded project types.
Example outcome of a worldwide IBM Notes Domino application analysis, including usage scan. Customers willing to decide on strategic options regarding the future of IBM Notes and Domino may want to take a look at our offerings: http://www.insight-notes.com
This document discusses organizing business process documentation from enterprise resource planning (ERP) projects. It proposes extracting process information from text documents and diagrams, linking the different representations, normalizing the data, and clustering the processes. This organized structure improves information reuse across projects, maintains consistency, and aids in duplicate detection. An evaluation of 240 process documents clustered into 23 groups found this approach boosted similarity for related business artifacts like requirements. Future work could utilize domain ontologies and extract standardized process models.
The document provides information about the Pega CPBA 7.1/7.2 certification exam, including the test domains and percentages, number of questions, time allotted, and passing score for each version. It also lists the topics covered in the training course, such as application design, case design, UI design, data modeling, automating business policies, and reporting. The course content includes practical exercises in requirements gathering, application express, direct capture of objectives, building reports, case management, data modeling, and UI design. The total course duration is 20-25 hours consisting of topics and hands-on exercises.
Apache SystemML Optimizer and Runtime techniques by Arvind Surve and Matthias...Arvind Surve
This deck includes Apache SystemML Runtime techniques. Those include parfor optimization, bufferpool optimization, spark specific rewrites, partitioning preserving operations, update in place, and ongoing research (Compressed Linear Algebra)
Apache SystemML Optimizer and Runtime techniques by Arvind Surve and Matthias...Arvind Surve
This session includes Apache SystemML Runtime techniques. Those include parfor optimization, bufferpool optimization, spark specific rewrites, partitioning preserving operations, update in place, and ongoing research (Compressed Linear Algebra)
RNNs for Recommendations and PersonalizationNick Pentreath
In the last few years, RNNs have achieved significant success in modeling time series and sequence data, in particular within the speech, language, and text domains. Recently, these techniques have been begun to be applied to session-based recommendation tasks, with very promising results. Nick Pentreath explores the latest research advances in this domain, as well as practical applications.
An introduction to Hadoop for large scale data analysisAbhijit Sharma
This document provides an overview of Hadoop and how it can be used for large scale data analysis. Some key points discussed include:
- Hadoop uses MapReduce, an programming model for processing large datasets in parallel across clusters of computers using a simple programming model.
- It also uses HDFS for reliable storage of very large files across clusters of commodity servers.
- Examples of how Hadoop can be used include distributed logging, search, analytics, and data mining of large datasets.
Big Data Architectures @ JAX / BigDataCon 2016Guido Schmutz
Mit der Architektur steht und fällt jedes IT-Projekt. Das gilt in noch stärkerem Maße für Big-Data-Projekte, denn hier konnten noch keine Standards über Jahrzehnte ihre Tauglichkeit beweisen. Dennoch verbreiten und etablieren sich auch hier gute und effektive Lösungen. Der Vortrag erklärt, welche Bausteine wichtig für die verschiedenen Einsatzmöglichkeiten im Big-Data-Umfeld sind, und wie sie in konkrete Lösungen gegossen werden können. Dabei beleuchtet er sowohl traditionelle Big-Data-Architekturen als auch aktuelle Ansätze, wie z. B. die Lambda- und die Kappa-Architektur. Ebenfalls ein Thema sind Stream-Processing-Infrastrukturen und ihre Kombination mit Big-Data-Technologien. Ausgehend von einer produkt- und technologieunabhängigen Referenzarchitektur stellt dieser Vortrag verschiedene Lösungsmöglichkeiten auf Basis von Open-Source-Komponenten vor.
1. The document discusses Enterprise Resource Planning (ERP) systems, including their history, components, implementation challenges, and critical views.
2. ERP systems emerged to integrate disparate business functions and processes, improve supply chain management, and provide real-time information to managers.
3. Major ERP vendors include SAP, Oracle, and BAAN. Successful implementation requires fitting the software to business needs, change management, and ongoing maintenance and upgrades.
Similar to Task Patterns in Collaborative Semantic Task Management as Means of Corporate Experience Preservation (20)
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
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.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
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.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3Data Hops
Free A4 downloadable and printable Cyber Security, Social Engineering Safety and security Training Posters . Promote security awareness in the home or workplace. Lock them Out From training providers datahops.com
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Task Patterns in Collaborative Semantic Task Management as Means of Corporate Experience Preservation
1. Task Patterns in Collaborative Semantic Task Management as Means of Corporate Experience Preservation Uwe Riss, SAP Research CEC Karlsruhe Benedikt Schmidt, SAP Research CEC Darmstadt Todor Stoitsev, SAP Research CEC Darmstadt 22. September 2009
35. Improvement by explicit enhancementIdentify Pattern Pattern Repository Enriched pattern support future tasks Pattern Provide Process Guidance by Pattern RequestPattern Enhance Task Pattern Context Similarity Task B Task A Task C Task D
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37. Abstraction Service = basic activity or a basic knowledge requirement for task execution
39. Decision/Action Alternatives = Filter Abstraction ServicesPTP * * * Abstraction Service Problem Decision Type Solution Direction Person Information Subtask Resource Purpose in Task Context Instance Concept Example Abstraction Services