Problem Prediction Model with Changes and Incidents
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We are living in an era where changes are taking place very quickly. Imagine a scenario where we can use Machine Learning to identify changes that are associated with incidents and problems. Then classify them using the Cynefin framework to determine the context of change according to the 5 domains: simple, complicated, complex, chaotic and disorder (natural science). Come up with a Decision Intelligence model that can provide the best answer to the potential problems caused by this ever evolving environment. Finally add the Requests to the Incident Model, as ITSM focus, on IT4IT Value Stream Network!
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The solution was designed to be implemented in two stages: Machine Learning and Application, according to the diagram below from the Data Science Academy.
Social_Distancing_DIS_Time_Series
This article aims to contribute to a population service strategy in the fight against Covid-19, through a scenario simulator with Artificial Intelligence and System Dynamics.
In February 2021, relevant changes were announced in Brazil in the fight against Covid-19, especially regarding vaccination. This article addresses a series of predictions to assist in the strategy of serving the population in the fight against this disease. It is a good opportunity to better understand the "invisible" factors that affect the people's contamination.
To help model these "invisible" factors, social distancing data was obtained, this data was passed as input to an Artificial Intelligence system and predictions were performed in Deep Learning (LSTM). The prediction of the social distancing variable was exported to calibrate the model in System Dynamics, with the Stella software, and the simulations were made with a Covid-19 model.
ICMS_Tax_Collection_Time_Series.pdf
This article aims to make the ICMS tax collection forecast considering linear and non-linear models with time series.
Modelos de previsão de Ocorrências
O presente artigo tem como objetivo a classificação de textos e a previsão das categorias de ocorrências, através do estudo de modelos de Inteligência Artificial, utilizando Machine Learning e Deep Learning para a classificação de textos e análise das previsões, sugerindo-se a melhor opção com o menor erro.
Inteligencia artificial serie_temporal_ dis_v1
O documento discute o uso de inteligência artificial e dinâmica de sistemas para simular cenários e estratégias de atendimento à população no combate à Covid-19 no Brasil. Os autores desenvolveram modelos preditivos com algoritmos como LSTM e redes neurais recorrentes para prever fatores como distanciamento social e ajudar na tomada de decisões. Os modelos foram adaptados de cursos de modelagem preditiva de séries temporais.
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Capacity prediction model
This document discusses developing a capacity prediction model for event management. It involves:
1. Developing a capacity model as part of event management to focus on major alarms from file system events monitored by Spectrum tool.
2. Correlating file system data with memory and CPU usage to predict the percentage of file system occupancy over time through statistical modeling.
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Project Organizational Responsibility Model - ORM
Using Machine Learning embedded in Organizational Responsibility Model, added to the ten characteristics of the CIO Master and the twelve competencies of the workforce can help lead the Digital Transformation of the traditional public organizations to the Exponential.
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A transformação digital do tradicional para o exponencial - v2
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MRO predict
1. O documento apresenta análises exploratórias de dados e modelos de classificação para prever se uma organização é tradicional ou exponencial com base em suas características. 2. É treinado um modelo de floresta aleatória que identifica "Demandas Pendentes" como a característica mais importante. 3. Também é construída uma árvore de decisão para classificação e são comparadas suas previsões com as do modelo de floresta aleatória.
MRO simula
1. O documento descreve a análise exploratória de dados (EDA) de um modelo de responsabilidade organizacional simulado no NetLogo para identificar as variáveis mais importantes.
2. Uma árvore de decisão é treinada usando as variáveis do modelo e a importância das variáveis é calculada, com "Demandas Atendidas" e "Demandas Pendentes" sendo as mais importantes.
3. Um gráfico é gerado mostrando a estrutura da árvore de decisão treinada.
O facin na prática com o projeto geo
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Modelo em rede de maturidade em governança corporativa e a lei das estatais
Modelo em rede de maturidade em governança corporativa e a lei das estataisGuttenberg Ferreira Passos
O documento discute um modelo de rede de maturidade em governança corporativa desenvolvido para mapear o alinhamento entre as recomendações do Decreto no 47.154 de Minas Gerais, que regulamenta a Lei das Estatais, e boas práticas de governança. O modelo consiste em dimensões e níveis de maturidade e permite visualizar as práticas associadas a cada nível e sua relação com os artigos do Decreto.Análise da Dispersão dos Esforços dos Funcionários
A Inteligência Artificial contribui para criar, ampliar e melhorar os sistemas de serviços, estabelecendo os algoritmos que irão subsidiar o processo de tomada de decisão no ambiente das organizações. Também contribuem para a solução dos problemas relacionados à gestão por resultados, visando à melhoria dos serviços públicos prestados, em face de uma demanda de serviços superior à capacidade instalada.
Facin e Modelo de Responsabilidade Organizacional
O documento descreve o Modelo de Conteúdo do Framework de Arquitetura Corporativa para Interoperabilidade (FACIN), apresentando suas nove visões e detalhando alguns elementos da Visão de Negócios, como capacidades de negócio, serviços, clientes e processos.
Understanding why caries is still a public health problem
Our article "Understanding why carries is still a public health problem" is a successful case published on System Dynamics Case Repository!
Modelo em Rede de Alinhamento das Ações aos Pilares da Governança
O documento discute o II Fórum de Governança e o alinhamento de ações governamentais aos pilares da governança corporativa. É apresentado um modelo para representar as relações entre diversas ações de governo voltadas para melhoria da gestão e governança. O modelo pode ser usado como padrão e estendido para outras entidades, visando maior participação social e benefícios para cidadãos.
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Modelo de Responsabilidade Organizacional e a Transformação Digital
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
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.
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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.
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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.
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Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
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Main news related to the CCS TSI 2023 (2023/1695)
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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 Milvus
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.
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Problem Prediction Model with Changes and Incidents
- 1. Guttenberg Ferreira Passoshttps://www.linkedin.com/posts/robakershoek_de vops-it4it-itil-activity-6616362686705876992-K1Fo Image: Rob Akershoek Problem Prediction Model with Changes and Incidents
- 2. Agenda 1. Challenges 2. Preprocessing Model 3. Analysis Model 4. Clustering model 5. Training Model 6. Prediction Model 7. Predictions 8. Conclusion
- 3. Challenges 1. Identify the most relevant key words in problems, changes and incidents (ITIL) in an Information Technology company; 2. Evaluate changes using various artificial intelligence algorithms to anticipate potential problems or incidents.
- 4. Guide to getting started with AI https://medium.com/@kozyr_91350/o-guia-definitivo- para-come%C3%A7ar-com-ia-e7e7dc68f376 Fonte: Cassie Kozyrkov
- 5. Orange Canvas • Sandeco https://www.youtube.com/watch?v=5bjY_WyZKm4 • Ajda Pretnar https://www.youtube.com/watch?v=HXjnDIgGDuI&t=10s
- 7. Read File
- 9. Bag of Words
- 10. Word Cloud
- 11. Corpus Viewer
- 12. Data Table
- 13. Analysis Model
- 14. Clustering model
- 15. Multidimensional Scale - MDS
- 16. Training Model
- 17. Test & Score
- 18. Confusion Matrix
- 19. Area Under the Curve - ROC
- 20. Prediction Model
- 21. Predictions
- 22. Classifiers AdaBoost https://en.wikipedia.org/wiki/AdaBoost KNN https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm Logistic Regression https://en.wikipedia.org/wiki/Logistic_regression Naive Bayes https://en.wikipedia.org/wiki/Naive_Bayes_classifier Random Forest https://en.wikipedia.org/wiki/Random_forest
- 24. Improvements incorporated 1. Identify the keywords (Tokens & Tags) of changes and problems to properly feed Bag of Words 2. Replace the contents of the Impact field of the problem file with another type of identification (Token & Tag) 3. Replace the contents of the Customers field from the problem file with the customer related to the Control Item - CI 4. Identify problem-causing keywords (Tokens & Tags) 5. Develop Incident Model 6. Address narrow scope with database-related issues
- 25. The Problem Prediction Model has the function of evaluate the changes (ITIL 4), using various artificial intelligence algorithms, to anticipate possible problems and incidents. The idea proposed is to address a narrow scope with issues related to the database theme, involves the migration of approximately 100 databases to an Exadata environment, with incidents already incorporated. We conclude that the Model is an important tool for change analysis aiming to identify the main occurrences and anticipate possible problems and incidents, using the Naive Bayes classifier from Orange software. Conclusion
- 26. Next steps 1. Initially maintain Change Management with ITIL 3 2. Establish a pilot project implementing Change Enabling with ITIL 4 3. Develop a Decision Model based on the Cynefin framework to determine the context of change according to the 5 simple, complicated, complex, chaotic and disorder realms 4. Add the Requests to the Incident Model, as ITSM focus, on IT4IT Value Stream Network
- 27. Next steps - Cynefin
- 28. Next steps - IT4IT