The document describes building a machine learning model using a boosted decision tree to predict satisfied and unsatisfied customers in a Santander customer satisfaction dataset from Kaggle. The model is developed using Azure ML, including splitting the training data into training and validation sets, training the model, tuning hyperparameters, and using the model to predict scores for the test set. Key steps are loading data, selecting features, training and evaluating the model, and reviewing learnings around important features and opportunities to improve predictions.
The document describes a proposed web-based student assessment data processing system using the CodeIgniter framework. The system aims to address issues with the current semi-computerized assessment process at SMK Negeri 1 Pandeglang, including errors during data entry and a time-consuming report generation process. The proposed system was analyzed using SWOT and other methods. It would feature a teacher interface to enter grades and an admin interface to manage data masters. Diagrams including use case, activity, class, and sequence diagrams were created to design the system's functionality and interactions. The system aims to streamline the assessment process and make it more efficient.
The document describes a project to develop a task handling system using Java full stack development with Spring Boot. A team of three students from Bannari Amman Institute of Technology created the system under the guidance of Dr. T. Kumareshan. The proposed system provides an online workspace for organizations to assign, modify the status of, and view tasks. It uses AngularJS for the client side, Spring Boot and MySQL for the server side. Key features include assigning tasks, updating tasks, deleting tasks, and viewing task lists. The system aims to efficiently track and manage tasks for advanced online task management.
Emotion Recognition By Textual Tweets Using Machine LearningIRJET Journal
This document discusses using machine learning techniques to perform sentiment analysis on tweets in order to predict election results in India. It begins with an introduction to sentiment analysis and how it can be applied to social media tweets. It then discusses existing methods for sentiment analysis that have certain disadvantages. The proposed system aims to improve accuracy by using techniques like Naive Bayes classification, support vector machines, decision trees, and long short-term memory networks. It presents the system design, implementation details using Python and various machine learning algorithms, and testing of the system to classify tweets by emotion and predict election outcomes.
https://imatge.upc.edu/web/publications/demonstration-open-source-framework-qualitative-evaluation-cbir-systems
Evaluating image retrieval systems in a quantitative way, for example by computing measures like mean average precision, allows for objective comparisons with a ground-truth. However, in cases where ground-truth is not available, the only alternative is to collect feedback from a user. Thus, qualitative assessments become important to better understand how the system works. Visualizing the results could be, in some scenarios, the only way to evaluate the results obtained and also the only opportunity to identify that a system is failing. This necessitates developing a User Interface (UI) for a Content Based Image Retrieval (CBIR) system that allows visualization of results and improvement via capturing user relevance feedback. A well-designed UI facilitates understanding of the performance of the system, both in cases where it works well and perhaps more importantly those which highlight the need for improvement. Our open-source system implements three components to facilitate researchers to quickly develop these capabilities for their retrieval engine. We present: a web-based user interface to visualize retrieval results and collect user annotations; a server that simplifies connection with any underlying CBIR system; and a server that manages the search engine data.
This document provides an overview of system development and information systems. It discusses reasons for creating or modifying systems, such as to correct problems or improve existing systems. It then describes the system development life cycle process, which involves six phases: preliminary investigation, system analysis, system design, system development, system implementation, and system operation and maintenance. It also discusses topics such as the roles of systems analysts, feasibility analysis, different approaches to system development, and implementation considerations.
The document describes building a machine learning model using a boosted decision tree to predict satisfied and unsatisfied customers in a Santander customer satisfaction dataset from Kaggle. The model is developed using Azure ML, including splitting the training data into training and validation sets, training the model, tuning hyperparameters, and using the model to predict scores for the test set. Key steps are loading data, selecting features, training and evaluating the model, and reviewing learnings around important features and opportunities to improve predictions.
The document describes a proposed web-based student assessment data processing system using the CodeIgniter framework. The system aims to address issues with the current semi-computerized assessment process at SMK Negeri 1 Pandeglang, including errors during data entry and a time-consuming report generation process. The proposed system was analyzed using SWOT and other methods. It would feature a teacher interface to enter grades and an admin interface to manage data masters. Diagrams including use case, activity, class, and sequence diagrams were created to design the system's functionality and interactions. The system aims to streamline the assessment process and make it more efficient.
The document describes a project to develop a task handling system using Java full stack development with Spring Boot. A team of three students from Bannari Amman Institute of Technology created the system under the guidance of Dr. T. Kumareshan. The proposed system provides an online workspace for organizations to assign, modify the status of, and view tasks. It uses AngularJS for the client side, Spring Boot and MySQL for the server side. Key features include assigning tasks, updating tasks, deleting tasks, and viewing task lists. The system aims to efficiently track and manage tasks for advanced online task management.
Emotion Recognition By Textual Tweets Using Machine LearningIRJET Journal
This document discusses using machine learning techniques to perform sentiment analysis on tweets in order to predict election results in India. It begins with an introduction to sentiment analysis and how it can be applied to social media tweets. It then discusses existing methods for sentiment analysis that have certain disadvantages. The proposed system aims to improve accuracy by using techniques like Naive Bayes classification, support vector machines, decision trees, and long short-term memory networks. It presents the system design, implementation details using Python and various machine learning algorithms, and testing of the system to classify tweets by emotion and predict election outcomes.
https://imatge.upc.edu/web/publications/demonstration-open-source-framework-qualitative-evaluation-cbir-systems
Evaluating image retrieval systems in a quantitative way, for example by computing measures like mean average precision, allows for objective comparisons with a ground-truth. However, in cases where ground-truth is not available, the only alternative is to collect feedback from a user. Thus, qualitative assessments become important to better understand how the system works. Visualizing the results could be, in some scenarios, the only way to evaluate the results obtained and also the only opportunity to identify that a system is failing. This necessitates developing a User Interface (UI) for a Content Based Image Retrieval (CBIR) system that allows visualization of results and improvement via capturing user relevance feedback. A well-designed UI facilitates understanding of the performance of the system, both in cases where it works well and perhaps more importantly those which highlight the need for improvement. Our open-source system implements three components to facilitate researchers to quickly develop these capabilities for their retrieval engine. We present: a web-based user interface to visualize retrieval results and collect user annotations; a server that simplifies connection with any underlying CBIR system; and a server that manages the search engine data.
This document provides an overview of system development and information systems. It discusses reasons for creating or modifying systems, such as to correct problems or improve existing systems. It then describes the system development life cycle process, which involves six phases: preliminary investigation, system analysis, system design, system development, system implementation, and system operation and maintenance. It also discusses topics such as the roles of systems analysts, feasibility analysis, different approaches to system development, and implementation considerations.
The document discusses prototype modeling. A prototype is a preliminary model or version of a final product that is created to test concepts or processes. There are several types of prototyping including throwaway, evolutionary, incremental, and extreme prototyping. The prototype modeling process involves requirements gathering, quick design, building the prototype, customer evaluation, review and updates. Prototypes allow users to provide feedback early in the development process and help reduce costs, time, and risks.
The document discusses the system development life cycle (SDLC), which describes the stages of an information system development project. It outlines the typical stages: recognition of need, feasibility study, analysis, design, implementation, post-implementation, maintenance, and prototyping. The feasibility study assesses the economic, technical, and behavioral factors. Analysis involves gathering requirements through tools like interviews and documentation. Design defines technical specifications and system flow. Implementation deploys the system. Prototyping allows refining the system through iterative testing and user feedback before final implementation.
This document outlines a movie recommendation system project built using collaborative filtering. The project aims to build a recommendation engine that suggests movies to users based on their preferences and watching history. It will use the MovieLens dataset and implement item-based collaborative filtering. The key steps include importing libraries, preprocessing the data, building the recommendation model using collaborative filtering, and evaluating the model's performance. Collaborative filtering works by comparing a user's preferences to other users to find patterns and provide personalized recommendations. The document also discusses some disadvantages of collaborative filtering like the cold-start problem and difficulty including additional metadata.
System Proposal(Personal Information & Leave Management System)Akila Jayarathna
The document proposes a web-based personal information and leave management system for a university. It analyzes alternative solutions such as a standalone system, manual system, or purchasing commercial software. A feasibility analysis finds that developing an in-house web-based system would be the most cost-effective solution compared to purchasing commercial software. The proposed system would allow online leave application and generate various reports on employee leave and personal information.
Se6162 analysis concept and principleskhaerul azmi
This document discusses software analysis concepts including requirement analysis, elicitation, and specification. It covers key principles such as understanding user needs, developing prototypes, and creating hierarchical models. Requirement elicitation techniques include interviews, meetings, use cases and scenarios. Analysis models the information domain, functions, and system behavior through data, functional and behavioral models. The specification captures requirements but separates functionality from implementation through a behavioral model.
java mini project for college students SWETALEENA2
This document describes a 360 degree feedback web application created using Java. The application allows various stakeholders like customers, vendors, agents, and internal employees to provide feedback on an organization's services and support. It schedules feedback collection, stores ratings and comments in a database, and generates reports and dashboards to analyze feedback. The application was created using technologies like Java, JSP, Servlets, MySQL, HTML, and CSS. It has separate feedback forms and dashboards for students, teachers, and HR. Users can log in based on their role to provide feedback which is stored in a database. The application aims to help organizations improve their services based on feedback from various stakeholders.
This webinar is going to cover what is a digital twin and how all stakeholders can benefit from their functionality. You will learn how model-based systems engineering enables digital engineering. Your host will discuss use cases, a realistic look at digital engineering and digital twins, and how you can use Innoslate to get started.
The Agenda
Here's what we're covering.
What is a Digital Twin
Benefits of Digital Twin
The Digital Engineering Path Enabled by MBSE
AR + MBSE Software
A More Realistic Digital Twin
Getting You Started with Digital Twins
Question Answer Session
The document discusses the Model-View-Controller (MVC) design pattern. MVC separates an application's data (model), user interface (view), and control logic (controller) to reduce failures. It provides modularity, allowing changes to one component without affecting others. MVC supports multiple views of the same data and powerful user interfaces through its separation of concerns.
A Comparative Study of Different types of Models in Software Development Life...IRJET Journal
This document compares and contrasts three common software development models: the waterfall model, iterative enhancement model, and prototyping model. It discusses the key stages and processes in each model, including requirements analysis, design, implementation, testing, and maintenance. The waterfall model is described as the classic sequential model, while the iterative and prototyping models allow for more flexibility and user feedback. The document analyzes the advantages and disadvantages of each approach and concludes each model tries to improve on the limitations of previous ones. The iterative model is seen as overcoming issues of the waterfall by allowing feedback, while the prototyping model is useful for complex or unestablished requirements.
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNINGIRJET Journal
1) The document discusses a study on handwritten digit recognition using machine learning. It reviews various digit recognition methods and analyzes an integrated system that achieved a minimum error rate of 0.32%.
2) The study uses a neural network model to recognize handwritten digits. It trains the model on over 60,000 images from MNIST and custom datasets.
3) Testing involves capturing images using a webcam in real-time, then preprocessing the images and running them through the trained neural network model to predict the digit. The model achieved high accuracy after training on large datasets.
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNINGIRJET Journal
This document summarizes a research paper on handwritten digit recognition using machine learning. The researchers trained a neural network model on over 60,000 images to recognize handwritten digits. The model was trained using two databases - the MNIST database and a self-collected database. It was tested on real-time images captured by a webcam. After training and testing, the integrated system achieved a minimum error rate of 0.32% in recognizing handwritten digits. The document also discusses the image processing techniques used in training and testing the model as well as the neural network architecture.
Tutorial for Machine Learning 101 (an all-day tutorial at Strata + Hadoop World, New York City, 2015)
The course is designed to introduce machine learning via real applications like building a recommender image analysis using deep learning.
In this talk we cover deployment of machine learning models.
The prototyping model involves building a prototype version of the software before developing the actual system. A prototype is a crude initial version that demonstrates some functionality but has limitations in reliability, performance, etc. compared to the final product. The prototyping model is useful when requirements are unclear so the prototype allows refining requirements based on customer feedback in an iterative process until an acceptable prototype is achieved to develop the final system from.
Anuj Vaghani presented on his internship experience working with data analytics and machine learning teams. He discussed key concepts like data analytics, machine learning, and the methodology he used. Anuj completed two projects - one analyzing hotel booking data to understand cancellation factors, and another predicting bike demand using regression models. He found factors like booking lead time and deposit type influenced cancellations. For bike demand, random forest and gradient boosting models achieved high accuracy. Anuj concluded by discussing future areas like deep learning and new opportunities in the field.
Application Insights and Jupyter Notebook(Opensource) combo to analyze large ...Sajeetharan
How to use App Insights and Jupyter notebook together to analyze large scale data with azure. We will see a demo on both using azure appinsights and azure notebooks.
The paper focuses on IoT as the latest trending technology in market and the challenges which a tester faces at both manual and automation front. The paper also sheds light on the scope of IoT in agile. The automation techniques which can be used are also elaborated in the same.
The document discusses software processes and iterative process models. It describes incremental delivery and spiral development as two iterative process models. Incremental delivery breaks development into increments with each delivering part of the functionality. Spiral development represents the process as a spiral with phases addressing objectives, risks, development and planning. Both models allow for iteration and incorporate user feedback earlier.
A prototype is an early sample or model created to test concepts or processes before final production. There are several types of prototyping models, including throwaway/rapid prototyping which uses minimal efforts to build prototypes that are discarded once requirements are understood, and evolutionary prototyping which builds functional prototypes to form the basis of future systems. The key steps in prototyping are requirements gathering, quick design, and building prototypes based on the design. Prototypes help expose issues, get early user feedback, and serve as a basis for specifications and testing before full system development.
The document discusses several software process models:
- The Linear Sequential (Waterfall) Model is a simple, systematic approach where each phase must be completed before moving to the next. It is best for small, well-defined projects.
- The Incremental Model applies the Linear Sequential Model iteratively to increments, delivering working software in stages. This allows for early delivery and flexibility.
- The Prototyping Model involves building prototypes to refine requirements through client feedback in iterations. This helps establish clear objectives.
- Rapid Application Development (RAD) is a fast version of the Linear Sequential Model using a component-based approach to accelerate delivery of fully functional projects.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The document discusses prototype modeling. A prototype is a preliminary model or version of a final product that is created to test concepts or processes. There are several types of prototyping including throwaway, evolutionary, incremental, and extreme prototyping. The prototype modeling process involves requirements gathering, quick design, building the prototype, customer evaluation, review and updates. Prototypes allow users to provide feedback early in the development process and help reduce costs, time, and risks.
The document discusses the system development life cycle (SDLC), which describes the stages of an information system development project. It outlines the typical stages: recognition of need, feasibility study, analysis, design, implementation, post-implementation, maintenance, and prototyping. The feasibility study assesses the economic, technical, and behavioral factors. Analysis involves gathering requirements through tools like interviews and documentation. Design defines technical specifications and system flow. Implementation deploys the system. Prototyping allows refining the system through iterative testing and user feedback before final implementation.
This document outlines a movie recommendation system project built using collaborative filtering. The project aims to build a recommendation engine that suggests movies to users based on their preferences and watching history. It will use the MovieLens dataset and implement item-based collaborative filtering. The key steps include importing libraries, preprocessing the data, building the recommendation model using collaborative filtering, and evaluating the model's performance. Collaborative filtering works by comparing a user's preferences to other users to find patterns and provide personalized recommendations. The document also discusses some disadvantages of collaborative filtering like the cold-start problem and difficulty including additional metadata.
System Proposal(Personal Information & Leave Management System)Akila Jayarathna
The document proposes a web-based personal information and leave management system for a university. It analyzes alternative solutions such as a standalone system, manual system, or purchasing commercial software. A feasibility analysis finds that developing an in-house web-based system would be the most cost-effective solution compared to purchasing commercial software. The proposed system would allow online leave application and generate various reports on employee leave and personal information.
Se6162 analysis concept and principleskhaerul azmi
This document discusses software analysis concepts including requirement analysis, elicitation, and specification. It covers key principles such as understanding user needs, developing prototypes, and creating hierarchical models. Requirement elicitation techniques include interviews, meetings, use cases and scenarios. Analysis models the information domain, functions, and system behavior through data, functional and behavioral models. The specification captures requirements but separates functionality from implementation through a behavioral model.
java mini project for college students SWETALEENA2
This document describes a 360 degree feedback web application created using Java. The application allows various stakeholders like customers, vendors, agents, and internal employees to provide feedback on an organization's services and support. It schedules feedback collection, stores ratings and comments in a database, and generates reports and dashboards to analyze feedback. The application was created using technologies like Java, JSP, Servlets, MySQL, HTML, and CSS. It has separate feedback forms and dashboards for students, teachers, and HR. Users can log in based on their role to provide feedback which is stored in a database. The application aims to help organizations improve their services based on feedback from various stakeholders.
This webinar is going to cover what is a digital twin and how all stakeholders can benefit from their functionality. You will learn how model-based systems engineering enables digital engineering. Your host will discuss use cases, a realistic look at digital engineering and digital twins, and how you can use Innoslate to get started.
The Agenda
Here's what we're covering.
What is a Digital Twin
Benefits of Digital Twin
The Digital Engineering Path Enabled by MBSE
AR + MBSE Software
A More Realistic Digital Twin
Getting You Started with Digital Twins
Question Answer Session
The document discusses the Model-View-Controller (MVC) design pattern. MVC separates an application's data (model), user interface (view), and control logic (controller) to reduce failures. It provides modularity, allowing changes to one component without affecting others. MVC supports multiple views of the same data and powerful user interfaces through its separation of concerns.
A Comparative Study of Different types of Models in Software Development Life...IRJET Journal
This document compares and contrasts three common software development models: the waterfall model, iterative enhancement model, and prototyping model. It discusses the key stages and processes in each model, including requirements analysis, design, implementation, testing, and maintenance. The waterfall model is described as the classic sequential model, while the iterative and prototyping models allow for more flexibility and user feedback. The document analyzes the advantages and disadvantages of each approach and concludes each model tries to improve on the limitations of previous ones. The iterative model is seen as overcoming issues of the waterfall by allowing feedback, while the prototyping model is useful for complex or unestablished requirements.
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNINGIRJET Journal
1) The document discusses a study on handwritten digit recognition using machine learning. It reviews various digit recognition methods and analyzes an integrated system that achieved a minimum error rate of 0.32%.
2) The study uses a neural network model to recognize handwritten digits. It trains the model on over 60,000 images from MNIST and custom datasets.
3) Testing involves capturing images using a webcam in real-time, then preprocessing the images and running them through the trained neural network model to predict the digit. The model achieved high accuracy after training on large datasets.
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNINGIRJET Journal
This document summarizes a research paper on handwritten digit recognition using machine learning. The researchers trained a neural network model on over 60,000 images to recognize handwritten digits. The model was trained using two databases - the MNIST database and a self-collected database. It was tested on real-time images captured by a webcam. After training and testing, the integrated system achieved a minimum error rate of 0.32% in recognizing handwritten digits. The document also discusses the image processing techniques used in training and testing the model as well as the neural network architecture.
Tutorial for Machine Learning 101 (an all-day tutorial at Strata + Hadoop World, New York City, 2015)
The course is designed to introduce machine learning via real applications like building a recommender image analysis using deep learning.
In this talk we cover deployment of machine learning models.
The prototyping model involves building a prototype version of the software before developing the actual system. A prototype is a crude initial version that demonstrates some functionality but has limitations in reliability, performance, etc. compared to the final product. The prototyping model is useful when requirements are unclear so the prototype allows refining requirements based on customer feedback in an iterative process until an acceptable prototype is achieved to develop the final system from.
Anuj Vaghani presented on his internship experience working with data analytics and machine learning teams. He discussed key concepts like data analytics, machine learning, and the methodology he used. Anuj completed two projects - one analyzing hotel booking data to understand cancellation factors, and another predicting bike demand using regression models. He found factors like booking lead time and deposit type influenced cancellations. For bike demand, random forest and gradient boosting models achieved high accuracy. Anuj concluded by discussing future areas like deep learning and new opportunities in the field.
Application Insights and Jupyter Notebook(Opensource) combo to analyze large ...Sajeetharan
How to use App Insights and Jupyter notebook together to analyze large scale data with azure. We will see a demo on both using azure appinsights and azure notebooks.
The paper focuses on IoT as the latest trending technology in market and the challenges which a tester faces at both manual and automation front. The paper also sheds light on the scope of IoT in agile. The automation techniques which can be used are also elaborated in the same.
The document discusses software processes and iterative process models. It describes incremental delivery and spiral development as two iterative process models. Incremental delivery breaks development into increments with each delivering part of the functionality. Spiral development represents the process as a spiral with phases addressing objectives, risks, development and planning. Both models allow for iteration and incorporate user feedback earlier.
A prototype is an early sample or model created to test concepts or processes before final production. There are several types of prototyping models, including throwaway/rapid prototyping which uses minimal efforts to build prototypes that are discarded once requirements are understood, and evolutionary prototyping which builds functional prototypes to form the basis of future systems. The key steps in prototyping are requirements gathering, quick design, and building prototypes based on the design. Prototypes help expose issues, get early user feedback, and serve as a basis for specifications and testing before full system development.
The document discusses several software process models:
- The Linear Sequential (Waterfall) Model is a simple, systematic approach where each phase must be completed before moving to the next. It is best for small, well-defined projects.
- The Incremental Model applies the Linear Sequential Model iteratively to increments, delivering working software in stages. This allows for early delivery and flexibility.
- The Prototyping Model involves building prototypes to refine requirements through client feedback in iterations. This helps establish clear objectives.
- Rapid Application Development (RAD) is a fast version of the Linear Sequential Model using a component-based approach to accelerate delivery of fully functional projects.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
11. • web2py framework follows the Model-View-Controller pattern of running web
applications unlike traditional patterns.
• Model is a part of the application that includes logic for the data. The objects in
model are used for retrieving and storing the data from the database.
• View is a part of the application, which helps in rendering the display of data to end
users. The display of data is fetched from Model.
• Controller is a part of the application, which handles user interaction. Controllers
can read data from a view, control user input, and send input data to the specific