“Hey, here are those new data files to add. I ‘cleaned’ them myself so it should be easy. Right?”
Words like these strike fear into the heart of all developers but integrating ‘dirty’ unstructured, denormalized and text heavy datasets from multiple locations is becoming the de facto standard when building out data platforms.
In this talk we will look at how we can augment our graph’s attributes using techniques from data mining (e.g. string similarity/distance measures) and Natural Language Processing (e.g. keyword extraction, named entity recognition). We will then walkthrough an example using this methodology to demonstrate the improvements in the accuracy of the resulting matches.
Comparison Study of Decision Tree Ensembles for RegressionSeonho Park
Nowadays, decision tree ensemble methods are widely used for solving classification and regression problem due to their rigorousness and robustness. To compare with classification, the performance in regression problem so far has not been yet addressed in detail. In this presentation, we review the state-of-art decision tree ensemble methodology in scikit-learn and xgboost for regression. Also, empirical study results are illustrated to compare their performance and computational efficiency.
Record linking refers to finding records that refer to the same entity across different data sources without a common identifier. This document discusses using logistic regression to classify record pairs as true or false matches. Features like string distances and attributes from related tables are used to train a logistic regression model. The trained model can then predict match probabilities for new record pairs. Storing these probabilities as "probabilistic foreign keys" allows linking records while preserving the original data and enabling manual review of uncertain matches.
Overview of tree algorithms from decision tree to xgboostTakami Sato
For my understanding, I surveyed popular tree algorithms on Machine Learning and their evolution. This is the first time I wrote a presentation in English. So, I am happy if you give me a feedback.
Evolving a Clean, Pragmatic Architecture at JBCNConf 2019Victor Rentea
Are you in a mood for a brainstorm? Join this critical review of the major decisions taken in a typical enterprise application architecture and learn to balance pragmatism with your design goals. Find out how to do just-in-time design to keep as much use-cases as simple as possible. The core purpose of this presentation is to learn to strike a **balance between pragmatism and maintainability** in your design. Without continuous refactoring, a simple design will inevitably degenerate into a Big Ball of Mud, under the assault of the new features and bugfixes. On the other hand, very highly-factored code can burden the take-off of the development and end up freezing the mindset in some rigid 'a-priori' design. The end goal of this talk is to challenge you to rethink critically the architecture of your own systems, and seek ways to simplify it to match your actual needs, with a pragmatic mindset. "Architecture is the art of postponing decisions", said Uncle Bob. This talk takes this idea further and explains an optimal mindset about designing enterprise applications: Evolving (Continuously Refactoring) a Pragmatic (Simple), Clean (aka Onion) Architecture, aiming to provide Developer Safety™️ and Comfort™️. It’s the philosophy that Victor distilled over the past 5 years, designing and implementing 9 applications as IBM Lead Architect, and delivering trainings and advises to many other companies. You’ll learn how to break data into pieces (Fit Entities, Value Objects, Data Transfer Objects), how to keep the logic simple (Facades, Domain Services, logic extraction patterns, Mappers, AOP), layering to enforce boundaries (keeping DTOs out of your logic, Dependency Inversion Principle), and many more, all in a dynamic, interactive and extremely entertaining session.
Webpage Personalization and User Profilingyingfeng
This document discusses techniques for webpage personalization and user profiling. It describes common properties of web personalization problems, including optimizing metrics like click-through rate (CTR) using large-scale sparse data. It then covers online logistic regression (OLR) and generalized matrix factorization (GMF) frameworks for CTR prediction. Experimental results on real-world datasets show that user profiles generated from matrix factorization models can provide significant click lifts over other profile methods when used as features for OLR.
Système de recommandations de produits sur un site marchand par Koby KARP, Data Scientist (Equancy) & Hervé MIGNOT, Partner at Equancy
La recommandation reste un outil clé pour la personnalisation des sites marchands et le sujet est loin d’être épuisé. La prise en compte de la particularité d’un marché peut nécessité d’adapter le traitement et les algorithmes utilisés. Après une revue des techniques de recommandations, nous présenterons la démarche spécifique que nous avons adopté. Le système a été développé sous Spark pour la préparation des données et le calcul des modèles de recommandations. Une API simple et son service ont été développé pour délivrer les recommandations aux applications clientes.
This document provides an introduction to building a guessing game with JavaScript. It covers defining variables, declaring functions, if/else statements, comparing values, parameters within functions, and setting up the game on Glitch. The document encourages learning code through free online programs and resources and shares testimonials from Thinkful graduates.
Haystack 2019 - Solving for Satisfaction: Introduction to Click Models - Eliz...OpenSource Connections
Relevance metrics like NDGC or ERR require graded judgements to evaluate query relevance performance. But what happens when we don't know what 'good' looks like ahead of time? This talk will look at using click modeling techniques to infer relevance judgements from user interaction logs.
Comparison Study of Decision Tree Ensembles for RegressionSeonho Park
Nowadays, decision tree ensemble methods are widely used for solving classification and regression problem due to their rigorousness and robustness. To compare with classification, the performance in regression problem so far has not been yet addressed in detail. In this presentation, we review the state-of-art decision tree ensemble methodology in scikit-learn and xgboost for regression. Also, empirical study results are illustrated to compare their performance and computational efficiency.
Record linking refers to finding records that refer to the same entity across different data sources without a common identifier. This document discusses using logistic regression to classify record pairs as true or false matches. Features like string distances and attributes from related tables are used to train a logistic regression model. The trained model can then predict match probabilities for new record pairs. Storing these probabilities as "probabilistic foreign keys" allows linking records while preserving the original data and enabling manual review of uncertain matches.
Overview of tree algorithms from decision tree to xgboostTakami Sato
For my understanding, I surveyed popular tree algorithms on Machine Learning and their evolution. This is the first time I wrote a presentation in English. So, I am happy if you give me a feedback.
Evolving a Clean, Pragmatic Architecture at JBCNConf 2019Victor Rentea
Are you in a mood for a brainstorm? Join this critical review of the major decisions taken in a typical enterprise application architecture and learn to balance pragmatism with your design goals. Find out how to do just-in-time design to keep as much use-cases as simple as possible. The core purpose of this presentation is to learn to strike a **balance between pragmatism and maintainability** in your design. Without continuous refactoring, a simple design will inevitably degenerate into a Big Ball of Mud, under the assault of the new features and bugfixes. On the other hand, very highly-factored code can burden the take-off of the development and end up freezing the mindset in some rigid 'a-priori' design. The end goal of this talk is to challenge you to rethink critically the architecture of your own systems, and seek ways to simplify it to match your actual needs, with a pragmatic mindset. "Architecture is the art of postponing decisions", said Uncle Bob. This talk takes this idea further and explains an optimal mindset about designing enterprise applications: Evolving (Continuously Refactoring) a Pragmatic (Simple), Clean (aka Onion) Architecture, aiming to provide Developer Safety™️ and Comfort™️. It’s the philosophy that Victor distilled over the past 5 years, designing and implementing 9 applications as IBM Lead Architect, and delivering trainings and advises to many other companies. You’ll learn how to break data into pieces (Fit Entities, Value Objects, Data Transfer Objects), how to keep the logic simple (Facades, Domain Services, logic extraction patterns, Mappers, AOP), layering to enforce boundaries (keeping DTOs out of your logic, Dependency Inversion Principle), and many more, all in a dynamic, interactive and extremely entertaining session.
Webpage Personalization and User Profilingyingfeng
This document discusses techniques for webpage personalization and user profiling. It describes common properties of web personalization problems, including optimizing metrics like click-through rate (CTR) using large-scale sparse data. It then covers online logistic regression (OLR) and generalized matrix factorization (GMF) frameworks for CTR prediction. Experimental results on real-world datasets show that user profiles generated from matrix factorization models can provide significant click lifts over other profile methods when used as features for OLR.
Système de recommandations de produits sur un site marchand par Koby KARP, Data Scientist (Equancy) & Hervé MIGNOT, Partner at Equancy
La recommandation reste un outil clé pour la personnalisation des sites marchands et le sujet est loin d’être épuisé. La prise en compte de la particularité d’un marché peut nécessité d’adapter le traitement et les algorithmes utilisés. Après une revue des techniques de recommandations, nous présenterons la démarche spécifique que nous avons adopté. Le système a été développé sous Spark pour la préparation des données et le calcul des modèles de recommandations. Une API simple et son service ont été développé pour délivrer les recommandations aux applications clientes.
This document provides an introduction to building a guessing game with JavaScript. It covers defining variables, declaring functions, if/else statements, comparing values, parameters within functions, and setting up the game on Glitch. The document encourages learning code through free online programs and resources and shares testimonials from Thinkful graduates.
Haystack 2019 - Solving for Satisfaction: Introduction to Click Models - Eliz...OpenSource Connections
Relevance metrics like NDGC or ERR require graded judgements to evaluate query relevance performance. But what happens when we don't know what 'good' looks like ahead of time? This talk will look at using click modeling techniques to infer relevance judgements from user interaction logs.
Large Scale Fuzzy Name Matching with a Custom ML Pipeline in Batch and Stream...Databricks
ING bank is a Dutch multinational, multi-product bank that offers banking services to 33 million retail and commercial customers in over 40 countries. At this scale, ING naturally faces a multitude of data consolidation tasks across its disparate sources. A common consolidation problem is fuzzy name matching: given a name (streaming) or a list of names (batch), find out the most similar name(s) from a different list.
Popular methods such as Levenshtein distance are not appropriate because of the time complexity and sheer volume of names involved. In this talk, we will introduce how we use a Spark custom ML pipeline and Structured Streaming to build fuzzy name matching products in batch and streaming. This can successfully match 8000 names per second against a 10 million name list, using a ten-node cluster. Firstly, we will give an introduction into the name matching problem.
Secondly, we will explain why Levenshtein distance approach is limited, and demonstrate a faster approach; token-based cosine similarity matching. Next, we will show how a ML pipeline helps to build an elegant solution. Here, we will deep dive into the detail of each stage, including customized preprocessing, tokenization, term-frequency, customized inverse document frequency, customized cosine similarity with distributed sparse matrix multiplication, and a customized supervision stage.
Finally, we will show how we deploy the ML pipeline within a batch data pipeline, and additionally as a fuzzy search engine in a streaming manner. Â The main conclusions will be: (1) a spark custom ML pipeline provides a powerful way to handle complicated data science problems (2) a uniform ML pipeline can serve both batch and streaming products easily from the same codebase.
This document discusses machine learning concepts including algorithms, data inputs/outputs, runtimes, and trends in academia vs industry. It notes that while academia focuses on algorithm complexity, industry prioritizes data-driven approaches using large datasets. Ensemble methods combining many simple models generally perform better than single complex models. Specific ML techniques discussed include word segmentation using n-gram probabilities, perceptrons for classification, SVD for recommendations and clustering, and crowdsourcing ensembles. The key lessons are that simple models with large data outperform complex models with less data, and that embracing many small independent models through ensembles is effective.
Dataiku at SF DataMining Meetup - Kaggle Yandex ChallengeDataiku
This is a presentation made on the 13th August 2014 at the SF Data Mining Meetup at Trulia. It's about Dataiku and the Kaggle Personalized Web Search Ranking challenge sponsored by Yandex
Adversarial and reinforcement learning-based approaches to information retrievalBhaskar Mitra
Traditionally, machine learning based approaches to information retrieval have taken the form of supervised learning-to-rank models. Recently, other machine learning approaches—such as adversarial learning and reinforcement learning—have started to find interesting applications in retrieval systems. At Bing, we have been exploring some of these methods in the context of web search. In this talk, I will share couple of our recent work in this area that we presented at SIGIR 2018.
Bootstrapping Entity Alignment with Knowledge Graph EmbeddingNanjing University
This document presents BootEA, a framework for bootstrapping entity alignment across knowledge graphs using knowledge graph embedding. BootEA models entity alignment as a classification task and trains alignment-oriented knowledge graph embeddings using an iterative process of parameter swapping, alignment prediction, labeling likely alignments, and editing alignments. Experimental results on five datasets show that BootEA significantly outperforms three state-of-the-art embedding-based entity alignment methods, particularly on sparse data.
Bootstrapping of PySpark Models for Factorial A/B TestsDatabricks
1. Factorial A/B testing involves running multiple experiments simultaneously by assigning each visitor to a variant in all tests, allowing for faster results than isolated tests.
2. Bootstrapping can be used to estimate the distribution of statistics like GLM coefficients from A/B test results, providing estimates of effect size and uncertainty.
3. Bootstrapping models in Spark can be parallelized using multithreading to submit batches of bootstrap iterations concurrently, improving performance by utilizing all CPU cores.
This document summarizes a presentation about the PostgreSQL query planner. It discusses areas where the planner can have problems, such as inaccurate row count estimates and suboptimal settings. Specific cases are presented where the planner may choose a poor plan, such as with common table expressions (WITH queries) and materialized views. The document also provides solutions and recommendations, such as using joins instead of WITH and intermediate tables for materialized views. Overall, the presentation aims to help users understand how the planner works and identify cases where its heuristics could lead to performance issues.
Fully Automated QA System For Large Scale Search And Recommendation Engines U...Spark Summit
1) The document describes a fully automated QA system for large scale search and recommendation engines using Spark.
2) It discusses key concepts in information retrieval like precision, recall, and learning to rank as well as challenges in building machine learning models for ranking like obtaining labeled training data.
3) The system architecture involves extracting features from query logs, calculating relevance scores from user click signals, and training machine learning models to improve ranking.
This document provides information about an upcoming DataWeave meetup session, including details about the speaker, agenda, and logistics. The speaker will discuss DataWeave basics like data types, operators, and expressions for transforming data to JSON, Java, and XML formats. The session will include hands-on examples using the Transform Message component. Attendees can ask questions in the chat and provide feedback after the meetup.
The document provides an overview of machine learning concepts and techniques. It begins with definitions of machine learning and common problem types like supervised, unsupervised, and reinforcement learning. Examples of machine learning algorithms for each problem type are given. The document then discusses best practices for machine learning projects, including framing the problem, preparing the data, selecting an appropriate model, and evaluating model performance. Feature engineering techniques for data preprocessing are also covered. The presentation aims to help audiences understand machine learning concepts and how to apply machine learning to real-world problems in one hour.
The document discusses various machine learning clustering algorithms. It explains that clustering groups unlabeled data into sets without supervision. Common clustering algorithms described include sequential clustering, K-means clustering, mixture modeling, and greedy hierarchical clustering. Each algorithm has advantages and disadvantages, such as K-means requiring specifying the number of clusters upfront or hierarchical clustering having a runtime of order n^2. The goal of clustering is to discover natural groups within the data.
Spiritsofts is the best Training Institutes for Power BI to expand your skills and knowledge. We Provides the best learning Environment. Obtain all the training by our expert professional which is having working experience from Top IT companies. The Training in is every thing we explained based on real time scenarios, it works which we do in companies.
MongoDB.local DC 2018: Tutorial - Data Analytics with MongoDBMongoDB
Data analytics can offer insights into your business and help take it to the next level. In this talk you'll learn about MongoDB tools for building visualizations, dashboards and interacting with your data. We'll start with exploratory data analysis using MongoDB Compass. Then, in a matter of minutes, we'll take you from 0 to 1 - connecting to your Atlas cluster via BI Connector and running analytical queries against it in Microsoft Excel. We'll also showcase the new MongoDB Charts product and you'll see how quick, easy and intuitive analytics can be on the MongoDB platform without flattening the data or spending time and effort on complicated and fragile ETL.
DataScienceLab2017_Оптимизация гиперпараметров машинного обучения при помощи ...GeeksLab Odessa
DataScienceLab, 13 мая 2017
Оптимизация гиперпараметров машинного обучения при помощи Байесовской оптимизации
Максим Бевза (Research Engineer at Grammarly)
Все алгоритмы машинного обучения нуждаются в настройке (тьюнинге). Часто мы используем Grid Search или Randomized Search или нашу интуицию для подбора гиперпараметров. Байесовская оптимизация поможет нам направить Randomized Search в те места, которые наиболее перспективны, так, чтобы тот же (или лучший) результат мы получили за меньшее количество итераций.
Все материалы: http://datascience.in.ua/report2017
This document is an introduction to extreme gradient boosting with XGBoost. It begins by discussing the basics of supervised classification, decision trees, and boosting. It then provides an overview of XGBoost, explaining that it is an optimized gradient boosting library that achieves state-of-the-art performance for many machine learning tasks. The document demonstrates XGBoost code examples and discusses decision trees, boosting, and when XGBoost should and should not be used.
This is the slide from my talk at FULokoja Ingressive meetup.
XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework. In prediction problems involving unstructured and structured data (images, text, etc.) artificial neural networks tend to outperform all other algorithms or frameworks. However, when it comes to small-to-medium structured/tabular data, decision tree-based algorithms are considered best-in-class right now. XGBoost model has the best combination of prediction performance and processing time compared to other algorithms.
Tag Extraction Final Presentation - CS185CSpring2014Naoki Nakatani
These slides were presented in class on May 7th 2014.
Task allocation
• George : ETL, Data Analysis, Machine Learning, Multi-label classification with Apache Spark
• Naoki : ETL, Data Analysis, Machine Learning, Feature Engineering, Multi-label classification with Apache Mahout
This document provides an overview of Data Quality Services (DQS) matching and Master Data Services (MDS). It discusses record matching, data issues that affect matching, the DQS matching process, and key components like the matching policy and knowledge base. It also introduces MDS and its configuration tools.
Slides covered during Analytics Boot Camp conducted with the help of IBM, Venturesity. Special credits to Kumar Rishabh (Google) and Srinivas Nv Gannavarapu (IBM)
Large Scale Fuzzy Name Matching with a Custom ML Pipeline in Batch and Stream...Databricks
ING bank is a Dutch multinational, multi-product bank that offers banking services to 33 million retail and commercial customers in over 40 countries. At this scale, ING naturally faces a multitude of data consolidation tasks across its disparate sources. A common consolidation problem is fuzzy name matching: given a name (streaming) or a list of names (batch), find out the most similar name(s) from a different list.
Popular methods such as Levenshtein distance are not appropriate because of the time complexity and sheer volume of names involved. In this talk, we will introduce how we use a Spark custom ML pipeline and Structured Streaming to build fuzzy name matching products in batch and streaming. This can successfully match 8000 names per second against a 10 million name list, using a ten-node cluster. Firstly, we will give an introduction into the name matching problem.
Secondly, we will explain why Levenshtein distance approach is limited, and demonstrate a faster approach; token-based cosine similarity matching. Next, we will show how a ML pipeline helps to build an elegant solution. Here, we will deep dive into the detail of each stage, including customized preprocessing, tokenization, term-frequency, customized inverse document frequency, customized cosine similarity with distributed sparse matrix multiplication, and a customized supervision stage.
Finally, we will show how we deploy the ML pipeline within a batch data pipeline, and additionally as a fuzzy search engine in a streaming manner. Â The main conclusions will be: (1) a spark custom ML pipeline provides a powerful way to handle complicated data science problems (2) a uniform ML pipeline can serve both batch and streaming products easily from the same codebase.
This document discusses machine learning concepts including algorithms, data inputs/outputs, runtimes, and trends in academia vs industry. It notes that while academia focuses on algorithm complexity, industry prioritizes data-driven approaches using large datasets. Ensemble methods combining many simple models generally perform better than single complex models. Specific ML techniques discussed include word segmentation using n-gram probabilities, perceptrons for classification, SVD for recommendations and clustering, and crowdsourcing ensembles. The key lessons are that simple models with large data outperform complex models with less data, and that embracing many small independent models through ensembles is effective.
Dataiku at SF DataMining Meetup - Kaggle Yandex ChallengeDataiku
This is a presentation made on the 13th August 2014 at the SF Data Mining Meetup at Trulia. It's about Dataiku and the Kaggle Personalized Web Search Ranking challenge sponsored by Yandex
Adversarial and reinforcement learning-based approaches to information retrievalBhaskar Mitra
Traditionally, machine learning based approaches to information retrieval have taken the form of supervised learning-to-rank models. Recently, other machine learning approaches—such as adversarial learning and reinforcement learning—have started to find interesting applications in retrieval systems. At Bing, we have been exploring some of these methods in the context of web search. In this talk, I will share couple of our recent work in this area that we presented at SIGIR 2018.
Bootstrapping Entity Alignment with Knowledge Graph EmbeddingNanjing University
This document presents BootEA, a framework for bootstrapping entity alignment across knowledge graphs using knowledge graph embedding. BootEA models entity alignment as a classification task and trains alignment-oriented knowledge graph embeddings using an iterative process of parameter swapping, alignment prediction, labeling likely alignments, and editing alignments. Experimental results on five datasets show that BootEA significantly outperforms three state-of-the-art embedding-based entity alignment methods, particularly on sparse data.
Bootstrapping of PySpark Models for Factorial A/B TestsDatabricks
1. Factorial A/B testing involves running multiple experiments simultaneously by assigning each visitor to a variant in all tests, allowing for faster results than isolated tests.
2. Bootstrapping can be used to estimate the distribution of statistics like GLM coefficients from A/B test results, providing estimates of effect size and uncertainty.
3. Bootstrapping models in Spark can be parallelized using multithreading to submit batches of bootstrap iterations concurrently, improving performance by utilizing all CPU cores.
This document summarizes a presentation about the PostgreSQL query planner. It discusses areas where the planner can have problems, such as inaccurate row count estimates and suboptimal settings. Specific cases are presented where the planner may choose a poor plan, such as with common table expressions (WITH queries) and materialized views. The document also provides solutions and recommendations, such as using joins instead of WITH and intermediate tables for materialized views. Overall, the presentation aims to help users understand how the planner works and identify cases where its heuristics could lead to performance issues.
Fully Automated QA System For Large Scale Search And Recommendation Engines U...Spark Summit
1) The document describes a fully automated QA system for large scale search and recommendation engines using Spark.
2) It discusses key concepts in information retrieval like precision, recall, and learning to rank as well as challenges in building machine learning models for ranking like obtaining labeled training data.
3) The system architecture involves extracting features from query logs, calculating relevance scores from user click signals, and training machine learning models to improve ranking.
This document provides information about an upcoming DataWeave meetup session, including details about the speaker, agenda, and logistics. The speaker will discuss DataWeave basics like data types, operators, and expressions for transforming data to JSON, Java, and XML formats. The session will include hands-on examples using the Transform Message component. Attendees can ask questions in the chat and provide feedback after the meetup.
The document provides an overview of machine learning concepts and techniques. It begins with definitions of machine learning and common problem types like supervised, unsupervised, and reinforcement learning. Examples of machine learning algorithms for each problem type are given. The document then discusses best practices for machine learning projects, including framing the problem, preparing the data, selecting an appropriate model, and evaluating model performance. Feature engineering techniques for data preprocessing are also covered. The presentation aims to help audiences understand machine learning concepts and how to apply machine learning to real-world problems in one hour.
The document discusses various machine learning clustering algorithms. It explains that clustering groups unlabeled data into sets without supervision. Common clustering algorithms described include sequential clustering, K-means clustering, mixture modeling, and greedy hierarchical clustering. Each algorithm has advantages and disadvantages, such as K-means requiring specifying the number of clusters upfront or hierarchical clustering having a runtime of order n^2. The goal of clustering is to discover natural groups within the data.
Spiritsofts is the best Training Institutes for Power BI to expand your skills and knowledge. We Provides the best learning Environment. Obtain all the training by our expert professional which is having working experience from Top IT companies. The Training in is every thing we explained based on real time scenarios, it works which we do in companies.
MongoDB.local DC 2018: Tutorial - Data Analytics with MongoDBMongoDB
Data analytics can offer insights into your business and help take it to the next level. In this talk you'll learn about MongoDB tools for building visualizations, dashboards and interacting with your data. We'll start with exploratory data analysis using MongoDB Compass. Then, in a matter of minutes, we'll take you from 0 to 1 - connecting to your Atlas cluster via BI Connector and running analytical queries against it in Microsoft Excel. We'll also showcase the new MongoDB Charts product and you'll see how quick, easy and intuitive analytics can be on the MongoDB platform without flattening the data or spending time and effort on complicated and fragile ETL.
DataScienceLab2017_Оптимизация гиперпараметров машинного обучения при помощи ...GeeksLab Odessa
DataScienceLab, 13 мая 2017
Оптимизация гиперпараметров машинного обучения при помощи Байесовской оптимизации
Максим Бевза (Research Engineer at Grammarly)
Все алгоритмы машинного обучения нуждаются в настройке (тьюнинге). Часто мы используем Grid Search или Randomized Search или нашу интуицию для подбора гиперпараметров. Байесовская оптимизация поможет нам направить Randomized Search в те места, которые наиболее перспективны, так, чтобы тот же (или лучший) результат мы получили за меньшее количество итераций.
Все материалы: http://datascience.in.ua/report2017
This document is an introduction to extreme gradient boosting with XGBoost. It begins by discussing the basics of supervised classification, decision trees, and boosting. It then provides an overview of XGBoost, explaining that it is an optimized gradient boosting library that achieves state-of-the-art performance for many machine learning tasks. The document demonstrates XGBoost code examples and discusses decision trees, boosting, and when XGBoost should and should not be used.
This is the slide from my talk at FULokoja Ingressive meetup.
XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework. In prediction problems involving unstructured and structured data (images, text, etc.) artificial neural networks tend to outperform all other algorithms or frameworks. However, when it comes to small-to-medium structured/tabular data, decision tree-based algorithms are considered best-in-class right now. XGBoost model has the best combination of prediction performance and processing time compared to other algorithms.
Tag Extraction Final Presentation - CS185CSpring2014Naoki Nakatani
These slides were presented in class on May 7th 2014.
Task allocation
• George : ETL, Data Analysis, Machine Learning, Multi-label classification with Apache Spark
• Naoki : ETL, Data Analysis, Machine Learning, Feature Engineering, Multi-label classification with Apache Mahout
This document provides an overview of Data Quality Services (DQS) matching and Master Data Services (MDS). It discusses record matching, data issues that affect matching, the DQS matching process, and key components like the matching policy and knowledge base. It also introduces MDS and its configuration tools.
Slides covered during Analytics Boot Camp conducted with the help of IBM, Venturesity. Special credits to Kumar Rishabh (Google) and Srinivas Nv Gannavarapu (IBM)
Similar to Improving Graph Based Entity Resolution with Data Mining and NLP (20)
Using Query Store in Azure PostgreSQL to Understand Query PerformanceGrant Fritchey
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
Orca: Nocode Graphical Editor for Container OrchestrationPedro J. Molina
Tool demo on CEDI/SISTEDES/JISBD2024 at A Coruña, Spain. 2024.06.18
"Orca: Nocode Graphical Editor for Container Orchestration"
by Pedro J. Molina PhD. from Metadev
Superpower Your Apache Kafka Applications Development with Complementary Open...Paul Brebner
Kafka Summit talk (Bangalore, India, May 2, 2024, https://events.bizzabo.com/573863/agenda/session/1300469 )
Many Apache Kafka use cases take advantage of Kafka’s ability to integrate multiple heterogeneous systems for stream processing and real-time machine learning scenarios. But Kafka also exists in a rich ecosystem of related but complementary stream processing technologies and tools, particularly from the open-source community. In this talk, we’ll take you on a tour of a selection of complementary tools that can make Kafka even more powerful. We’ll focus on tools for stream processing and querying, streaming machine learning, stream visibility and observation, stream meta-data, stream visualisation, stream development including testing and the use of Generative AI and LLMs, and stream performance and scalability. By the end you will have a good idea of the types of Kafka “superhero” tools that exist, which are my favourites (and what superpowers they have), and how they combine to save your Kafka applications development universe from swamploads of data stagnation monsters!
🏎️Tech Transformation: DevOps Insights from the Experts 👩💻campbellclarkson
Connect with fellow Trailblazers, learn from industry experts Glenda Thomson (Salesforce, Principal Technical Architect) and Will Dinn (Judo Bank, Salesforce Development Lead), and discover how to harness DevOps tools with Salesforce.
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
Odoo releases a new update every year. The latest version, Odoo 17, came out in October 2023. It brought many improvements to the user interface and user experience, along with new features in modules like accounting, marketing, manufacturing, websites, and more.
The Odoo 17 update has been a hot topic among startups, mid-sized businesses, large enterprises, and Odoo developers aiming to grow their businesses. Since it is now already the first quarter of 2024, you must have a clear idea of what Odoo 17 entails and what it can offer your business if you are still not aware of it.
This blog covers the features and functionalities. Explore the entire blog and get in touch with expert Odoo ERP consultants to leverage Odoo 17 and its features for your business too.
An Overview of Odoo ERP
Odoo ERP was first released as OpenERP software in February 2005. It is a suite of business applications used for ERP, CRM, eCommerce, websites, and project management. Ten years ago, the Odoo Enterprise edition was launched to help fund the Odoo Community version.
When you compare Odoo Community and Enterprise, the Enterprise edition offers exclusive features like mobile app access, Odoo Studio customisation, Odoo hosting, and unlimited functional support.
Today, Odoo is a well-known name used by companies of all sizes across various industries, including manufacturing, retail, accounting, marketing, healthcare, IT consulting, and R&D.
The latest version, Odoo 17, has been available since October 2023. Key highlights of this update include:
Enhanced user experience with improvements to the command bar, faster backend page loading, and multiple dashboard views.
Instant report generation, credit limit alerts for sales and invoices, separate OCR settings for invoice creation, and an auto-complete feature for forms in the accounting module.
Improved image handling and global attribute changes for mailing lists in email marketing.
A default auto-signature option and a refuse-to-sign option in HR modules.
Options to divide and merge manufacturing orders, track the status of manufacturing orders, and more in the MRP module.
Dark mode in Odoo 17.
Now that the Odoo 17 announcement is official, let’s look at what’s new in Odoo 17!
What is Odoo ERP 17?
Odoo 17 is the latest version of one of the world’s leading open-source enterprise ERPs. This version has come up with significant improvements explained here in this blog. Also, this new version aims to introduce features that enhance time-saving, efficiency, and productivity for users across various organisations.
Odoo 17, released at the Odoo Experience 2023, brought notable improvements to the user interface and added new functionalities with enhancements in performance, accessibility, data analysis, and management, further expanding its reach in the market.
Boost Your Savings with These Money Management AppsJhone kinadey
A money management app can transform your financial life by tracking expenses, creating budgets, and setting financial goals. These apps offer features like real-time expense tracking, bill reminders, and personalized insights to help you save and manage money effectively. With a user-friendly interface, they simplify financial planning, making it easier to stay on top of your finances and achieve long-term financial stability.
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
React.js, a JavaScript library developed by Facebook, has gained immense popularity for building user interfaces, especially for single-page applications. Over the years, React has evolved and expanded its capabilities, becoming a preferred choice for mobile app development. This article will explore why React.js is an excellent choice for the Best Mobile App development company in Noida.
Visit Us For Information: https://www.linkedin.com/pulse/what-makes-reactjs-stand-out-mobile-app-development-rajesh-rai-pihvf/
WWDC 2024 Keynote Review: For CocoaCoders AustinPatrick Weigel
Overview of WWDC 2024 Keynote Address.
Covers: Apple Intelligence, iOS18, macOS Sequoia, iPadOS, watchOS, visionOS, and Apple TV+.
Understandable dialogue on Apple TV+
On-device app controlling AI.
Access to ChatGPT with a guest appearance by Chief Data Thief Sam Altman!
App Locking! iPhone Mirroring! And a Calculator!!
14 th Edition of International conference on computer visionShulagnaSarkar2
About the event
14th Edition of International conference on computer vision
Computer conferences organized by ScienceFather group. ScienceFather takes the privilege to invite speakers participants students delegates and exhibitors from across the globe to its International Conference on computer conferences to be held in the Various Beautiful cites of the world. computer conferences are a discussion of common Inventions-related issues and additionally trade information share proof thoughts and insight into advanced developments in the science inventions service system. New technology may create many materials and devices with a vast range of applications such as in Science medicine electronics biomaterials energy production and consumer products.
Nomination are Open!! Don't Miss it
Visit: computer.scifat.com
Award Nomination: https://x-i.me/ishnom
Conference Submission: https://x-i.me/anicon
For Enquiry: Computer@scifat.com
8 Best Automated Android App Testing Tool and Framework in 2024.pdfkalichargn70th171
Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.
The Comprehensive Guide to Validating Audio-Visual Performances.pdfkalichargn70th171
Ensuring the optimal performance of your audio-visual (AV) equipment is crucial for delivering exceptional experiences. AV performance validation is a critical process that verifies the quality and functionality of your AV setup. Whether you're a content creator, a business conducting webinars, or a homeowner creating a home theater, validating your AV performance is essential.
A Comprehensive Guide on Implementing Real-World Mobile Testing Strategies fo...kalichargn70th171
In today's fiercely competitive mobile app market, the role of the QA team is pivotal for continuous improvement and sustained success. Effective testing strategies are essential to navigate the challenges confidently and precisely. Ensuring the perfection of mobile apps before they reach end-users requires thoughtful decisions in the testing plan.
What to do when you have a perfect model for your software but you are constrained by an imperfect business model?
This talk explores the challenges of bringing modelling rigour to the business and strategy levels, and talking to your non-technical counterparts in the process.
2. Hello, I’m David
Bechberger
Architect and Developer
● Distributed systems
● High performance low
latency big data platforms
● Graph Databases
● Teach and Mentor fellow
developers
www.bechbergerconsulting.com
www.bechberger.com
@bechbd
www.linkedin.com/in/davebechberger
4. What is Entity Resolution
The process of linking digital entities in data to real world entities.
5. I am known by many names but you may call
me:
● Data referencing
● Record Linkage
● Canonicalization
● Coreference resolution
● Merge/purge
● Entity Clustering
● ….
6. Why is it Hard?
● Structured versus Unstructured
● Name Ambiguity
● Typos/Transposition/Data Errors
● Missing/Incomplete Data
● Changing Data
● Abbreviations
7. Two types of ER problems
Ones with canonical data Ones without canonical data
15. Problem - Matching Product Data
● Product catalog data from Amazon and Google*
● Already deduplicated
● ~1300 Amazon Products, ~3200 Google Products
● Contains a list of perfect matches for testing against
*Datasets from Database Leipzig Group and is available at: https://dbs.uni-
leipzig.de/de/research/projects/object_matching/fever/benchmark_datasets_for_entity_resolution
16. Goal
Match Amazon data with Google data to build out the basis for a
master data management solution
17. What are we starting with?
Title Manufacturer Description
clickart 950 000 -
premier image pack
(dvd-rom)
broderbund
ca international -
arcserve lap/desktop
oem 30pk
computer associates oem arcserve backup
v11.1 win 30u for
laptops and desktops
learning quickbooks
2007
intuit learning quickbooks
2007
eu063av aba
microsoft windows xp
professional
hp eu063av aba :
usually ships in 24
hours...
ID
Title
Description
Origin
NameManufacturer
built_by
Product
18. How are we going to get there?
1. Bipartite and Pattern Matching
2. Iteratively add attributes to data
3. Try and match on weighted attributes
25. Find Manufacturers in Amazon data
● Fuzzy match to find unique
● Create and link nodes to
unique manufacturers
● Found 227 manufacturers Intuit
Intuit
Corp
Quick
Book
Intuit
Corp
built_by
built_by
CanonicalOriginal
26. Find Manufacturers in Google data
● ~7% had manufacturers (232/3229)
● 224 products matched existing manufacturers
● Found 8 more unique manufacturers
27. Validate Canonical Manufacturers
● Review and validate canonical
data
● Add edges between data that
represent the same entity
Sony
Sony
Corp
Intuit
Corp
Intuit
is_same_asis_same_as
28. Build out the Canonical Manufacturer graph
● Found 235 unique manufacturers
● 14 aliases
● Canonical Manufacturers added
to graph with aliases
Intuit
Corp
Intuit
is_same_as
Micro
soft
Sony
29. What’s our graph look like now?
Intuit
Intuit
Corp
Intuit
Corp
Quick
Book
Intuit
Turbo
Tax
Micro
soft
Sony
is_same_as
built_by
built_bybuilt_by
built_by
30. Manufacturer Pattern Matching
● Added Manufacturer
Traversal into Pattern
Match
● Found 534 matches
Intuit
Intuit
Corp
Intuit
Corp
Quick
Book
CanonicalOriginal
Intuit
Turbo
Tax
34. A quick word on Similarity Measurements
● Many different algorithms, each solves a different problem
● Know your data
● Research the options and
● Choose the right one for your data
35. Most Google Data Missing Manufacturer
Or is it?
Example:
eu063av aba microsoft windows xp
professional - license and media
- 1 user - cto - english
36. Named Entity Recognition
Process of classifying entities in strings into known categories
microsoft xbox 360: forza motorsport 2
sony playstation 2: karaoke revolution: american idol bundle
ibm(r) viavoice(r) advanced edition 10
37. Damereau-Levenstein Distance
● Measures the edit distance
between two strings
● Handles insertions,
deletions, transposition and
substitutions
Sony
Snoy Snyo
1 2
2
39. Find similarity between titles
Amazon Title Google Title
ms visual studio 2011 plus video studio 11 plus
Spiderman 3 ps2 activision 81935 spiderman 3
ps2
kids power fun for girls Topic entertainment kids
power fun for girls
40. Jaccard Index
● Set similarity measures
between finite sets (A, B)
● Works on n-Grams
● Calculated as Intersection
over Union
“J(A,B) = |A∩B|/|A⋃B|”
N=1 (Unigram)
This is a sentence
this, is, a,
sentence
N=2 (Bigram)
This is a sentence
this is, is a,
a sentence
N=3 (Trigram)
This is a sentence
this is a,
is a sentence
41. A = Dragon Natural Speaking 9.0
B = Dragon Natural 9.0 Professional
A ⋃ B = 5
A ∩ B = 3
Jaccard Index = ⅗ = 0.60
Jaccard Index
A B
Dragon
Natural
Speaking
9.0
Professional
43. Find similarity between descriptions
● Use TF-IDF finds the relative importance of words in a
document
● Cosine similarity compares two vectors and gives the similarity
between them
44. TF = # of times a word appears
# words in a document
IDF = # of documents
# of documents
with term
TF-IDF
Word TF-IDF Score
unique 4.43
bag 4.34
original 2.945
professional 1.336
log( )
48. What does our graph looks like now?
Intuit
Corp
Intuit
is_same_as
Quick
Book
Turbo
Tax
Intuit Corp
Intuit
built_by
built_by
distance:2
distance:2
distance:3
distance:3
jaccard:0.6cosine_similarity:0.75
49. Aggregating Traversal
● Aggregate all the values into a weighted sum*
● Highest sum was most likely
Value = cosine_similarity + jaccard + (manufacturer simplest
traversal path where distance is <=2 and path length is <=3)
*For this talk I used evenly weighted values, in practice this needs calculated
50. What does our traversal look like?
Intuit
Corp
Intuit
Quick
Book
Turbo
Tax
Value = cosine_similarity + jaccard + (traversal paths <3)
Not an architect that just draws boxes and lines, I get my hands dirty by actually helping to build these things
What this means is resolving data from one or more datasets into a canonical representation of that entity.
E.g. I have facebook, linkedin, google, twitter etc but there is only one singular entity that is me. Entity resolution is the process of taking each of those disparate data sources and linking them to the singular real world me entity.
Entity Resolution is not a new problem, its one that has become more important as we get more and more representation of yourself and we want mine interesting data from them
Deduplication, Record Linkage, Data referencing, Canonicalization, Coreference resolution, Merge/purge, Object identification, Entity clustering, Object consolidation, Identity uncertainty, Reference reconciliation
Its not if you have structured/clean and consistent data, but in reality it isnt
Dave versus David
Mispelled names
Missing items
Wife changed name
Canonical Examples - Countries of the world (195), Fortune 500 companies
Non-canonical examples - probably the most common, the canonical list has to be made from the data
Examples are: people, places, products
Not going to talk about Dedupe or blocking clustering
A little bit on canonicalization but mostly on linking records
MDM - Getting master data from multiple systems
Customers - linking customers from multiple different internal systems (email, chat, phone)
Rec engines - Linking sales and product data across divisions
Intrustion detection - linking IP spoofs to the same person
Fraud - Linking fraudulent transactions on multiple cards to same person
Combining the best of Graph techniques with standard data mining and NLP techniques to provide a better outcome
Lots of different
String similarity - The process of comparing two strings and finding out how similar/dissimilar they are
Named Entity Recognition - Process of classifying entities in text into predefined categories
Shingling - process of tokenizing data to gauge similarity
Aggregating Traversals - Using traversals to calculate weighed sums
Pattern Matching - find patterns
Inferring relationships
Path traversals
NER works by using labelled training set data to determine entities
Used canonical manufacturers as training set data
Input the titles
Good for comparing shorter string segments like names
TF-IDF turns each document into a vector of numbers
Values are then normalized using the dot product
Cosine similarity compares the normalized vectors
Produces a normalized vector of relative importance of words
Similar scores are close to 1
Unrelated scores are close to 0
Opposites are close to -1
Summed up the distance between items with cosine similarity, jaccard index and simplest path traversal where distance<=2 and length<=3
Locality Sensitive Hashing - create hash codes for data to find others most like it
Apache Commons for cosine-similarity and Jaccard Index
Java Similairty for Damerau-Levensthein
OpenNLP - for tokenizing and NER
Tinkerpop for traversals