The document discusses practical computing issues that arise when working with large datasets. It begins by noting that many statistical analyses can be done on a single laptop. It then discusses storing very large datasets, which may require terabytes of storage. The document outlines some basic computing concepts for working with big data, including software engineering practices, databases, and distributed computing.
The document discusses various applications of dimension reduction techniques to extract low-dimensional representations from high-dimensional data for purposes of prediction, descriptive analysis, and input into subsequent causal analysis. It provides examples of such applications using Google search data, genetic data, medical claims data, credit scores, online purchases, and congressional roll call votes. It also discusses issues around text as data, including bag-of-words representations and the use of automated and manual steps in text analysis.
This document discusses recommendation systems and topic modeling for documents using machine learning techniques. It begins by introducing recommendation systems and different types of recommendation literature, including item similarity, collaborative filtering, and hierarchical models. It then discusses bringing in user choice data and different collaborative filtering approaches like k-nearest neighbor prediction and matrix factorization. The document also covers topic modeling, including latent Dirichlet allocation, and how topic models can be combined with user choice models. It concludes by discussing challenges in causal inference when using machine learning.
Nowadays Sentiment Analysis play an important Role in each field such as Stock market, product reviews, news article, political debates which help us to determining current trend in the market regarding specific product, event, issues. Here we are apply sentiment analysis on microblogging platforms such as twitter, Facebook which is used by different people to express their opinion with respect to different kind of foods in the field of home’schef. This paper explain different methods of text preprocessing and applies them with a naive Bayes classifier in a big data, distributed computing platform with the goal of creating a scalable sentiment analysis solution that can classify text into positive or negative categories. We apply negation handling, word n-grams, stemming, and feature selection to evaluate how different combinations of these pre-processing methods affect performance and efficiency.
This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.
Module 1 introduction to machine learningSara Hooker
We believe in building technical capacity all over the world.
We are building and teaching an accessible introduction to machine learning for students passionate about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our work, visit www.deltanalytics.org
This document summarizes a discussion between Susan Athey and Guido Imbens on the relationship between machine learning and causal inference. It notes that while machine learning excels at prediction problems using large datasets, it has weaknesses when it comes to causal questions. Econometrics and statistics literature focuses more on formal theories of causality. The document proposes combining the strengths of both fields by developing machine learning methods that can estimate causal effects, accounting for issues like endogeneity and treatment effect heterogeneity. It outlines some open problems and directions for future research at the intersection of these fields.
If you are curious what is ML all about, this is a gentle introduction to Machine Learning and Deep Learning. This includes questions such as why ML/Data Analytics/Deep Learning ? Intuitive Understanding o how they work and some models in detail. At last I share some useful resources to get started.
The document discusses various applications of dimension reduction techniques to extract low-dimensional representations from high-dimensional data for purposes of prediction, descriptive analysis, and input into subsequent causal analysis. It provides examples of such applications using Google search data, genetic data, medical claims data, credit scores, online purchases, and congressional roll call votes. It also discusses issues around text as data, including bag-of-words representations and the use of automated and manual steps in text analysis.
This document discusses recommendation systems and topic modeling for documents using machine learning techniques. It begins by introducing recommendation systems and different types of recommendation literature, including item similarity, collaborative filtering, and hierarchical models. It then discusses bringing in user choice data and different collaborative filtering approaches like k-nearest neighbor prediction and matrix factorization. The document also covers topic modeling, including latent Dirichlet allocation, and how topic models can be combined with user choice models. It concludes by discussing challenges in causal inference when using machine learning.
Nowadays Sentiment Analysis play an important Role in each field such as Stock market, product reviews, news article, political debates which help us to determining current trend in the market regarding specific product, event, issues. Here we are apply sentiment analysis on microblogging platforms such as twitter, Facebook which is used by different people to express their opinion with respect to different kind of foods in the field of home’schef. This paper explain different methods of text preprocessing and applies them with a naive Bayes classifier in a big data, distributed computing platform with the goal of creating a scalable sentiment analysis solution that can classify text into positive or negative categories. We apply negation handling, word n-grams, stemming, and feature selection to evaluate how different combinations of these pre-processing methods affect performance and efficiency.
This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.
Module 1 introduction to machine learningSara Hooker
We believe in building technical capacity all over the world.
We are building and teaching an accessible introduction to machine learning for students passionate about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our work, visit www.deltanalytics.org
This document summarizes a discussion between Susan Athey and Guido Imbens on the relationship between machine learning and causal inference. It notes that while machine learning excels at prediction problems using large datasets, it has weaknesses when it comes to causal questions. Econometrics and statistics literature focuses more on formal theories of causality. The document proposes combining the strengths of both fields by developing machine learning methods that can estimate causal effects, accounting for issues like endogeneity and treatment effect heterogeneity. It outlines some open problems and directions for future research at the intersection of these fields.
If you are curious what is ML all about, this is a gentle introduction to Machine Learning and Deep Learning. This includes questions such as why ML/Data Analytics/Deep Learning ? Intuitive Understanding o how they work and some models in detail. At last I share some useful resources to get started.
This document provides an introduction to text analytics using IBM SPSS Modeler. It defines key terms related to text analytics and outlines the main steps in the text analytics process: extraction, categorization, and visualization. It then provides a tutorial on using IBM SPSS Modeler to perform text analytics, including sourcing text, extracting concepts and relationships, categorizing records, and visualizing results. Templates and resources are described that can be used to start an interactive workbench session in Modeler for exploring text analytics.
Text analytics is used to extract structured data from unstructured text sources like social media posts, reviews, emails and call center notes. It involves acquiring and preparing text data, processing and analyzing it using algorithms like decision trees, naive bayes, support vector machines and k-nearest neighbors to extract terms, entities, concepts and sentiment. The results are then visualized to support data-driven decision making for applications like measuring customer opinions and providing search capabilities. Popular tools for text analytics include RapidMiner, KNIME, SPSS and R.
An Introduction to Text Analytics: 2013 Workshop presentationSeth Grimes
This document provides an introduction to text analytics. It discusses perspectives on text analytics from different roles like IT support, researchers, and solution providers. It explains how text analytics can boost business results by analyzing unstructured text data from sources like emails, social media, surveys etc. It discusses how text analytics transforms information retrieval to information access by extracting semantics, entities, topics and relationships from text. It also provides definitions and explanations of key concepts in text analytics like entities, features, metadata, natural language processing, information extraction, categorization, classification and evaluation metrics.
Module 9: Natural Language Processing Part 2Sara Hooker
This document provides an overview of natural language processing techniques for gathering and analyzing text data, including web scraping, topic modeling, and clustering. It discusses gathering text data through APIs or web scraping using tools like Beautiful Soup. It also covers representing text numerically using bag-of-words and TF-IDF, visualizing documents in multi-dimensional spaces based on word frequencies, and using k-means clustering to group similar documents together based on cosine or Euclidean distances between their vectors. The document uses examples of Netflix movie descriptions to illustrate these NLP techniques.
Strategies for Practical Active Learning, Robert MunroRobert Munro
In many real-world Machine Learning applications, you need to continually update your models with new training data to improve and maintain accuracy as your model is applied. However, it is often difficult to decide what new data needs to be labeled for training, and what is the best workflow and interfaces for labeling. This training will focus on how you can use Active Learning to improve your training data at scale with common Deep Learning frameworks. At the end of this session, you will understand several Active Learning strategies. We will use the example of applying Active Learning to the ImageNet data set using the TensorFlow Deep Learning framework.
This document provides an overview of key concepts in statistics for data science, including:
- Descriptive statistics like measures of central tendency (mean, median, mode) and variation (range, variance, standard deviation).
- Common distributions like the normal, binomial, and Poisson distributions.
- Statistical inference techniques like hypothesis testing, t-tests, and the chi-square test.
- Bayesian concepts like Bayes' theorem and how to apply it in R.
- How to use R and RCommander for exploring and visualizing data and performing statistical analyses.
Module 8: Natural language processing Pt 1Sara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
The document discusses predictive text analytics, including predicting text completions, disambiguating text, and correcting errors. It also discusses extracting entities, concepts, facts, and sentiments from unstructured text sources for applications like search, knowledge discovery, and predictive analytics. Key challenges include the complexity of human language with features like ambiguity and context.
The document summarizes key concepts in machine learning, including defining learning, types of learning (induction vs discovery, guided learning vs learning from raw data, etc.), generalisation and specialisation, and some simple learning algorithms like Find-S and the candidate elimination algorithm. It discusses how learning can be viewed as searching a generalisation hierarchy to find a hypothesis that covers the examples. The candidate elimination algorithm maintains the version space - the set of hypotheses consistent with the training examples - by updating the general and specific boundaries as new examples are processed.
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
This document provides an introduction to machine learning, including definitions, types, and case studies. It begins with an agenda and overview of artificial intelligence applications. It then defines machine learning as a field that allows computers to learn without being explicitly programmed. The main types of machine learning are described as supervised, unsupervised, semi-supervised, and reinforcement learning. Example case studies on Netflix recommendations, cancer diagnosis, and Amazon inventory are outlined. The document concludes with tips on prerequisites and resources for studying machine learning, including mathematics, programming tools, and course recommendations.
1. Introduction and how to get into Data
2. Data Engineering and skills needed
3. Comparison of Data Analytics for statistic and real time streaming data
4. Bayesian Reasoning for Data
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Module 4: Model Selection and EvaluationSara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Lexalytics Text Analytics Workshop: Perfect Text AnalyticsLexalytics
This document summarizes and promotes the text analytics capabilities of Perfect Text Analytics. It discusses how Perfect is fast, usable, consistent, provides new knowledge, is inclusive of all text, and is trainable. Customer use cases are presented in reputation management, politics, market intelligence, hospitality, financial services, pharma, and opinion mining. The document outlines planned enhancements over the next year, including sarcasm detection, foreign language support, and more customizable tools. Overall, it argues that text analytics can provide valuable insights across many industries when combined with business logic.
Statistical Modeling in 3D: Describing, Explaining and PredictingGalit Shmueli
This document discusses statistical modeling approaches for explaining, predicting, and describing. It notes that explanatory modeling focuses on testing causal hypotheses, predictive modeling focuses on predicting new observations, and descriptive modeling approximates distributions or relationships. The document argues that these goals are different and the best model for one purpose is not necessarily best for another. It cautions against conflating explanation and prediction, and notes that explanatory power does not necessarily indicate predictive power or vice versa. The document examines differences in how data is approached and models are designed and evaluated for these different purposes.
This document discusses preliminary data analysis techniques. It begins by explaining that data analysis is done to make sense of collected data. The basic steps of preliminary analysis are editing, coding, and tabulating data. Editing involves checking for errors and inconsistencies. Coding transforms raw data into numerical codes for analysis. Tabulation involves counting how many cases fall into each coded category. Examples of tabulations like simple counts and cross-tabulations are provided to show relationships between variables. Preliminary analysis helps detect errors and develop hypotheses for further statistical testing.
Real-time Recommendations for Retail: Architecture, Algorithms, and DesignJuliet Hougland
Users are constantly searching for new content and to stay competitive organizations must act immediately based on up-to-date data. Outdated recommendations decrease the likelihood of presenting the right offer and make it harder to maintain customer loyalty. In order to provide the most relevant recommendations and increase engagement, organizations must track customer interactions and re-score recommendations on the fly.
Data sources have expanded dramatically to include a wealth of historical data and a constant influx of behavior data. The key to moving from predictive models, applied in batch, to models that provide responses in real time, is to focus on the efficiency of model application. The speed that recommendations can be served is influenced by:
Architecture of the recommendation serving platform
Choice of recommendation algorithm
Datastore access patterns
In this presentation, we’ll discuss how developers can use open source components like HBase and Kiji to develop low-latency recommendation models that can be easily deployed by e-commerce companies. We will give practical advice on how to choose models and design data stores that make use of the architecture and quickly serve new recommendations.
This document provides an introduction to text analytics using IBM SPSS Modeler. It defines key terms related to text analytics and outlines the main steps in the text analytics process: extraction, categorization, and visualization. It then provides a tutorial on using IBM SPSS Modeler to perform text analytics, including sourcing text, extracting concepts and relationships, categorizing records, and visualizing results. Templates and resources are described that can be used to start an interactive workbench session in Modeler for exploring text analytics.
Text analytics is used to extract structured data from unstructured text sources like social media posts, reviews, emails and call center notes. It involves acquiring and preparing text data, processing and analyzing it using algorithms like decision trees, naive bayes, support vector machines and k-nearest neighbors to extract terms, entities, concepts and sentiment. The results are then visualized to support data-driven decision making for applications like measuring customer opinions and providing search capabilities. Popular tools for text analytics include RapidMiner, KNIME, SPSS and R.
An Introduction to Text Analytics: 2013 Workshop presentationSeth Grimes
This document provides an introduction to text analytics. It discusses perspectives on text analytics from different roles like IT support, researchers, and solution providers. It explains how text analytics can boost business results by analyzing unstructured text data from sources like emails, social media, surveys etc. It discusses how text analytics transforms information retrieval to information access by extracting semantics, entities, topics and relationships from text. It also provides definitions and explanations of key concepts in text analytics like entities, features, metadata, natural language processing, information extraction, categorization, classification and evaluation metrics.
Module 9: Natural Language Processing Part 2Sara Hooker
This document provides an overview of natural language processing techniques for gathering and analyzing text data, including web scraping, topic modeling, and clustering. It discusses gathering text data through APIs or web scraping using tools like Beautiful Soup. It also covers representing text numerically using bag-of-words and TF-IDF, visualizing documents in multi-dimensional spaces based on word frequencies, and using k-means clustering to group similar documents together based on cosine or Euclidean distances between their vectors. The document uses examples of Netflix movie descriptions to illustrate these NLP techniques.
Strategies for Practical Active Learning, Robert MunroRobert Munro
In many real-world Machine Learning applications, you need to continually update your models with new training data to improve and maintain accuracy as your model is applied. However, it is often difficult to decide what new data needs to be labeled for training, and what is the best workflow and interfaces for labeling. This training will focus on how you can use Active Learning to improve your training data at scale with common Deep Learning frameworks. At the end of this session, you will understand several Active Learning strategies. We will use the example of applying Active Learning to the ImageNet data set using the TensorFlow Deep Learning framework.
This document provides an overview of key concepts in statistics for data science, including:
- Descriptive statistics like measures of central tendency (mean, median, mode) and variation (range, variance, standard deviation).
- Common distributions like the normal, binomial, and Poisson distributions.
- Statistical inference techniques like hypothesis testing, t-tests, and the chi-square test.
- Bayesian concepts like Bayes' theorem and how to apply it in R.
- How to use R and RCommander for exploring and visualizing data and performing statistical analyses.
Module 8: Natural language processing Pt 1Sara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
The document discusses predictive text analytics, including predicting text completions, disambiguating text, and correcting errors. It also discusses extracting entities, concepts, facts, and sentiments from unstructured text sources for applications like search, knowledge discovery, and predictive analytics. Key challenges include the complexity of human language with features like ambiguity and context.
The document summarizes key concepts in machine learning, including defining learning, types of learning (induction vs discovery, guided learning vs learning from raw data, etc.), generalisation and specialisation, and some simple learning algorithms like Find-S and the candidate elimination algorithm. It discusses how learning can be viewed as searching a generalisation hierarchy to find a hypothesis that covers the examples. The candidate elimination algorithm maintains the version space - the set of hypotheses consistent with the training examples - by updating the general and specific boundaries as new examples are processed.
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
This document provides an introduction to machine learning, including definitions, types, and case studies. It begins with an agenda and overview of artificial intelligence applications. It then defines machine learning as a field that allows computers to learn without being explicitly programmed. The main types of machine learning are described as supervised, unsupervised, semi-supervised, and reinforcement learning. Example case studies on Netflix recommendations, cancer diagnosis, and Amazon inventory are outlined. The document concludes with tips on prerequisites and resources for studying machine learning, including mathematics, programming tools, and course recommendations.
1. Introduction and how to get into Data
2. Data Engineering and skills needed
3. Comparison of Data Analytics for statistic and real time streaming data
4. Bayesian Reasoning for Data
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Module 4: Model Selection and EvaluationSara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Lexalytics Text Analytics Workshop: Perfect Text AnalyticsLexalytics
This document summarizes and promotes the text analytics capabilities of Perfect Text Analytics. It discusses how Perfect is fast, usable, consistent, provides new knowledge, is inclusive of all text, and is trainable. Customer use cases are presented in reputation management, politics, market intelligence, hospitality, financial services, pharma, and opinion mining. The document outlines planned enhancements over the next year, including sarcasm detection, foreign language support, and more customizable tools. Overall, it argues that text analytics can provide valuable insights across many industries when combined with business logic.
Statistical Modeling in 3D: Describing, Explaining and PredictingGalit Shmueli
This document discusses statistical modeling approaches for explaining, predicting, and describing. It notes that explanatory modeling focuses on testing causal hypotheses, predictive modeling focuses on predicting new observations, and descriptive modeling approximates distributions or relationships. The document argues that these goals are different and the best model for one purpose is not necessarily best for another. It cautions against conflating explanation and prediction, and notes that explanatory power does not necessarily indicate predictive power or vice versa. The document examines differences in how data is approached and models are designed and evaluated for these different purposes.
This document discusses preliminary data analysis techniques. It begins by explaining that data analysis is done to make sense of collected data. The basic steps of preliminary analysis are editing, coding, and tabulating data. Editing involves checking for errors and inconsistencies. Coding transforms raw data into numerical codes for analysis. Tabulation involves counting how many cases fall into each coded category. Examples of tabulations like simple counts and cross-tabulations are provided to show relationships between variables. Preliminary analysis helps detect errors and develop hypotheses for further statistical testing.
Real-time Recommendations for Retail: Architecture, Algorithms, and DesignJuliet Hougland
Users are constantly searching for new content and to stay competitive organizations must act immediately based on up-to-date data. Outdated recommendations decrease the likelihood of presenting the right offer and make it harder to maintain customer loyalty. In order to provide the most relevant recommendations and increase engagement, organizations must track customer interactions and re-score recommendations on the fly.
Data sources have expanded dramatically to include a wealth of historical data and a constant influx of behavior data. The key to moving from predictive models, applied in batch, to models that provide responses in real time, is to focus on the efficiency of model application. The speed that recommendations can be served is influenced by:
Architecture of the recommendation serving platform
Choice of recommendation algorithm
Datastore access patterns
In this presentation, we’ll discuss how developers can use open source components like HBase and Kiji to develop low-latency recommendation models that can be easily deployed by e-commerce companies. We will give practical advice on how to choose models and design data stores that make use of the architecture and quickly serve new recommendations.
The document provides an overview of fundamentals of database design including definitions of key concepts like data, information, and databases. It discusses the purpose of databases and database management systems. It also covers topics like selecting a database system, database development best practices, and data entry considerations.
eScience: A Transformed Scientific MethodDuncan Hull
The document discusses the concept of eScience, which involves synthesizing information technology and science. It explains how science is becoming more data-driven and computational, requiring new tools to manage large amounts of data. It recommends that organizations foster the development of tools to help with data capture, analysis, publication, and access across various scientific disciplines.
INTRODUCTION TO Database Management System (DBMS)Prof Ansari
shared collection of logically related data, designed to meet the information needs of multiple users in an organization. The term database is often erroneously referred to as a synonym for a “database management system DBMS)”. They are not equivalent and it will be explained in the next section.
Stream Meets Batch for Smarter Analytics- Impetus White PaperImpetus Technologies
For Impetus’ White Papers archive, visit- http://www.impetus.com/whitepaper
The paper discusses how the traditional batch and real time paradigm can work together to deliver smarter, quicker and better insights on large volumes of data picking the right strategy and right technology.
Ch-ch-ch-ch-changes....Stitch Triggers - Andrew MorganMongoDB
Intelligent apps are emerging as the next frontier in analytics and application development. Learn how to build intelligent apps on MongoDB powered by Google Cloud with TensorFlow for machine learning and DialogFlow for artificial intelligence. Get your developers and data scientists to finally work together to build applications that understand your customer, automate their tasks, and provide knowledge and decision support.
The document discusses the data warehouse lifecycle and key components. It covers topics like source systems, data staging, presentation area, business intelligence tools, dimensional modeling concepts, fact and dimension tables, star schemas, slowly changing dimensions, dates, hierarchies, and physical design considerations. Common pitfalls discussed include becoming overly focused on technology, tackling too large of projects, and neglecting user acceptance.
The document discusses map reduce and how it can be used for recommendation systems. It describes how map reduce works by mapping data into key-value pairs and then reducing them. This allows large amounts of sparse, unstructured data to be processed efficiently across many machines. It then gives an example of how map reduce could be used to build a sequential web access-based recommendation system by mapping log data into a pattern tree that is continuously updated and used to provide recommendations.
This document discusses the key characteristics of Big Data - volume, variety, velocity, and veracity. It provides examples and explanations of each characteristic. Volume refers to the large amount of data. Variety means the different types and sources of data. Velocity is about the speed at which data is processed. Veracity relates to the quality and trustworthiness of the data. The document emphasizes that understanding these characteristics is important for effectively managing and analyzing Big Data.
Shant Hovsepian, CTO of Arcadia Data and a panel of experts details the trade-offs between a number of architectures that provide self-service access to data, and industry researcher Mark Madsen discusses the pros and cons of architectures, deployment strategies, and customer examples of BI on big data.
Topics include:
- Traditional BI platforms based on semantic layers and SQL/MDX generation
- Server and desktop BI tools based on direct mapping of data
- Distributed BI platforms (e.g., MPP and data native)
- OLAP- and SQL-on-Hadoop engines
BIG DATA | How to explain it & how to use it for your career?Tuan Yang
If you ask people what BIG DATA is they often say it is about a lot of data. But the world has ALWAYS had a lot of data. It is about datafication – a word so new even spellcheck functions don’t know it is a real word!
Learn more about:
» How BIG DATA changes career paths of even the most unsuspecting?
» How BIG DATA changes the way business decision are made?
» How BIG DATA changes who makes those decisions & the reshuffle of the balance of power it causes?
» What BIG DATA skills can you bring to the office tomorrow to increase your value to the firm
This document discusses the challenges and opportunities presented by the increasing volume and complexity of biological data. It outlines four main areas: 1) Developing methods to efficiently store, access, and analyze large datasets; 2) Broadening our understanding of gene function beyond a small number of well-studied genes; 3) Accelerating research through improved sharing of data, results, and methods; and 4) Leveraging exploratory analysis of integrated datasets to generate new insights. The author advocates for lossy data compression, streaming analysis, preprint sharing, improved metadata collection, and incentivizing open data practices.
This document provides an overview of fundamentals of database design. It discusses what a database is, the difference between data and information, why databases are needed, how to select a database system, basic database definitions and building blocks, quality control considerations, and data entry methods. The overall purpose of a database management system is to transform data into information, information into knowledge, and knowledge into action.
This document provides an overview of fundamentals of database design. It discusses what a database is, the difference between data and information, why databases are needed, how to select a database system, basic database definitions and building blocks, quality control considerations, and data entry methods. The overall purpose of a database management system is to transform data into information, information into knowledge, and knowledge into action.
Gerenral insurance Accounts IT and Investmentvijayk23x
The document provides an overview of topics that may be covered in accounting, IT and investment exams, including:
1. The exam questions will be split between investment, IT, accounting standards and ratios, and preparation of financial accounts.
2. IT topics include storage units, network types, protocols, programming languages, databases, data warehousing concepts like data marts, operational data stores, and dimensional modeling techniques like star and snowflake schemas.
3. Key concepts in machine learning, deep learning, big data, data lakes and artificial intelligence are also defined.
This document discusses the concept of big data. It defines big data as massive volumes of structured and unstructured data that are difficult to process using traditional database techniques due to their size and complexity. It notes that big data has the characteristics of volume, variety, and velocity. The document also discusses Hadoop as an implementation of big data and how various industries are generating large amounts of data.
FISCAL STIMULUS IN ECONOMIC UNIONS: WHAT ROLE FOR STATESNBER
1) State deficits can boost job growth in the deficit state but also in neighboring states, showing significant spillover effects. Coordinated fiscal policies across states are more cost-effective than individual state policies.
2) Federal aid to states, when coordinated, can effectively stimulate the overall economy. Targeted aid linked to services for lower income households is more effective than untargeted aid.
3) The economic stimulus of the American Recovery and Reinvestment Act could have been 30% more effective if it relied more on targeted aid and less on untargeted aid. Coordinated fiscal policies that account for spillovers across economic regions are optimal for stimulus programs.
Business in the United States Who Owns it and How Much Tax They PayNBER
This document analyzes business ownership and tax payments in the United States using administrative tax data from 2011. It finds:
1. Pass-through business income, such as from partnerships and S-corporations, is highly concentrated.
2. The average federal income tax rate on pass-through business income is 19%.
3. 30% of income earned by partnerships cannot be uniquely traced to an identifiable, ultimate owner.
Redistribution through Minimum Wage Regulation: An Analysis of Program Linkag...NBER
This document analyzes the program linkages and budgetary spillovers of minimum wage regulation using data from recent federal minimum wage increases. It finds that wages increased for some low-skilled workers but employment declined significantly. While safety net programs provided some income replacement, earnings and tax revenues decreased substantially. Overall, the analysis suggests minimum wage increases reallocated income from employers and taxpayers to low-wage workers, with program and tax revenue spillovers of approximately $1-2 billion annually.
The Distributional Effects of U.S. Clean Energy Tax CreditsNBER
This document summarizes a study examining the distributional effects of US clean energy tax credits from 2006-2012. It finds that higher-income households claimed a disproportionate share of the $18 billion in credits. Specifically, the study analyzes tax return data to see who claimed credits for investments like home weatherization, solar panels, hybrid vehicles, and electric vehicles. It aims to provide insights into how the inequitable distribution may inform future program design and the debate around subsidies versus carbon taxes.
An Experimental Evaluation of Strategies to Increase Property Tax Compliance:...NBER
This document summarizes a study that tested different strategies for increasing property tax compliance in Philadelphia. The researchers worked with the city's Department of Revenue to randomly assign taxpayers with overdue property taxes to receive one of four letters: a standard letter, or a standard letter plus an additional sentence appealing to civic duty, public services benefits, or potential home loss. They found the civic duty appeal significantly increased tax payments, especially for those with lower debts. Appealing to public services benefits also showed some effect on higher debt taxpayers. The researchers conclude strategically targeting messages could further improve compliance.
The document discusses using machine learning methods to estimate heterogeneous causal effects. It proposes an approach of using regression trees on a transformed outcome variable to estimate individual treatment effects. However, this approach is critiqued as it can introduce noise. An improved approach is presented that uses the sample average treatment effect within each leaf as the estimator, and uses the variance of predictions for model fitting criteria and a matching estimator for out-of-sample evaluation. The approach separates the tasks of model selection and treatment effect estimation to enable valid statistical inference on estimated effects in subgroups.
This document discusses various machine learning techniques including:
1. Tree pruning involves first growing a large tree and then pruning branches that do not improve the objective function. This prevents early stopping.
2. Boosting uses multiple weak learners sequentially to get an additive model that approximates the regression function. It combines many simple models to create a powerful ensemble model.
3. Unsupervised learning techniques like principal component analysis and clustering are used to find patterns in data without an outcome variable. These include reducing dimensions and partitioning data into subgroups.
This document summarizes key points from a lecture on diffusion, identification, and network formation. It discusses how diffusion of products can be modeled, including information passing between neighbors. Estimation techniques are described to model information diffusion on actual networks by simulating propagation over time. The challenges of identification when networks are endogenous are also covered. Forming models of network formation that account for link dependencies is an important area of current research.
This document provides an overview of social and economic networks. It discusses why networks are important to study, as interactions are shaped by relationships. Some examples of networks are presented, such as marriage networks, friendship networks in high schools, military alliances, and interbank payment networks. The document then discusses how to represent networks mathematically and introduces concepts like degree, paths, average path length, and degree distributions. It also covers homophily, or the tendency for similar people to connect, and shows examples of homophily along attributes. Finally, it introduces the idea of centrality and influence within a network, discussing measures like degree centrality and eigenvector centrality.
Daron Acemoglu presents a document on networks, games over networks, and peer effects. The document discusses how networks can be used to model externalities and peer effects. It presents a model of a game over networks where players' payoffs are determined by their own actions, the actions of their network neighbors, and potential strategic interactions. The best responses in this game are characterized. Under certain conditions, such as the game being a potential game, the game will have a unique Nash equilibrium where each player's action is determined by their position in the network. The document discusses applications of this type of network game model.
The document discusses how economic shocks propagate through networks of production and inputs. It begins by presenting a simple model of an economy consisting of sectors that use each other's outputs as inputs. Shocks to individual sectors can spread to other sectors through this production network. While diversification across many sectors could cause microeconomic shocks to "wash out", the structure of the network influences how shocks aggregate. Asymmetric networks with some sectors having outsized importance can lead to greater aggregate volatility than more regular networks where all sectors are equally important. Empirical analysis of input-output data supports the theory by finding significant downstream effects of sectoral shocks.
The NBER Working Paper Series at 20,000 - Joshua GansNBER
This document discusses publication lags in economics research, with working papers appearing years before peer-reviewed published work. It questions whether publication means anything given the large number of working papers now available. It also considers options for the National Bureau of Economic Research's web repository, such as providing open access to working papers along with links to related materials, peer reviews, and published versions of the papers.
The NBER Working Paper Series at 20,000 - Claudia GoldinNBER
This document analyzes trends in the NBER Working Paper series from 1978 to 2013. It finds that the number of working papers published annually has increased dramatically over time, from around 100 in the late 1970s to over 1,200 by 2013. The number of NBER research programs has also expanded significantly, from 7 originally to over 20 currently. Individual working papers now tend to involve more programs and more authors than in the past as well. The working paper series has become less specialized and more collaborative over four decades of growth and evolution.
The NBER Working Paper Series at 20,000 - James PoterbaNBER
This document summarizes the origin and evolution of the NBER Working Paper series from its beginning in 1972 to the present. It started as an outlet for NBER research and has grown tremendously over time. Some key points:
- The first working paper was published in June 1973 and there were only 3 papers in the first month.
- Growth accelerated after Martin Feldstein became NBER President in 1977, with over 200 papers published in 1981.
- There are now over 20,000 working papers published and about 5.5 million downloads per year from around the world.
- The most popular papers focus on topics like financial crises, economic growth, and corporate governance.
The NBER Working Paper Series at 20,000 - Scott SternNBER
The NBER Working Paper series recently reached 20,000 papers published and is recognized as one of the leading economics working paper series in the world. According to 2014 Google Scholar Metrics, the NBER Working Paper series ranked 18th out of thousands of journals by its H-5 index, which measures the productivity and impact of published work. The high ranking of the NBER Working Paper series demonstrates its important role in disseminating new economic research and ideas worldwide.
The NBER Working Paper Series at 20,000 - Glenn EllisonNBER
This document summarizes trends in the publication process and the role of working papers. It finds that publication times at economics journals have increased significantly over the past 30 years. Acceptance rates at top journals have also declined. These changes mean that published papers cannot address current issues or reflect the latest state of knowledge as quickly. The document also finds that working papers, such as those from the NBER, play an increasingly important role, as economists can disseminate their work more quickly through working paper series than through the traditional publication process. NBER working papers account for a large share of papers eventually published in top journals and those NBER papers go on to be well-cited.
- The document summarizes a lecture on using micro data with characteristics-based choice models. It discusses two key advantages of micro data: 1) It provides information on how observed individual characteristics interact with product characteristics. 2) It includes data on individuals who did not purchase products as well as second choices, giving insight into unobserved product characteristics.
- The model specifies utility as depending on observed and unobserved individual characteristics as well as product characteristics. Micro data on first choices matches individual characteristics to chosen products, while second choice data helps account for unobserved characteristics by holding individual conditions constant.
Econometrics of High-Dimensional Sparse ModelsNBER
The document discusses high-dimensional sparse econometric models where the number of predictors (p) is much larger than the sample size (n). It outlines an approach for estimating regression functions using penalization methods like the LASSO. Specifically, it discusses:
1. Using the LASSO estimator to minimize squared errors while penalizing the l1-norm of coefficients, inducing sparsity.
2. Choosing the optimal penalty level as a function of the error variance and sample size. Variants like the square-root LASSO provide a tuning-free approach.
3. Examples showing how sparse approximations can better capture patterns in population data than traditional low-dimensional approximations.
High-Dimensional Methods: Examples for Inference on Structural EffectsNBER
This document describes a study that uses high-dimensional methods to estimate the effect of 401(k) eligibility on measures of accumulated assets. It begins by outlining the baseline model and notes areas for improvement, such as controlling for income. It then discusses using regularization like LASSO for variable selection in high-dimensional settings. The document explores more flexible specifications by generating many interaction and polynomial terms but notes the need for dimension reduction. It describes using LASSO to select important variables from a large set. The results select a parsimonious set of variables and estimate similar 401(k) effects as the baseline.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
2. Introduction
We have focused on the statistical / econometric issues that arise with
big data
In the time that remains, we want to spend a little time on the
practical issues...
3. Introduction
We have focused on the statistical / econometric issues that arise with
big data
In the time that remains, we want to spend a little time on the
practical issues...
E.g., where do you actually put a 2 TB dataset?
4. Introduction
We have focused on the statistical / econometric issues that arise with
big data
In the time that remains, we want to spend a little time on the
practical issues...
E.g., where do you actually put a 2 TB dataset?
Goal: Sketch some basic computing ideas relevant to working with
large datasets.
5. Introduction
We have focused on the statistical / econometric issues that arise with
big data
In the time that remains, we want to spend a little time on the
practical issues...
E.g., where do you actually put a 2 TB dataset?
Goal: Sketch some basic computing ideas relevant to working with
large datasets.
Caveat: We are all amateurs
6. The Good News
Much of what we've talked about here you can do on your laptop
Your OS knows how to do parallel computing (multiple processors,
multiple cores)
Many big datasets are 5 GB
Save the data to local disk, re up Stata or R, and o you go...
7. How Big is Big?
Congressional record text (1870-2010) ≈50 GB
Congressional record pdfs (1870-2010) ≈500 GB
Nielsen scanner data (34k stores, 2004-2010) ≈5 TB
Wikipedia (2013) ≈6 TB
20% Medicare claims data (1997-2009) ≈10 TB
Facebook (2013) ≈100,000 TB
All data in the world ≈2.7 billion TB
16. This Talk
We are not software engineers or computer scientists.
But we have learned that most common problems in social sciences
have analogues in these elds and there are standard solutions.
Goal is to highlight a few of these that we think are especially valuable
to researchers.
Focus on incremental changes: one step away from common practice.
21. Manual Approach
Two main problems with this approach
Replication: how can we be sure we'll nd our way back to the exact
same numbers?
Eciency: what happens if we change our mind about the right
specication?
23. Fully Automated Approach
File: rundirectory.bat
stattransfer export_to_csv.stc
statase -b mergefiles.do
statase -b cleandata.do
statase -b regressions.do
statase -b figures.do
pdflatex tv_potato.tex
All steps controlled by a shell script
Order of steps unambiguous
Easy to call commands from dierent packages
24. Make
Framework to go from source to target
Tracks dependencies and revisions
Avoids rebuilding components that are up to date
Used to build executable les
26. After Some Editing
Dates demarcate versions, initials demarcate authors
Why do this?
Facilitates comparison
Facilitates undo
27. What's Wrong with the Approach?
Why not do this?
It's a pain: always have to remember to tag every new le
It's confusing:
Which log le came from regressions_022713_mg.do?
Which version of cleandata.do makes the data used by
regressions_022413.do?
It fails the market test: No software rm does it this way
28. Version Control
Software that sits on top of your lesystem
Keeps track of multiple versions of the same le
Records date, authorship
Manages conicts
Benets
Single authoritative version of the directory
Edit without fear: an undo command for everything
42. Research Assistant Output
county state cnty_pop state_pop region
36037 NY 3817735 43320903 1
36038 NY 422999 43320903 1
36039 NY 324920 . 1
36040 . 143432 43320903 1
. NY . 43320903 1
37001 VA 3228290 7173000 3
37002 VA 449499 7173000 3
37003 VA 383888 7173000 4
37004 VA 483829 7173000 3
43. Causes for Concern
county state cnty_pop state_pop region
36037 NY 3817735 43320903 1
36038 NY 422999 43320903 1
36039 NY 324920 . 1
36040 . 143432 43320903 1
. NY . 43320903 1
37001 VA 3228290 7173000 3
37002 VA 449499 7173000 3
37003 VA 383888 7173000 4
37004 VA 483829 7173000 3
44. Relational Databases
county state population
36037 NY 3817735
36038 NY 422999
36039 NY 324920
36040 NY 143432
37001 VA 3228290
37002 VA 449499
37003 VA 383888
37004 VA 483829
state population region
NY 43320903 1
VA 7173000 3
Each variable is an attribute of an element of the table
Each table has a key
Tables are connected by foreign keys (state eld in the county table)
45. Steps
Store data in normalized format as above
Can use at les, doesn't have to be fancy relational database software
Construct a second set of les with key transformations
e.g., log population
Merge data together and run analysis
70. Code and Data
Data are getting larger
Research is getting more collaborative
Need to manage code and data responsibly for collaboration and
replicability
Learn from the pros, not from us
72. What is a Database?
Database Theory
Principles for how to store / organize / retrieve data eciently
(normalization, indexing, optimization, etc.)
Database Software
Manages storage / organization / retrieval of data (SQL, Oracle,
Access, etc.)
Economists rarely use this software because we typically store data in
at les interact with them using statistical programs
When we receive extracts from large datasets (the census, Medicare
claims, etc.) someone else often interacts with the database on the
back end
73. Normalization
Database Normalization is the process of organizing the elds and
tables of a relational database to minimize redundancy and
dependency. Normalization usually involves dividing large tables into
smaller (and less redundant) tables and dening relationships between
them.
75. Indexing
Medicare claims data for 1997-2010 are roughly 10 TB
These data are stored at NBER in thousands of zipped SAS les
76. Indexing
Medicare claims data for 1997-2010 are roughly 10 TB
These data are stored at NBER in thousands of zipped SAS les
To extract, say, all claims for heart disease patients aged 55-65, you
would need to read every line of every one of those les
THIS IS SLOW!!!
77. Indexing
The obvious solution, long understood for book, libraries, economics
journals, and so forth, is to build an index
Database software handles this automatically
Allows you to specify elds that will be often used for lookups,
subsetting, etc. to be indexed
For the Medicare data, we could index age, gender, type of treatment,
etc. to allow much faster extraction
78. Indexing
Benets
Fast lookups
Easy to police data constraints
Costs
Storage
Time
Database optimization is the art of tuning database structure and
indexing for a specic set of needs
80. Data Warehouses
Traditional databases are optimized for operational environments
Bank transactions
Airline reservations
etc.
Characteristics
Many small reads and writes
Many users accessing simultaneously
Premium on low latency
Only care about current state
81. Data Warehouses
In analytic / research environments, however, the requirements are
dierent
Frequent large reads, infrequent writes
Relatively little simultaneous access
Value throughput relative to latency
May care about history as well as current state
Need to create and re-use many custom extracts
82. Data Warehouses
In analytic / research environments, however, the requirements are
dierent
Frequent large reads, infrequent writes
Relatively little simultaneous access
Value throughput relative to latency
May care about history as well as current state
Need to create and re-use many custom extracts
Database systems tuned to these requirements are commonly called
data warehouses
85. Distributed Computing
Denition: Computation shared among many independent processors
Terminology
Distributed vs. Parallel (latter usually refers to systems with shared
memory)
Cluster vs. Grid (latter usually more decentralized heterogeneous)
86. On Your Local Machine
Your OS can run multiple processors each with multiple cores
Your video card has hundreds of cores
Stata, R, Matlab, etc. can all exploit these resources to do parallel
computing
87. On Your Local Machine
Your OS can run multiple processors each with multiple cores
Your video card has hundreds of cores
Stata, R, Matlab, etc. can all exploit these resources to do parallel
computing
Stata
Buy appropriate MP version of Stata
Software does the rest
88. On Your Local Machine
Your OS can run multiple processors each with multiple cores
Your video card has hundreds of cores
Stata, R, Matlab, etc. can all exploit these resources to do parallel
computing
Stata
Buy appropriate MP version of Stata
Software does the rest
R / Matlab
Install appropriate add-ins (parallel package in R, parallel computing
toolbox in Matlab)
Include parallel commands in code (e.g., parfor in place of for in
Matlab)
89. On Cluster / Grid
Resources abound
University / department computing clusters
Non-commercial scientic computing grids (e.g., XSEDE)
Commercial grids (e.g., Amazon EC2)
90. On Cluster / Grid
Resources abound
University / department computing clusters
Non-commercial scientic computing grids (e.g., XSEDE)
Commercial grids (e.g., Amazon EC2)
Most of these run Linux w/ distribution handled by a batch scheduler
Write code using your favorite application, then send it to scheduler
with a bash script
91. MapReduce
MapReduce is a programming model that facilitates distributed
computing
Developed by Google around 2004, though ideas predate that
92. MapReduce
MapReduce is a programming model that facilitates distributed
computing
Developed by Google around 2004, though ideas predate that
Most algorithms for distributed data processing can be represented in
two steps
Map: Process individual chunk of data to generate an intermediate
summary
Reduce: Combine summaries from dierent chunks to produce a
single output le
93. MapReduce
MapReduce is a programming model that facilitates distributed
computing
Developed by Google around 2004, though ideas predate that
Most algorithms for distributed data processing can be represented in
two steps
Map: Process individual chunk of data to generate an intermediate
summary
Reduce: Combine summaries from dierent chunks to produce a
single output le
If you structure your code this way, MapReduce software will handle
all the details of distribution:
Partitioning data
Scheduling execution across nodes
Managing communication between machines
Handling errors / machine failures
94. MapReduce: Examples
Count words in a large collection of documents
Map: Document i → Set of (word, count) pairs Ci
Reduce: Collapse {Ci }, summing count within word
95. MapReduce: Examples
Count words in a large collection of documents
Map: Document i → Set of (word, count) pairs Ci
Reduce: Collapse {Ci }, summing count within word
Extract medical claims for 65-year old males
Map: Record set i → Subset of i that are 65-year old males Hi
Reduce: Append elements of {Hi }
96. MapReduce: Examples
Count words in a large collection of documents
Map: Document i → Set of (word, count) pairs Ci
Reduce: Collapse {Ci }, summing count within word
Extract medical claims for 65-year old males
Map: Record set i → Subset of i that are 65-year old males Hi
Reduce: Append elements of {Hi }
Compute marginal regression for text analysis (e.g., Gentzkow
Shapiro 2010)
Map: Counts xij of phrase j → Parameters ˆαj , ˆβj from
E (xij |yi ) = αj + βj xij
Reduce: Append ˆαj , ˆβj
98. MapReduce: Implementation
MapReduce is the original software developed by Google
Hadoop is the open-source version most people use (developed by
Apache)
Amazon has a hosted implementation (Amazon EMR)
99. MapReduce: Implementation
MapReduce is the original software developed by Google
Hadoop is the open-source version most people use (developed by
Apache)
Amazon has a hosted implementation (Amazon EMR)
How does it work?
Write your code as two functions called map and reduce
Send code data to scheduler using bash script
100. Distributed File Systems
Data transfer is the main bottleneck in distributed systems
For big data, it makes sense to distribute data as well as computation
Data broken up into chunks, each of which lives on a separate node
File system keeps track of where the pieces are and allocates jobs so
computation happens close to data whenever possible
101. Distributed File Systems
Data transfer is the main bottleneck in distributed systems
For big data, it makes sense to distribute data as well as computation
Data broken up into chunks, each of which lives on a separate node
File system keeps track of where the pieces are and allocates jobs so
computation happens close to data whenever possible
Tight coupling between MapReduce software and associated le
systems
MapReduce → Google File System (GFS)
Hadoop → Hadoop Distributed File System (HDFS)
Amazon EMR → Amazon S3
102. Distributed File Systems
Legend:
Data messages
Control messages
Application
(file name, chunk index)
(chunk handle,
chunk locations)
GFS master
File namespace
/foo/bar
Instructions to chunkserver
Chunkserver state
GFS chunkserverGFS chunkserver
(chunk handle, byte range)
chunk data
chunk 2ef0
Linux file system Linux file system
GFS client
Figure 1: GFS Architecture
and replication decisions using global knowledge. However,
we must minimize its involvement in reads and writes so
that it does not become a bottleneck. Clients never read
and write file data through the master. Instead, a client asks
the master which chunkservers it should contact. It caches
this information for a limited time and interacts with the
chunkservers directly for many subsequent operations.
Let us explain the interactions for a simple read with refer-
ence to Figure 1. First, using the fixed chunk size, the client
translates the file name and byte offset specified by the ap-
plication into a chunk index within the file. Then, it sends
the master a request containing the file name and chunk
index. The master replies with the corresponding chunk
tent TCP connection to the chunkserver over an extended
period of time. Third, it reduces the size of the metadata
stored on the master. This allows us to keep the metadata
in memory, which in turn brings other advantages that we
will discuss in Section 2.6.1.
On the other hand, a large chunk size, even with lazy space
allocation, has its disadvantages. A small file consists of a
small number of chunks, perhaps just one. The chunkservers
storing those chunks may become hot spots if many clients
are accessing the same file. In practice, hot spots have not
been a major issue because our applications mostly read
large multi-chunk files sequentially.
However, hot spots did develop when GFS was first used
105. Scenario 1: Not-So-Big Data
My data is 100 gb or less
Advice
Store data locally in at les (csv, Stata, R, etc.)
Organize data in normalized tables for robustness and clarity
Run code serially or (if computation is slow) in parallel
106. Scenario 2: Big Data, Small Analysis
My raw data is 100 gb, but the extracts I actually use for analysis
are 100 gb
107. Scenario 2: Big Data, Small Analysis
My raw data is 100 gb, but the extracts I actually use for analysis
are 100 gb
Example
Medicare claims data → analyze heart attack spending by patient by
year
Nielsen scanner data → analyze average price by store by month
108. Scenario 2: Big Data, Small Analysis
My raw data is 100 gb, but the extracts I actually use for analysis
are 100 gb
Example
Medicare claims data → analyze heart attack spending by patient by
year
Nielsen scanner data → analyze average price by store by month
Advice
Store data in relational database optimized to produce analysis extracts
eciently
Store extracts locally in at les (csv, Stata, R, etc.)
Organize extracts in normalized tables for robustness and clarity
Run code serially or (if computation is slow) in parallel
109. Scenario 2: Big Data, Small Analysis
My raw data is 100 gb, but the extracts I actually use for analysis
are 100 gb
Example
Medicare claims data → analyze heart attack spending by patient by
year
Nielsen scanner data → analyze average price by store by month
Advice
Store data in relational database optimized to produce analysis extracts
eciently
Store extracts locally in at les (csv, Stata, R, etc.)
Organize extracts in normalized tables for robustness and clarity
Run code serially or (if computation is slow) in parallel
Note: Gains to database increase for more structured data. For
completely unstructured data, you may be better o using distributed
le system + map reduce to create extracts.
110. Scenario 3: Big Data, Big Analysis
My data is 100 GB and my analysis code needs to touch all of the
data
111. Scenario 3: Big Data, Big Analysis
My data is 100 GB and my analysis code needs to touch all of the
data
Example
2 TB of SEC ling text → run variable selection using all data
112. Scenario 3: Big Data, Big Analysis
My data is 100 GB and my analysis code needs to touch all of the
data
Example
2 TB of SEC ling text → run variable selection using all data
Advice
Store data in distributed le system
Use MapReduce or other distributed algorithms for analysis