Automating the Capture of Data Transformation Metadata from Statistical Analysis Software
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Python for Economists Alex Bell
Python for Economists Alex Bell [email protected] http://xkcd.com/353 This version: October 2016. If you have not already done so, download the files for the exercises here. Contents 1 Introduction to Python 3 1.1 Getting Set-Up................................................. 3 1.2 Syntax and Basic Data Structures...................................... 3 1.2.1 Variables: What Stata Calls Macros ................................ 4 1.2.2 Lists.................................................. 5 1.2.3 Functions ............................................... 6 1.2.4 Statements............................................... 7 1.2.5 Truth Value Testing ......................................... 8 1.3 Advanced Data Structures .......................................... 10 1.3.1 Tuples................................................. 10 1.3.2 Sets .................................................. 11 1.3.3 Dictionaries (also known as hash maps) .............................. 11 1.3.4 Casting and a Recap of Data Types................................. 12 1.4 String Operators and Regular Expressions ................................. 13 1.4.1 Regular Expression Syntax...................................... 14 1.4.2 Regular Expression Methods..................................... 16 1.4.3 Grouping RE's ............................................ 18 1.4.4 Assertions: Non-Capturing Groups................................. 19 1.4.5 Portability of REs (REs in Stata).................................. 20 1.5 Working with the Operating System.................................... -
Informal Data Transformation Considered Harmful
Informal Data Transformation Considered Harmful Eric Daimler, Ryan Wisnesky Conexus AI out the enterprise, so that data and programs that depend on that data need not constantly be re-validated for ev- ery particular use. Computer scientists have been develop- ing techniques for preserving data integrity during trans- formation since the 1970s (Doan, Halevy, and Ives 2012); however, we agree with the authors of (Breiner, Subrah- manian, and Jones 2018) and others that these techniques are insufficient for the practice of AI and modern IT sys- tems integration and we describe a modern mathematical approach based on category theory (Barr and Wells 1990; Awodey 2010), and the categorical query language CQL2, that is sufficient for today’s needs and also subsumes and unifies previous approaches. 1.1 Outline To help motivate our approach, we next briefly summarize an application of CQL to a data science project undertaken jointly with the Chemical Engineering department of Stan- ford University (Brown, Spivak, and Wisnesky 2019). Then, in Section 2 we review data integrity and in Section 3 we re- view category theory. Finally, we describe the mathematics of our approach in Section 4, and conclude in Section 5. We present no new results, instead citing a line of work summa- rized in (Schultz, Spivak, and Wisnesky 2017). Image used under a creative commons license; original 1.2 Motivating Case Study available at http://xkcd.com/1838/. In scientific practice, computer simulation is now a third pri- mary tool, alongside theory and experiment. Within -
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Annex 2: List of tested and analyzed data sharing tools (non-exhaustive) Below are the specifications of the tools surveyed, as to February 2015, with some updates from April 2016. The tools selected in the context of EU BON are available in the main text of the publication and are described in more details. This list is also available on the EU BON Helpdesk website, where it will be regularly updated as needed. Additional lists are available through the GBIF resources page, the DataONE software tools catalogue, the BioVel BiodiversityCatalogue and the BDTracker A.1 GBIF Integrated Publishing Toolkit (IPT) Main usage, purpose, selected examples The Integrated Publishing Toolkit is a free open source software tool written in Java which is used to publish and share biodiversity data sets and metadata through the GBIF network. Designed for interoperability, it enables the publishing of content in databases or text files using open standards, namely, the Darwin Core and the Ecological Metadata Language. It also provides a 'one-click' service to convert data set metadata into a draft data paper manuscript for submission to a peer-reviewed journal. Currently, the IPT supports three core types of data: checklists, occurrence datasets and sample based data (plus datasets at metadata level only). The IPT is a community-driven tool. Core development happens at the GBIF Secretariat but the coding, documentation, and internationalization are a community effort. New versions incorporate the feedback from the people who actually use the IPT. In this way, users can help get the features they want by becoming involved. The user interface of the IPT has so far been translated into six languages: English, French, Spanish, Traditional Chinese, Brazilian Portuguese, Japanese (Robertson et al, 2014). -
Automating the Capture of Data Transformations from Statistical Scripts in Data Documentation Jie Song George Alter H
C2Metadata: Automating the Capture of Data Transformations from Statistical Scripts in Data Documentation Jie Song George Alter H. V. Jagadish University of Michigan University of Michigan University of Michigan Ann Arbor, Michigan Ann Arbor, Michigan Ann Arbor, Michigan [email protected] [email protected] [email protected] ABSTRACT CCS CONCEPTS Datasets are often derived by manipulating raw data with • Information systems → Data provenance; Extraction, statistical software packages. The derivation of a dataset transformation and loading. must be recorded in terms of both the raw input and the ma- nipulations applied to it. Statistics packages typically provide KEYWORDS limited help in documenting provenance for the resulting de- data transformation, data documentation, data provenance rived data. At best, the operations performed by the statistical ACM Reference Format: package are described in a script. Disparate representations Jie Song, George Alter, and H. V. Jagadish. 2019. C2Metadata: Au- make these scripts hard to understand for users. To address tomating the Capture of Data Transformations from Statistical these challenges, we created Continuous Capture of Meta- Scripts in Data Documentation. In 2019 International Conference data (C2Metadata), a system to capture data transformations on Management of Data (SIGMOD ’19), June 30-July 5, 2019, Am- in scripts for statistical packages and represent it as metadata sterdam, Netherlands. ACM, New York, NY, USA, 4 pages. https: in a standard format that is easy to understand. We do so by //doi.org/10.1145/3299869.3320241 devising a Structured Data Transformation Algebra (SDTA), which uses a small set of algebraic operators to express a 1 INTRODUCTION large fraction of data manipulation performed in practice. -
What Is a Data Warehouse?
What is a Data Warehouse? By Susan L. Miertschin “A data warehouse is a subject oriented, integrated, time variant, nonvolatile, collection of data in support of management's decision making process.” https: //www. bus iness.auc. dk/oe kostyr /file /What_ is_a_ Data_ Ware house.pdf 2 What is a Data Warehouse? “A copy of transaction data specifically structured for query and analysis” 3 “Data Warehousing is the coordination, architected, and periodic copying of data from various sources, both inside and outside the enterprise, into an environment optimized for analytical and informational processing” ‐ Alan Simon Data Warehousing for Dummies 4 Business Intelligence (BI) • “…implies thinking abstractly about the organization, reasoning about the business, organizing large quantities of information about the business environment.” p. 6 in Giovinazzo textbook • Purpose of BI is to define and execute a strategy 5 Strategic Thinking • Business strategist – Always looking forward to see how the company can meet the objectives reflected in the mission statement • Successful companies – Do more than just react to the day‐to‐day environment – Understand the past – Are able to predict and adapt to the future 6 Business Intelligence Loop Business Intelligence Figure 1‐1 p. 2 Giovinazzo • Encompasses entire loop shown Business Strategist • Data Storage + ETC = OLAP Data Mining Reports Data Warehouse Data Storage • Data WhWarehouse + Tools (yellow) = Extraction,Transformation, Cleaning DiiDecision Support CRM Accounting Finance HR System 7 The Data Warehouse Decision Support Systems Central Repository Metadata Dependent Data DtData Mar t EtExtrac tion DtData Log Administration Cleansing/Tranformation External Extraction Source Extraction Store Independent Data Mart Operational Environment Figure 1-2 p. -
POLITECNICO DI TORINO Repository ISTITUZIONALE
POLITECNICO DI TORINO Repository ISTITUZIONALE Rethinking Software Network Data Planes in the Era of Microservices Original Rethinking Software Network Data Planes in the Era of Microservices / Miano, Sebastiano. - (2020 Jul 13), pp. 1-175. Availability: This version is available at: 11583/2841176 since: 2020-07-22T19:49:25Z Publisher: Politecnico di Torino Published DOI: Terms of use: Altro tipo di accesso This article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository Publisher copyright (Article begins on next page) 08 October 2021 Doctoral Dissertation Doctoral Program in Computer and Control Enginering (32nd cycle) Rethinking Software Network Data Planes in the Era of Microservices Sebastiano Miano ****** Supervisor Prof. Fulvio Risso Doctoral examination committee Prof. Antonio Barbalace, Referee, University of Edinburgh (UK) Prof. Costin Raiciu, Referee, Universitatea Politehnica Bucuresti (RO) Prof. Giuseppe Bianchi, University of Rome “Tor Vergata” (IT) Prof. Marco Chiesa, KTH Royal Institute of Technology (SE) Prof. Riccardo Sisto, Polytechnic University of Turin (IT) Politecnico di Torino 2020 This thesis is licensed under a Creative Commons License, Attribution - Noncommercial- NoDerivative Works 4.0 International: see www.creativecommons.org. The text may be reproduced for non-commercial purposes, provided that credit is given to the original author. I hereby declare that, the contents and organisation of this dissertation constitute my own original work and does not compromise in any way the rights of third parties, including those relating to the security of personal data. ........................................ Sebastiano Miano Turin, 2020 Summary With the advent of Software Defined Networks (SDN) and Network Functions Virtualization (NFV), software started playing a crucial role in the computer net- work architectures, with the end-hosts representing natural enforcement points for core network functionalities that go beyond simple switching and routing. -
Data Warehousing on AWS
Data Warehousing on AWS March 2016 Amazon Web Services – Data Warehousing on AWS March 2016 © 2016, Amazon Web Services, Inc. or its affiliates. All rights reserved. Notices This document is provided for informational purposes only. It represents AWS’s current product offerings and practices as of the date of issue of this document, which are subject to change without notice. Customers are responsible for making their own independent assessment of the information in this document and any use of AWS’s products or services, each of which is provided “as is” without warranty of any kind, whether express or implied. This document does not create any warranties, representations, contractual commitments, conditions or assurances from AWS, its affiliates, suppliers or licensors. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. Page 2 of 26 Amazon Web Services – Data Warehousing on AWS March 2016 Contents Abstract 4 Introduction 4 Modern Analytics and Data Warehousing Architecture 6 Analytics Architecture 6 Data Warehouse Technology Options 12 Row-Oriented Databases 12 Column-Oriented Databases 13 Massively Parallel Processing Architectures 15 Amazon Redshift Deep Dive 15 Performance 15 Durability and Availability 16 Scalability and Elasticity 16 Interfaces 17 Security 17 Cost Model 18 Ideal Usage Patterns 18 Anti-Patterns 19 Migrating to Amazon Redshift 20 One-Step Migration 20 Two-Step Migration 20 Tools for Database Migration 21 Designing Data Warehousing Workflows 21 Conclusion 24 Further Reading 25 Page 3 of 26 Amazon Web Services – Data Warehousing on AWS March 2016 Abstract Data engineers, data analysts, and developers in enterprises across the globe are looking to migrate data warehousing to the cloud to increase performance and lower costs. -
TSP 5.0 Reference Manual
TSP 5.0 Reference Manual Bronwyn H. Hall and Clint Cummins TSP International 2005 Copyright 2005 by TSP International First edition (Version 4.0) published 1980. TSP is a software product of TSP International. The information in this document is subject to change without notice. TSP International assumes no responsibility for any errors that may appear in this document or in TSP. The software described in this document is protected by copyright. Copying of software for the use of anyone other than the original purchaser is a violation of federal law. Time Series Processor and TSP are trademarks of TSP International. ALL RIGHTS RESERVED Table Of Contents 1. Introduction_______________________________________________1 1. Welcome to the TSP 5.0 Help System _______________________1 2. Introduction to TSP ______________________________________2 3. Examples of TSP Programs _______________________________3 4. Composing Names in TSP_________________________________4 5. Composing Numbers in TSP _______________________________5 6. Composing Text Strings in TSP_____________________________6 7. Composing TSP Commands _______________________________7 8. Composing Algebraic Expressions in TSP ____________________8 9. TSP Functions _________________________________________10 10. Character Set for TSP ___________________________________11 11. Missing Values in TSP Procedures _________________________13 12. LOGIN.TSP file ________________________________________14 2. Command summary_______________________________________15 13. Display Commands _____________________________________15 -
Towards a Fully Automated Extraction and Interpretation of Tabular Data Using Machine Learning
UPTEC F 19050 Examensarbete 30 hp August 2019 Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Per Hedbrant Master Thesis in Engineering Physics Department of Engineering Sciences Uppsala University Sweden Abstract Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Teknisk- naturvetenskaplig fakultet UTH-enheten Motivation A challenge for researchers at CBCS is the ability to efficiently manage the Besöksadress: different data formats that frequently are changed. Significant amount of time is Ångströmlaboratoriet Lägerhyddsvägen 1 spent on manual pre-processing, converting from one format to another. There are Hus 4, Plan 0 currently no solutions that uses pattern recognition to locate and automatically recognise data structures in a spreadsheet. Postadress: Box 536 751 21 Uppsala Problem Definition The desired solution is to build a self-learning Software as-a-Service (SaaS) for Telefon: automated recognition and loading of data stored in arbitrary formats. The aim of 018 – 471 30 03 this study is three-folded: A) Investigate if unsupervised machine learning Telefax: methods can be used to label different types of cells in spreadsheets. B) 018 – 471 30 00 Investigate if a hypothesis-generating algorithm can be used to label different types of cells in spreadsheets. C) Advise on choices of architecture and Hemsida: technologies for the SaaS solution. http://www.teknat.uu.se/student Method A pre-processing framework is built that can read and pre-process any type of spreadsheet into a feature matrix. Different datasets are read and clustered. An investigation on the usefulness of reducing the dimensionality is also done. -
Dataone Operations Documentation Release 2.0.1
DataONE Operations Documentation Release 2.0.1 DataONE Jul 01, 2020 Contents 1 Contents 3 1.1 Project Resources............................................3 1.2 Member Nodes Resources........................................ 23 1.3 Coordinating Nodes........................................... 61 1.4 Development............................................... 77 1.5 DataONE Environments......................................... 81 1.6 Glossary................................................. 82 2 Indices and Tables 87 Index 89 i ii DataONE Operations Documentation, Release 2.0.1 Operations documentation for the DataONE project and deployed infrastructure. Contents 1 DataONE Operations Documentation, Release 2.0.1 2 Contents CHAPTER 1 Contents 1.1 Project Resources 1.1.1 Communications Real Time DataONE uses Slack for messaging between project participants. Anyone may join the DataONE Slack organization by requesting an invite at: https://slack.dataone.org/ Video Conferencing DatONE uses Zoom for video conferencing within the project and for webinars. Project participants needing Zoom for project purposes should contact support @ dataone.org. Discussion Lists DataONE uses Google Groups for email list management. The DataONE.org Google Groups are managed as a dataone.org subdomain of nceas.ucsb.edu and includes the following lists: 3 DataONE Operations Documentation, Release 2.0.1 Address Description [email protected] For contacting administrators of the DataONE infrastructure. [email protected] A voluntary participation mailing list for disseminating information to the community with interest in DataONE. [email protected] Internal communications for the DataONE core development team. [email protected] Member Node forum, for discussion related to Member Nodes and dissem- inating information to DataONE Member Node operators. [email protected] General development related discussion. [email protected] Private list for the DataONE User Group Committee members. -
Data Workflows with Stata and Python
In [1]: from IPython.display import IFrame import ipynb_style from epstata import Stpy import pandas as pd from itertools import combinations from importlib import reload In [2]: reload(ipynb_style) ipynb_style.clean() #ipynb_style.presentation() #ipynb_style.pres2() Out[2]: Data Workflows in Stata and Python (http://www.stata.com) (https://www.python.org) Data Workflows in Stata and Python Dejan Pavlic, Education Policy Research Initiative, University of Ottawa Stephen Childs (presenter), Office of Institutional Analysis, University of Calgary (http://ucalgary.ca) (http://uottawa.ca/en) (http://socialsciences.uottawa.ca/irpe- epri/eng/index.asp) Introduction About this talk Objectives know what Python is and what advantages it has know how Python can work with Stata Please save questions for the end. Or feel free to ask me today or after the conference. Outline Introduction Overall Motivation About Python Building Blocks Running Stata from Python Pandas Python language features Workflows ETL/Data Cleaning Stata code generation Processing Stata output About Me Started using Stata in grad school (2006). Using Python for about 3 years. Post-Secondary Education sector University of Calgary - Institutional Analysis (https://oia.ucalgary.ca/Contact) Education Policy Research Initiative (http://socialsciences.uottawa.ca/irpe-epri/eng/index.asp) - University of Ottawa (a Stata shop) Motivation Python is becoming very popular in the data world. Python skills are widely applicable. Python is powerful and flexible and will help you get more done, -
Panel Discussion Presentation: "Dataone: Facilitating Escience Through Collaboration"
University of Massachusetts Medical School eScholarship@UMMS University of Massachusetts and New England Area Librarian e-Science Symposium 2011 e-Science Symposium Apr 6th, 12:00 AM - 11:15 AM Panel Discussion presentation: "DataONE: Facilitating eScience through Collaboration" Suzie Allard University of Tennessee Follow this and additional works at: https://escholarship.umassmed.edu/escience_symposium Part of the Library and Information Science Commons This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License. Repository Citation Allard, S. (2011). Panel Discussion presentation: "DataONE: Facilitating eScience through Collaboration". University of Massachusetts and New England Area Librarian e-Science Symposium. https://doi.org/ 10.13028/9dck-z235. Retrieved from https://escholarship.umassmed.edu/escience_symposium/2011/ program/9 Creative Commons License This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License. This material is brought to you by eScholarship@UMMS. It has been accepted for inclusion in University of Massachusetts and New England Area Librarian e-Science Symposium by an authorized administrator of eScholarship@UMMS. For more information, please contact [email protected]. DataONE: Facilitating eScience through Collaboration Suzie Allard, Ph.D. University of Tennessee Presented at the Third University of Massachusetts & New England Area Librarian eScience Symposium 6 April 2011 A prominent feature of e-Science is the generation of immense data sets that can be rapidly disseminated to other researchers via the internet. -- Donna Kafel, MLIS Project Coordinator for the New England e-Science Portal for Librarians “e-Science and Its Relevance for Research Libraries” Environmental Science Challenges 3 Vision: Provide universal access to data about life on earth and the environment that sustains it.