by Sławek Staworko, (joint work with Peter Buneman), University of Edinburgh, presented at the 3rd PRELIDA Consolidation and Dissemination Workshop, Riva, Italy, October, 17, 2014. More information about the workshop at: prelida.eu
This document discusses best practices for Python package development including setup.py, testing, linting, documentation and continuous integration. It provides examples of using Pipenv, pytest, tox, coverage and Travis CI/Appveyor for testing and CI. Standard library modules like functools, itertools, pathlib and abc are demonstrated. API design, documentation, versioning and community guidelines are also covered.
Python offers lists as a useful tool for storing and iterating over sequences of data. Lists can store multiple values unlike variables which store single values. Lists have attributes like name, values, length calculated by len(list), and indexes which start from 0 to len(list)-1. Functions in Python perform specific tasks and help organize programs into modular chunks. Variables inside functions have local scope and lifetime only during function execution, being destroyed when the function returns.
Python is useful for academics working in various fields like physics, economics, social systems, and biology. It has many advantages over other languages like MATLAB, including having a large number of packages, being easily extensible, and its interactive nature. However, some academics care more about factors like speed, compatibility with legacy code, and reproducibility. Improving Python's performance relative to MATLAB, simplifying integration with compiled code, and enhancing default plotting options in Matplotlib could increase its adoption in academic settings.
Metadata Provenance Tutorial Part 2: Interoperable Metadata ProvenanceMagnus Pfeffer
Tutorial held at the Semantic Web in Libraries conference in Hamburg, Germany, at November 25th 2013. The tutorial was held together with Kai Eckert, who did Part 1.
Abstract:
When metadata is distributed, combined, and enriched as Linked Data, the tracking of its provenance becomes a hard issue. Using data encumbered with licenses that require attribution of authorship may eventually become impracticable as more and more data sets are aggregated - one of the main motivations for the call to open data under permissive licenses like CC0. Nonetheless, there are important scenarios where keeping track of provenance information becomes a necessity. A typical example is the enrichment of existing data with automatically obtained data, for instance as a result of automatic indexing. Ideally, the origins, conditions, rules and other means of production of every statement are known and can be used to put it into the right context.
Part 1 - Metadata Provenance in RDF: In RDF, the mere representation of provenance - i.e., statements about statements - is challenging. We explore the possibilities, from the unloved reification and other proposed alternative Linked Data practices through to named graphs and recent developments regarding the upcoming next version of RDF.
Part 2 - Interoperable Metadata Provenance: As with metadata itself, common vocabularies and data models are needed to express basic provenance information in an interoperable fashion. We investigate the PROV model that is currently developed by the W3C Provenance Working Group and compare it to Dublin Core as a representative of a flat, descriptive metadata schema.
We actively encourage participants to present their own use cases and open challenges at this workshop. Please contact the organizers for details.
Prior experience: The workshop is intended for participants who have mastered the basics of linked data and want to delve into expressing provenance. Beside a basic understanding of RDF, the linked data principles and the use of ontologies (like Dublin Core or Bibo) to express bibliographic metadata no specialised knowledge is required.
This presentation about Python Interview Questions will help you crack your next Python interview with ease. The video includes interview questions on Numbers, lists, tuples, arrays, functions, regular expressions, strings, and files. We also look into concepts such as multithreading, deep copy, and shallow copy, pickling and unpickling. This video also covers Python libraries such as matplotlib, pandas, numpy,scikit and the programming paradigms followed by Python. It also covers Python library interview questions, libraries such as matplotlib, pandas, numpy and scikit. This video is ideal for both beginners as well as experienced professionals who are appearing for Python programming job interviews. Learn what are the most important Python interview questions and answers and know what will set you apart in the interview process.
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S. students to programming and computer science. This course will give you hands-on development experience and prepare you for a career as a professional Python programmer.
What is this course about?
The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python from the basics and go on to master web development & game development in Python. Gain hands on experience creating a flappy bird game clone & website functionalities in Python.
What are the course objectives?
By the end of this online Python training course, you will be able to:
1. Internalize the concepts & constructs of Python
2. Learn to create your own Python programs
3. Master Python Django & advanced web development in Python
4. Master PyGame & game development in Python
5. Create a flappy bird game clone
The Python training course is recommended for:
1. Any aspiring programmer can take up this bundle to master Python
2. Any aspiring web developer or game developer can take up this bundle to meet their training needs
Learn more at https://www.simplilearn.com/mobile-and-software-development/python-development-training
mooc_presentataion_mayankmanral on the subject puthongarvitbisht27
Python prr ppt hai so plzz nsjsiskbdbdjdjdkdjdndndndndmmdmdndndndndndndndnndndndnndndndmememdmsjakksmwbshskammanahaialemneeuislmeheuiekememejkeksmwjejekekmemenejekmemrme
This document discusses best practices for Python package development including setup.py, testing, linting, documentation and continuous integration. It provides examples of using Pipenv, pytest, tox, coverage and Travis CI/Appveyor for testing and CI. Standard library modules like functools, itertools, pathlib and abc are demonstrated. API design, documentation, versioning and community guidelines are also covered.
Python offers lists as a useful tool for storing and iterating over sequences of data. Lists can store multiple values unlike variables which store single values. Lists have attributes like name, values, length calculated by len(list), and indexes which start from 0 to len(list)-1. Functions in Python perform specific tasks and help organize programs into modular chunks. Variables inside functions have local scope and lifetime only during function execution, being destroyed when the function returns.
Python is useful for academics working in various fields like physics, economics, social systems, and biology. It has many advantages over other languages like MATLAB, including having a large number of packages, being easily extensible, and its interactive nature. However, some academics care more about factors like speed, compatibility with legacy code, and reproducibility. Improving Python's performance relative to MATLAB, simplifying integration with compiled code, and enhancing default plotting options in Matplotlib could increase its adoption in academic settings.
Metadata Provenance Tutorial Part 2: Interoperable Metadata ProvenanceMagnus Pfeffer
Tutorial held at the Semantic Web in Libraries conference in Hamburg, Germany, at November 25th 2013. The tutorial was held together with Kai Eckert, who did Part 1.
Abstract:
When metadata is distributed, combined, and enriched as Linked Data, the tracking of its provenance becomes a hard issue. Using data encumbered with licenses that require attribution of authorship may eventually become impracticable as more and more data sets are aggregated - one of the main motivations for the call to open data under permissive licenses like CC0. Nonetheless, there are important scenarios where keeping track of provenance information becomes a necessity. A typical example is the enrichment of existing data with automatically obtained data, for instance as a result of automatic indexing. Ideally, the origins, conditions, rules and other means of production of every statement are known and can be used to put it into the right context.
Part 1 - Metadata Provenance in RDF: In RDF, the mere representation of provenance - i.e., statements about statements - is challenging. We explore the possibilities, from the unloved reification and other proposed alternative Linked Data practices through to named graphs and recent developments regarding the upcoming next version of RDF.
Part 2 - Interoperable Metadata Provenance: As with metadata itself, common vocabularies and data models are needed to express basic provenance information in an interoperable fashion. We investigate the PROV model that is currently developed by the W3C Provenance Working Group and compare it to Dublin Core as a representative of a flat, descriptive metadata schema.
We actively encourage participants to present their own use cases and open challenges at this workshop. Please contact the organizers for details.
Prior experience: The workshop is intended for participants who have mastered the basics of linked data and want to delve into expressing provenance. Beside a basic understanding of RDF, the linked data principles and the use of ontologies (like Dublin Core or Bibo) to express bibliographic metadata no specialised knowledge is required.
This presentation about Python Interview Questions will help you crack your next Python interview with ease. The video includes interview questions on Numbers, lists, tuples, arrays, functions, regular expressions, strings, and files. We also look into concepts such as multithreading, deep copy, and shallow copy, pickling and unpickling. This video also covers Python libraries such as matplotlib, pandas, numpy,scikit and the programming paradigms followed by Python. It also covers Python library interview questions, libraries such as matplotlib, pandas, numpy and scikit. This video is ideal for both beginners as well as experienced professionals who are appearing for Python programming job interviews. Learn what are the most important Python interview questions and answers and know what will set you apart in the interview process.
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S. students to programming and computer science. This course will give you hands-on development experience and prepare you for a career as a professional Python programmer.
What is this course about?
The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python from the basics and go on to master web development & game development in Python. Gain hands on experience creating a flappy bird game clone & website functionalities in Python.
What are the course objectives?
By the end of this online Python training course, you will be able to:
1. Internalize the concepts & constructs of Python
2. Learn to create your own Python programs
3. Master Python Django & advanced web development in Python
4. Master PyGame & game development in Python
5. Create a flappy bird game clone
The Python training course is recommended for:
1. Any aspiring programmer can take up this bundle to master Python
2. Any aspiring web developer or game developer can take up this bundle to meet their training needs
Learn more at https://www.simplilearn.com/mobile-and-software-development/python-development-training
mooc_presentataion_mayankmanral on the subject puthongarvitbisht27
Python prr ppt hai so plzz nsjsiskbdbdjdjdkdjdndndndndmmdmdndndndndndndndnndndndnndndndmememdmsjakksmwbshskammanahaialemneeuislmeheuiekememejkeksmwjejekekmemenejekmemrme
Getting started in Python presentation by Laban KGDSCKYAMBOGO
Python Overview and getting started in Python Language. It includes on how to install, run it and carrying out some simple python codes in different environments(IDLEs)
This document provides an overview of learning Python in three hours. It covers Python's history, installing and running Python, basic data types like integers, floats and strings. It also discusses sequence types like lists, tuples and strings, and how lists are mutable while tuples are immutable. The document includes examples of basic syntax like assignment, conditionals, functions and modules. It provides guidance on naming conventions and discusses the Python interpreter, editors and development environments.
This document provides an overview and introduction to Python programming. It covers setting up Python, background on the language, basic syntax like printing, variables, operators, control structures, functions, and data structures. It encourages participation and practicing the concepts by following along. The goal is to teach the fundamentals of Python in an interactive class format.
The document discusses techniques for optimizing memory usage through bit packing and value type polymorphism. It describes:
1. Bit packing techniques like storing multiple values in a single integer using bitwise operations to reduce memory usage. This includes examples of packing booleans and enums.
2. Using a "tagged union" approach to represent different value types polymorphically by storing a type tag and common data in a single value.
3. The concept of "value type polymorphism" where subtypes all fit within a size budget by using a tag to differentiate them while presenting a common API. This allows efficiently representing types in a compiler.
This document provides an overview of the Python programming language. It discusses Python's history and key features such as being an interpreted, object-oriented, and functional language. The document also covers installing Python, running Python scripts and programs, basic datatypes like integers and strings, sequence types like lists and tuples, and other basic concepts like functions, variables, and flow control.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It explains key concepts like variable assignment, conditional statements, functions, modules and packages. The document also compares mutable lists and immutable tuples, and covers common list operations.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It explains key concepts like variable assignment, basic operations, slicing of sequences, and how lists are mutable but tuples are immutable. The document is intended to teach Python fundamentals in about three hours.
This document provides an overview of the Python programming language. It discusses Python's history and key features such as being object-oriented, scalable, and functional from the beginning. It also covers installing Python, running Python programs, basic datatypes like integers and strings, sequence types like lists and tuples, and other basic concepts like functions, comments, and whitespace.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It explains key concepts like variable assignment, basic operations, slicing of sequences, and how lists are mutable but tuples are immutable. The document is intended to teach Python fundamentals in about three hours.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It explains key concepts like variable assignment, basic operations, slicing of sequences, and how lists are mutable but tuples are immutable. The document is intended to teach Python fundamentals in about three hours.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It explains key concepts like variable assignment, conditional statements, functions, modules and packages. The document also compares mutable lists and immutable tuples, and covers common list operations.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It explains key concepts like variable assignment, conditional statements, functions, modules and packages. The document also compares mutable lists and immutable tuples, and covers common list operations.
This document provides an overview of the Python programming language. It discusses Python's history and key features such as being an interpreted, object-oriented, and functional language. The document also covers installing Python, running Python scripts and programs, basic datatypes like integers and strings, sequence types like lists and tuples, and other basic concepts like functions, variables, and flow control.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It explains key concepts like variable assignment, basic operations, slicing of sequences, and how lists are mutable but tuples are immutable. The document is intended to teach Python fundamentals in about three hours.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, and lists. It also covers Python concepts like functions, modules, conditionals, loops, classes and objects.
The document provides an overview of the Python programming language. It discusses Python's history and key features such as being scalable, object oriented, and functional. It also covers installing Python, running Python programs, basic datatypes like integers and strings, sequence types like lists and tuples, and other basic concepts like functions, variables, and control flow.
This document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It also covers topics like functions, modules, files, and classes in Python.
Python is a general-purpose programming language that is highly readable. It uses English keywords and has fewer syntactical constructions than other languages. Python supports object-oriented, interactive, and procedural programming. It has various data types like numbers, strings, lists, tuples and dictionaries. Python uses constructs like if/else, for loops, functions and classes to control program flow and structure code.
This document discusses steps towards a data value chain, including big data, public open data, and linked (open) data. It provides definitions and examples for each topic. For big data, it discusses the large volumes of data being created and challenges in working with such data. For public open data, it outlines principles like completeness and ease of access. It also shows examples of apps using open government data. For linked open data, it discusses moving from a web of documents to a web of interconnected data through using URIs and typed links. It also shows the growth of the linked open data cloud over time.
Preserving linked data: sustainability and organizational infrastructurePRELIDA Project
by Mariella Guercio (Sapienza Università di Roma), presented at the 3rd PRELIDA Consolidation and Dissemination Workshop, Riva, Italy, October, 17, 2014. More information about the workshop at: prelida.eu
Getting started in Python presentation by Laban KGDSCKYAMBOGO
Python Overview and getting started in Python Language. It includes on how to install, run it and carrying out some simple python codes in different environments(IDLEs)
This document provides an overview of learning Python in three hours. It covers Python's history, installing and running Python, basic data types like integers, floats and strings. It also discusses sequence types like lists, tuples and strings, and how lists are mutable while tuples are immutable. The document includes examples of basic syntax like assignment, conditionals, functions and modules. It provides guidance on naming conventions and discusses the Python interpreter, editors and development environments.
This document provides an overview and introduction to Python programming. It covers setting up Python, background on the language, basic syntax like printing, variables, operators, control structures, functions, and data structures. It encourages participation and practicing the concepts by following along. The goal is to teach the fundamentals of Python in an interactive class format.
The document discusses techniques for optimizing memory usage through bit packing and value type polymorphism. It describes:
1. Bit packing techniques like storing multiple values in a single integer using bitwise operations to reduce memory usage. This includes examples of packing booleans and enums.
2. Using a "tagged union" approach to represent different value types polymorphically by storing a type tag and common data in a single value.
3. The concept of "value type polymorphism" where subtypes all fit within a size budget by using a tag to differentiate them while presenting a common API. This allows efficiently representing types in a compiler.
This document provides an overview of the Python programming language. It discusses Python's history and key features such as being an interpreted, object-oriented, and functional language. The document also covers installing Python, running Python scripts and programs, basic datatypes like integers and strings, sequence types like lists and tuples, and other basic concepts like functions, variables, and flow control.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It explains key concepts like variable assignment, conditional statements, functions, modules and packages. The document also compares mutable lists and immutable tuples, and covers common list operations.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It explains key concepts like variable assignment, basic operations, slicing of sequences, and how lists are mutable but tuples are immutable. The document is intended to teach Python fundamentals in about three hours.
This document provides an overview of the Python programming language. It discusses Python's history and key features such as being object-oriented, scalable, and functional from the beginning. It also covers installing Python, running Python programs, basic datatypes like integers and strings, sequence types like lists and tuples, and other basic concepts like functions, comments, and whitespace.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It explains key concepts like variable assignment, basic operations, slicing of sequences, and how lists are mutable but tuples are immutable. The document is intended to teach Python fundamentals in about three hours.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It explains key concepts like variable assignment, basic operations, slicing of sequences, and how lists are mutable but tuples are immutable. The document is intended to teach Python fundamentals in about three hours.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It explains key concepts like variable assignment, conditional statements, functions, modules and packages. The document also compares mutable lists and immutable tuples, and covers common list operations.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It explains key concepts like variable assignment, conditional statements, functions, modules and packages. The document also compares mutable lists and immutable tuples, and covers common list operations.
This document provides an overview of the Python programming language. It discusses Python's history and key features such as being an interpreted, object-oriented, and functional language. The document also covers installing Python, running Python scripts and programs, basic datatypes like integers and strings, sequence types like lists and tuples, and other basic concepts like functions, variables, and flow control.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It explains key concepts like variable assignment, basic operations, slicing of sequences, and how lists are mutable but tuples are immutable. The document is intended to teach Python fundamentals in about three hours.
The document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, and lists. It also covers Python concepts like functions, modules, conditionals, loops, classes and objects.
The document provides an overview of the Python programming language. It discusses Python's history and key features such as being scalable, object oriented, and functional. It also covers installing Python, running Python programs, basic datatypes like integers and strings, sequence types like lists and tuples, and other basic concepts like functions, variables, and control flow.
This document provides an overview of the Python programming language. It discusses Python's history, how to install and run Python, basic data types like integers, floats, strings, lists and tuples. It also covers topics like functions, modules, files, and classes in Python.
Python is a general-purpose programming language that is highly readable. It uses English keywords and has fewer syntactical constructions than other languages. Python supports object-oriented, interactive, and procedural programming. It has various data types like numbers, strings, lists, tuples and dictionaries. Python uses constructs like if/else, for loops, functions and classes to control program flow and structure code.
This document discusses steps towards a data value chain, including big data, public open data, and linked (open) data. It provides definitions and examples for each topic. For big data, it discusses the large volumes of data being created and challenges in working with such data. For public open data, it outlines principles like completeness and ease of access. It also shows examples of apps using open government data. For linked open data, it discusses moving from a web of documents to a web of interconnected data through using URIs and typed links. It also shows the growth of the linked open data cloud over time.
Preserving linked data: sustainability and organizational infrastructurePRELIDA Project
by Mariella Guercio (Sapienza Università di Roma), presented at the 3rd PRELIDA Consolidation and Dissemination Workshop, Riva, Italy, October, 17, 2014. More information about the workshop at: prelida.eu
Organizational and Economic Issues in Linked Data PreservationPRELIDA Project
by Jose Maria Garcia (UIBK/STI Innsbruck), presented at the 3rd PRELIDA Consolidation and Dissemination Workshop, Riva, Italy, October, 17, 2014. More information about the workshop at: prelida.eu
CEDAR: From Fragment to Fabric - Dutch Census Data in a Web of Global Cultura...PRELIDA Project
by Ashkan Ashkpour, Albert Meroño-Peñuela, Christophe Gueret (http://cedar-project.nl/), presented at the 3rd PRELIDA Consolidation and Dissemination Workshop, Riva, Italy, October, 17, 2014. More information about the workshop at: prelida.eu
Privacy‐Aware Preservation: Challenges from the Perspective of a Linked Data ...PRELIDA Project
The document discusses challenges for preserving privacy in linked data from the perspective of a linked data privacy auditing framework. It describes work on ontologies (L2TAP+SCIP) for publishing privacy log events as linked data to enable log integration and encoding of privacy-related events. The framework allows expressing privacy policies and preferences, and performing query-based auditing of linked data uses and flows to check for privacy violations. Examples show how the framework could be applied to an medical research study dataset to log access requests and ensure privacy policy compliance.
The document summarizes the goals and status of the Media Ecology Project (MEP). The MEP aims to 1) realize a sustainability project around cultural memory and media history using linked data, 2) develop networked scholarship around online archival content, and 3) support the work of archives in relation to public memory. It is currently in beta development, working simultaneously on building a research environment, engaging learning models, recruiting partners, and developing tools. Pilot projects include working with the Library of Congress paper print collection and multi-archival projects on newsreels and broadcast news.
HIBERLINK: Reference Rot and Linked Data: Threat and RemedyPRELIDA Project
This document discusses reference rot in linked data and proposes remedies. It defines reference rot as occurring when links to web resources no longer point to the original content. Empirical evidence from analyses of journal articles and e-theses shows that over one third of references experience rot. Proposed remedies include a Hiberlink plug-in to enable proactive archiving, augmenting links with temporal context using the Missing Link approach, and a HiberActive system for repositories to actively archive references. The goal is to increase the chances of accessing referenced content over time by embedding archiving solutions into existing authoring and publishing workflows.
CEDAR & PRELIDA Preservation of Linked Socio-Historical DataPRELIDA Project
by Albert Meroño, presented at the 3rd PRELIDA Consolidation and Dissemination Workshop, Riva, Italy, October, 17, 2014. More information about the workshop at: prelida.eu
DIACHRON Preservation: Evolution Management for PreservationPRELIDA Project
by Giorgos Flouris (FORTH), presented at the 3rd PRELIDA Consolidation and Dissemination Workshop, Riva, Italy, October, 17, 2014. More information about the workshop at: prelida.eu
by Yannis Stavrakas (“Athena” Research Center
), presented at the 3rd PRELIDA Consolidation and Dissemination Workshop, Riva, Italy, October, 17, 2014. More information about the workshop at: prelida.eu
by Sotiris Batsakis & Grigoris Antoniou, presented at the 3rd PRELIDA Consolidation and Dissemination Workshop, Riva, Italy, October, 17, 2014. More information about the workshop at: prelida.eu
D.3.1: State of the Art - Linked Data and Digital PreservationPRELIDA Project
by D. Giaretta (APARSEN), presented at the 3rd PRELIDA Consolidation and Dissemination Workshop, Riva, Italy, October, 17, 2014. More information about the workshop at: prelida.eu
Introduction to PRELIDA Consolidation and Dissemination WorkshopPRELIDA Project
by Carlo Meghini (ISTI CNR, Pisa), presented at the 3rd PRELIDA Consolidation and Dissemination Workshop, Riva, Italy, October, 17, 2014. More information about the workshop at: prelida.eu
D3.1 State of the art assessment on Linked Data and Digital PreservationPRELIDA Project
The presentation was given by René van Horik from Data Archiving & Networked Services, The Netherlands, at the PRELIDA Midterm Workshop in Catania, April 2014.
The document discusses the PRELIDA project which aims to identify differences between linked data and digital preservation communities and analyze gaps between the two. The objectives are to collect use cases of long-term preservation of linked data and identify challenges of applying existing preservation approaches to linked data. Issues discussed include differences in preservation requirements for linked data versus other data types and whether linked data preservation can be viewed as a special case of web archiving.
Towards long-term preservation of linked data - the PRELIDA projectPRELIDA Project
This document summarizes a presentation about preserving linked data over the long term. It introduces the PRELIDA project, which aims to bridge the digital preservation and linked data communities. The presentation discusses what digital preservation can provide for linked data, such as file format standards, archival storage services, and documentation practices. It also outlines challenges for preserving linked data, like its dynamic and distributed nature. The PRELIDA project seeks to address these challenges through research and bringing the communities together.
PRELIDA is a 24-month FP7 project starting in January 2013 with the objectives of bridging the linked data and digital preservation communities. It aims to make each community aware of the other's work and challenges. The project will collect linked data use cases, create a state of the art on linked data and digital preservation technologies, set up a technology observatory, and identify challenges through workshops. The working group, comprising stakeholders, academia, companies and standardization bodies, will help achieve these objectives by providing input and reviewing results.
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
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
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
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2. Preservation of evolving data
Tom
cat
has
tuna
eats
Tom
cat
has
Apr 1
dies
Tom
dog
has
dog
food eats
Version 1 Version 2 Version 3
…
Archive
• Version retrieval
• Timeline queries
• Storage space efficiency
3. Approaches to data
preservation
• Store all versions
• Store the original databases and log the changes
• Hybrid approach of the above two
• store the initial and every 10th version
• store log changes for the intermediate versions
• Annotation based approach!
• never delete data but annotate its validity with
time intervals
5. What exactly is the input?
Delta = difference between two databases expressed with
two atomic operations: inserting a triple and deleting a triple
Tom
cat
has
tuna
eats
Tom
cat
has
Apr 1
dies
Tom
dog
has
dog
food eats
delete (cat, eats, tuna)
insert (cat, dies, Apr 1)
delete (Tom, has, cat)
insert (Tom, has, dog)
inset (dog, eats, dog food)
delete (cat, dies, Apr 1)
Snapshots
Deltas
Snapshots = complete database instances
6. Challenges in preserving
evolving data with annotations
1. The task is relatively simple if deltas are know:!
• deleting a triple closes its interval!
• adding a triple opens a new interval !
2. It gets complicated when only snapshots are given!
• it boils down to computing deltas!
• main challenge: identify objects that are the same across
versions of the database
Entity resolution problem!
which data object represent the same entity across different versions!
well-studied database problem in various different settings
(from duplicate elimination to record matching)
7. Entity resolution and RDF
URI (Uniform resource identifier)
URIs are supposed to make things easy but…
• RDF has also blank nodes
• URIs don’t exactly solve the problem in the
context of evolving/merged ontologies…
Two different RDF nodes need not represent different objects
8. Blank nodes
• LOD initiative frowns upon them
• Blank nodes are commonplace (and misused?)
Tom
cat
has
Peter
believes
Tom cathas
Peter believes
_bsubject
pred
object
_b
2.4 -0.4
Reification Complex number
9. Blank nodes (cont.)
1. Reification (Peter believes that Tom has a cat)
2. Data structures (complex types)
3. Anonymization (Tom has a pet)
Assumptions on reasonable use of blank nodes:!
1. Represent concrete objects !
2. The objects can be identified from the context
10. Deblanking
_b1
7 end
_b2
3
_b3
5
LISP-style encoding
list of numbers [5,3,7]
head
head
head
tail
tail
tail
#(7,end)
7 end
_b2
3
_b3
5
head
head
head
tail
tail
tail
#(7,end)
7 end
#(3,7,end)
3
_b3
5
head
head
head
tail
tail
tail
#(7,end)
7 end
#(3,7,end)
3
#(5,3,7,end)
5
head
head
head
tail
tail
tail
Assumption: graph has no cycles consisting of blanks only
Assumption: identity of a blank node is determined by its contents
11. Experiements
• 10 versions of Experimental Factor Ontology (EFO)
data expressed in OWL
• 200k triples in the 1st version, 290k in the last
• On average 20k blank nodes in each version
• 920k triples overall (blank nodes are independent)
• many triples do not last more than 1 version
13. Improving space efficiency
Peter
Edinburgh +44 712 4567
phone [1–10]lives [1–10]
Peter
Edinburgh +44 712 4567
phonelives
[1–10]Lift common intervals to subject
dog
has [1–5]
dog
has [1–5]
• Intervals moved from all but 33.7k triples (of total 285k)
• Number of subjects with histories is 34.3k
• Total number of intervals is reduced from 285k to 60k
• The size of the index reduced by almost 80%
15. Conclusions
• Annotation offers an attractive way of representing
an evolving RDF dataset (need for nested RDF?)
• Evolution of data may require more complex atomic
operations. For instance, vocabulary evolution:
adding, splitting, merging classes. (can
bisimulation help here?)