What is it about?
-How to easily describe your solutions using a pre-defined template compliant with ADMS-AP;
-How to automatically transform your solutions’ descriptions into machine readable language, i.e., RDF;
-How to get your interoperability solutions ready to be shared on Joinup.
In this lecture we analyze document oriented databases. In particular we consider why there are the first approach to nosql and what are the main features. Then, we analyze as example MongoDB. We consider the data model, CRUD operations, write concerns, scaling (replication and sharding).
Finally we presents other document oriented database and when to use or not document oriented databases.
Presentation slides of the workshop on "Introduction to Pig" at Fifth Elephant, Bangalore, India on 26th July, 2012.
http://fifthelephant.in/2012/workshop-pig
What is it about?
-How to easily describe your solutions using a pre-defined template compliant with ADMS-AP;
-How to automatically transform your solutions’ descriptions into machine readable language, i.e., RDF;
-How to get your interoperability solutions ready to be shared on Joinup.
In this lecture we analyze document oriented databases. In particular we consider why there are the first approach to nosql and what are the main features. Then, we analyze as example MongoDB. We consider the data model, CRUD operations, write concerns, scaling (replication and sharding).
Finally we presents other document oriented database and when to use or not document oriented databases.
Presentation slides of the workshop on "Introduction to Pig" at Fifth Elephant, Bangalore, India on 26th July, 2012.
http://fifthelephant.in/2012/workshop-pig
This presentation about HBase will help you understand what is HBase, what are the applications of HBase, how is HBase is different from RDBMS, what is HBase Storage, what are the architectural components of HBase and at the end, we will also look at some of the HBase commands using a demo. HBase is an essential part of the Hadoop ecosystem. It is a column-oriented database management system derived from Google’s NoSQL database Bigtable that runs on top of HDFS. After watching this video, you will know how to store and process large datasets using HBase. Now, let us get started and understand HBase and what it is used for.
Below topics are explained in this HBase presentation:
1. What is HBase?
2. HBase Use Case
3. Applications of HBase
4. HBase vs RDBMS
5. HBase Storage
6. HBase Architectural Components
What is this Big Data Hadoop training course about?
Simplilearn’s Big Data Hadoop training course lets you master the concepts of the Hadoop framework and prepares you for Cloudera’s CCA175 Big data certification. The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Achievers IT React JS Certification Course in Bangalore. ... UI Development training in Marathahalli ; also benefits as UI training online and UI Developer
Google Professional-Data-Engineer Cheat Sheet PDF ~ Brain DumpsRoberthar
Get more information visits it : https://www.dumpskey.com/google/professional-data-engineer-braindumps
Dumpskey.com study materials are highly customised as per the syllabus of Google for Professional-Data-Engineer Certification exam. Getting Google Cloud Data Engineer Professional exam is possible only when you choose to opt for good study materials. The Dumpskey exam questions are well structured and you get them in easy to use formats, Practice soft and PDF file.
The columnar roadmap: Apache Parquet and Apache ArrowDataWorks Summit
The Hadoop ecosystem has standardized on columnar formats—Apache Parquet for on-disk storage and Apache Arrow for in-memory. With this trend, deep integration with columnar formats is a key differentiator for big data technologies. Vertical integration from storage to execution greatly improves the latency of accessing data by pushing projections and filters to the storage layer, reducing time spent in IO reading from disk, as well as CPU time spent decompressing and decoding. Standards like Arrow and Parquet make this integration even more valuable as data can now cross system boundaries without incurring costly translation. Cross-system programming using languages such as Spark, Python, or SQL can becomes as fast as native internal performance.
In this talk we’ll explain how Parquet is improving at the storage level, with metadata and statistics that will facilitate more optimizations in query engines in the future. We’ll detail how the new vectorized reader from Parquet to Arrow enables much faster reads by removing abstractions as well as several future improvements. We will also discuss how standard Arrow-based APIs pave the way to breaking the silos of big data. One example is Arrow-based universal function libraries that can be written in any language (Java, Scala, C++, Python, R, ...) and will be usable in any big data system (Spark, Impala, Presto, Drill). Another is a standard data access API with projection and predicate push downs, which will greatly simplify data access optimizations across the board.
Speaker
Julien Le Dem, Principal Engineer, WeWork
In this lecture we analyze key-values databases. At first we introduce key-value characteristics, advantages and disadvantages.
Then we analyze the major Key-Value data stores and finally we discuss about Dynamo DB.
In particular we consider how Dynamo DB: How is implemented
1. Motivation Background
2. Partitioning: Consistent Hashing
3. High Availability for writes: Vector Clocks
4. Handling temporary failures: Sloppy Quorum
5. Recovering from failures: Merkle Trees
6. Membership and failure detection: Gossip Protocol
This presentation contains the introduction to NOSQL databases, it's types with examples, differentiation with 40 year old relational database management system, it's usage, why and we should use it.
Παρουσίαση για το σεμινάριο “Δεδομένα Βιβλιοθηκών στο μελλοντικό ψηφιακό περιβάλλον - FRBR και Linked Data”
Το σεμινάριο διοργανώθηκε από τις Βιβλιοθήκες του Τμήματος Νομικής του ΕΚΠΑ και του Πανεπιστημίου Πειραιώς στις 18 και 19 Ιουνίου 2012, υπό την επιμέλεια της Ομάδας Βάσεων Δεδομένων και Πληροφοριακών Συστημάτων του Εργαστηρίου Ψηφιακών Βιβλιοθηκών και Ηλεκτρονικής Δημοσίευσης του Τμήματος Αρχειονομίας - Βιβλιοθηκονομίας του Ιονίου Πανεπιστημίου
Διδάσκοντες:
- Μανόλης Πεπονάκης (MLIS)
- Δρ. Μιχάλης Σφακάκης
- Δρ. Χρήστος Παπαθεοδώρου
This presentation about HBase will help you understand what is HBase, what are the applications of HBase, how is HBase is different from RDBMS, what is HBase Storage, what are the architectural components of HBase and at the end, we will also look at some of the HBase commands using a demo. HBase is an essential part of the Hadoop ecosystem. It is a column-oriented database management system derived from Google’s NoSQL database Bigtable that runs on top of HDFS. After watching this video, you will know how to store and process large datasets using HBase. Now, let us get started and understand HBase and what it is used for.
Below topics are explained in this HBase presentation:
1. What is HBase?
2. HBase Use Case
3. Applications of HBase
4. HBase vs RDBMS
5. HBase Storage
6. HBase Architectural Components
What is this Big Data Hadoop training course about?
Simplilearn’s Big Data Hadoop training course lets you master the concepts of the Hadoop framework and prepares you for Cloudera’s CCA175 Big data certification. The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Achievers IT React JS Certification Course in Bangalore. ... UI Development training in Marathahalli ; also benefits as UI training online and UI Developer
Google Professional-Data-Engineer Cheat Sheet PDF ~ Brain DumpsRoberthar
Get more information visits it : https://www.dumpskey.com/google/professional-data-engineer-braindumps
Dumpskey.com study materials are highly customised as per the syllabus of Google for Professional-Data-Engineer Certification exam. Getting Google Cloud Data Engineer Professional exam is possible only when you choose to opt for good study materials. The Dumpskey exam questions are well structured and you get them in easy to use formats, Practice soft and PDF file.
The columnar roadmap: Apache Parquet and Apache ArrowDataWorks Summit
The Hadoop ecosystem has standardized on columnar formats—Apache Parquet for on-disk storage and Apache Arrow for in-memory. With this trend, deep integration with columnar formats is a key differentiator for big data technologies. Vertical integration from storage to execution greatly improves the latency of accessing data by pushing projections and filters to the storage layer, reducing time spent in IO reading from disk, as well as CPU time spent decompressing and decoding. Standards like Arrow and Parquet make this integration even more valuable as data can now cross system boundaries without incurring costly translation. Cross-system programming using languages such as Spark, Python, or SQL can becomes as fast as native internal performance.
In this talk we’ll explain how Parquet is improving at the storage level, with metadata and statistics that will facilitate more optimizations in query engines in the future. We’ll detail how the new vectorized reader from Parquet to Arrow enables much faster reads by removing abstractions as well as several future improvements. We will also discuss how standard Arrow-based APIs pave the way to breaking the silos of big data. One example is Arrow-based universal function libraries that can be written in any language (Java, Scala, C++, Python, R, ...) and will be usable in any big data system (Spark, Impala, Presto, Drill). Another is a standard data access API with projection and predicate push downs, which will greatly simplify data access optimizations across the board.
Speaker
Julien Le Dem, Principal Engineer, WeWork
In this lecture we analyze key-values databases. At first we introduce key-value characteristics, advantages and disadvantages.
Then we analyze the major Key-Value data stores and finally we discuss about Dynamo DB.
In particular we consider how Dynamo DB: How is implemented
1. Motivation Background
2. Partitioning: Consistent Hashing
3. High Availability for writes: Vector Clocks
4. Handling temporary failures: Sloppy Quorum
5. Recovering from failures: Merkle Trees
6. Membership and failure detection: Gossip Protocol
This presentation contains the introduction to NOSQL databases, it's types with examples, differentiation with 40 year old relational database management system, it's usage, why and we should use it.
Παρουσίαση για το σεμινάριο “Δεδομένα Βιβλιοθηκών στο μελλοντικό ψηφιακό περιβάλλον - FRBR και Linked Data”
Το σεμινάριο διοργανώθηκε από τις Βιβλιοθήκες του Τμήματος Νομικής του ΕΚΠΑ και του Πανεπιστημίου Πειραιώς στις 18 και 19 Ιουνίου 2012, υπό την επιμέλεια της Ομάδας Βάσεων Δεδομένων και Πληροφοριακών Συστημάτων του Εργαστηρίου Ψηφιακών Βιβλιοθηκών και Ηλεκτρονικής Δημοσίευσης του Τμήματος Αρχειονομίας - Βιβλιοθηκονομίας του Ιονίου Πανεπιστημίου
Διδάσκοντες:
- Μανόλης Πεπονάκης (MLIS)
- Δρ. Μιχάλης Σφακάκης
- Δρ. Χρήστος Παπαθεοδώρου
This paper was presented by Vassilis Papakonstantinou at the 17th ACM Symposium on Access Control Models and Technologies (ACM SACMAT 2012) in Newark, USA, June 20 - 22, 2012.
Abstract:
The Resource Description Framework (RDF) has become the defacto standard for representing information in the Semantic Web. Given the increasing amount of sensitive RDF data available on the Web, it becomes increasingly critical to guarantee secure access to this content. In this paper we advocate the use of an abstract access control model to ensure the selective exposure of RDF information. The model is defined by a set of abstract operators. Tokens are used to label RDF triples with access information. Abstract operators model RDF Schema inference rules and propagation of labels along the RDF Schema (RDFS) class and property hierarchies. In this way, the access label of a triple is a complex expression that involves the labels of the triples and the operators applied to obtain said label. Different applications can then adopt different concrete access policies that encode an assignment of the abstract tokens and operators to concrete (specific) values. Following this approach, changes in the interpretation of abstract tokens and operators can be easily implemented resulting in a very flexible mechanism that allows one to easily experiment with different concrete access policies (defined per context or user). To demonstrate the feasibility of the approach, we implemented our ideas on top of the MonetDB and PostgreSQL open source database systems. We conducted an initial set of experiments which showed that the overhead for using abstract expressions is roughly linear to the number of triples considered; performance is also affected by the characteristics of the dataset, such as the size and depth of class and property hierarchies as well as the considered concrete policy.
Presented in : JIST2015, Yichang, China
Prototype: http://rc.lodac.nii.ac.jp/rdf4u/
Video: https://www.youtube.com/watch?v=z3roA9-Cp8g
Abstract: It is known that Semantic Web and Linked Open Data (LOD) are powerful technologies for knowledge management, and explicit knowledge is expected to be presented by RDF format (Resource Description Framework), but normal users are far from RDF due to technical skills required. As we learn, a concept-map or a node-link diagram can enhance the learning ability of learners from beginner to advanced user level, so RDF graph visualization can be a suitable tool for making users be familiar with Semantic technology. However, an RDF graph generated from the whole query result is not suitable for reading, because it is highly connected like a hairball and less organized. To make a graph presenting knowledge be more proper to read, this research introduces an approach to sparsify a graph using the combination of three main functions: graph simplification, triple ranking, and property selection. These functions are mostly initiated based on the interpretation of RDF data as knowledge units together with statistical analysis in order to deliver an easily-readable graph to users. A prototype is implemented to demonstrate the suitability and feasibility of the approach. It shows that the simple and flexible graph visualization is easy to read, and it creates the impression of users. In addition, the attractive tool helps to inspire users to realize the advantageous role of linked data in knowledge management.
Relational databases are rigid-structured data sources characterized by complex relationships among a set of relations (tables). Making sense of such relationships is a challenging problem because users must consider multiple relations, understand their ensemble of integrity constraints, interpret dozens of attributes, and draw complex SQL queries for each desired data exploration. In this scenario, we introduce a twofold methodology; we use a hierarchical graph representation to efficiently model the database relationships and, on top of it, we designed a visualization technique for rapidly relational exploration. Our results demonstrate that the exploration of databases is profoundly simplified as the user is able to visually browse the data with little or no knowledge about its structure, dismissing the need of complex SQL queries. We believe our findings will bring a novel paradigm in what concerns relational data comprehension.
Two graph data models : RDF and Property Graphsandyseaborne
Talk given at ApacheConEU Big Data 2015.
This talk describes the two common graph data approaches, RDF and Property Graphs. It concludes with observations about the different emphasis of each and where each is focused.
RDF2Vec: RDF Graph Embeddings for Data MiningPetar Ristoski
Linked Open Data has been recognized as a valuable source for background information in data mining. However, most data mining tools require features in propositional form, i.e., a vector of nominal or numerical features associated with an instance, while Linked Open Data sources are graphs by nature. In this paper, we present RDF2Vec, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs. We generate sequences by leveraging local information from graph sub-structures, harvested by Weisfeiler-Lehman Subtree RDF Graph Kernels and graph walks, and learn latent numerical representations of entities in RDF graphs. Our evaluation shows that such vector representations outperform existing techniques for the propositionalization of RDF graphs on a variety of different predictive machine learning tasks, and that feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be easily reused for different tasks.
"SPARQL Cheat Sheet" is a short collection of slides intended to act as a guide to SPARQL developers. It includes the syntax and structure of SPARQL queries, common SPARQL prefixes and functions, and help with RDF datasets.
The "SPARQL Cheat Sheet" is intended to accompany the SPARQL By Example slides available at http://www.cambridgesemantics.com/2008/09/sparql-by-example/ .
Tutorial on RDFa, to be held at ISWC2010 in Shanghai, China. (I was supposed to hold the tutorial but last minute issues made it impossible for me to travel there...)
This presentation is an introduction to RDFa, as the fourth assignment of the IST 681 in iSchool, Syracuse University. The presentation is made by Kai Li, who is a library student in Syracuse University..
Presentation of SPARQL Anything at the MEI Linked Data IG Meeting in July 2021. We try SPARQL Anything with MEI XML files and experiment with simple and difficult tasks.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
8. Chapter: 2 (Tutorial)
8
</rdf:RDF>
2. <?xml version="1.0"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
<rdf:Seq rdf:about="http://example.org/favourite-fruit">
<rdf:li rdf:resource="http://example.org/banana"/>
<rdf:li rdf:resource="http://example.org/apple"/>
<rdf:li rdf:resource="http://example.org/pear"/>
</rdf:Seq>
</rdf:RDF>
Both are equivalent and produces same RDF graph as shown below in figure because rdf:li, special property
element, is equivalent to rdf:_1, rdf:_2 in order.
Complete example of rdf:datatype:
RDF/XML:
<?xml version="1.0"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:ex="http://example.org/stuff/1.0/">
<rdf:Description rdf:about="http://example.org/item01">
<ex:size rdf:datatype="http://www.w3.org/2001/XMLSchema#int">123</ex:size>
</rdf:Description>
</rdf:RDF>
Triples:
<http://example.org/item01> <http://example.org/stuff/1.0/size>
"123"^^<http://www.w3.org/2001/XMLSchema#int>.
Notation 3:
@prefix eg: <http://example.org/>.
@prefix ex: <http://example.org/stuff/1.0/>.
eg:item01 ex:size "123".
Reification: statements about statements.
1. {:John :Loves :Mary} :accordingTo :Bill.
2. Mary claims that John’s name is “John Smith”.
3. Romeo thought that {Juliet was dead}.
Romeo thought that Juliet was dead.