Talk given in Tecnocampus Mataró.
In recent years, the mobile and web software industry has advanced fast. Each year new tools and frameworks emerge, fueled by the open source community. This presentation was prepared by Geemba in order to introduce students to this new landscape and modern tecnologies.
JSON-LD is a set of W3C standards track specifications for representing Linked Data in JSON. It is fully compatible with the RDF data model, but allows developers to work with data entirely within JSON.
More information on JSON-LD can be found at http://json-ld.org/
The document discusses various data exchange formats and their pros and cons for serializing and transmitting structured data. It begins with an overview of JSON and its limitations before examining alternatives like Protocol Buffers, XML, MessagePack, and ASN.1. The presenter provides details on ASN.1's advanced features like parametrized types and string ranges. While ASN.1 is very rich, the quality of open source libraries is variable. In summary, the document analyzes different data serialization options beyond JSON and their tradeoffs.
JSON-LD provides a standard representation for expressing linked data using JSON objects. It allows objects to represent entities with keys as properties, and arrays to express property values. Contexts define terms and associate properties and values with IRIs. JSON-LD brings the benefits of linked data to web applications by mapping JSON structures to RDF triples.
Linked Data in Use: Schema.org, JSON-LD and hypermedia APIs - Front in Bahia...Ícaro Medeiros
In my talk I walk throgh Semantic Web initiatives, like RDF and SPARQL, linked data principles, discuss some implementation and adoption issues and talk about semantic annotation in HTML. Semantic annotation using the Schema.org vocabulary is demonstrated using both HTML 5 Microdata or JSON-LD input. There is a strong highlight in benefits seen in Google search results with Rich Snippets, Actions in Email, and Google Now with real examples.
Presentation of the paper "On Using JSON-LD to Create Evolvable RESTful Services" at the 3rd International Workshop on RESTful Design (WS-REST 2012) at WWW2012 in Lyon, France
Integrating and Interpreting Social Data from Heterogeneous SourcesMatthew Rowe
The document discusses integrating and interpreting social data from heterogeneous sources. It outlines challenges like information overload from large amounts of social data published daily. It proposes linking social data from different sources using semantics. It describes generating metadata for social data fragments by lifting them to RDF representations. This includes assigning URIs, content, hashtags, timestamps, locations, and owners. The goal is to integrate social data and enable fine-grained, geo-localized analysis of relevant past social data.
Slides for the course Big Data and Automated Content Analysis, in which students of the social sciences (communication science) learn how to conduct analyses using Python.
JSON-LD is a set of W3C standards track specifications for representing Linked Data in JSON. It is fully compatible with the RDF data model, but allows developers to work with data entirely within JSON.
More information on JSON-LD can be found at http://json-ld.org/
The document discusses various data exchange formats and their pros and cons for serializing and transmitting structured data. It begins with an overview of JSON and its limitations before examining alternatives like Protocol Buffers, XML, MessagePack, and ASN.1. The presenter provides details on ASN.1's advanced features like parametrized types and string ranges. While ASN.1 is very rich, the quality of open source libraries is variable. In summary, the document analyzes different data serialization options beyond JSON and their tradeoffs.
JSON-LD provides a standard representation for expressing linked data using JSON objects. It allows objects to represent entities with keys as properties, and arrays to express property values. Contexts define terms and associate properties and values with IRIs. JSON-LD brings the benefits of linked data to web applications by mapping JSON structures to RDF triples.
Linked Data in Use: Schema.org, JSON-LD and hypermedia APIs - Front in Bahia...Ícaro Medeiros
In my talk I walk throgh Semantic Web initiatives, like RDF and SPARQL, linked data principles, discuss some implementation and adoption issues and talk about semantic annotation in HTML. Semantic annotation using the Schema.org vocabulary is demonstrated using both HTML 5 Microdata or JSON-LD input. There is a strong highlight in benefits seen in Google search results with Rich Snippets, Actions in Email, and Google Now with real examples.
Presentation of the paper "On Using JSON-LD to Create Evolvable RESTful Services" at the 3rd International Workshop on RESTful Design (WS-REST 2012) at WWW2012 in Lyon, France
Integrating and Interpreting Social Data from Heterogeneous SourcesMatthew Rowe
The document discusses integrating and interpreting social data from heterogeneous sources. It outlines challenges like information overload from large amounts of social data published daily. It proposes linking social data from different sources using semantics. It describes generating metadata for social data fragments by lifting them to RDF representations. This includes assigning URIs, content, hashtags, timestamps, locations, and owners. The goal is to integrate social data and enable fine-grained, geo-localized analysis of relevant past social data.
Slides for the course Big Data and Automated Content Analysis, in which students of the social sciences (communication science) learn how to conduct analyses using Python.
Sharding with MongoDB allows scaling a database horizontally across multiple servers. It involves splitting data into chunks and distributing those chunks across shards. The mongos router directs read and write operations to the appropriate shards. Documents are sharded based on a shard key to ensure related data resides on the same shard. Queries are routed efficiently based on the shard key. Splits and migrations balance data as needs change over time.
Audio available: https://www.liferay.com/web/events-symposium-north-america/recap
Liferay makes it easy to integrate your application with powerful search engines. However, it may be hard to diagnose why your most important content isn't showing up the way you need it to. This session will recap the key concepts for indexing and querying with Liferay Search, and present a number of techniques to guarantee your documents will be found with best possible relevance.
André de Oliveira joined Liferay in early 2014 as a senior engineer and leads the Search Infrastructure team. He's been a Java developer and architect for the last 15 years. Ever since discovering Elasticsearch, he's vowed never to write another SQL WHERE clause again.
This document summarizes Simon Bagreev's experience at the Heroku Waza 2013 conference. It includes:
1) Simon attended talks on various topics related to building applications on Heroku including performance, databases, APIs, and mobile development.
2) Simon found the talks on performance optimization, Heroku secrets, Postgres features, and mobile development to be the most interesting and informative.
3) The Postgres talk in particular made Simon realize how many powerful features are available in Postgres beyond the basic functionality, such as arrays, JSON, full text search and more.
MongoDB World 2019: How to Keep an Average API Response Time Less than 5ms wi...MongoDB
MDBW 19 Slide and Video Tagging info
MDBW 19 Slide and Video Tagging info
100%
9
eluc
0 of 0
Context:
Do you need high query performance on your MongoDB? I will share the story about how our team build a stable filter search API platform called ‘Mach’ with 6 MongoDBs, which has 4ms average response time while taking 4,000+ read QPS and 6,000+ write QPS.
shared with one person, off - only specific people can access.
Do you need high query performance on your MongoDB? I will share the story about how our team build a stable filter search API platform called ‘Mach’ with 6 MongoDBs, which has 4ms average response time while taking 4,000+ read QPS and 6,000+ write QPS.
This document discusses MongoDB aggregation pipelines and their capabilities. It begins with an introduction to Tom Schreiber, who is a senior consulting engineer at MongoDB. It then provides examples of aggregation pipelines that find the two highest paid employees per department. It demonstrates how to do this in different ways using SQL queries and a Ruby implementation. It explains how aggregation pipelines allow data to be easily worked with and processed in series using composable stages like in other functional programming languages and libraries. Overall, the document shows how aggregation pipelines provide a powerful yet easy way to query and transform data in MongoDB.
The core Search frameworks in Liferay 7 have been significantly retooled to benefit not only from Liferay's new modular architecture, but also from one of the most innovative players in the market: Elasticsearch, which replaces Lucene as the default search engine in Portal. This session will cover topics like clustering and scalability, unveil improvements (both Elasticsearch and Solr) like aggregations, filters, geolocation, "more like this" and other new query types, and also hot new features for the Enterprise like out-of-the-box Marvel cluster monitoring and Shield security.
André "Arbo" Oliveira joined Liferay in early 2014 as a senior engineer and leads the Search Infrastructure team. He's been writing code for a living for 22 years, 14 of them as a Java developer and architect. Ever since discovering Elasticsearch, he's vowed never to write another SQL WHERE clause again.
Faster and better search results with ElasticsearchEnrico Polesel
Slides from the 25th october talk at the 2019 Plone conference.
Elasticsearch allows Plone to rely on a search engine that is scalable and performant, compared to the regular search feature.
After an introduction about the strengths of the Elastic Stack, you will see how to take advantage of analyzers, tokenizers, custom and boost scoring, geo-search and.
1) JSON-LD has seen widespread adoption with over 2 million HTML pages including it and it being a required format for Linked Data platforms.
2) A primary goal of JSON-LD was to allow JSON developers to use it similarly to JSON while also providing mechanisms to reshape JSON documents into a deterministic structure for processing.
3) JSON-LD 1.1 includes additional features like using objects to index into collections, scoped contexts, and framing capabilities.
This document provides an overview of Elasticsearch including:
- Elasticsearch is a database server that is document-based and schema-free, built on Apache Lucene.
- It allows for full-text search across fields in documents and supports CRUD operations via a RESTful API.
- Common operations demonstrated include indexing, updating, retrieving, and deleting documents.
- Advanced search capabilities include filtering, phrase matching, and highlighting search terms.
- Analytics features like aggregations allow grouping and metrics like average age to be computed across document fields and buckets.
A Semantic Description Language for RESTful Data Services to Combat SemaphobiaMarkus Lanthaler
The document proposes a semantic description language (SEREDASj) to provide machine-readable descriptions of RESTful web services. It aims to address the lack of standards for describing REST APIs and help combat "semaphobia", the fear of semantics. The language builds on previous work but is tailored specifically for REST by focusing on simplicity and supporting many use cases including discovery and composition of RESTful services.
Semantic Optimization with Structured Data - SMX MunichCraig Bradford
Craig Bradford, SMX Munich
The future of structured data isn’t about understanding what a thing is, it’s about understanding what a thing can do. It is allowing us to move past "strings to things" and into actions and anticipatory search. In this presentation I cover:
How structured data has changed (strings to things)
How to get apps indexed by Google
Using structured data to say what a thing can do (things to actions)
Email markup for events and more
The future of Google Now (actions to anticipation)
Some predictions and trends about what comes next
This document provides an overview of key concepts for working with Elasticsearch including:
- What documents and their metadata fields like _index, _type, and _id represent
- How to index, retrieve, update, delete and check for existence of documents
- Using versions for optimistic concurrency control
- Partially updating documents with scripts
- Retrieving multiple documents with _mget
- Reducing overhead with bulk indexing operations
- Setting default types to reduce repetition
I'm Andrea D'Ubaldo, I am a software developer and cyber security enthusiast. The purpose of this presentation is to warn people about google "hacking".
I don't pretend to teach, I only love sharing knowledge. Hope you enjoy ! Comments and remarks are welcome.
------------------------------------------------
Summary
- What is Google dorks
- Queries syntax
- Queries examples
- Conclusion
Google Dork Definition
"A Google dork is an employee who unknowingly exposes sensitive corporate information on the Internet. The word dork is slang for a slow-witted or in-ept person."
Margaret Rouse
Director, WhatIs.com at TechTarget
@WhatIsDotCom
What is
Google dorks is a powerful advanced search, an instrument to perform queries on Google search engine.
How it works
That queries allows the user to find detailed information over the internet, such files, hidden pages, sensitive documents and so on.
Why use
But...dork queries are considered by many an “hacking technique”. Because of his nature, the dorks can be used for different purposes, often bad purpose and we shall then see...
Queries syntax
a) inurl
Find that word or sentences in the URL
inurl: php?id=
b) related
Find that related websites
related:www.google.com
c) filetype
research by file type
filetype:pdf shakespeare
d) site
Restrict to a specific site
site:fakesite.com
e) intitle
Find that word or sentences in the title of a website
intitle: search
...Other syntax characters and operators.
Examples :
- Search files containing username and password
- Discover vulnerable server, affected by SQL Injection
- Pages containing login portal
- Sensitive directory
Credits and References
What is Google dork? – Margaret Rouse
What is Google dork? - WhatIs.com - TechTarget
whatis.techtarget.com
Conclusion
Be careful and protect your data!
Google hacking
https://en.wikipedia.org/wiki/Google_hacking
Wikipedia.
Google Hacking Database (GHDB)
https://www.exploit-db.com/google-hacking-database/
Exploit Database
Special thanks to all the people who made and released these awesome resources for free:
Presentation template by SlidesCarnival (http://www.slidescarnival.com/)
Photographs by Unsplash (http://unsplash.com/)
This document provides an introduction to using ElasticSearch. It discusses installing ElasticSearch and making API calls. It demonstrates indexing employee documents with fields like name, age, interests. It shows how to search for documents by field values or full text, do phrase searches, and highlight search terms. It also introduces analytics capabilities like aggregations to analyze field values.
Aligning Web Services with the Semantic Web to Create a Global Read-Write Gra...Markus Lanthaler
Presentation of the paper "Aligning Web Services with the Semantic Web to Create a Global Read-Write Graph of Data" gave at the 9th IEEE European Conference on Web Services (ECOWS 2011) in Lugano, Switzerland.
Despite significant research and development efforts, the vision of the Semantic Web yielding to a Web of Data has not yet become reality. Even though initiatives such as Linking Open Data gained traction recently, the Web of Data is still clearly outpaced by the growth of the traditional, document-based Web. Instead of releasing data in the form of RDF, many publishers choose to publish their data in the form of Web services. The reasons for this are manifold. Given that RESTful Web services closely resemble the document-based Web, they are not only perceived as less complex and disruptive, but also provide read-write interfaces to the underlying data. In contrast, the current Semantic Web is essentially read-only which clearly inhibits net-working effects and engagement of the crowd. On the other hand, the prevalent use of proprietary schemas to represent the data published by Web services inhibits generic browsers or crawlers to access and understand this data; the consequence are islands of data instead of a global graph of data forming the envisioned Semantic Web. We thus propose a novel approach to integrate Web services into the Web of Data by introducing an algorithm to translate SPARQL queries to HTTP requests. The aim is to create a global read-write graph of data and to standardize the mashup development process. We try to keep the approach as familiar and simple as possible to lower the entry barrier and foster the adoption of our approach. Thus, we based our proposal on SEREDASj, a semantic description language for RESTful data services, for making proprietary JSON service schemas accessible.
The document introduces a database being created to organize information about the Harry Potter universe. It contains over 772 unique characters, 100 spells, and details from both the books and movies. The database aims to help users quickly find answers to their questions. It discusses some challenges in developing the database, such as differences between the books and movies, and ensuring accurate data. Entity-relationship diagrams and definitions of entities like characters, spells, and schools are provided to illustrate the database structure.
Back to Basics Webinar 3: Schema Design Thinking in DocumentsMongoDB
This is the third webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will explain the architecture of document databases.
Back to Basics Webinar 3 - Thinking in DocumentsJoe Drumgoole
- The document discusses modeling data in MongoDB based on cardinality and access patterns.
- It provides examples of embedding related data for one-to-one and one-to-many relationships, and references for large collections.
- The document recommends considering read/write patterns and embedding objects for efficient access, while breaking out data if it grows too large.
How to use Schema to enrich search results and improve your CTR - Andrew Mart...SearchNorwich
This document discusses structured data and schema.org for search engine optimization. It introduces JSON-LD as a format for communicating structured data to search engines. It provides examples of using schema.org types like Person, Organization, Product, and Event for jobs, companies, e-commerce, and events. It also discusses using Sitelink Searchbox schema for adding an on-site search box to search engine results pages. The presentation emphasizes testing structured data, starting niche, and being patient with results. Useful links are provided for learning more about structured data implementation.
The document discusses schema design basics for MongoDB, including terms, considerations for schema design, and examples of modeling different types of data structures like trees, single table inheritance, and many-to-many relationships. It provides examples of creating indexes, evolving schemas, and performing queries and updates. Key topics covered include embedding data versus normalization, indexing, and techniques for modeling one-to-many and many-to-many relationships.
Sharding with MongoDB allows scaling a database horizontally across multiple servers. It involves splitting data into chunks and distributing those chunks across shards. The mongos router directs read and write operations to the appropriate shards. Documents are sharded based on a shard key to ensure related data resides on the same shard. Queries are routed efficiently based on the shard key. Splits and migrations balance data as needs change over time.
Audio available: https://www.liferay.com/web/events-symposium-north-america/recap
Liferay makes it easy to integrate your application with powerful search engines. However, it may be hard to diagnose why your most important content isn't showing up the way you need it to. This session will recap the key concepts for indexing and querying with Liferay Search, and present a number of techniques to guarantee your documents will be found with best possible relevance.
André de Oliveira joined Liferay in early 2014 as a senior engineer and leads the Search Infrastructure team. He's been a Java developer and architect for the last 15 years. Ever since discovering Elasticsearch, he's vowed never to write another SQL WHERE clause again.
This document summarizes Simon Bagreev's experience at the Heroku Waza 2013 conference. It includes:
1) Simon attended talks on various topics related to building applications on Heroku including performance, databases, APIs, and mobile development.
2) Simon found the talks on performance optimization, Heroku secrets, Postgres features, and mobile development to be the most interesting and informative.
3) The Postgres talk in particular made Simon realize how many powerful features are available in Postgres beyond the basic functionality, such as arrays, JSON, full text search and more.
MongoDB World 2019: How to Keep an Average API Response Time Less than 5ms wi...MongoDB
MDBW 19 Slide and Video Tagging info
MDBW 19 Slide and Video Tagging info
100%
9
eluc
0 of 0
Context:
Do you need high query performance on your MongoDB? I will share the story about how our team build a stable filter search API platform called ‘Mach’ with 6 MongoDBs, which has 4ms average response time while taking 4,000+ read QPS and 6,000+ write QPS.
shared with one person, off - only specific people can access.
Do you need high query performance on your MongoDB? I will share the story about how our team build a stable filter search API platform called ‘Mach’ with 6 MongoDBs, which has 4ms average response time while taking 4,000+ read QPS and 6,000+ write QPS.
This document discusses MongoDB aggregation pipelines and their capabilities. It begins with an introduction to Tom Schreiber, who is a senior consulting engineer at MongoDB. It then provides examples of aggregation pipelines that find the two highest paid employees per department. It demonstrates how to do this in different ways using SQL queries and a Ruby implementation. It explains how aggregation pipelines allow data to be easily worked with and processed in series using composable stages like in other functional programming languages and libraries. Overall, the document shows how aggregation pipelines provide a powerful yet easy way to query and transform data in MongoDB.
The core Search frameworks in Liferay 7 have been significantly retooled to benefit not only from Liferay's new modular architecture, but also from one of the most innovative players in the market: Elasticsearch, which replaces Lucene as the default search engine in Portal. This session will cover topics like clustering and scalability, unveil improvements (both Elasticsearch and Solr) like aggregations, filters, geolocation, "more like this" and other new query types, and also hot new features for the Enterprise like out-of-the-box Marvel cluster monitoring and Shield security.
André "Arbo" Oliveira joined Liferay in early 2014 as a senior engineer and leads the Search Infrastructure team. He's been writing code for a living for 22 years, 14 of them as a Java developer and architect. Ever since discovering Elasticsearch, he's vowed never to write another SQL WHERE clause again.
Faster and better search results with ElasticsearchEnrico Polesel
Slides from the 25th october talk at the 2019 Plone conference.
Elasticsearch allows Plone to rely on a search engine that is scalable and performant, compared to the regular search feature.
After an introduction about the strengths of the Elastic Stack, you will see how to take advantage of analyzers, tokenizers, custom and boost scoring, geo-search and.
1) JSON-LD has seen widespread adoption with over 2 million HTML pages including it and it being a required format for Linked Data platforms.
2) A primary goal of JSON-LD was to allow JSON developers to use it similarly to JSON while also providing mechanisms to reshape JSON documents into a deterministic structure for processing.
3) JSON-LD 1.1 includes additional features like using objects to index into collections, scoped contexts, and framing capabilities.
This document provides an overview of Elasticsearch including:
- Elasticsearch is a database server that is document-based and schema-free, built on Apache Lucene.
- It allows for full-text search across fields in documents and supports CRUD operations via a RESTful API.
- Common operations demonstrated include indexing, updating, retrieving, and deleting documents.
- Advanced search capabilities include filtering, phrase matching, and highlighting search terms.
- Analytics features like aggregations allow grouping and metrics like average age to be computed across document fields and buckets.
A Semantic Description Language for RESTful Data Services to Combat SemaphobiaMarkus Lanthaler
The document proposes a semantic description language (SEREDASj) to provide machine-readable descriptions of RESTful web services. It aims to address the lack of standards for describing REST APIs and help combat "semaphobia", the fear of semantics. The language builds on previous work but is tailored specifically for REST by focusing on simplicity and supporting many use cases including discovery and composition of RESTful services.
Semantic Optimization with Structured Data - SMX MunichCraig Bradford
Craig Bradford, SMX Munich
The future of structured data isn’t about understanding what a thing is, it’s about understanding what a thing can do. It is allowing us to move past "strings to things" and into actions and anticipatory search. In this presentation I cover:
How structured data has changed (strings to things)
How to get apps indexed by Google
Using structured data to say what a thing can do (things to actions)
Email markup for events and more
The future of Google Now (actions to anticipation)
Some predictions and trends about what comes next
This document provides an overview of key concepts for working with Elasticsearch including:
- What documents and their metadata fields like _index, _type, and _id represent
- How to index, retrieve, update, delete and check for existence of documents
- Using versions for optimistic concurrency control
- Partially updating documents with scripts
- Retrieving multiple documents with _mget
- Reducing overhead with bulk indexing operations
- Setting default types to reduce repetition
I'm Andrea D'Ubaldo, I am a software developer and cyber security enthusiast. The purpose of this presentation is to warn people about google "hacking".
I don't pretend to teach, I only love sharing knowledge. Hope you enjoy ! Comments and remarks are welcome.
------------------------------------------------
Summary
- What is Google dorks
- Queries syntax
- Queries examples
- Conclusion
Google Dork Definition
"A Google dork is an employee who unknowingly exposes sensitive corporate information on the Internet. The word dork is slang for a slow-witted or in-ept person."
Margaret Rouse
Director, WhatIs.com at TechTarget
@WhatIsDotCom
What is
Google dorks is a powerful advanced search, an instrument to perform queries on Google search engine.
How it works
That queries allows the user to find detailed information over the internet, such files, hidden pages, sensitive documents and so on.
Why use
But...dork queries are considered by many an “hacking technique”. Because of his nature, the dorks can be used for different purposes, often bad purpose and we shall then see...
Queries syntax
a) inurl
Find that word or sentences in the URL
inurl: php?id=
b) related
Find that related websites
related:www.google.com
c) filetype
research by file type
filetype:pdf shakespeare
d) site
Restrict to a specific site
site:fakesite.com
e) intitle
Find that word or sentences in the title of a website
intitle: search
...Other syntax characters and operators.
Examples :
- Search files containing username and password
- Discover vulnerable server, affected by SQL Injection
- Pages containing login portal
- Sensitive directory
Credits and References
What is Google dork? – Margaret Rouse
What is Google dork? - WhatIs.com - TechTarget
whatis.techtarget.com
Conclusion
Be careful and protect your data!
Google hacking
https://en.wikipedia.org/wiki/Google_hacking
Wikipedia.
Google Hacking Database (GHDB)
https://www.exploit-db.com/google-hacking-database/
Exploit Database
Special thanks to all the people who made and released these awesome resources for free:
Presentation template by SlidesCarnival (http://www.slidescarnival.com/)
Photographs by Unsplash (http://unsplash.com/)
This document provides an introduction to using ElasticSearch. It discusses installing ElasticSearch and making API calls. It demonstrates indexing employee documents with fields like name, age, interests. It shows how to search for documents by field values or full text, do phrase searches, and highlight search terms. It also introduces analytics capabilities like aggregations to analyze field values.
Aligning Web Services with the Semantic Web to Create a Global Read-Write Gra...Markus Lanthaler
Presentation of the paper "Aligning Web Services with the Semantic Web to Create a Global Read-Write Graph of Data" gave at the 9th IEEE European Conference on Web Services (ECOWS 2011) in Lugano, Switzerland.
Despite significant research and development efforts, the vision of the Semantic Web yielding to a Web of Data has not yet become reality. Even though initiatives such as Linking Open Data gained traction recently, the Web of Data is still clearly outpaced by the growth of the traditional, document-based Web. Instead of releasing data in the form of RDF, many publishers choose to publish their data in the form of Web services. The reasons for this are manifold. Given that RESTful Web services closely resemble the document-based Web, they are not only perceived as less complex and disruptive, but also provide read-write interfaces to the underlying data. In contrast, the current Semantic Web is essentially read-only which clearly inhibits net-working effects and engagement of the crowd. On the other hand, the prevalent use of proprietary schemas to represent the data published by Web services inhibits generic browsers or crawlers to access and understand this data; the consequence are islands of data instead of a global graph of data forming the envisioned Semantic Web. We thus propose a novel approach to integrate Web services into the Web of Data by introducing an algorithm to translate SPARQL queries to HTTP requests. The aim is to create a global read-write graph of data and to standardize the mashup development process. We try to keep the approach as familiar and simple as possible to lower the entry barrier and foster the adoption of our approach. Thus, we based our proposal on SEREDASj, a semantic description language for RESTful data services, for making proprietary JSON service schemas accessible.
The document introduces a database being created to organize information about the Harry Potter universe. It contains over 772 unique characters, 100 spells, and details from both the books and movies. The database aims to help users quickly find answers to their questions. It discusses some challenges in developing the database, such as differences between the books and movies, and ensuring accurate data. Entity-relationship diagrams and definitions of entities like characters, spells, and schools are provided to illustrate the database structure.
Back to Basics Webinar 3: Schema Design Thinking in DocumentsMongoDB
This is the third webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will explain the architecture of document databases.
Back to Basics Webinar 3 - Thinking in DocumentsJoe Drumgoole
- The document discusses modeling data in MongoDB based on cardinality and access patterns.
- It provides examples of embedding related data for one-to-one and one-to-many relationships, and references for large collections.
- The document recommends considering read/write patterns and embedding objects for efficient access, while breaking out data if it grows too large.
How to use Schema to enrich search results and improve your CTR - Andrew Mart...SearchNorwich
This document discusses structured data and schema.org for search engine optimization. It introduces JSON-LD as a format for communicating structured data to search engines. It provides examples of using schema.org types like Person, Organization, Product, and Event for jobs, companies, e-commerce, and events. It also discusses using Sitelink Searchbox schema for adding an on-site search box to search engine results pages. The presentation emphasizes testing structured data, starting niche, and being patient with results. Useful links are provided for learning more about structured data implementation.
The document discusses schema design basics for MongoDB, including terms, considerations for schema design, and examples of modeling different types of data structures like trees, single table inheritance, and many-to-many relationships. It provides examples of creating indexes, evolving schemas, and performing queries and updates. Key topics covered include embedding data versus normalization, indexing, and techniques for modeling one-to-many and many-to-many relationships.
This document provides an overview of schema design and data modeling for both relational and non-relational databases. It discusses the history of data modeling including hierarchical and relational models. The goals of data modeling are to avoid anomalies, minimize redesign, and make the model informative for users. Common data modeling patterns like one-to-many, many-to-many, and tree structures are explained. Specific examples are given for modeling comments, products, and categories in a non-relational database.
Intro to MongoDB
Get a jumpstart on MongoDB, use cases, and next steps for building your first app with Buzz Moschetti, MongoDB Enterprise Architect.
@BuzzMoschetti
MongoDB .local Chicago 2019: Best Practices for Working with IoT and Time-ser...MongoDB
Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe.
This talk covers:
• Common components of an IoT solution
• The challenges involved with managing time-series data in IoT applications
• Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance.
• How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts
At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.
Media owners are turning to MongoDB to drive social interaction with their published content. The way customers consume information has changed and passive communication is no longer enough. They want to comment, share and engage with publishers and their community through a range of media types and via multiple channels whenever and wherever they are. There are serious challenges with taking this semi-structured and unstructured data and making it work in a traditional relational database. This webinar looks at how MongoDB’s schemaless design and document orientation gives organisation’s like the Guardian the flexibility to aggregate social content and scale out.
MongoDB Europe 2016 - ETL for Pros – Getting Data Into MongoDB The Right WayMongoDB
The document discusses best practices for extracting, transforming, and loading (ETL) large amounts of data into MongoDB. It describes common mistakes made in ETL processes, such as performing nested queries to retrieve and assemble documents, and building documents within the database itself using update operations. The presentation provides a case study comparing these inefficient approaches to loading order, item, and tracking data from relational tables into MongoDB documents.
MongoDB, PHP and the cloud - php cloud summit 2011Steven Francia
An introduction to using MongoDB with PHP.
Walking through the basics of schema design, connecting to a DB, performing CRUD operations and queries in PHP.
MongoDB runs great in the cloud, but there are some things you should know. In this session we'll explore scaling and performance characteristics of running Mongo in the cloud as well as best practices for running on platforms like Amazon EC2.
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB
Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe.
This talk covers:
Common components of an IoT solution
The challenges involved with managing time-series data in IoT applications
Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance.
How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts
At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.
Webinar: General Technical Overview of MongoDB for Dev TeamsMongoDB
In this talk we will focus on several of the reasons why developers have come to love the richness, flexibility, and ease of use that MongoDB provides. First we will give a brief introduction of MongoDB, comparing and contrasting it to the traditional relational database. Next, we’ll give an overview of the APIs and tools that are part of the MongoDB ecosystem. Then we’ll look at how MongoDB CRUD (Create, Read, Update, Delete) operations work, and also explore query, update, and projection operators. Finally, we will discuss MongoDB indexes and look at some examples of how indexes are used.
MongoDB .local Munich 2019: Best Practices for Working with IoT and Time-seri...MongoDB
Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe.
This talk covers:
• Common components of an IoT solution
• The challenges involved with managing time-series data in IoT applications
• Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance.
• How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts
At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.
This document summarizes a presentation on best practices for extracting, transforming, and loading (ETL) large amounts of data from relational databases into MongoDB documents. The presentation discusses common mistakes made in ETL processes, including making nested database queries, building documents within the database, and loading all data into memory at once. It then analyzes a case study involving importing order, item, and tracking data from relational tables into normalized MongoDB documents.
MongoDB is a scalable, high-performance, open-source document database that provides dynamic queries and indexing. It aims to provide the power of relational databases with the scalability and flexibility of non-relational databases. Key features include ease of use, scaling capabilities, dynamic queries similar to SQL, and speed comparable to key-value stores while supporting rich querying like relational databases.
This document provides an overview of MongoDB and the Mongoid ODM for modeling data and applications without SQL and active record. It discusses how MongoDB is a non-relational, schema-less database that stores data as JSON-like documents rather than in tables. Mongoid allows modeling data similarly to active record but translates it to the document structure of MongoDB. The document uses a sample rails app to demonstrate modeling products, users, and reviews without active record or SQL.
Baton rouge - sql vs no sql and azure data factoryDiponkar Paul
NoSQL databases have grown in popularity in recent years due to the flexibility of data modeling and scaling up capabilities. NoSQL databases also have been used in the big data landscape. The demo rich session will elaborate the difference between SQL and NoSQL. And data moving capabilities from NoSQL database MongoDB to Azure Data Lake by using Azure data factory.
SQL vs. NoSQL and Moving data by Azure Data FactoryDiponkar Paul
NoSQL databases have grown in popularity in recent years due to the flexibility of data modeling and scaling up capabilities. NoSQL databases also have been used in the big data landscape. The demo rich session will elaborate the difference between SQL and NoSQL. And end to end solution for data moving capabilities from NoSQL database MongoDB to Azure Data Lake by using Azure data factory.
Relational databases are central to web applications, but they have also been the primary source of pain when it comes to scale and performance. Recently, non-relational databases (also referred to as NoSQL) have arrived on the scene. This session explains not only what MongoDB is and how it works, but when and how to gain the most benefit.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
2. Backend (server)
+
Mobile Apps
++
PHP + MySQL
Web Apps
HTML + JavaScript + CSSAndroid Java / Kotlin
iOS Swift / ObjectiveC
/
Traditional landscape
/
5. SELECT associations2.object_id, associations2.term_id, associations2.cat_ID, associations2
FROM (SELECT objects_tags.object_id, objects_tags.term_id, wp_cb_tags2cats.cat_ID, categor
FROM (SELECT wp_term_relationships.object_id, wp_term_taxonomy.term_id, wp_term_taxono
FROM wp_term_relationships
LEFT JOIN wp_term_taxonomy ON wp_term_relationships.term_taxonomy_id = wp_term_tax
ORDER BY object_id ASC, term_id ASC)
AS objects_tags
LEFT JOIN wp_cb_tags2cats ON objects_tags.term_id = wp_cb_tags2cats.tag_ID
LEFT JOIN (SELECT wp_term_relationships.object_id, wp_term_taxonomy.term_id as cat_ID,
FROM wp_term_relationships
LEFT JOIN wp_term_taxonomy ON wp_term_relationships.term_taxonomy_id = wp_term_tax
WHERE wp_term_taxonomy.taxonomy = 'category'
GROUP BY object_id, cat_ID, term_taxonomy_id
ORDER BY object_id, cat_ID, term_taxonomy_id)
AS categories on wp_cb_tags2cats.cat_ID = categories.term_id
WHERE objects_tags.term_id = wp_cb_tags2cats.tag_ID
GROUP BY object_id, term_id, cat_ID, term_taxonomy_id
ORDER BY object_id ASC, term_id ASC, cat_ID ASC)
AS associations2
LEFT JOIN categories ON associations2.object_id = categories.object_id
WHERE associations2.cat_ID <> categories.cat_ID
GROUP BY object_id, term_id, cat_ID, term_taxonomy_id
ORDER BY object_id, term_id, cat_ID, term_taxonomy_id
Looks familiar?
Source: How to reduce a long SQL query based on CREATE VIEW? (Stackoverflow)
6. SELECT associations2.object_id, associations2.term_id, associations2.cat_ID, associations2
FROM (SELECT objects_tags.object_id, objects_tags.term_id, wp_cb_tags2cats.cat_ID, categor
FROM (SELECT wp_term_relationships.object_id, wp_term_taxonomy.term_id, wp_term_taxono
FROM wp_term_relationships
LEFT JOIN wp_term_taxonomy ON wp_term_relationships.term_taxonomy_id = wp_term_tax
ORDER BY object_id ASC, term_id ASC)
AS objects_tags
LEFT JOIN wp_cb_tags2cats ON objects_tags.term_id = wp_cb_tags2cats.tag_ID
LEFT JOIN (SELECT wp_term_relationships.object_id, wp_term_taxonomy.term_id as cat_ID,
FROM wp_term_relationships
LEFT JOIN wp_term_taxonomy ON wp_term_relationships.term_taxonomy_id = wp_term_tax
WHERE wp_term_taxonomy.taxonomy = 'category'
GROUP BY object_id, cat_ID, term_taxonomy_id
ORDER BY object_id, cat_ID, term_taxonomy_id)
AS categories on wp_cb_tags2cats.cat_ID = categories.term_id
WHERE objects_tags.term_id = wp_cb_tags2cats.tag_ID
GROUP BY object_id, term_id, cat_ID, term_taxonomy_id
ORDER BY object_id ASC, term_id ASC, cat_ID ASC)
AS associations2
LEFT JOIN categories ON associations2.object_id = categories.object_id
WHERE associations2.cat_ID <> categories.cat_ID
GROUP BY object_id, term_id, cat_ID, term_taxonomy_id
ORDER BY object_id, term_id, cat_ID, term_taxonomy_id
Looks familiar?
Source: How to reduce a long SQL query based on CREATE VIEW? (Stackoverflow)
7. Looks familiar?
id name email data
1 Ragnar Lodbrok lodbrok@mail.com
{"phone":"(297)-707-
8575","twitter":"foobar",
"instagram":"foobar"}
2 Björn Ironside bironside@mail.com
{"phone":"(297)-707-
8575","twitter":"foobar",
"instagram":"foobar"}
3 Lagertha lagertha@mail.com
{"phone":"(297)-707-
8575","twitter":"foobar",
"instagram":"foobar"}
8. Looks familiar?
id name email data
1 Ragnar Lodbrok lodbrok@mail.com
{"phone":"(297)-707-
8575","twitter":"foobar",
"instagram":"foobar"}
2 Björn Ironside bironside@mail.com
{"phone":"(297)-707-
8575","twitter":"foobar",
"instagram":"foobar"}
3 Lagertha lagertha@mail.com
{"phone":"(297)-707-
8575","twitter":"foobar",
"instagram":"foobar"}
Commonly done in order to:
- Handle inconsistent data
- Avoid modifying the schema
- …
9. Looks familiar?
id name email data
1 Ragnar Lodbrok lodbrok@mail.com
{"phone":"(297)-707-
8575","twitter":"foobar",
"instagram":"foobar"}
2 Björn Ironside bironside@mail.com
{"phone":"(297)-707-
8575","twitter":"foobar",
"instagram":"foobar"}
3 Lagertha lagertha@mail.com
{"phone":"(297)-707-
8575","twitter":"foobar",
"instagram":"foobar"}
Commonly done in order to:
- Handle inconsistent data
- Avoid modifying the schema
- …
19. Design of a NoSQL database
“When designing data models, always consider
the application usage of the data as well as the
inherent structure of the data itself.”
- MongoDB manual
20. Design of a NoSQL database
• Duplication can be the right choice.
• Favour few collections over many.
• Data doesn’t have to be structured.
“When designing data models, always consider
the application usage of the data as well as the
inherent structure of the data itself.”
- MongoDB manual
22. Advantages Disadvantages
• Flexible schema
• Speed
• Scaling
• Flexible schema
• Memory in disk
• Data integrity
(application-side
validation)
23. Learn more
• Official Website
• Recommended reads:
• Thinking in Documents
• 6 Rules of Thumb for MongoDB Schema Design
• Free online course: Data Wrangling with MongoDB
• MongoDB Source Code
27. Model – Eloquent ORM
Implementation of Active Record pattern
$user = User::find(2);
$user->name;
$user->email;
$user->update([
'email' => 'new@mail.com'
]);
$user->delete();
User::create([
'name' => 'Bill Finger',
'email' => 'finger@mail.com'
]);
id name email
1 Bob Kane kane@mail.com
2 Jerry Robinson robinson@mail.com
3 Bill Finger finger@mail.com
DATABASE (SQL)
CODE (PHP)
29. Model – Eloquent ORM
Convention over configuration
<?php
use IlluminateDatabaseEloquentModel;
class Post extends Model {
public function author() {
return $this->belongsTo(User::class);
}
public function comments() {
return $this->hasMany(Comment::class);
}
}
DATABASE
CODE
30. Model – Query Builder
$users = DB::table('users')
->join('contacts', 'users.id', '=', 'contacts.user_id')
->join('orders', 'users.id', '=', 'orders.user_id')
->select('users.*', 'contacts.phone', 'orders.price')
->get();
$usersCount = DB::table('users')->count();
$usersByStatus = DB::table('users')
->select(DB::raw('count(*) as user_count, status'))
->where('status', '<>', 1)
->groupBy('status')
->get();
COUNT
GROUP BY
JOIN
ETC…
32. Controller - Routing
GET mywebsite.com/user/3
Route::get('user/{id}', 'UsersController@show');
class UsersController {
public function show($id) {
return User::find($id);
}
}
33. Controller - Routing
POST mywebsite.com/login
Route::post('login', 'HomeController@login');
class UsersController {
public function login() {
$credentials = [
'email' => request('email'),
'password' => request('password')
];
if (Auth::attempt($credentials))
return redirect('/home');
}
}
34. View - Blade
@if (auth()->check())
{!! Form::open(['url' => '/login']) !!}
{!! Form::text('email'); !!}
{!! Form::password('password'); !!}
{!! Form::submit('Login'); !!}
{!! Form::close() !!}
@else
<div class="container">
Hello, {{ auth()->user()->name }}.
</div>
@endif
<div class="container">
Hello, Bill Finger.
</div>
<form action="/login">
<input type="text" name="email">
<input type="password" name="password">
<input type="submit" value="Login">
</form>
If the user isn’t logged in
If the user is logged in
Laravel Blade can be thought
as a superset of php when
rendering views.
35. Out of the box with Laravel
Authentication Mailing Testing
Form
Validation
Command
Line Tools
Sessions
Database
Migrations
Task
Scheduling
And more…
41. What can Typescript do for you?
• Javascript superset.
• Compiles into Javascript.
• Compatible in all browsers.
• Provides the following:
• Classes
• Interfaces
• Typings
• File imports
• …
42. Example
class Car{
public run() {
console.log('I am a running car!');
}
}
let car = new Car();
car.run();
function createCar() {
return {
run: function() {
console.log('I am a running car!');
}
};
}
var car = createCar();
car.run();
JAVASCRIPT
TYPESCRIPT
43. Example
abstract class Vehicle {
abstract public run(): void;
}
class Car extends Vehicle {
public run() {
console.log('I am a running car!');
}
}
let car = new Car();
car.run();
TYPESCRIPT
44. Example
export default abstract class Vehicle {
abstract public run(): void;
}
Vehicle.ts
import Vehicle from './Vehicle.ts';
export default class Car extends Vehicle {
public run() {
console.log('I am a running car!');
}
}
import Car from './Car';
let car = new Car();
car.run();
Car.ts
index.ts
61. What can Sass do for you?
• CSS superset.
• Compiles into CSS.
• Compatible in all browsers.
• Provides the following:
• Variables
• Functions
• Nesting
• Loops
• Conditionals
• File imports
• …
62. SASS
Example
$font-stack: Helvetica, sans-serif;
$primary-color: #333;
body {
font: 100% $font-stack;
color: $primary-color;
}
nav {
ul {
margin: 0;
padding: 0;
list-style: none;
}
li { display: inline-block; }
a {
display: block;
padding: 6px 12px;
text-decoration: none;
}
}
body {
font: 100% Helvetica, sans-serif;
color: #333;
}
nav ul {
margin: 0;
padding: 0;
list-style: none;
}
nav li {
display: inline-block;
}
nav a {
display: block;
padding: 6px 12px;
text-decoration: none;
}
CSS
69. Native functionality
Via Cordova Plugins
• Code written once per platform, Javascript interface
• Huge collection of available plugins:
• Camera
• GPS
• Push Notifications
• Bluetooth
• NFC
• …
• Program your own!
70. Learn more
• Official Website
• Components Interactive Documentation
• Ionic Source Code
72. You also need to know…
Package managers
• npmjs.com
• Repository for node
(Javascript) libraries
• 400.000+ packages
• packagist.org
• Repository for php
libraries
• 140.000+ packages