This document discusses trends driving the adoption of NoSQL databases, including increasing data size, connectivity of information, semi-structured data, and distributed application architectures. It describes four categories of NoSQL databases - aggregate-oriented, key-value stores, column family (BigTable), and document databases - and provides examples and comparisons of their pros and cons.
Enterprise Data World 2016 | FIBO extension to Schema.org | FIBO SEO | Christ...Christopher Regan
Financial Industry Business Ontology (FIBO) extension to Schema.org presentation by Christopher Regan & Richard Wallis and the EDW 2016 conference in San Diego, April 20th, 2016. Chris Regan is a Digital Technologist and works in Southern California. https://www.linkedin.com/in/christophermregan/
The Information Workbench - Linked Data and Semantic Wikis in the EnterprisePeter Haase
The Information Workbench is a platform for Linked Data applications in the enterprise. Targeting the full life-cycle of Linked Data applications, it facilitates the integration and processing of Linked Data following a Data-as-a-Service paradigm.
In this talk we present how we use Semantic Wiki technologies in the Information Workbench for the development of user interfaces for interacting with the Linked Data. The user interface can be easily customized using a large set of widgets for data integration, interactive visualization, exploration and analytics, as well as the collaborative acquisition and authoring of Linked Data. The talk will feature a live demo illustrating an example application, a Conference Explorer integrating data about the SMWCon conference, publications and social media.
We will also present solutions and applications of the Information Workbench in a variety of other domains, including the Life Sciences and Data Center Management.
This presentation by Shana McDanold of Georgetown University was presented during the NISO Virtual Conference, BIBFRAME & Real World Applications of Linked Bibliographic Data, held on June 15, 2016
Big Linked Data - Creating Training CurriculaEUCLID project
This presentation includes an overview of the basic rules to follow when developing training and education curricula for Linked Data and Big Linked Data
Enterprise Data World 2016 | FIBO extension to Schema.org | FIBO SEO | Christ...Christopher Regan
Financial Industry Business Ontology (FIBO) extension to Schema.org presentation by Christopher Regan & Richard Wallis and the EDW 2016 conference in San Diego, April 20th, 2016. Chris Regan is a Digital Technologist and works in Southern California. https://www.linkedin.com/in/christophermregan/
The Information Workbench - Linked Data and Semantic Wikis in the EnterprisePeter Haase
The Information Workbench is a platform for Linked Data applications in the enterprise. Targeting the full life-cycle of Linked Data applications, it facilitates the integration and processing of Linked Data following a Data-as-a-Service paradigm.
In this talk we present how we use Semantic Wiki technologies in the Information Workbench for the development of user interfaces for interacting with the Linked Data. The user interface can be easily customized using a large set of widgets for data integration, interactive visualization, exploration and analytics, as well as the collaborative acquisition and authoring of Linked Data. The talk will feature a live demo illustrating an example application, a Conference Explorer integrating data about the SMWCon conference, publications and social media.
We will also present solutions and applications of the Information Workbench in a variety of other domains, including the Life Sciences and Data Center Management.
This presentation by Shana McDanold of Georgetown University was presented during the NISO Virtual Conference, BIBFRAME & Real World Applications of Linked Bibliographic Data, held on June 15, 2016
Big Linked Data - Creating Training CurriculaEUCLID project
This presentation includes an overview of the basic rules to follow when developing training and education curricula for Linked Data and Big Linked Data
Spring Data Graph is an integration library for the open source graph database Neo4j and has been around for over a year, evolving from its infancy as brainchild of Rod Johnson and Emil Eifrem. It supports transparent AspectJ based POJO to Graph Mapping, a Neo4jTemplate API and extensive support for Spring Data Repositories. It can work with an embedded graph database or with the standalone Neo4j Server.
The session starts with a short introduction to graph databases. Following that, the different approaches using Spring Data Graph are explored in the Cineasts.net web-app, a social movie database which is also the application of the tutorial in the Spring Data Graph Guidebook. The session will also cover creating a green-field project using the Spring Roo Addon for Spring Data Graph and deploying the App to CloudFoundry.
"Get Ready for Big Data" presentation from Gilbane Boston 2011; for more details, see http://gilbaneboston.com/conference_program.html#t2 and http://pbokelly.blogspot.com/2011/12/gilbane-boston-2011-big-data.html
This deck talks about the basic overview of NoSQL technologies, implementation vendors/products, case studies, and some of the core implementation algorithms. The presentation also describes a quick overview of "Polyglot Persistency", "NewSQL" like emerging trends.
The deck is targeted to beginners who wants to get an overview of NoSQL databases.
From the Feb 19 2014 NISO Virtual Conference: The Semantic Web Coming of Age: Technologies and Implementations
The Web of Data - Ralph Swick, Domain Lead of the Information and Knowledge Domain at W3C
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
No Sql Movement
1. it's not "Never SQL"
NOSQL is simply…
Not Only SQL
NOSQL no-seek-wool n. Describes ongoing
trend where developers increasingly opt for
non-relational databases to help solve their
problems, in an effort to use the right tool for
the right job
4. Trend 2: Connectedness
GGG
Onotologies
RDFa
Folksonomies
Information connectivity
Tagging
Wikis
UGC
Blogs
Feeds
Hypertext
Text
Documents
5. Trend 3: Semi-structured information
• Individualisation of content
– 1970’s salary lists, all elements exactly one job
– 2000’s salary lists, we need many job columns!
• All encompassing “entire world views”
• Store more data about each entity
• Trend accelerated by the decentralization of
content generation
– Age of participation (“web 2.0”)
12. Key-Value Stores
• “Dynamo: Amazon’s Highly Available Key-
Value Store” (2007)
• Data model:
– Global key-value mapping
– Big scalable HashMap
– Highly fault tolerant (typically)
• Examples:
– Riak, Redis, Voldemort
13. Pros and Cons
• Strengths
– Simple data model
– Great at scaling out horizontally
• Scalable
• Available
• Weaknesses:
– Simplistic data model
– Poor for complex data
14. Column Family (BigTable)
• Google’s “Bigtable: A Distributed Storage
System for Structured Data” (2006)
• Data model:
– A big table, with column families
– Map-reduce for querying/processing
• Examples:
– HBase, HyperTable, Cassandra
15. Pros and Cons
• Strengths
– Data model supports semi-structured data
– Naturally indexed (columns)
– Good at scaling out horizontally
• Weaknesses:
– Unsuited for interconnected data
16. Document Databases
• Data model
– Collections of documents
– A document is a key-value collection
– Index-centric, lots of map-reduce
• Examples
– CouchDB, MongoDB
17. Pros and Cons
• Strengths
– Simple, powerful data model (just like SVN!)
– Good scaling (especially if sharding supported)
• Weaknesses:
– Unsuited for interconnected data
– Query model limited to keys (and indexes)
• Map reduce for larger queries
18. Graph Databases
• Data model:
– Nodes with properties
– Named relationships with properties
– Hypergraph, sometimes
• Examples:
– Neo4j (of course), Sones GraphDB, OrientDB,
InfiniteGraph, AllegroGraph
19. Pros and Cons
• Strengths
– Powerful data model
– Fast
• For connected data, can be many orders of magnitude
faster than RDBMS
• Weaknesses:
– Sharding
• Though they can scale reasonably well
• And for some domains you can shard too!
20.
21. Disclaimer
• I don’t hold any sort of copyright on any of the content used
including the photos, logos and text and trademarks used.
They all belong to the respective individual and companies
• I am not responsible for, and expressly disclaims all liability
for, damages of any kind arising out of use, reference to, or
reliance on any information contained within this slide .
UGC = User Generated ContentGGG = Giant Global Graph (what the web will become)Ontologies are the structural frameworks for organizing information and are used in artificial intelligence, the Semantic Web, systems engineering, software engineering, biomedical informatics, library science, enterprise bookmarking, and information architecture as a form of knowledge representation about the world or some part of it. The creation of domain ontologies is also fundamental to the definition and use of an enterprise architecture frameworkA folksonomy is a system of classification derived from the practice and method of collaboratively creating and managing tags to annotate and categorize content;[1][2] this practice is also known as collaborative tagging,[3] social classification, social indexing, and social tagging. Folksonomy, a term coined by Thomas Vander Wal, is a portmanteau offolk and taxonomy.RDFa (or Resource Description Framework – in – attributes) is a W3C Recommendation that adds a set of attribute-level extensions toXHTML for embedding rich metadata within Web documents. The RDF data-model mapping enables its use for embedding RDFsubject-predicate-object expressions within XHTML documents, it also enables the extraction of RDF model triples by compliant user agents.
This is strictly about connected data – joins kill performance there.No bashing of RDBMS performance for tabular transaction processingGreen line denotes “zone of SQL adequacy”
Fowler points out that KV/Column/Document stores are all aggregates: they’re different from graphs because they enforce structure at design time – as an aggregate of data.Clump of data that can be co-located on a cluster instance and which is accessed together.“a fundamental unit of storage which is a rich structure of closely related data: for key-value stores it's the value, for document stores it's the document, and for column-family stores it's the column family. In DDD terms, this group of data is an aggregate.”
History – Amazon decide that they always wanted the shopping basket to be available, but couldn’t take a chance on RDBMSSo they built their ownBig risk, but simple data model and well-known computing science underpinning it (e.g. consistent hashing, Bloom filters for sensible replication)+ Massive read/write scale- Simplistic data model moves heavy lifting into the app tier (e.g. map reduce)
Mongo DB has a reputation for taking liberties with durability to get speedCouch DB has good multimaster replication from Lotus Notes
People talk about Codd’s relational model being mature because it was proposed in 1969 – 42 years old.Euler’s graph theory was proposed in 1736 – 275 years old.
Can’t easily shard graphs like documents or KV stores.This means that high performance graph databases are limited in terms of data set size that can be handled by a single machine.Can use replicas to speed things up (and improve availability) but limits data set size limited to a single machine’s disk/memory.Some domains can shard easily (.e.g geo, most web apps) using consistent routing approach and cache sharding – we’ll cover that later.