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Graphs of the various type conversions provided
Converting between standard OCaml types
The OCaml standard library provides many convertion functions of the form Foo.{to,of}_bar between its various types. These are incomplete – and somewhat arbitrary – so it’s often necessary to go via intermediate types to achieve the convertion you want. (Either that or roll your own with a fold or iter function.)
What’s worse, you may need to mix forwards and backwards conversions (to_ and of_ style respectively), requiring you to search both forwards and backwards in the documentation to get the conversion you need. For example, consider the case of ('k, 'v) Hashtbl.t -> ('k * 'v) list:
- Looking in the docs for [
Hashtbl][hashtbl-doc], we see that there’s no - direct conversion to an association list. We might give up and build our own
- one with
Hashtbl.fold. On OCaml 4.07+, we haveHashtbl.to_seq : ('k, 'v) Hashtbl.t -> ('k, 'v) Seq.t, so let’s start by using that. - We look in the docs for [
Seq][seq-doc], and find no conversions. Once again, - we could use get distracted and use
Seq.fold_leftto build our own. Or, we - could start searching backwards from the type we want…
- In the docs for [
List][list-doc], we find the functionList.of_seq : 'a Seq.t -> 'a List.t. Now we can complete the chain:
let hashtbl_to_list h = Hashtbl.to_seq h |> List.of_seq
OCaml
Rather than keeping the entire Stdlib documentation open in browser tabs, it can
be helpful to view all of the available conversions as a directed graph. That’s
what this repository provides. (These graphs show the conversions available on
OCaml 4.11.0; your mileage may vary.)
#data visualization #data analysis
What is GEEK
Buddha Community
1656899776
Graph-scroll: Simple Scrolling Events for D3 Graphs
graph-scroll.js
Simple scrolling events for d3 graphs. Based on stack
Demo/Documentation
graph-scroll takes a selection of explanatory text sections and dispatches active events as different sections are scrolled into to view. These active events can be used to update a chart's state.
d3.graphScroll()
.sections(d3.selectAll('#sections > div'))
.on('active', function(i){ console.log(i + 'th section active') })
The top most element scrolled fully into view is classed graph-scroll-active. This makes it easy to highlight the active section with css:
#sections > div{
opacity: .3
}
#sections div.graph-scroll-active{
opacity: 1;
}
To support headers and intro images/text, we use a container element containing the explanatory text and graph.
<h1>Page Title</div>
<div id='container'>
<div id='graph'></div>
<div id='sections'>
<div>Section 0</div>
<div>Section 1</div>
<div>Section 2</div>
</div>
</div>
<h1>Footer</h1>
If these elements are passed to graphScroll as selections with container and graph, every element in the graph selection will be classed graph-scroll-graph if the top of the container is out of view.
d3.graphScroll()
.graph(d3.selectAll('#graph'))
.container(d3.select('#container'))
.sections(d3.selectAll('#sections > div'))
.on('active', function(i){ console.log(i + 'th section active') })
When the graph starts to scroll out of view, position: sticky keeps the graph element stuck to the top of the page while the text scrolls by.
#container{
position: relative;
}
#sections{
width: 340px;
}
#graph{
margin-left: 40px;
width: 500px;
position: sticky;
top: 0px;
float: right;
}
On mobile centering the graph and sections while adding a some padding for the first slide is a good option:
@media (max-width: 925px) {
#graph{
width: 100%;
margin-left: 0px;
float: none;
}
#sections{
position: relative;
margin: 0px auto;
padding-top: 400px;
}
}
Adjust the amount of pixels before a new section is triggered is also helpful on mobile (Defaults to 200 pixels):
graphScroll.offset(300)
To update or replace a graphScroll instance, pass a string to eventId to remove the old event listeners:
graphScroll.eventId('uniqueId1')Author: 1wheel
Source Code: https://github.com/1wheel/graph-scroll
License: MIT license
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Graph Therapy: The Year of the Graph Newsletter, June/May 2020
Parts of the world are still in lockdown, while others are returning to some semblance of normalcy. Either way, while the last few months have given some things pause, they have boosted others. It seems like developments in the world of Graphs are among those that have been boosted.
An abundance of educational material on all things graph has been prepared and delivered online, and is now freely accessible, with more on the way.
Graph databases have been making progress and announcements, repositioning themselves by a combination of releasing new features, securing additional funds, and entering strategic partnerships.
A key graph database technology, RDF*, which enables compatibility between RDF and property graph databases, is gaining momentum and tool support.
And more cutting edge research combining graph AI and knowledge graphs is seeing the light, too. Buckle up and enjoy some graph therapy.
Stanford’s series of online seminars featured some of the world’s leading experts on all things graph. If you missed it, or if you’d like to have an overview of what was said, you can find summaries for each lecture in this series of posts by Bob Kasenchak and Ahren Lehnert. Videos from the lectures are available here.
Stanford Knowledge Graph Course Not-Quite-Live-Blog
Stanford University’s computer science department is offering a free class on Knowledge Graphs available to the public. Stanford is also making recordings of the class available via the class website.
Another opportunity to get up to speed with educational material: The entire program of the course “Information Service Engineering” at KIT - Karlsruhe Institute of Technology, is delivered online and made freely available on YouTube. It includes topics such as ontology design, knowledge graph programming, basic graph theory, and more.
Information Service Engineering at KIT
Knowledge representation as a prerequisite for knowledge graphs. Learn about knowledge representation, ontologies, RDF(S), OWL, SPARQL, etc.
Ontology may sound like a formal term, while knowledge graph is a more approachable one. But the 2 are related, and so is ontology and AI. Without a consistent, thoughtful approach to developing, applying, evolving an ontology, AI systems lack underpinning that would allow them to be smart enough to make an impact.
The ontology is an investment that will continue to pay off, argue Seth Earley and Josh Bernoff in Harvard Business Review, making the case for how businesses may benefit from a knowldge-centric approach
Is Your Data Infrastructure Ready for AI?
Even after multiple generations of investments and billions of dollars of digital transformations, organizations struggle to use data to improve customer service, reduce costs, and speed the core processes that provide competitive advantage. AI was supposed to help with that.
Besides AI, knowledge graphs have a part to play in the Cloud, too. State is good, and lack of support for Stateful Cloud-native applications is a roadblock for many enterprise use-cases, writes Dave Duggal.
Graph knowledge bases are an old idea now being revisited to model complex, distributed domains. Combining high-level abstraction with Cloud-native design principles offers efficient “Context-as-a-Service” for hydrating stateless services. Graph knowledge-based systems can enable composition of Cloud-native services into event-driven dataflow processes.
Kubernetes also touches upon Organizational Knowledge, and that may be modeled as a Knowledge Graph.
Graph Knowledge Base for Stateful Cloud-Native Applications
Extending graph knowledge bases to model distributed systems creates a new kind of information system, one intentionally designed for today’s IT challenges.
The Enterprise Knowledge Graph Foundation was recently established to define best practices and mature the marketplace for EKG adoption, with a launch webinar on June the 23rd.
The Foundation defines its mission as including adopting semantic standards, developing best practices for accelerated EKG deployment, curating a repository of reusable models and resources, building a mechanism for engagement and shared knowledge, and advancing the business cases for EKG adoption.
Enterprise Knowledge Graph Maturity Model
The Enterprise Knowledge Graph Maturity Model (EKG/MM) is the industry-standard definition of the capabilities required for an enterprise knowledge graph. It establishes standard criteria for measuring progress and sets out the practical questions that all involved stakeholders ask to ensure trust, confidence and usage flexibility of data. Each capability area provides a business summary denoting its importance, a definition of the added value from semantic standards and scoring criteria based on five levels of defined maturity.
Enterprise Knowledge Graphs is what the Semantic Web Company (SWC) and Ontotext have been about for a long time, too. Two of the vendors in this space that have been around for the longer time just announced a strategic partnership: Ontotext, a graph database and platform provider, meets SWC, a management and added value layer that sits on top.
SWC and Ontotext CEOs emphasize how their portfolios are complementary, while the press release states that the companies have implemented a seamless integration of the PoolParty Semantic Suite™ v.8 with the GraphDB™ and Ontotext Platform, which offers benefits for many use cases.
#database #artificial intelligence #graph databases #rdf #graph analytics #knowledge graph #graph technology
1621327800
Graphs and Knowledge Connexions. The Year of the Graph Newsletter, Autumn 2020
As 2020 is coming to an end, let’s see it off in style. Our journey in the world of Graph Analytics, Graph Databases, Knowledge Graphs and Graph AI culminate.
The representation of the relationships among data, information, knowledge and --ultimately-- wisdom, known as the data pyramid, has long been part of the language of information science. Digital transformation has made this relevant beyond the confines of information science. COVID-19 has brought years’ worth of digital transformation in just a few short months.
In this new knowledge-based digital world, encoding and making use of business and operational knowledge is the key to making progress and staying competitive. So how do we go from data to information, and from information to knowledge? This is the key question Knowledge Connexions aims to address.
Graphs in all shapes and forms are a key part of this.
Knowledge Connexions is a visionary event featuring a rich array of technological building blocks to support the transition to a knowledge-based economy: Connecting data, people and ideas, building a global knowledge ecosystem.
The Year of the Graph will be there, in the workshop “From databases to platforms: the evolution of Graph databases”. George Anadiotis, Alan Morrison, Steve Sarsfield, Juan Sequeda and Steven Xi bring many years of expertise in the domain, and will analyze Graph Databases from all possible angles.
This is the first step in the relaunch of the Year of the Graph Database Report. Year of the Graph Newsletter subscribers just got a 25% discount code. To be always in the know, subscribe to the newsletter, and follow the newly launched Year of the Graph account on Twitter! In addition to getting the famous YotG news stream every day, you will also get a 25% discount code.
#database #machine learning #artificial intelligence #data science #graph databases #graph algorithms #graph analytics #emerging technologies #knowledge graphs #semantic technologies
1595932020
Graph Therapy: The Year of the Graph Newsletter, June/May 2020
Parts of the world are still in lockdown, while others are returning to some semblance of normalcy. Either way, while the last few months have given some things pause, they have boosted others. It seems like developments in the world of Graphs are among those that have been boosted.
An abundance of educational material on all things graph has been prepared and delivered online, and is now freely accessible, with more on the way.
Graph databases have been making progress and announcements, repositioning themselves by a combination of releasing new features, securing additional funds, and entering strategic partnerships.
A key graph database technology, RDF*, which enables compatibility between RDF and property graph databases, is gaining momentum and tool support.
And more cutting edge research combining graph AI and knowledge graphs is seeing the light, too. Buckle up and enjoy some graph therapy.
Stanford’s series of online seminars featured some of the world’s leading experts on all things graph. If you missed it, or if you’d like to have an overview of what was said, you can find summaries for each lecture in this series of posts by Bob Kasenchak and Ahren Lehnert. Videos from the lectures are available here.
Stanford Knowledge Graph Course Not-Quite-Live-Blog
Stanford University’s computer science department is offering a free class on Knowledge Graphs available to the public. Stanford is also making recordings of the class available via the class website.
Another opportunity to get up to speed with educational material: The entire program of the course “Information Service Engineering” at KIT - Karlsruhe Institute of Technology, is delivered online and made freely available on YouTube. It includes topics such as ontology design, knowledge graph programming, basic graph theory, and more.
Information Service Engineering at KIT
Knowledge representation as a prerequisite for knowledge graphs. Learn about knowledge representation, ontologies, RDF(S), OWL, SPARQL, etc.
Ontology may sound like a formal term, while knowledge graph is a more approachable one. But the 2 are related, and so is ontology and AI. Without a consistent, thoughtful approach to developing, applying, evolving an ontology, AI systems lack underpinning that would allow them to be smart enough to make an impact.
The ontology is an investment that will continue to pay off, argue Seth Earley and Josh Bernoff in Harvard Business Review, making the case for how businesses may benefit from a knowldge-centric approach
Is Your Data Infrastructure Ready for AI?
Even after multiple generations of investments and billions of dollars of digital transformations, organizations struggle to use data to improve customer service, reduce costs, and speed the core processes that provide competitive advantage. AI was supposed to help with that.
Besides AI, knowledge graphs have a part to play in the Cloud, too. State is good, and lack of support for Stateful Cloud-native applications is a roadblock for many enterprise use-cases, writes Dave Duggal.
Graph knowledge bases are an old idea now being revisited to model complex, distributed domains. Combining high-level abstraction with Cloud-native design principles offers efficient “Context-as-a-Service” for hydrating stateless services. Graph knowledge-based systems can enable composition of Cloud-native services into event-driven dataflow processes.
Kubernetes also touches upon Organizational Knowledge, and that may be modeled as a Knowledge Graph.
Graph Knowledge Base for Stateful Cloud-Native Applications
Extending graph knowledge bases to model distributed systems creates a new kind of information system, one intentionally designed for today’s IT challenges.
The Enterprise Knowledge Graph Foundation was recently established to define best practices and mature the marketplace for EKG adoption, with a launch webinar on June the 23rd.
The Foundation defines its mission as including adopting semantic standards, developing best practices for accelerated EKG deployment, curating a repository of reusable models and resources, building a mechanism for engagement and shared knowledge, and advancing the business cases for EKG adoption.
Enterprise Knowledge Graph Maturity Model
The Enterprise Knowledge Graph Maturity Model (EKG/MM) is the industry-standard definition of the capabilities required for an enterprise knowledge graph. It establishes standard criteria for measuring progress and sets out the practical questions that all involved stakeholders ask to ensure trust, confidence and usage flexibility of data. Each capability area provides a business summary denoting its importance, a definition of the added value from semantic standards and scoring criteria based on five levels of defined maturity.
Enterprise Knowledge Graphs is what the Semantic Web Company (SWC) and Ontotext have been about for a long time, too. Two of the vendors in this space that have been around for the longer time just announced a strategic partnership: Ontotext, a graph database and platform provider, meets SWC, a management and added value layer that sits on top.
SWC and Ontotext CEOs emphasize how their portfolios are complementary, while the press release states that the companies have implemented a seamless integration of the PoolParty Semantic Suite™ v.8 with the GraphDB™ and Ontotext Platform, which offers benefits for many use cases.
#database #artificial intelligence #graph databases #rdf #graph analytics #knowledge graph #graph technology
1593156510
Basic Data Types in Python | Python Web Development For Beginners
At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
Table of Contents hide
III Built-in data types in Python
Mutable objects
The Size and declared value and its sequence of the object can able to be modified called mutable objects.
Mutable Data Types are list, dict, set, byte array
Immutable objects
The Size and declared value and its sequence of the object can able to be modified.
Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.
id() and type() is used to know the Identity and data type of the object
a**=25+**85j
type**(a)**
output**:<class’complex’>**
b**={1:10,2:“Pinky”****}**
id**(b)**
output**:**238989244168
Built-in data types in Python
a**=str(“Hello python world”)****#str**
b**=int(18)****#int**
c**=float(20482.5)****#float**
d**=complex(5+85j)****#complex**
e**=list((“python”,“fast”,“growing”,“in”,2018))****#list**
f**=tuple((“python”,“easy”,“learning”))****#tuple**
g**=range(10)****#range**
h**=dict(name=“Vidu”,age=36)****#dict**
i**=set((“python”,“fast”,“growing”,“in”,2018))****#set**
j**=frozenset((“python”,“fast”,“growing”,“in”,2018))****#frozenset**
k**=bool(18)****#bool**
l**=bytes(8)****#bytes**
m**=bytearray(8)****#bytearray**
n**=memoryview(bytes(18))****#memoryview**
Numbers (int,Float,Complex)
Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.
#signed interger
age**=**18
print**(age)**
Output**:**18
Python supports 3 types of numeric data.
int (signed integers like 20, 2, 225, etc.)
float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)
complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)
A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).
String
The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.
# String Handling
‘Hello Python’
#single (') Quoted String
“Hello Python”
# Double (") Quoted String
“”“Hello Python”“”
‘’‘Hello Python’‘’
# triple (‘’') (“”") Quoted String
In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.
The operator “+” is used to concatenate strings and “*” is used to repeat the string.
“Hello”+“python”
output**:****‘Hello python’**
"python "*****2
'Output : Python python ’
#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type