Semantic Web, SW
Services, Grid, Cloud
Martin Kuba, ÚVT MU
makub@ics.muni.cz
Outline
● Semantic Web
○ expressing semantics
○ RDF, OWL, ontologies
○ Semantic Web Services
● Grid
● Cloud
● Containers
● Infrastructure as Code
Semantic Web
● idea introduced by Tim Berners Lee
(inventor of WWW) in 2001
● “The Semantic Web is not a separate Web
but an extension of the current one, in which
information is given well-defined meaning,
better enabling computers and people to
work in cooperation”
● web instead of platform for distributed
presentations would be platform for
distributed knowledge
Semantic Continuum
● semantics = meaning
● semantic in SW means machine-processable
● semantic continuum (Uschold 2003)
a. implicit semantics in the minds of humans
b. explicit informal semantics (text description in natural
language, e.g. HTML specification)
c. formal semantics for humans (in formal language
processed by humans)
d. formal semantics for machine processing
● goal is to create robotic decision-making
devices
● metadata - data about data
Expressing Semantics
● folksonomies
● microdata
● RDF triples and RDF Schema vocabularies
● OWL-DL ontologies for automated reasoning
Folksonomies
● keyword metadata as tags
● e.g. an image of a dog may be tagged with
tags dog, collie or pet
● (+) low entry barrier, no user training
● (-) no synonym control, flat structure
● tag clouds
Microdata
● competing Microdata, Microformats, RDFa
● nesting semantics within existing content on web
pages
● RDFa only inside XML, not in HTML5
● Microdata provides JavaScript API
● Microdata use namespace-qualified vocabularies
predefined at data-vocabulary.org or schema.org
● supported by Google search engine
● opposite vision than in 2000:
○ XML with CSS or XSLT - semantic markup with
presentational metadata
○ HTML5 with Microdata - presentational markup with
semantic metadata
Comparison of Microdata and others
JSON-LD
● Microdata are deprecated since 2016
● Microdata DOM API is deprecated since
2018 in Mozilla, removed since Firefox 49
● JSON-LD (JavaScript Object Notation for
Linked Data) replaces them
● W3C Recommendation since 2014
● Google parses JSON-LD and uses them in
searches
Example of JSON-LD
RDF - Resource Description Framework
● statements about web resources
● triples subject-predicate-object
● subject and predicate are URIs
● object can be a URI or a data value
● reification - an RDF statement is assigned a
URI and treated as a resource
● producers and consumers of RDF
statements must agree on the semantics of
the resource identifiers, conveyed by some
controlled vocabulary
RDF Schema
● tool for defining controlled vocabularies
● defines
○ classes of things
○ properties (binary predicates)
○ subsumption relationships (subclasses, subproperties)
○ rdf:type - resource is an instance of a class
● SPARQL (SPARQL Protocol and RDF Query
Language) is an SQL-like language for
querying RDF graphs
● entailment rules allow to entail e.g. that when
a resource is in a particular class, then it is
also in all its superclasses
RDF Schema example
● RDFS can define two classes:
○ Person
○ Student as subclass of Person
● a RDF statement may state that a resource
representing John Doe is of rdf:type Student
● by entailment, John Doe is also a Person
OWL
● Web Ontology Language defined by W3C
● ontology is a term from artificial intelligence
● ontology is “an explicit (written) formal
conceptualization”, used for capturing
knowledge about some domain of interest
● “conceptualization is an abstract simplified view of some selected part of the world, containing
the objects, concepts, and other entities that are presumed of interest for some particular
purpose and the relationships between them”
● OWL 1 released in 2004, OWL 2 in 2009
● two different (incompatible) semantics
○ RDF based - OWL Full
○ DL (Description Logics) based - OWL DL
Types of logic
● Propositional logic
○ formulae made of atomic propositions with values
true or false, and logical connectives like negation
(¬A), and (A∧B), or (A∨B) and implication (A→B)
○ sound, complete and decidable in finite time
● Predicate logic
○ adds predicates, quantifiers, terms
○ formulae look like ∀x∃y(P(x)→Q(f(y)))
● First order predicate logic
○ quantifiers can range only over elements of sets
○ sound and complete, but not decidable
● Description logics
○ logics designed to be as expressive as possible
while retaining decidability
OWL DL
● Description Logics is a decidable fragment of
First Order Predicate Logic (FOL) plus
decidable extensions
● reasoners - software able to entail complete
inferable knowledge in finite time
● OWL DL uses:
○ classes
○ individuals
○ properties (binary relations)
■ object properties (between two objects)
■ data properties (between object and data literal)
● can use SWRL (Sem. Web Rule Language)
Prefix(:=<http://goo.gl/kWFWj#>)
Prefix(xsd:=<http://www.w3.org/2001/XMLSchema#>
Ontology(<http://goo.gl/kWFWj>
Declaration( Class( :Person ) )
Declaration( Class( :MarriedPerson ))
Declaration( NamedIndividual( :Martin ) )
Declaration( NamedIndividual(:Lenka ) )
Declaration( ObjectProperty(:hasSpouse ) )
Declaration( DataProperty(:hasEmail ) )
SymmetricObjectProperty( :hasSpouse )
FunctionalObjectProperty( :hasSpouse )
ClassAssertion( :Person :Lenka )
ClassAssertion( :Person :Martin )
DifferentIndividuals( :Martin :Lenka )
ObjectPropertyAssertion( :hasSpouse :Martin :Lenka )
DataPropertyAssertion( :hasEmail :Martin "makub@ics.muni.cz"^^xsd:string )
SubClassOf( :MarriedPerson :Person )
EquivalentClasses( :MarriedPerson ObjectSomeValuesFrom( :hasSpouse :Person ))
OWL DL Tools
● ontology editor with GUI - Protege
○ http://protege.stanford.edu/
● reasoners
○ Pellet
○ HermiT
○ FACT++
○ Stardog
● Java API for OWL - OWL API
○ http://owlapi.sourceforge.net/
Limits of OWL DL
● based on FOL ∀x∃y(P(x)→Q(f(y)))
● cannot express
○ fuzzy expressions - “It often rains in autumn.”
○ non-monotonicity - “Birds fly, penguin is a bird, but penguin does not fly.”
○ propositional attitudes - “Eve thinks that 2 is not a prime number.”
○ modal logic
■ possibility and necessity - “It is possible that it will rain today.”
■ epistemic modalities - “Eve knows that 2 is a prime number.”
■ temporal logic - “I am always hungry.”
■ deontic logic - “You must do this.”
● Transparent Intensional Logic (TIL)
○ can express anything that can be said
○ has no calculus or reasoning algorithms
Semantic Web Services
● research efforts OWL-S, WSDL-S, WSMO
● semantics can enhance discovery
○ on the semantic continuum move it from b) to d)
○ e.g. search for "getHardDriveQuote" can find also
"getQuoteForHardDrive" (synonym) and "getSCSIDriveQuote"
(subsumed term)
● web service semantics
○ Data semantics - it defines meaning of the data, i.e. inputs and outputs of operations
○ Functional semantics - it defines meaning of the operations, i.e. how they transform input
to output
○ QoS semantics - it provides meaning for quality aspects, like price, availability, level of
trust etc. Service selection may be based on such characteristics.
○ Execution semantics - it provides details like preconditions and effects of service
invocation, conversation patters of service invocation etc
Grid
● term introduced in 1998 by Carl
Kesselman and Ian Foster in book
"The Grid: Blueprint for a New Computing
Infrastructure"
● analogy to electrical power grid
● "A computational grid is a hardware and
software infrastructure that provides
dependable, consistent, pervasive, and
inexpensive access to high-end
computational capabilities."
● in 2001 in article "The Anatomy of the
Grid" added Virtual Organizations
What is The Grid ?
● coordinates resources that are not subject to
centralized control
● using standard, open, general-purpose
protocols and interfaces
● to deliver nontrivial qualities of service.
Grid Usage
● high performance computing (HPC)
○ research of medical drugs
○ gravitation waves research
○ earthquake prediction
○ electronic chip engineering
○ ...
● large data
○ Large Hadron Collider in CERN
● expensive scientific instruments
○ large microscope in Japan
● remote cooperation
○ teleconferences, remote surgery, ...
Grid Middleware
● not a single middleware
○ in U.S.A. Globus
○ in Europe gLite
○ in Germany UNICORE
● services
○ information services (Globus: MDS, gLite: BDII)
○ gridFTP - striped transfer, third party transfer
○ resource allocation (Globus: GRAM, gLite: WMS)
○ virtual organization membership (VOMS)
● Computing Element
○ grid gate, batch system, cluster of worker nodes
● Storage Element
○ disk servers, disk arrays, tape storage
Grid Security
● based on X509 certificates and PKI (Public
Key Infrastructure)
● list of selected CAs (Certification Authorities)
maintained by IGTF (International Grid Trust
Federation)
● allows so-called proxy certificates
○ short-lived (24 hours, 1 week)
○ a certificate signed by a user certificate or proxy cert.
○ can be delegated to a running job
● VOMS (Virtual Organisation Membership
Service) issues attribute certificates specifying
user privileges on resources
European Grid History
● in 2001-2003 project DataGrid
○ for processing massive data produced by Large
Hadron Collider in CERN
● in 2004-2010 projects EGEE I, II, III
● in 2010-2014 EGI (European Grid Infrastructure) built in
project InSPIRE
● 2015-2017 project EGI-Engage
● EGI in November 2016
○ 730000 CPUs
○ 285 PB disk storage, 280 PB archive storage
● EGI consists of NGIs (National Grid Infrastructures)
● Czech NGI is MetaCentrum, operated by CESNET,
collects 18260 CPUs, 4.7 PB disk storage, 15.6 PB tape
storage
Cloud Computing
● use of computing resources (hardware and
software) that are delivered as a service
over a network
● in 1960 utility computing
● in 2006 Amazon released AWS (Amazon
Web Service)
○ EC2 (Elastic Compute Cloud)
○ S3 (Simple Storage Service)
Cloud definition
● cloud computing is a general term for
anything that involves delivering hosted
services over the Internet
● definition by NIST (National Institute of Standards and Technology, U.S.
Department of Commerce): Cloud computing is a model for enabling
ubiquitous, convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, servers, storage,
applications, and services) that can be rapidly provisioned and released
with minimal management effort or service provider interaction.
● five essential characteristics: on-demand self-service, broad network
access, resource pooling, rapid elasticity and measured service
Cloud Service Models
● Software as a Service (SaaS)
● Platform as a Service (PaaS)
● Infrastructure as a Service (IaaS)
SaaS - Software as a Service
● best known to computer users, the only one they
directly use, provides device independence
● examples:
○ web mail – GMail, Hotmail
○ social networking and messaging – Facebook, Google+,
Twitter
○ on-line office suites – Google Docs, Microsoft Office 365
○ file services – Dropbox, Google Drive, Microsoft
OneDrive, ownCloud
○ image libraries – Picasa, Flickr
○ video libraries – YouTube, Vimeo
○ communication tools – Adobe Connect, WebEx
○ business software – Salesforce, NetSuite
PaaS - Platform as a Service
● platform is a software environment used to
develop and run applications
● not visible to end users, targeted to application
developers and maintainers delivering their SaaS
applications
● examples:
○ Google App Engine (provides PHP, Python, Java, Go)
○ Amazon Elastic Beanstalk (provides Ruby, PHP, Python, .NET, Java,
JavaScript)
○ Heroku (provides Ruby, PHP, Python, Java, JavaScript, Perl)
○ Microsoft Azure Websites (provides PHP, Python, .NET, JavaScript)
○ Red Hat OpenShift (provides Ruby, Python, PHP, JavaScript, Perl, Java,
Haskell, .NET)
IaaS - Infrastructure as a Service
● provides a virtual data center
● IaaS provider provides virtual machines
(VMs) with complete operating systems
● many VMs can be hosted on a single
physical machine running hypervisor
software (Xen, KVM, VMWare)
● resources hired from an IaaS cloud can be
used directly (e.g. on-demand movie
rendering) or as a layer under a PaaS or
SaaS cloud
IaaS Providers Provide
● disk images with pre-installed popular
operating systems (various versions of
Linux, MS-Windows)
● networking services - virtual local area
networks, virtual private networks, IP
addresses, firewalls,
load balancers, domain name service (DNS)
● storage services - virtual block storage, file
storage, object storage, relational database
storage,no-SQL storage, tape archive
storage, content delivery network (CDN)
IaaS Examples
● providers:
○ Amazon Elastic Compute Cloud
○ Google Compute Engine
○ Microsoft Azure
○ Rackspace Cloud Servers
● software:
○ OpenNebula
○ OpenStack
○ Eucalyptus
○ VMware vCloud Suite
Cloud Service Models Summary
● Software-as-a-Service model provides on-demand
access to software, either as downloadable code executed
on client computers, or through remote API calls to code
executed on servers
● Platform-as-a-Service model provides on-demand
software environment for deploying applications. The
environment includes concrete programming languages,
their specific libraries, and additional
services like SQL and no-SQL storage. PaaS cloud is
usually used as a layer under SaaS cloud services.
● Infrastructure-as-a-Service model provides on-demand
resources from a virtual data center. The resources can be
used directly or as a layer under PaaS or SaaS cloud
services.
Masaryk University / CERIT-SC Cloud
● private IaaS cloud
● based on OpenNebula with KVM hypervisors
● disk images with Debian Linux, CentOS,
SciLinux, Ubuntu and MS-Windows
● archive in the form of HSM (Hierarchical
Storage Management) with layers
○ RAID disk arrays
○ massive arrays of idle disks (MAIDs)
○ magnetic tapes
● in March 2019 it provides 5000 CPUs
Docker containers
● real Virtual Machines have some overhead
● Docker is a tool for deployment of software in
so-called containers
● a container is an isolated environment with complete
system libraries, running inside a hosting OS
● a container is in principle a chroot directory with
cgroups and namespaces, with exportable directories
and TCP ports for linking to other containers
● a container can contain i.e. Ubuntu 18.04 with
Apache, but can run on any Linux, MacOS X or
Windows host
Docker containers (2)
● Docker containers are like .deb or .rpm packages, but
OS-independent
● versioned repository of containers on DockerHub
● official containers for famous software (e.g. Postgres,
Apache, Ubuntu, Centos, …)
● anybody can create a new container by modifying
another container
● containers can be linked using TCP ports
(e.g. Apache+PHP -> PostgreSQL)
● persistent data can be stored outside of containers
using exported directories (called volumes)
Infrastructure as Code (IaC)
● managing computer data centers through
machine-readable definition files
● no interactive configuration tools
● necessary when managing hundreds of
machines
● definitions may be in a version control
system (e.g. git)
● tools like Puppet, Ansible, SaltStack
● declarative approach defines the desired
state of machines
● idempotent actions - can be run repeatedly
with the same result as when run just once
Ansible example
That’s it.
Thank you for your attention