• Today in the Knowledge Economy and with the advancement of technologies and Rapid Sophistication of People in Urban Areas there is a need to make cities SMART to conserve Energy and resources for a long period of time. So one initiative in which San Carlos Calif has taken an initiative that with the help of Mobile Apps along with Sensors to implement Smart Parking Solutions they can keep a track of Parking Space nearby the Place or Shop where they want to track their Vehicle.
• Another way is that our cities are connecting hospitals to expand medical services via TELEMEDICINE this program help the patients to avoid long journeys and wait time and with the help of internet Doctor can diagnose Patients Problem.
• SMART Countries of Asia has been using Renewable Resources as in India there is a scarcity of resources. So India has to use renewable sources of energy. we have to use Solar cookers and Solar heaters which reduces our Consumption of LPG and increases our dependence on Solar Power Plants to generate Electricity.
• SMART Education which is the signal of Development and growth prospects in the country by using ICT Methods as India has to use Smart Technology Methods as Said By SAMSUNG to take an Initiative from the Secondary Schools so that their Brain get sharped from the very first day to compete and survive in this competitive world and for getting admission in reputed universities.
• SMART Cities must have Public Transport facility available at short distances in the form of Buses and Metros or even rapid metros so that People avoid using their own vehicles to go for any domestic work and even office work •
The Internet of things describes physical objects that are embedded with sensors, processing ability, software, and other technologies that connect and exchange data with other devices and systems over the Internet or other communications networks.
Getting started on your natural language processing project? First you'll need to extract some features from your corpus. Frequency, Syntax parsing, word vectors are good ones to start with.
• Today in the Knowledge Economy and with the advancement of technologies and Rapid Sophistication of People in Urban Areas there is a need to make cities SMART to conserve Energy and resources for a long period of time. So one initiative in which San Carlos Calif has taken an initiative that with the help of Mobile Apps along with Sensors to implement Smart Parking Solutions they can keep a track of Parking Space nearby the Place or Shop where they want to track their Vehicle.
• Another way is that our cities are connecting hospitals to expand medical services via TELEMEDICINE this program help the patients to avoid long journeys and wait time and with the help of internet Doctor can diagnose Patients Problem.
• SMART Countries of Asia has been using Renewable Resources as in India there is a scarcity of resources. So India has to use renewable sources of energy. we have to use Solar cookers and Solar heaters which reduces our Consumption of LPG and increases our dependence on Solar Power Plants to generate Electricity.
• SMART Education which is the signal of Development and growth prospects in the country by using ICT Methods as India has to use Smart Technology Methods as Said By SAMSUNG to take an Initiative from the Secondary Schools so that their Brain get sharped from the very first day to compete and survive in this competitive world and for getting admission in reputed universities.
• SMART Cities must have Public Transport facility available at short distances in the form of Buses and Metros or even rapid metros so that People avoid using their own vehicles to go for any domestic work and even office work •
The Internet of things describes physical objects that are embedded with sensors, processing ability, software, and other technologies that connect and exchange data with other devices and systems over the Internet or other communications networks.
Getting started on your natural language processing project? First you'll need to extract some features from your corpus. Frequency, Syntax parsing, word vectors are good ones to start with.
Entity Resolution is the task of disambiguating manifestations of real world entities through linking and grouping and is often an essential part of the data wrangling process. There are three primary tasks involved in entity resolution: deduplication, record linkage, and canonicalization; each of which serve to improve data quality by reducing irrelevant or repeated data, joining information from disparate records, and providing a single source of information to perform analytics upon. However, due to data quality issues (misspellings or incorrect data), schema variations in different sources, or simply different representations, entity resolution is not a straightforward process and most ER techniques utilize machine learning and other stochastic approaches.
Smart Cities and Big Data - Research Presentationannegalang
Research presentation on smart cities (sensor technology) and big data, presented in a graduate course I took on Transmedia Design and Digital Culture.
GraphAware: Insights Discovery with KGs: Bringing Archives to Life (GraphSumm...Neo4j
With the advances in the domain of NLP and NLU in recent years, such as the GPT-3 and other Large Language Models, the industry is finally mature enough to empower organisations to unlock the incredible knowledge potential hidden within omnipresent unstructured data sources. In this presentation, Dr. Vlasta Kus from GraphAware talked about the state-of-the-art technologies and complex pipelines employed with a goal of turning an archive of a major US foundation into a Knowledge Graph which enables surprise (aha-moments), massive modelling complexity and provides previously unavailable level of insights and pattern discovery.
IoT Solutions for Smart Energy Smart Grid and Smart Utility ApplicationsEurotech
Smart Energy Smart Grid and Smart Infrastructure - Many Applications and Devices
An introduction to Eurotech' s IoT Field-to-Application Building Blocks for the Energy and Utility Industry
FIWARE Overview Webinar - 27th March 2019
Corresponding webinar recording: https://youtu.be/97JsnnpPLrA
Chapter: Fundamentals
Difficulty: 0
Audience: Anyone
Basic introduction describing what FIWARE is, why you need it and how the elements of the FIWARE Catalogue can help accelerate the development of your Smart Solution.
Overview of the W3C Semantic Sensor Network (SSN) ontologyRaúl García Castro
The slides include an overview of the W3C Semantic Sensor Network (SSN) ontology along with an example of its use in a coastal flood emergency planning use case in the FP7 SSG4Env project.
Entity Resolution is the task of disambiguating manifestations of real world entities through linking and grouping and is often an essential part of the data wrangling process. There are three primary tasks involved in entity resolution: deduplication, record linkage, and canonicalization; each of which serve to improve data quality by reducing irrelevant or repeated data, joining information from disparate records, and providing a single source of information to perform analytics upon. However, due to data quality issues (misspellings or incorrect data), schema variations in different sources, or simply different representations, entity resolution is not a straightforward process and most ER techniques utilize machine learning and other stochastic approaches.
Smart Cities and Big Data - Research Presentationannegalang
Research presentation on smart cities (sensor technology) and big data, presented in a graduate course I took on Transmedia Design and Digital Culture.
GraphAware: Insights Discovery with KGs: Bringing Archives to Life (GraphSumm...Neo4j
With the advances in the domain of NLP and NLU in recent years, such as the GPT-3 and other Large Language Models, the industry is finally mature enough to empower organisations to unlock the incredible knowledge potential hidden within omnipresent unstructured data sources. In this presentation, Dr. Vlasta Kus from GraphAware talked about the state-of-the-art technologies and complex pipelines employed with a goal of turning an archive of a major US foundation into a Knowledge Graph which enables surprise (aha-moments), massive modelling complexity and provides previously unavailable level of insights and pattern discovery.
IoT Solutions for Smart Energy Smart Grid and Smart Utility ApplicationsEurotech
Smart Energy Smart Grid and Smart Infrastructure - Many Applications and Devices
An introduction to Eurotech' s IoT Field-to-Application Building Blocks for the Energy and Utility Industry
FIWARE Overview Webinar - 27th March 2019
Corresponding webinar recording: https://youtu.be/97JsnnpPLrA
Chapter: Fundamentals
Difficulty: 0
Audience: Anyone
Basic introduction describing what FIWARE is, why you need it and how the elements of the FIWARE Catalogue can help accelerate the development of your Smart Solution.
Overview of the W3C Semantic Sensor Network (SSN) ontologyRaúl García Castro
The slides include an overview of the W3C Semantic Sensor Network (SSN) ontology along with an example of its use in a coastal flood emergency planning use case in the FP7 SSG4Env project.
IOT is connecting every physical object in the world using wireless technologies to track and control them from every where in the world...Every object is uniquely identified using ip addresses(IPv6)
“Semantic Technologies for Smart Services” diannepatricia
Rudi Studer, Full Professor in Applied Informatics at the Karlsruhe Institute of Technology (KIT), Institute AIFB, presentation “Semantic Technologies for Smart Services” as part of the Cognitive Systems Institute Speaker Series, December 15, 2016.
A Unified Semantic Engine for Internet of Things and Smart Cities: From Senso...Amélie Gyrard
A Unified Semantic Engine for Internet of Things and Smart Cities: From Sensor Data to End-Users Applications
The 8th IEEE International Conference on Internet of Things (iThings 2015), 11-13 December 2015, Sydney, Australia
Amelie Gyrard, Martin Serrano
The Internet of Things, Ambient Intelligence, and the Move Towards Intelligen...George Vanecek
With the successful adoption of cloud-based services and the increasing capabilities of smart connected/wireless devices, the software and consumer electronics industries are turning towards innovating solutions within the Internet-of-Things (IoT) to offer consumers (and enterprises) smart solutions that take the dynamics of the real-world into consideration.
The vision is to bring the awareness of what happens in the real-world, how people live and how smart devices operate in the real world into the view and control of the digital world. Here the digital world is the totality of the Internet, the Web, and the private and public cloud services.
In this session, we will look at key technical trends and their increasing interdependency in the areas of real-world Sensing, Perception, Machine Learning, Context-awareness, dynamic Trust Determination, Semantic Web and Artificial Intelligence which are now enabling ambient intelligence and driving the emergence of Intelligence Systems within the Internet of Things. We will also look at the challenges that such interdependencies expose, and the opportunities that their solutions offer to the industry.
Dynamic Semantics for the Internet of Things PayamBarnaghi
Ontology Summit 2015 : Track A Session - Ontology Integration in the Internet of Things - Thu 2015-02-05,
http://ontolog-02.cim3.net/wiki/ConferenceCall_2015_02_05
Using the Open Science Data Cloud for Data Science ResearchRobert Grossman
The Open Science Data Cloud is a petabyte scale science cloud for managing, analyzing, and sharing large datasets. We give an overview of the Open Science Data Cloud and how it can be used for data science research.
The 3TU.Datacentrum repository of research data hosts datasets as well as other objects representing measuring devices, locations, time periods and the like. Virtually all metadata is in rdf so the repository can be approached as an rdf graph. We will show how this is implemented with Fedora Commons, heavily leaning on rdf queries and xslt2.0. As a result of this architecture, it is relatively easy to make the repository linked-data-enabled by generating OAI/ORE resource maps.
While most of the metadata is rdf, most of the data is in NetCDF. Although not very well known in the library world, this is very popular format in various fields of science and engineering. It comes with its own data server Opendap which offers a rich API to interact with the data. Our repository is therefore a hybrid Fedora + Opendap setup and we will show how the two are integrated into a unified view and how they are kept in sync on ingest.
This was presented at the ELAG conference, Palma de Mallorca 2012.
A distributed system in its most simplest definition is a group of computers working together as to
appear as a single computer to the end-user. These machines have a shared state, operate
concurrently and can fail independently without affecting the whole system’s uptime.
This is in line with ever-growing technological expansion of the world, distributed systems are
becoming more and more widespread. Take a look at the increasing number of available
computer technologies/innovation around, this is sporadically increasing, and this result in
intense computational requirement.
Yeah, Moore’s law proposed more computing power by fitting more transistors (which
approximately doubles every two years) into a simple chip using cost-efficient approach - cool,
but over the past 5 years, there has been little deviation from this - ability to scale horizontally
and not just vertically alone.
Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...confluent
Tinder’s Quickfire Pipeline powers all things data at Tinder. It was originally built using AWS Kinesis Firehoses and has since been extended to use both Kafka and other event buses. It is the core of Tinder’s data infrastructure. This rich data flow of both client and backend data has been extended to service a variety of needs at Tinder, including Experimentation, ML, CRM, and Observability, allowing backend developers easier access to shared client side data. We perform this using many systems, including Kafka, Spark, Flink, Kubernetes, and Prometheus. Many of Tinder’s systems were natively designed in an RPC first architecture.
Things we’ll discuss decoupling your system at scale via event-driven architectures include:
– Powering ML, backend, observability, and analytical applications at scale, including an end to end walk through of our processes that allow non-programmers to write and deploy event-driven data flows.
– Show end to end the usage of dynamic event processing that creates other stream processes, via a dynamic control plane topology pattern and broadcasted state pattern
– How to manage the unavailability of cached data that would normally come from repeated API calls for data that’s being backfilled into Kafka, all online! (and why this is not necessarily a “good” idea)
– Integrating common OSS frameworks and libraries like Kafka Streams, Flink, Spark and friends to encourage the best design patterns for developers coming from traditional service oriented architectures, including pitfalls and lessons learned along the way.
– Why and how to avoid overloading microservices with excessive RPC calls from event-driven streaming systems
– Best practices in common data flow patterns, such as shared state via RocksDB + Kafka Streams as well as the complementary tools in the Apache Ecosystem.
– The simplicity and power of streaming SQL with microservices
Similar to Semantic technologies for the Internet of Things (20)
Scientific and Academic Research: A Survival Guide PayamBarnaghi
Payam Barnaghi
Centre for Vision, Speech and Signal Processing (CVSSP)
Electrical and Electronic Engineering Department
University of Surrey
February 2019
invited talk at iPHEM16, Innovation in Pre-hospital Emergency Medicine, Kent Surrey and Sussex Air Ambulance Trust, July 2016, Brighton, United Kingdom
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
1. 1
Semantic technologies for the Internet of
Things
Payam Barnaghi
Institute for Communication Systems (ICS)
University of Surrey
Guildford, United Kingdom
International “IoT 360″ Summer School
October 29th – November 1st, 2014 – Rome, Italy
2. 2
Things, Data, and lots of it
image courtesy: Smarter Data - I.03_C by Gwen Vanhee
3. Data in the IoT
− Data is collected by sensory devices and also crowd
sensing sources.
− It is time and location dependent.
− It can be noisy and the quality can vary.
− It is often continuous - streaming data.
− There are other important issues such as:
− Device/network management
− Actuation and feedback (command and control)
− Service and entity descriptions are also important.
4. 4
“Raw data is both an oxymoron and
bad data”
Geoff Bowker, 2005
Source: Kate Crawford, "Algorithmic Illusions: Hidden Biases of Big Data", Strata 2013.
5. 5
From data to actionable information
Wisdom?
Knowledge
Information
Data
Actionable information
Abstractions and perceptions
Structured data (with semantics)
Raw sensory data
7. Semantics and Data
− Data with semantic annotations
− Provenance, quality of information
− Interpretable formats
− Links and interconnections
− Background knowledge, domain information
− Hypotheses, expert knowledge
− Adaptable and context-aware solutions
7
8. Interoperable and Semantically described
Data is the starting point to create an
efficient set of Actions.
The goal is often to create actionable
information.
9. Wireless Sensor (and Actuator)
Networks
Inference/
Processing
of IoT data
Core network
“Web of Things”
Gateway e.g. Internet
Protocols?
Data
Aggregation/
Fusion
Sink
node Gateway
End-user
Interoperable/
Computer services
Operating
Systems?
Services?
Protocols?
In-node
Data
Processing
Interoperable/
Machine-interpretable
representations
Interoperable/
Machine-interpretable
Representations?
- The networks typically run Low Power Devices
- Consist of one or more sensors, could be different type of sensors (or actuators)
Machine-interpretable
representations
10. 10
What we are going to study
− The sensors (and in general “Things”) are increasingly being
connected with Web infrastructure.
− This can be supported by embedded devices that directly support
IP and web-based connection (e.g. 6LowPAN and CoAp) or devices
that are connected via gateway components.
− Broadening the IoT to the concept of “Web of Things”
− There are already standards such as Sensor Web Enablement
(SWE) set developed by the Open Geospatial Consortium (OGC)
that are widely being adopted in industry, government and
academia.
− While such frameworks provide some interoperability, semantic
technologies are increasingly seen as key enabler for integration
of IoT data and broader Web information systems.
11. Data formats
11
Observation and measurement data-annotation
Tags
Location
Source: Cosm.com
12. Observation and measurement data
15, C, 08:15, 51.243057, -0.589444
12
value
Unit of
measurement
Time
Longitude
Latitude
How to make the data representations more machine-readable
and machine-interpretable;
13. Observation and measurement data
15, C, 08:15, 51.243057, -0.589444
13
<value>
<unit>
<Time>
<Longitude>
<Latitude>
What about this?
<value>15</value>
<unit>C</unit>
<time>08:15</time>
<longitude>51.243057</longitude>
<latitude>-0.58944</latitude>
14. Extensible Markup Language (XML)
− XML is a simple, flexible text format that is used
for data representation and annotation.
− XML was originally designed for large-scale
electronic publishing.
− XML plays a key role in the exchange of a wide
variety of data on the Web and elsewhere.
− It is one of the most widely-used formats for
sharing structured information.
14
15. XML Document Example
<?xml version="1.0"?>
<measurement>
<value>15</value>
<unit>C</unit>
<time>08:15</time>
<longitude>51.243057</longitude>
<latitude>-0.58944</latitude>
</measurement>
15
XML Prolog- the XML
declaration
XML
elements
XML documents
MUST be “well
formed”
Root element
17. Well Formed XML Documents
− A "Well Formed" XML document has correct XML
syntax.
− XML documents must have a root element
− XML elements must have a closing tag
− XML tags are case sensitive
− XML elements must be properly nested
− XML attribute values must be quoted
Source: W3C Schools, http://www.w3schools.com/ 17
18. Validating XML Documents
− A "Valid" XML document is a "Well Formed" XML
document, which conforms to the structure of the
document defined in an XML Schema.
− XML Schema defines the structure and a list of
defined elements for an XML document.
18
19. XML Schema- example
<xs:element name=“measurement">
<xs:complexType>
<xs:sequence>
<xs:element name=“value" type="xs:decimal"/>
<xs:element name=“unit" type="xs:string"/>
<xs:element name=“time" type="xs:time"/>
<xs:element name=“longitude" type="xs:double"/>
<xs:element name=“latitude" type="xs:double"/>
</xs:sequence>
</xs:complexType>
</xs:element>
19
- XML Schema defines the structure and elements
- An XML document then becomes an instantiation of the document defined
by the schema;
20. XML Documents–
revisiting the example
<?xml version="1.0"?>
<measurement>
<value>15</value>
<unit>C</unit>
<time>08:15</time>
<longitude>51.243057</longitude>
<latitude>-0.58944</latitude>
</measurement>
20
<?xml version="1.0"?> “But what about this?”
<sensor_data>
<reading>15</reading>
<u>C</u>
<timestamp>08:15</timestamp>
<long>51.243057</long>
<lat>-0.58944</lat>
</sensor_data>
21. 21
XML
− Meaning of XML-Documents is intuitively clear
− due to "semantic" Mark-Up
− tags are domain-terms
− But, computers do not have intuition
− tag-names do not provide semantics for machines.
− DTDs or XML Schema specify the structure of
documents, not the meaning of the document
contents
− XML lacks a semantic model
− has only a "surface model”, i.e. tree
Source: Semantic Web, John Davies, BT, 2003.
22. XML:
limitations for semantic markup
− XML representation makes no commitment on:
− Domain specific ontological vocabulary
−Which words shall we use to describe a given set of concepts?
− Ontological modelling primitives
−How can we combine these concepts, e.g. “car is a-kind-of
(subclass-of) vehicle”
requires pre-arranged agreement on
vocabulary and primitives
Only feasible for closed collaboration
agents in a small & stable community
pages on a small & stable intranet
.. not for sharable Web-resources
Source: Semantic Web, John Davies, BT, 2003. 22
23. Semantic Web technologies
− XML provide a metadata format.
− It defines the elements but does not provide
any modelling primitive nor describes the
meaningful relations between different
elements.
− Using semantic technologies to solve these
issues.
23
24. A bit of history
− “The Semantic Web is an extension of the current web
in which information is given well-defined meaning,
better enabling computers and people to work in co-operation.“
(Tim Berners-Lee et al, 2001)
24
Image source: Miller 2004
25. Semantics & the IoT
− The Semantic Sensor (&Actuator) Web is an extension
of the current Web/Internet in which information is given
well-defined meaning, better enabling objects, devices
and people to work in co-operation and to also enable
autonomous interactions between devices and/or
objects.
25
26. Resource Description
Framework (RDF)
− A W3C standard
− Relationships between documents
− Consisting of triples or sentences:
− <subject, property, object>
− <“Sensor”, hasType, “Temperature”>
− <“Node01”, hasLocation, “Room_BA_01” >
− RDFS extends RDF with standard “ontology
vocabulary”:
− Class, Property
− Type, subClassOf
− domain, range
26
27. RDF for semantic annotation
− RDF provides metadata about resources
− Object -> Attribute-> Value triples or
− Object -> Property-> Subject
− It can be represented in XML
− The RDF triples form a graph
27
31. Let’s add a bit more structure
(complexity?)
31
xsd:decimal
Location
hasValue
hasTime
xsd:double
xsd:time
xsd:double
xsd:string
hasLongitude
hasLatitude
hasUnit
Measurement
hasLocation
32. An instance of our model
32
15
Location
#0126
hasValue
hasTime
51.243057
08:15
-0.589444
C
hasLongitude
hasLatitude
hasUnit
Measurement
#0001
hasLocation
33. RDF: Basic Ideas
−Resources
−Every resource has a URI (Universal Resource
Identifier)
−A URI can be a URL (a web address) or a some
other kind of identifier;
−An identifier does not necessarily enable
access to a resources
−We can think of a resources as an object that
we want to describe it.
−Car
−Person
−Places, etc.
33
34. RDF: Basic Ideas
− Properties
− Properties are special kind of resources;
− Properties describe relations between resources.
− For example: “hasLocation”, “hasType”, “hasID”,
“sratTime”, “deviceID”,.
− Properties in RDF are also identified by URIs.
− This provides a global, unique naming scheme.
− For example:
−“hasLocation” can be defined as:
− URI: http://www.loanr.it/ontologies/DUL.owl#hasLocation
− SPARQL is a query language for the RDF data.
−SPARQL provide capabilities to query RDF graph patterns
along with their conjunctions and disjunctions.
34
35. Ontologies
− The term ontology is originated from philosophy.
In that context it is used as the name of a
subfield of philosophy, namely, the study of the
nature of existence.
− In the Semantic Web:
− An ontology is a formal specification of a domain;
concepts in a domain and relationships between the
concepts (and some logical restrictions).
35
36. Ontologies and Semantic Web
− In general, an ontology describes a set of
concepts in a domain.
− An ontology consists of a finite list of terms and
the relationships between the terms.
− The terms denote important concepts (classes of
objects) of the domain.
− For example, in a university setting, staff
members, students, courses, modules, lecture
theatres, and schools are some important
concepts.
36
37. Web Ontology Language (OWL)
− RDF(S) is useful to describe the concepts and their
relationships, but does not solve all possible requirements
− Complex applications may want more possibilities:
− similarity and/or differences of terms (properties or classes)
− construct classes, not just name them
− can a program reason about some terms? e.g.:
− each «Sensor» resource «A» has at least one «hasLocation»
− each «Sensor» resource «A» has maximum one ID
− This lead to the development of Web Ontology Language or
OWL.
37
38. OWL
− OWL provide more concepts to express meaning
and semantics than XML and RDF(S)
− OWL provides more constructs for stating logical
expressions such as: Equality, Property
Characteristics, Property Restrictions, Restricted
Cardinality, Class Intersection, Annotation
Properties, Versioning, etc.
Source: http://www.w3.org/TR/owl-features/ 38
39. Ontology engineering
− An ontology: classes and properties (also referred
to as schema ontology)
− Knowledge base: a set of individual instances of
classes and their relationships
− Steps for developing an ontology:
− defining classes in the ontology and arranging the
classes in a taxonomic (subclass–superclass) hierarchy
− defining properties and describing allowed values and
restriction for these properties
− Adding instances and individuals
40. Basic rules for designing ontologies
− There is no one correct way to model a domain;
there are always possible alternatives.
− The best solution almost always depends on the
application that you have in mind and the required
scope and details.
− Ontology development is an iterative process.
− The ontologies provide a sharable and extensible form to
represent a domain model.
− Concepts that you choose in an ontology should
be close to physical or logical objects and
relationships in your domain of interest (using
meaningful nouns and verbs).
41. A simple methodology
1. Determine the domain and scope of the model that you want to
design your ontology.
2. Consider reusing existing concepts/ontologies; this will help to
increase the interoperability of your ontology.
3. Enumerate important terms in the ontology; this will determine
what are the key concepts that need to be defined in an ontology.
4. Define the classes and the class hierarchy; decide on the classes
and the parent/child relationships
5. Define the properties of classes; define the properties that relate
the classes;
6. Define features of the properties; if you are going to add
restriction or other OWL type restrictions/logical expressions.
7. Define/add instances
41
42. Semantic technologies in the IoT
− Applying semantic technologies to IoT can
support:
− Interoperability
− effective data access and integration
− resource discovery
− reasoning and processing of data
− knowledge extraction (for automated decision making
and management)
42
43. 43
Data/Service description frameworks
− There are standards such as Sensor Web Enablement
(SWE) set developed by the Open Geospatial Consortium
that are widely being adopted in industry, government and
academia.
− While such frameworks provide some interoperability,
semantic technologies are increasingly seen as key enabler
for integration of IoT data and broader Web information
systems.
44. Revisiting goals of the
Internet of Things
− A primary goal of interconnecting devices and
collecting/processing data from them is to create
situation awareness and enable applications,
machines, and human users to better understand
their surrounding environments.
− The understanding of a situation, or context,
potentially enables services and applications to
make intelligent decisions and to respond to the
dynamics of their environments.
44
45. 45
Sensor Markup Language (SensorML)
Source: http://www.mitre.org/
The Sensor Model
Language Encoding
(SensorML) defines
models and XML
encoding to represent the
geometric, dynamic, and
observational
characteristics of sensors
and sensor systems.
46. Using semantics
− Find all available resources (which can provide
data) and data related to “Room A” (which is an
object in the linked data)?
− What is “Room A”? What is its location? returns “location”
data
− What type of data is available for “Room A” or that “location”?
(sensor types)
− Predefined Rules can be applied based on
available data
− (TempRoom_A > 80°C) AND (SmokeDetectedRoom_A position==TRUE)
FireEventRoom_A
46
47. Semantic modelling
− Lightweight: experiences show that a lightweight
ontology model that well balances expressiveness
and inference complexity is more likely to be
widely adopted and reused; also large number of
IoT resources and huge amount of data need
efficient processing
− Compatibility: an ontology needs to be consistent
with those well designed, existing ontologies to
ensure compatibility wherever possible.
− Modularity: modular approach to facilitate
ontology evolution, extension and integration
with external ontologies.
47
48. Existing models- SSN Ontology
− W3C Semantic Sensor Network Incubator Group’s
SSN ontology (mainly for sensors and sensor
networks, platforms and systems).
http://www.w3.org/2005/Incubator/ssn/
49. Stimulus-Sensor-Observation
- The SSO Ontology Design Pattern developed
following the principle of minimal ontological
commitments to make it reusable for a variety of
application areas.
-Introduces a minimal set of classes and relations
centered around the notions of stimuli, sensor, and
observations.
-Defines stimuli as the (only) link to the physical
environment.
49
52. 52
SSN Ontology
Ontology Link: http://www.w3.org/2005/Incubator/ssn/ssnx/ssn
M. Compton et al, "The SSN Ontology of the W3C Semantic Sensor Network Incubator Group", Journal of Web Semantics, 2012.
53. 53
53
W3C SSN Ontology
makes observations
of this type
What it
measures
Where it is
units
SSN-XG ontologies
SSN-XG annotations
SSN-XG Ontology Scope
54. What SSN does not model
− Sensor types and models
− Networks: communication, topology
− Representation of data and units of measurement
− Location, mobility or other dynamic behaviours
− Control and actuation
− ….
54
55. Web of Things
− Integrating the real world data
into the Web and providing
Web-based interactions with
the IoT resources is also often
discussed under umbrella term
of “Web of Things” (WoT).
− WoT data is not only large in
scale and volume, but also
continuous, with rich
spatiotemporal dependency.
55
56. Web of Things
Connecting sensor, actuator and other devices to the World
Wide Web.
“Things’ data and capabilities are exposed as web
data/services.
Enables an interoperable usage of IoT resources (e.g.
sensors, devices, their data and capabilities) by enabling
web based discovery, access, tasking, and alerting.
56
58. 58
The world of IoT and Semantics:
Challenges and issues
59. 59
Some good existing models:
SSN Ontology
Ontology Link: http://www.w3.org/2005/Incubator/ssn/ssnx/ssn
M. Compton et al, "The SSN Ontology of the W3C Semantic Sensor Network Incubator Group", Journal of Web Semantics, 2012.
60. Semantic Sensor Web
60
“The semantic sensor Web enables
interoperability and advanced analytics
for situation awareness and other
advanced applications from
heterogeneous sensors.”
(Amit Sheth et al, 2008)
62. 62
We have good models and description
frameworks;
The problem is that having good
models and developing ontologies is
not enough.
63. 63
Semantic descriptions are intermediary
solutions, not the end product.
They should be transparent to the end-user
and probably to the data producer
as well.
64. A WoT/IoT Framework
WSN
WSN
WSN
WSN
WSN
Network-enabled
Devices
Semantically
annotate data
64
Gateway
CoAP
HTTP
CoAP
CoAP
HTTP
6LowPAN
Semantically
annotate data
http://mynet1/snodeA23/readTemp?
WSN
MQTT
MQTT
Gateway
And several other
protocols and solutions…
65. Publishing Semantic annotations
− We need a model (ontology) – this is often the easy part
for a single application.
− Interoperability between the models is a big issue.
− Express-ability vs Complexity is a challenge
− How and where to add the semantics
− Where to publish and store them
− Semantic descriptions for data, streams, devices
(resources) and entities that are represented by the
devices, and description of the services.
65
67. Hyper/CAT
- Servers provide catalogues of resources to
clients.
- A catalogue is an array of URIs.
- Each resource in the catalogue is annotated
with metadata (RDF-like triples).
67 Source: Toby Jaffey, HyperCat Consortium, http://www.hypercat.io/standard.html
68. Hyper/CAT model
68 Source: Toby Jaffey, HyperCat Consortium, http://www.hypercat.io/standard.html
69. 69
Complex models are (sometimes) good
for publishing research papers….
But they are often difficult to
implement and use in real world
products.
70. What happens afterwards is more important
− How to index and query the annotated data
− How to make the publication suitable for constrained
environments and/or allow them to scale
− How to query them (considering the fact that here we are
dealing with live data and often reducing the processing
time and latency is crucial)
− Linking to other sources
70
71. The IoT is a dynamic, online and rapidly
changing world
71
isPartOf
Annotation for the (Semantic) Web
Annotation for the IoT
Image sources: ABC Australia and 2dolphins.com
73. 73
Creating common vocabularies and
taxonomies are also equally important
e.g. event taxonomies.
74. 74
We should accept the fact that
sometimes we do not need (full)
semantic descriptions.
Think of the applications and use-cases
before starting to annotate the data.
75. 75
Semantic descriptions can be fairly
static on the Web;
In the IoT, the meaning of data and
the annotations can change over
time/space…
77. Dynamic Semantics
<iot:measurement>
<iot:type> temp</iot:type>
<iot:unit>Celsius</iot:unit>
<time>12:30:23UTC</time>
<iot:accuracy>80%</iot:accuracy>
<loc:long>51.2365<loc:lat>
<loc:lat>0.5703</loc:lat>
</iot:measurment>
- But this could be also a
function of time and
location;
- What would be the
accuracy 5 seconds after
the measurement?
- Should it be a part of this
model?
77
78. Dynamic annotations for data in the
process chain
S. Kolozali et al, A Knowledge-based Approach for Real-Time IoT Data Stream Annotation and Processing", iThings 2014, 2014. 78
79. Dynamic annotations for provenance data
S. Kolozali et al, A Knowledge-based Approach for Real-Time IoT Data Stream Annotation and Processing", iThings 2014, 2014. 79
81. Extraction of events and semantics from social media
81
Tweets from a city
City Infrastructure
https://osf.io/b4q2t/
P. Anantharam, P. Barnaghi, K. Thirunarayan, A. Sheth, "Extracting city events from social streams,“, 2014.
83. Overall, we need semantic technologies
in the IoT and these play a key role in
providing interoperability.
84. However, we should design and use
the semantics carefully and
consider the constraints and
dynamicity of the IoT environments.
85. #1: Design for large-scale and provide tools and
APIs.
#2: Think of who will use the semantics and how
when you design your models.
#3: Provide means to update and change the
semantic annotations.
85
86. #4: Create tools for validation and interoperability
testing.
#5: Create taxonomies and vocabularies.
#6: Of course you can always create a better
model, but try to re-use existing ones as much as
you can.
86
87. #7: Link your data and descriptions to other
existing resources.
#8: Define rules and/or best practices for providing
the values for each attribute.
#9: Remember the widely used semantic
descriptions on the Web are simple ones like
FOAF.
87
88. #10: Semantics are only one part of the solution
and often not the end-product so the focus of the
design should be on creating effective methods,
tools and APIs to handle and process the
semantics.
Query methods, machine learning, reasoning and
data analysis techniques and methods should be
able to effectively use these semantics.
88
89. Data analytics framework
Ambient
Intelligence
Social
systems Interactions Interactions
89
Data Data
Data:
Domain
Knowledge
Domain
Knowledge
Social
systems
Open
Interfaces
Open
Interfaces
Ambient
Intelligence
Quality and
Quality and
Trust
Trust
Privacy and
Security
Privacy and
Security
Open Data Open Data
91. IoT data: semantic related issues
− The current IoT data communications often rely on binary or syntactic data
models which lack of providing machine interpretable meanings to the
data.
− Syntactic representation or in some cases XML-based data
− Often no general agreement on annotating the data
− requires a pre-agreement between different parties to be able to
process and interpret the data
− Limited reasoning based on the content and context data
− Limited interoperability in data and resource/device description level
− Data integration and fusion issues
92. Requirements
− Structured representation of concepts
− Machine-interpretable descriptions
− Reasoning mechanisms
− Access mechanism to heterogeneous resource descriptions with
diverse capabilities
− Automated interactions and horizontal integration with existing
applications
93. What are the challenges?
− The models provide the basic description frameworks, but
alignment between different models and frameworks are required.
− Semantics are the starting point, reasoning and interpretation of
data is required for automated processes.
− Real interoperability happens when data/services from different
frameworks and providers can be interchanged and used with
minimised intervention.
94. Possible solutions?
− The semantic Web has faced this problem earlier.
− Proposed solution: using machine-readable and machine-interpretable
meta-data
− Important not: machine-interpretable but not machine-untreatable!
− Well defined standards and description frameworks: RDF, OWL, SPARQL
− Variety of open-source, commercial tools for creating/managing/querying
and accessing semantic data
− Jena, Sesame, Protégé, …
− An Ontology defines conceptualisation of a domain.
− Terms and concepts
− A common vocabulary
− Relationships between the concepts
− There are several existing and emerging ontologies in the IoT domain.
− HyperCat model
− W3C SSN ontology
− And many more
− Automated annotation methods, dynamic semantics
95. How to adapt the solutions?
− Creating ontologies and defining data models are not enough
− tools to create and annotate data
− data handling components
− Complex models and ontologies look good, but
− design lightweight versions for constrained environments
− think of practical issues
− make it as much as possible compatible and/or link it to the other
existing ontologies
− Domain knowledge and instances
− Common terms and vocabularies
− Location, unit of measurement, type, theme, …
− Link it to other resource
− Linked-data
− URIs and naming
− In many cases, semantic annotations and semantic processing
should be intermediary not the end products.
96. What are the practical steps?
− Linked data approach is a promising way of integrating data from
different sources and interlinking semantic descriptions;
− Alignment between different description models for
Services/Resources/Entities;
− Using common models (e.g. HyperCat, SSNO) and developing
applications and services that use these information represented
based on the models;
− Ontology learning from real world data;
− Dynamic and automated annotations;
− Semantic processing, scalable (distributed) repository, discovery,
query and analysis support;
− Tools and support for real-time and streaming (semantically
annotated) data;
97. Quiz
− Design a simple ontology (model) to describe
operating system and different sensors on a
smart phone.
98. Q&A
− Payam Barnaghi, University of
Surrey/EU FP7 CityPulse Project
http://www.ict-citypulse.eu/
@pbarnaghi
p.barnaghi@surrey.ac.uk