A time efficient approach for detecting errors in big sensor data on cloudNexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE CODE PLEASE CALL BEOLOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM ,EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
The document discusses grid computing and the development of computational grids. Key points:
- Grids allow for sharing of computing power and resources across geographic locations through networked supercomputers, databases, and instruments.
- Major organizations like NASA, DOE, and NSF are working to build computational grids for applications like scientific simulations and instrument control.
- Indiana University is involved in grid research through various departments and projects focused on resource sharing, portals, middleware, and more.
This document provides an introduction and overview of grid computing. It defines grid computing as the collection of computer resources from multiple locations to reach a common goal. Key points include: grids link computing resources from different computers and use middleware to connect users' jobs to these resources; grids allow massive computing power by combining hundreds of computers; potential applications include computational services, data services, and information services; advantages include solving larger problems faster and better resource utilization, while disadvantages include evolving standards and a learning curve.
A time efficient approach for detecting errors in big sensor data on cloudLeMeniz Infotech
This document describes a proposed approach for detecting errors in large sensor data sets on the cloud. It aims to leverage the computation power and scalability of cloud platforms to more efficiently detect and locate errors compared to traditional methods. Specifically, the approach uses the network topology of sensor networks to distribute error detection tasks across cloud resources. An experiment on a cloud platform showed this approach can significantly reduce error detection time while maintaining accuracy compared to processing large sensor data sets locally.
“It’s Not About Sensor Making, it’s About Sense Making” - Moriya Kassis @Prod...Product of Things
This document discusses how deep learning can be used to make sense of large amounts of data from sensors and IoT devices. Deep learning algorithms can learn directly from experience without needing explicit programming of rules and features, allowing systems to adapt quickly to new data sources. The key aspects are defining the neural network architecture, optimizing parameters which can take weeks, and then running computations quickly. Deep learning enables enhanced scalability, flexibility and portability for real-time systems like smart sensors. The goal is not just sensor data but using intelligence to augment human abilities through derived insights.
It’s Not About Sensor Making, it’s About Sense MakingMoriya Kassis
Deep learning involves learning through layers which allows a computer to build a hierarchy of complex concepts out of simpler concepts. Just like Product Management, the objective of Deep Learning is to solve ‘intuitive’ problems i.e. problems characterized by High dimensionality and no rules.
In this talk, Moriya discussed with us how deep is the future of IoT, how is it changing the way we create products and what will be its implications.
Contextualised Cognitive Perspective for Linked Sensor Data iammyr
This document proposes a contextualized cognitive approach for linking sensor data. It discusses using ontologies to describe sensor concepts and contexts. Current solutions like sensor ontologies and context classification architectures have limitations. The proposal is to use a cognitive approach inspired by human cognition, delimited to the sensor environment. Context would be described using domain-agnostic, event, and upper-level ontologies. Future work includes validating ontology choices and implementing the approach with user feedback. The goal is improving understanding of sensor reality by using the linked open data cloud to enhance classification and emulate human cognition.
The document discusses the grid, which allows for integrated and collaborative use of geographically separated computing resources. Grid computing enables sharing and aggregation of distributed autonomous resources dynamically based on availability, capability, performance, cost and user requirements. Key characteristics of grid systems include coordinating resources not controlled by a central authority, using open standards, and providing quality of service.
Coupling-Based Internal Clock Synchronization for Large Scale Dynamic Distrib...Angelo Corsaro
This paper studies the problem of realizing a common software clock among a large set of nodes without an external time reference (i.e., internal clock synchronization), any centralized control and where nodes can join and leave the distributed system at their will. The paper proposes an internal clock synchronization algorithm which combines the gossip-based paradigm with a nature-inspired approach, coming from the coupled oscillators phenomenon, to cope with scale and churn. The algorithm works on the top of an overlay network and uses a uniform peer sampling service to fullfill each node’s local view. Therefore, differently from clock synchronization protocols for small scale and static distributed systems, here each node synchronizes regularly with only the neighbors in its local view and not with the whole system. Theoretical and empirical evaluations of the convergence speed and of the synchronization error of the coupled-based internal clock synchronization algorithm have been carried out, showing how convergence time and the synchronization error depends on the coupling factor and on the local view size. Moreover the variation of the synchronization error with respect to churn and the impact of a sudden variation of the number of nodes have been analyzed to show the stability of the algorithm. In all these contexts, the algorithm shows nice performance and very good self-organizing properties. Finally, we showed how the assumption on the existence of a uniform peer-sampling service is instrumental for the good behavior of the algorithm.
Grid computing involves connecting geographically distributed computers and resources into a single network to create a virtual supercomputer. Key aspects of grid computing include combining computational power from multiple computers, providing single sign-on access to distributed resources, and distributing programs across processes or computers. Popular software for implementing grids includes Globus, Condor, Legion, and NetSolve. Grids are useful for tasks like distributed supercomputing, high-throughput computing, and data-intensive computing.
Inroduction to grid computing by gargi shankar vermagargishankar1981
Grid computing allows for sharing and coordination of distributed computer resources to address large-scale computation problems. It enables dynamic, scalable, and inexpensive access to computing power by connecting computers and other resources together with open standards. Key aspects of grid computing include dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities through coordination of distributed and often heterogeneous resources not subject to centralized control.
Grid computing is a distributed computing system where a group of connected computers work together as a single large computing resource. It allows users to submit tasks that are divided into independent subtasks and distributed across available grid resources. Key benefits include solving larger problems faster through collaboration and making better use of existing hardware. While standards are still evolving, grid computing has enabled projects like the Large Hadron Collider which involves over 1,800 physicists across 32 countries.
Grid computing allows for the sharing and aggregation of distributed computing resources like computers, networks, databases and instruments. It provides a large virtual computing system for end users and applications. Key characteristics include facilitating solutions to large, complex problems across locations and organizations through integrated and collaborative use of heterogeneous resources. Popular applications include medical research, astronomy, climate modeling and more. Examples of operational grids discussed are TeraGrid, Pauá Grid Project and academic research projects like SETI@home.
Proximity aware local-recoding anonymization with map reduce for scalable big...Nexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE CODE PLEASE CALL BEOLOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM ,EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
Towards application development for the internet of things updatedPankesh Patel
The document discusses developing a domain model for Internet of Things (IoT) applications. It identifies common IoT behaviors like data collection, sense-compute-actuate, and intermittent sensing. An IoT domain model is presented that captures key concepts like entities, sensors, actuators, devices, and software components, as well as their relationships. The domain model provides benefits like a common understanding of IoT terminology, modeling invariant properties, and enabling modular application design.
This document provides an overview of grid computing. It defines a grid as a collection of distributed heterogeneous computing and data resources available through network tools and protocols. It discusses several examples of grid computing projects like SETI@home, Distributed.net, and virtual organizations. It also covers types of grids based on shared resources, topology, and behavior. The document outlines the layered structure of a grid and standards like OGSA, OGSI, and GSI that enable interoperability. It provides descriptions of key grid components like resource brokers, information services, security, data transfer, job submission, and problem solving environments.
This document discusses sensitivity analysis of smart meter data for privacy negotiation in IoT applications. It proposes an algorithm to detect sensitive points in smart meter data using kurtosis, Hampel identifier, and modified Rosner filter. The algorithm computes a sensitivity density to quantify privacy. Results on a public dataset show the algorithm detects sensitivity with high accuracy while preserving privacy of appliances like fridges. Future work aims to reduce complexity and improve privacy quantification when analyzing collective energy usage patterns across households.
Presented By Ashok.J 3 rd BCA - AVVM Sri Pushpam College, Poondi , Tanjor
Slide 2: GRID COMPUTING Conceptual View Of Grid Computing ?
What Is Grid Computing?: What Is Grid Computing? Grid computing is the collection of computer resources from multiple locations to reach a common goal. GRID COMPUTING
Slide 4: How Grid Computing Works? GRID COMPUTING
Slide 5: Types Of Grid Data Grid Collaboration Grid Network Grid Utility Grid GRID COMPUTING Computational Grid
Slide 6: Grid topologies
Slide 7: Intra grids A Typical intra grid topology exist within S ingle Organization, providing a basic set of grid Services
Slide 8: Extra grids An Extra grid, Typically involves more than one security provider , and the level Management complexity increases
Slide 9: Inter Grids An inter grid requires the dynamic integration of applications, resources and service with patterns, Customers access via WAN/ Internet
Slide 10: A Simple Grid GRID COMPUTING
Slide 11: Complex Inter grid GRID COMPUTING
Slide 12: Grid Scheduled An application is one or more jobs that are scheduled to run a Grid GRID COMPUTING
Slide 13: Advantages : Can solve larger, more complex problems in a shorter time Easier to collaborate with other organizations Make better use of existing hardware GRID COMPUTING
Slide 14: Disa dvantages : Grid software and standards are still evolving Learning curve to get started Non-interactive job submission GRID COMPUTING
Slide 15: BENEFITS OF GRID COMPUTING GRID COMPUTING Exploiting underutilized resources Parallel CPU capacity Virtual organizations for collaboration and virtual resources Access to additional resources Resource balancing Reliability Management
Presented By Ashok.J ashokmannai0005@gmail.com
Dynamic Semantics for Semantics for Dynamic IoT EnvironmentsPayamBarnaghi
This document discusses the need for dynamic semantics to handle the complex and changing nature of data in IoT environments. It notes that while semantic models and ontologies exist and are helpful for interoperability, they need to be designed simply and account for the dynamic nature of IoT data. Semantic annotations may change over time and location, and tools are needed to update them automatically. Overall, semantics are an important part of solving interoperability but must be implemented carefully considering the constraints of IoT environments.
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
Semantic Technologies for the Internet of Things: Challenges and Opportunities PayamBarnaghi
The document discusses semantic technologies for the Internet of Things (IoT), outlining both challenges and opportunities. It notes that IoT data is heterogeneous, distributed, noisy, incomplete, time and location dependent, and dynamic. Semantic descriptions could help address issues of interoperability and machine interpretability, but real-world implementation faces challenges of complexity versus expressiveness, where and how to publish semantics, and handling dynamic data meanings. Simplicity is important, and semantics should be designed with the intended uses and users in mind. Semantics are an intermediary that must effectively enable tools, APIs, querying, and data analysis to be useful for applications.
IoT-Lite: A Lightweight Semantic Model for the Internet of ThingsPayamBarnaghi
This document presents IoT-Lite, a lightweight semantic model for annotating data in the Internet of Things. IoT-Lite aims to address issues of heterogeneity and interoperability in IoT systems by providing a simple way to semantically describe sensors, actuators, and other devices. It reuses existing models like SSN and defines best practices for annotation. Evaluations show IoT-Lite imposes minimal overhead on data size and query time compared to other semantic models. The goal of IoT-Lite is to make semantic descriptions transparent and easy to implement for both end users and data producers.
How to make data more usable on the Internet of ThingsPayamBarnaghi
This document provides an overview of making data from the Internet of Things (IoT) more usable. It discusses how sensor devices and "things" are becoming more connected and generating large amounts of data. It describes challenges around discovery, access, search, and interpretation of heterogeneous IoT data at large scales. The document advocates using semantic technologies like ontologies and linked data to help interpret and integrate IoT data with broader web information. It provides examples of sensor markup languages and the W3C SSN ontology for annotating sensor data. Overall, the summary discusses the growing amount of data from the IoT, challenges in making it usable, and how semantic technologies can help address those challenges.
A study of existing ontologies in the io t domainSof Ouni
The document discusses existing ontologies in the Internet of Things (IoT) domain. It identifies core concepts needed for an IoT ontology by defining competency questions using the 4W1H methodology. These concepts include sensor, platform, testbed, service, location, and context. The document then surveys existing IoT ontologies based on these concepts and how they address areas like sensor discovery, data description, capabilities, extensibility, and data access. It aims to identify gaps in current ontologies to help define a unified standard ontology for the IoT domain.
Information Engineering in the Age of the Internet of Things PayamBarnaghi
The document discusses information engineering challenges in the age of the Internet of Things (IoT). It notes that while semantic models and ontologies are useful, simplicity is important for real-world implementation. Dynamic and streaming IoT data also requires approaches different from traditional semantic web techniques. The document provides several "design commandments" focused on usability, interoperability, and accounting for the constraints of IoT environments. Overall, it argues that semantics are just one part of effectively handling and processing IoT data.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Call for Papers- Special Session: Applications of Computational Intelligence, Internet of Things and Cutting Edge Technologies
Christo Ananth, Dr.Akhatov Akmal Rustamovich, Dr.Muhtor Nasirov
Professor, Samarkand State University, Uzbekistan
A Survey Of Context-Aware Mobile Computing ResearchKelly Lipiec
This document provides a survey of research on context-aware mobile computing systems and applications. It discusses definitions of context, including categories of context like computing context, user context, physical context, and time context. The document also defines context as the set of environmental states and settings that determine an application's behavior or where application events occur and are interesting to the user. It surveys context-aware applications and how they sense and model contextual information. The difficulties and potential solutions outlined will help researchers make context-aware computing a reality.
This document discusses big data mining and the Internet of Things. It first presents challenges with big data mining including modeling big data characteristics, identifying key challenges, and issues with statistical analysis of IoT data. It then describes an architecture called IOT-StatisticDB that provides a generalized schema for storing sensor data from IoT devices and a distributed system for parallel computing and statistical analysis of IoT big data. The system includes query operators for data retrieval and statistical analysis of IoT data in areas like transportation networks.
This document discusses big data mining and the Internet of Things. It first presents challenges with big data mining including modeling big data characteristics, identifying key challenges, and issues with statistical analysis of IoT data. It then describes an architecture called IOT-StatisticDB that provides a generalized schema for storing sensor data from IoT devices and a distributed system for parallel computing and statistical analysis of IoT big data. The system includes query operators for data retrieval and statistical analysis of IoT data in areas like transportation networks.
Assignment Of Sensing Tasks To IoT Devices Exploitation Of A Social Network ...Dustin Pytko
The document proposes exploiting a Social Internet of Things (SIoT) paradigm to assign sensing tasks to IoT devices in a mobile crowdsensing (MCS) scenario. A new algorithm is presented to fairly allocate resources and sensing tasks among devices while extending device lifetime. The algorithm creates an energy consumption profile for each device and task that is shared over the SIoT network. Emulations showed the proposed approach extended the time for the first device's battery to deplete by over 40% compared to alternative approaches.
This document discusses providing sensor data as a service. It proposes an event collaboration model where sensor data is pushed to a database when it changes, rather than requiring polling. This would allow users to access up-to-date data through queries. The system would contain various sensors that store data in a database, and provide an interface for users to access visualizations and downloads of the sensor data in different formats like CSV and JSON.
This document discusses providing sensor data as a service. It proposes an event collaboration model where sensor data is pushed to a database when it changes, rather than requiring polling. This would allow users to access up-to-date data through queries. The system would contain various sensors that store data in a database, and provide an interface for users to access visualizations and downloads of the sensor data in different formats like CSV and JSON.
Data Modelling and Knowledge Engineering for the Internet of ThingsCory Andrew Henson
Tutorial on Data Modelling and Knowledge Engineering for the Internet of Things, presented at EKAW 2012, Galway City, Ireland, October 8-12, 2012
http://knoesis.org/iot-tutorial-ekaw2012/
The document discusses the integration of Internet of Things (IoT) and cloud computing, referred to as Cloud of Things. It identifies several key issues with this integration, such as protocol support, energy efficiency, resource allocation, identity management, and security/privacy. Potential solutions are provided for some of the issues. The conclusion discusses the need for more study on the impact of these issues based on the specific IoT application and services provided.
This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analy- sis, and reasoning will be discussed. The tutorial will de- scribe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions re- lated to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, do- main knowledge integration and management, querying large- scale IoT data, and AI applications for automated knowledge extraction from real world data.
Related: Semantic Sensor Web: http://knoesis.org/projects/ssw
Physical-Cyber-Social Computing: http://wiki.knoesis.org/index.php/PCS
Similar to Semantic IoT Semantic Inter-Operability Practices - Part 2 (20)
Mechanisms for Real World Services, presented in the session: "Empowering IoT through virtual objects and cognitive technologies", at the Internet of Things Workshop, during the Future Internet Week, Poznan, Poland, 24-28 October 2011
Internet of Things Environment for Service Creation and Testing (IoT.est)iotest
S Nechifor, Internet of Things Environment for Service Creation and Testing (IoT.est), at CompArch 2012 Main Program (Industry Day, Wednesday 27 June), Bertinoro, Italy, 25-28 June 2012
Semantic Interoperability Issues and Approaches in the IoT.est Projectiotest
P Barnaghi, Semantic Interoperability Issues and Approaches in the IoT.est Project, at the IERC AC4 Semantic interoperability Workshop (during the IoT-week 2012), Venice, Italy, 19 June 2012
The IoT.est project involves 8 partners from 7 countries to develop a test-driven service creation environment for Internet of Things enabled business services. The environment will allow acquisition of sensor and device data and control of actuators across domains. It will also facilitate run-time monitoring and autonomous service adaptation to changes in environment, context, and network parameters. The IoT.est architecture builds upon existing IoT platforms by providing common interfaces, resource control and management, security and privacy mechanisms, and discovery services.
Naming, Search and Discovery in IoT: Issues and proposed solutions in the FP7...iotest
Naming, Search and Discovery in IoT: Issues and proposed solutions in the FP7 EU IoT.est Project, presented at the IERC AC2 meeting at the FIA (Future Internet Assembly), Aalborg, Denmark, 9 May 2012
Environment for Service Creation and Testing in the Internet of Thingsiotest
K. Moessner, Environment for Service Creation and Testing in the Internet of Things, at the IERC AC14 Cognitive Technologies in IoT (during the IoT-week 2012), Venice, Italy, 18 June 2012
This document discusses semantic interoperability challenges for Internet of Things (IoT) services and resources. It proposes using semantic models and metadata tagging to provide structured, machine-interpretable representations of IoT concepts like resources, entities, and services. This would allow automated processing and integration of diverse IoT data and services. However, challenges remain in aligning different models and achieving real interoperability across frameworks. Practical solutions may involve linking related models and proposing common abstract descriptions.
Evolving the way we create and test services for the Internet of Thingsiotest
Evolving the way we create and test services for the Internet of Things presented at RCIS 2012 - the Sixth International Conference on Research Challenges in Information Science, Valencia, Spain, 16-18 May 2012
Distributed semantic repository and discovery architectureiotest
The document discusses service discovery architecture in the IoT.est project. It proposes using geospatial indexing and distributed semantic repositories for service discovery to ensure accuracy through semantic search and efficiency through search scope reduction. While R-Tree updates are expensive, most service changes are constrained to gateways, reducing the need for propagation of update messages and computational complexity of updates in dynamic IoT environments.
Delegation Inheritance in Odoo 17 and Its Use CasesCeline George
There are 3 types of inheritance in odoo Classical, Extension, and Delegation. Delegation inheritance is used to sink other models to our custom model. And there is no change in the views. This slide will discuss delegation inheritance and its use cases in odoo 17.
Slide Presentation from a Doctoral Virtual Open House presented on June 30, 2024 by staff and faculty of Capitol Technology University
Covers degrees offered, program details, tuition, financial aid and the application process.
No, it's not a robot: prompt writing for investigative journalismPaul Bradshaw
How to use generative AI tools like ChatGPT and Gemini to generate story ideas for investigations, identify potential sources, and help with coding and writing.
A talk from the Centre for Investigative Journalism Summer School, July 2024
How to Add Colour Kanban Records in Odoo 17 NotebookCeline George
In Odoo 17, you can enhance the visual appearance of your Kanban view by adding color-coded records using the Notebook feature. This allows you to categorize and distinguish between different types of records based on specific criteria. By adding colors, you can quickly identify and prioritize tasks or items, improving organization and efficiency within your workflow.
Front Desk Management in the Odoo 17 ERPCeline George
Front desk officers are responsible for taking care of guests and customers. Their work mainly involves interacting with customers and business partners, either in person or through phone calls.
Integrated Marketing Communications (IMC)- Concept, Features, Elements, Role of advertising in IMC
Advertising: Concept, Features, Evolution of Advertising, Active Participants, Benefits of advertising to Business firms and consumers.
Classification of advertising: Geographic, Media, Target audience and Functions.
Beyond the Advance Presentation for By the Book 9John Rodzvilla
In June 2020, L.L. McKinney, a Black author of young adult novels, began the #publishingpaidme hashtag to create a discussion on how the publishing industry treats Black authors: “what they’re paid. What the marketing is. How the books are treated. How one Black book not reaching its parameters casts a shadow on all Black books and all Black authors, and that’s not the same for our white counterparts.” (Grady 2020) McKinney’s call resulted in an online discussion across 65,000 tweets between authors of all races and the creation of a Google spreadsheet that collected information on over 2,000 titles.
While the conversation was originally meant to discuss the ethical value of book publishing, it became an economic assessment by authors of how publishers treated authors of color and women authors without a full analysis of the data collected. This paper would present the data collected from relevant tweets and the Google database to show not only the range of advances among participating authors split out by their race, gender, sexual orientation and the genre of their work, but also the publishers’ treatment of their titles in terms of deal announcements and pre-pub attention in industry publications. The paper is based on a multi-year project of cleaning and evaluating the collected data to assess what it reveals about the habits and strategies of American publishers in acquiring and promoting titles from a diverse group of authors across the literary, non-fiction, children’s, mystery, romance, and SFF genres.
Webinar Innovative assessments for SOcial Emotional SkillsEduSkills OECD
Presentations by Adriano Linzarini and Daniel Catarino da Silva of the OECD Rethinking Assessment of Social and Emotional Skills project from the OECD webinar "Innovations in measuring social and emotional skills and what AI will bring next" on 5 July 2024
How to Store Data on the Odoo 17 WebsiteCeline George
Here we are going to discuss how to store data in Odoo 17 Website.
It includes defining a model with few fields in it. Add demo data into the model using data directory. Also using a controller, pass the values into the template while rendering it and display the values in the website.
How to Configure Time Off Types in Odoo 17Celine George
Now we can take look into how to configure time off types in odoo 17 through this slide. Time-off types are used to grant or request different types of leave. Only then the authorities will have a clear view or a clear understanding of what kind of leave the employee is taking.
Semantic IoT Semantic Inter-Operability Practices - Part 2
1. IoT Semantic Inter-Operability Event
Part 2: IoT semantic interoperability practices
Presenter: Gilbert Cassar
Centre for Communication Systems Research, University of Surrey
Contributors: Dr. Payam Barnaghi, Dr. Martin Serrano, Mr. Phillippe Cousin
2. “People want answers, not numbers”
(Steven Glaser, UC Berkley)
Sink
node Gateway
Core network
e.g. Internet
What is the temperature at home?Freezing!
3. Turning Data into Wisdom
Data
Information
Knowledge
Wisdom
Raw sensory data
Structured data (with
semantics)
Abstraction and perceptions
Actionable intelligence
4. Components Related to Things
Physical world objects
e.g. A room, a car, A person;
Feature of Interest
e.g. Temperature of the room, Location of the car, heart-
rate of the person;
Sensors
e.g. Temperature sensor, GPS, pulse sensor
5. How to say what a Sensor is and
what it measures
Sink
node
Gateway
6. Semantics and IoT Data
Creating ontologies and defining data models is 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 compatible as possible 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 resources
Linked-data
URIs and naming
7. 7
Semantics and Linked-data
The principles in designing the linked data are
defined as:
using URI’s as names for things;
using HTTP URI’s to enable people to look up those
names;
provide useful RDF information related to URI’s that are
looked up by machine or people;
including RDF statements that link to other URI’s to
enable discovery of other related concepts of the Web
of Data;
9. 9
Myth and reality
#1: If we create an Ontology our data is
interoperable
Reality: there are/could be a number of ontologies for a domain
Ontology mapping
Reference ontologies
Standardisation efforts
#2: Semantic data will make my data machine-
understandable and my system will be intelligent.
Reality: it is still meta-data, machines don’t understand it but can
interpret it. It still does need intelligent processing, reasoning mechanism
to process and interpret the data.
10. 10
Myth and reality
#3: It’s a Hype! Ontologies and semantic data are
too much overhead; we deal with tiny devices in IoT.
Reality: Ontologies are a way to share and agree on a common vocabulary
and knowledge; at the same time there are machine-interpretable and
represented in interoperable and re-usable forms;
You don’t necessarily need to add semantic metadata in the source- it could be
added to the data at a later stage (e.g. in a gateway);
Legacy applications can ignore it or to be extended to work with it.
11. The Importance of Domain Knowledge
Created with the help of domain experts.
Provides a common understanding of the domain for
people and machines to refer to.
Allows machines to determine the relationship
between assertions coming from the same domain.
What’s the relationship between ‘temperature’ and ‘weather’?
Easier to provide suggestions to engineers building a
semantic description of their sensor.
12. Exercises 1
Open the following ontologies in Protégé:
Quantity and Dimensions ontologies:
http://purl.oclc.org/NET/ssnx/qu/qu
http://purl.oclc.org/NET/ssnx/qu/qu-rec20
Units ontology:
http://localhost:8080/InteropOntologyMatchingTool/Ontos/Units.owl
http://qudt.org/1.1/schema/dimension
14. Input and Output Parameters
A very important part of any semantically
annotated service description.
Used by:
Discovery Engines.
Semantic Matchmakers.
Composition Engines.
Compensation Engines.
17. Filters Used By Semantic Matchmakers
Where A and B are parameter
types.
The Subsumes filter is less useful
than the other two because when A
is more generic than B, A cannot
interoperate with B in most cases.
18. QU-rec20 Ontology
Ontology for Quantity Kinds and Units: units and
quantities definitions
This ontology imports the qu ontology derived from
the work done by the SysML 1.2 QUDV working
group (see http://purl.oclc.org/NET/ssnx/qu/qu for
details).
Defines a huge variety of dimensions and could be
used a common domain for describing the type of
data measured by a sensor.
19. QUDT Ontology
Ontology for Quantities, Units, Dimensions and Data
Types.
Developed by TopQuadrant and NASA.
Another standardisation effort. Compare with the
QU-rec20 ontology.
20. QoS/QoI Ontology
Created as part of the IoT.est Project
http://ict-iotest.eu/iotest/
Contains various definitions for Quality of
Service and Quality of Information
attributes that could be used to describe a
service parameter.
21. Useful Domain Ontologies
Quantity and Dimensions ontologies:
http://purl.oclc.org/NET/ssnx/qu/qu
http://purl.oclc.org/NET/ssnx/qu/qu-rec20
Units ontology:
http://localhost:8080/InteropOntologyMatchingTool/Ontos/Units.owl
http://qudt.org/1.1/schema/dimension
23. Exercises 2: create a parameter ontology
Considering reuse of the existing ontologies (using
‘import’ in Protégé)
Consider the following parameter attributes:
Data Type
Unit of Measure
Response Time
Location
More information also means more overhead.
24. Exercise 3: Comparing your parameter
model with others’
Copy your parameter description on a usb stick.
Transfer it to the Virtual Machine of another person sat at your
table.
Save it in the folder:
Home/apache/apache-tomcat-6.0.36/webapps/docs/ontology/
The URL of your model should now be:
http://localhost:8080/InteropOntologyCheckingTool/docs/ontology/yourontology.owl
Use the Interoperability tool at:
http://localhost:8080/InteropOntologyCheckingTool/
Compare your parameter model to the other person’s model
to check how interoperable they are.
25. Exercise 3: Discussion
How interoperable is your model with other
people’s model?
Have you re-used existing structures (for example
from the IoT.est service model) ?