Recently, cloud computing technologies have been employed for large-scale machine-to-machine (M2M) systems, as they could potentially offer better solutions for managing monitoring data and analytics applications to support the needs of different consumers. However, t here exist complex relationships between monitored objects, monitoring data, analysis features, and stakeholders in M2M that require efficient ways to handle
these complex relationships. This paper presents techniques for linking and managing monitored objects, sustainability monitoring data and analytics applications for different stakeholders in cloud-based M2M systems. We describe a Platform-as-a-Service
for sustainability governance that implements these techniques.
We also illustrate our prototype based on a real-world cloud system for facility monitoring.
Automating Data Science over a Human Genomics Knowledge BaseVaticle
# Automating Data Science over a Human Genomics Knowledge Base
Radouane Oudrhiri, the CTO of Eagle Genomics, will talk about how Eagle Genomics is building a platform for automating data science over a human genomics knowledge base. Rad will dive into the architecture Eagle Genomics and also discuss how Grakn serves as the knowledge base foundation of the system. Rad also give a brief history of databases, semantic expressiveness and how Grakn fits in the big picture.
# Radouane Oudrhiri, CTO, Eagle Genomics
Radouane has an extensive experience in leading world-class software and data-intensive system developments in different industries from Telecom to Healthcare, Nuclear, Automotive, Financials. Radouane is Lean/Six Sigma Master Black Belt with speciality in high-tech, IT and Software engineering and he is recognised as the leader and early adaptor of Lean/Six Sigma and DFSS to IT and Software. He is a fellow of the Royal Statistical Society (RSS) and member of the ISO Technical Committee (TC69: Applications of Statistical methods) where he is co-author of the Lean & Six Sigma Standard (ISO 18404) as well as the new standard under development (Design for Six Sigma). He is also part of the newly formed international Group on Big Data (nominated by BSI as the UK representative/expert). Radouane has also been Chair of the working group on Measurement Systems for Automated Processes/Systems within the ISPE (International Society for Pharmaceutical Engineering).
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch: https://bit.ly/2DYsUhD
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spent most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Attend this webinar and learn:
- How data virtualization can accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- How popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc. integrate with Denodo
- How you can use the Denodo Platform with large data volumes in an efficient way
- How Prologis accelerated their use of Machine Learning with data virtualization
2014: Treparel Big Data Text Analytics & VisualizationTreparel
Text and content analytics have become a source of competitive advantage, enabling business, government agencies, and researchers to extract unprecedented value from unstructured data.
Treparel (Delft, The Netherlands) is a independent provider of Text analytics and Visualization software. Organizations like Philips, Bayer, Abbott, NXP Semiconductors are using KMX Text Analytics software to gain faster, reliable, precise insights in large complex unstructured data sets.
The KMX API allows software and service companies to enhance their unstructured data analysis capabilities by embedding world class machine learning based clustering, categorization and visualization.
A recent review by IDC states: “KMX visualization capabilities around its auto-categorization and clustering offer immediate insight into unstructured data sets and appear to be adaptable and customizable to customer needs. Its approach to auto-categorization utilizes statistical principles and machine learning that require significantly less training and tuning on the part of customers than other approaches.”
Analytic Platforms in the Real World with 451Research and Calpont_July 2012Calpont Corporation
Matt Aslett, 451 Research, and Bob Wilkinson, VP Engineering for Calpont, discuss the emergence of the analytic platform, its place the new ecosystem for Big Data, considerations for selection, and applied use cases of Calpont’s analytic platform, InfiniDB, in Telco and Mobile Advertising.
Pay-per-use LAN and Wi-Fi network infrastructure is available to help your business :
- Pay only for what is used (AND no capital investment is required)
- Minimize paying for unused infrastructure, so infrastructure costs align with business revenues
- Refocus your IT staff on business applications and communications
And don't miss slide 11...on the 10 differences from other offers.
this is part 3 of the series on Data Mesh ... looking at the intersection of microservices architecture concepts, data integration / replication technologies and log-based stream integration techniques. This webinar was mostly a demonstration, but several slides used to setup the demo are included here as a PDF for viewers.
Automating Data Science over a Human Genomics Knowledge BaseVaticle
# Automating Data Science over a Human Genomics Knowledge Base
Radouane Oudrhiri, the CTO of Eagle Genomics, will talk about how Eagle Genomics is building a platform for automating data science over a human genomics knowledge base. Rad will dive into the architecture Eagle Genomics and also discuss how Grakn serves as the knowledge base foundation of the system. Rad also give a brief history of databases, semantic expressiveness and how Grakn fits in the big picture.
# Radouane Oudrhiri, CTO, Eagle Genomics
Radouane has an extensive experience in leading world-class software and data-intensive system developments in different industries from Telecom to Healthcare, Nuclear, Automotive, Financials. Radouane is Lean/Six Sigma Master Black Belt with speciality in high-tech, IT and Software engineering and he is recognised as the leader and early adaptor of Lean/Six Sigma and DFSS to IT and Software. He is a fellow of the Royal Statistical Society (RSS) and member of the ISO Technical Committee (TC69: Applications of Statistical methods) where he is co-author of the Lean & Six Sigma Standard (ISO 18404) as well as the new standard under development (Design for Six Sigma). He is also part of the newly formed international Group on Big Data (nominated by BSI as the UK representative/expert). Radouane has also been Chair of the working group on Measurement Systems for Automated Processes/Systems within the ISPE (International Society for Pharmaceutical Engineering).
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch: https://bit.ly/2DYsUhD
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spent most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Attend this webinar and learn:
- How data virtualization can accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- How popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc. integrate with Denodo
- How you can use the Denodo Platform with large data volumes in an efficient way
- How Prologis accelerated their use of Machine Learning with data virtualization
2014: Treparel Big Data Text Analytics & VisualizationTreparel
Text and content analytics have become a source of competitive advantage, enabling business, government agencies, and researchers to extract unprecedented value from unstructured data.
Treparel (Delft, The Netherlands) is a independent provider of Text analytics and Visualization software. Organizations like Philips, Bayer, Abbott, NXP Semiconductors are using KMX Text Analytics software to gain faster, reliable, precise insights in large complex unstructured data sets.
The KMX API allows software and service companies to enhance their unstructured data analysis capabilities by embedding world class machine learning based clustering, categorization and visualization.
A recent review by IDC states: “KMX visualization capabilities around its auto-categorization and clustering offer immediate insight into unstructured data sets and appear to be adaptable and customizable to customer needs. Its approach to auto-categorization utilizes statistical principles and machine learning that require significantly less training and tuning on the part of customers than other approaches.”
Analytic Platforms in the Real World with 451Research and Calpont_July 2012Calpont Corporation
Matt Aslett, 451 Research, and Bob Wilkinson, VP Engineering for Calpont, discuss the emergence of the analytic platform, its place the new ecosystem for Big Data, considerations for selection, and applied use cases of Calpont’s analytic platform, InfiniDB, in Telco and Mobile Advertising.
Pay-per-use LAN and Wi-Fi network infrastructure is available to help your business :
- Pay only for what is used (AND no capital investment is required)
- Minimize paying for unused infrastructure, so infrastructure costs align with business revenues
- Refocus your IT staff on business applications and communications
And don't miss slide 11...on the 10 differences from other offers.
this is part 3 of the series on Data Mesh ... looking at the intersection of microservices architecture concepts, data integration / replication technologies and log-based stream integration techniques. This webinar was mostly a demonstration, but several slides used to setup the demo are included here as a PDF for viewers.
Data Virtualization. An Introduction (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3uiXVoC
What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad of consumers.
Watch on-demand this session to learn:
- What is Data Virtualization?
- Why do I need Data Virtualization in my organization?
- How do I implement Data Virtualization in my enterprise? Where does it fit..?
A REVIEW ON CLASSIFICATION OF DATA IMBALANCE USING BIGDATAIJMIT JOURNAL
Classification is one among the data mining function that assigns items in a collection to target categories
or collection of data to provide more accurate predictions and analysis. Classification using supervised
learning method aims to identify the category of the class to which a new data will fall under. With the
advancement of technology and increase in the generation of real-time data from various sources like
Internet, IoT and Social media it needs more processing and challenging. One such challenge in
processing is data imbalance. In the imbalanced dataset, majority classes dominate over minority classes
causing the machine learning classifiers to be more biased towards majority classes and also most
classification algorithm predicts all the test data with majority classes. In this paper, the author analysis
the data imbalance models using big data and classification algorithm
A Review on Classification of Data Imbalance using BigDataIJMIT JOURNAL
Classification is one among the data mining function that assigns items in a collection to target categories or collection of data to provide more accurate predictions and analysis. Classification using supervised learning method aims to identify the category of the class to which a new data will fall under. With the advancement of technology and increase in the generation of real-time data from various sources like Internet, IoT and Social media it needs more processing and challenging. One such challenge in processing is data imbalance. In the imbalanced dataset, majority classes dominate over minority classes causing the machine learning classifiers to be more biased towards majority classes and also most classification algorithm predicts all the test data with majority classes. In this paper, the author analysis the data imbalance models using big data and classification algorithm.
Cloud Analytics Ability to Design, Build, Secure, and Maintain Analytics Solu...YogeshIJTSRD
Cloud Analytics is another area in the IT field where different services like Software, Infrastructure, storage etc. are offered as services online. Users of cloud services are under constant fear of data loss, security threats, and availability issues. However, the major challenge in these methods is obtaining real time and unbiased datasets. Many datasets are internal and cannot be shared due to privacy issues or may lack certain statistical characteristics. As a result of this, researchers prefer to generate datasets for training and testing purposes in simulated or closed experimental environments which may lack comprehensiveness. Advances in sensor technology, the Internet of things IoT , social networking, wireless communications, and huge collection of data from years have all contributed to a new field of study Big Data is discussed in this paper. Through this analysis and investigation, we provide recommendations for the research public on future directions on providing data based decisions for cloud supported Big Data computing and analytic solutions. This paper concentrates upon the recent trends in Big Data storage and analysing, in the clouds, and also points out the security limitations. Rajan Ramvilas Saroj "Cloud Analytics: Ability to Design, Build, Secure, and Maintain Analytics Solutions on the Cloud" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd43728.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/43728/cloud-analytics-ability-to-design-build-secure-and-maintain-analytics-solutions-on-the-cloud/rajan-ramvilas-saroj
Denodo DataFest 2016: Data Science: Operationalizing Analytical Models in Rea...Denodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/yVJnti
Data virtualization starts with democratizing data access for business users, but goes well beyond to enable entire analytics life cycle. This session will discuss the critical role of data virtualization in the four key phases of big data analytics: Discovery of raw and enriched data, Analytic Exploration, Real-time Operationalization, and Predictive Intervention.
In this session, you will learn:
• Design of advanced analytics with view towards business goal realization
• The role of data virtualization in enabling analytics through four key phases
• How to exploit product capabilities relevant to each stage
• Creating a system of governed self-service and collaborative analytics
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
Watch Paul's session from Fast Data Strategy on-demand here: https://goo.gl/3veKqw
"Through 2020, 50% of enterprises will implement some form of data virtualization as one enterprise production option for data integration" according to Gartner. It is clear that data virtualization has become a driving force for companies to implement an agile, real-time and flexible enterprise data architecture.
Attend this session to learn:
• What data virtualization actually means and how it differs from traditional data integration approaches
• The most important use cases and key patterns of data virtualization
• The benefits of data virtualization
Rapid changes in the technology lead to increased variety of data sources. These varied data sources
generating data in the large volume and with extremely high speed. To accommodate and use this data in decision
making systems is the big challenge. To make fullest use of the valuable data generated by different systems, target
users of the analysis systems need to be increased. In general knowledge discovery process using the tools which are
available requires the handsome expertise in the domain as well as in the technology. The project ITDA (Integrated
Tool for Data Analysis) focuses to provide the complete platform for multidimensional data analysis to enhance the
decision making process in every domain. This projects provides all the techniques required to perform
multidimensional data analysis and avoids the overheads occurred by the traditional cube architecture followed by
most of the analytics system. Modelling the available data in the multidimensional form is the basis and crucial step
for multidimensional analysis. This work describes the multidimensional modelling aspect and its implementation
using ITDA project.
Watch full webinar here: https://buff.ly/2XXbNB7
What started to evolve as the most agile and real-time enterprise data fabric, Data Virtualization is proving to go beyond its initial promise and is becoming one of the most important enterprise big data fabrics.
Attend this session to learn:
*What data virtualization really is
*How it differs from other enterprise data integration technologies
*Why data virtualization is finding enterprise wide deployment inside some of the largest organizations
Virtualisation de données : Enjeux, Usages & BénéficesDenodo
Watch full webinar here: https://bit.ly/3oah4ng
Gartner a récemment qualifié la Data Virtualisation comme étant une pièce maitresse des architectures d’intégration de données.
Découvrez :
- Les bénéfices d’une plateforme de virtualisation de données
- La multiplication des usages : Lakehouse, Data Science, Big Data, Data Service & IoT
- La création d’une vue unifiée de votre patrimoine de données sans transiger sur la performance
- La construction d’une architecture d’intégration Agile des données : on-premise, dans le cloud ou hybride
The advent of hybrid clouds, multi clouds and app driven business models hasnecessitated a network that has to be robust, secure and scalable to meet rapidly changing business expectations.
Watch full webinar here: https://bit.ly/2SaBj5l
You will often hear that "data is the new gold". In this context, data management is one of the areas that has received more attention by the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
In this webinar we will discuss the technology trends that will drive the enterprise data strategies in the years to come. Don't miss it if you want to keep yourself informed about how to convert your data to strategic assets in order to complete the data-driven transformation in your company.
Join us for an exciting session that will cover:
- The most interesting trends in data management
- How to build a logical data fabric architecture?
- How to manage your data integration strategy in the new hybrid world?
- Our predictions on how those trends will change the data management world
- How can companies monetize the data through data-as-a-service infrastructure?
- What is the role of the voice computing in the future of data analytics?
Data Virtualization. An Introduction (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3uiXVoC
What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad of consumers.
Watch on-demand this session to learn:
- What is Data Virtualization?
- Why do I need Data Virtualization in my organization?
- How do I implement Data Virtualization in my enterprise? Where does it fit..?
A REVIEW ON CLASSIFICATION OF DATA IMBALANCE USING BIGDATAIJMIT JOURNAL
Classification is one among the data mining function that assigns items in a collection to target categories
or collection of data to provide more accurate predictions and analysis. Classification using supervised
learning method aims to identify the category of the class to which a new data will fall under. With the
advancement of technology and increase in the generation of real-time data from various sources like
Internet, IoT and Social media it needs more processing and challenging. One such challenge in
processing is data imbalance. In the imbalanced dataset, majority classes dominate over minority classes
causing the machine learning classifiers to be more biased towards majority classes and also most
classification algorithm predicts all the test data with majority classes. In this paper, the author analysis
the data imbalance models using big data and classification algorithm
A Review on Classification of Data Imbalance using BigDataIJMIT JOURNAL
Classification is one among the data mining function that assigns items in a collection to target categories or collection of data to provide more accurate predictions and analysis. Classification using supervised learning method aims to identify the category of the class to which a new data will fall under. With the advancement of technology and increase in the generation of real-time data from various sources like Internet, IoT and Social media it needs more processing and challenging. One such challenge in processing is data imbalance. In the imbalanced dataset, majority classes dominate over minority classes causing the machine learning classifiers to be more biased towards majority classes and also most classification algorithm predicts all the test data with majority classes. In this paper, the author analysis the data imbalance models using big data and classification algorithm.
Cloud Analytics Ability to Design, Build, Secure, and Maintain Analytics Solu...YogeshIJTSRD
Cloud Analytics is another area in the IT field where different services like Software, Infrastructure, storage etc. are offered as services online. Users of cloud services are under constant fear of data loss, security threats, and availability issues. However, the major challenge in these methods is obtaining real time and unbiased datasets. Many datasets are internal and cannot be shared due to privacy issues or may lack certain statistical characteristics. As a result of this, researchers prefer to generate datasets for training and testing purposes in simulated or closed experimental environments which may lack comprehensiveness. Advances in sensor technology, the Internet of things IoT , social networking, wireless communications, and huge collection of data from years have all contributed to a new field of study Big Data is discussed in this paper. Through this analysis and investigation, we provide recommendations for the research public on future directions on providing data based decisions for cloud supported Big Data computing and analytic solutions. This paper concentrates upon the recent trends in Big Data storage and analysing, in the clouds, and also points out the security limitations. Rajan Ramvilas Saroj "Cloud Analytics: Ability to Design, Build, Secure, and Maintain Analytics Solutions on the Cloud" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd43728.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/43728/cloud-analytics-ability-to-design-build-secure-and-maintain-analytics-solutions-on-the-cloud/rajan-ramvilas-saroj
Denodo DataFest 2016: Data Science: Operationalizing Analytical Models in Rea...Denodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/yVJnti
Data virtualization starts with democratizing data access for business users, but goes well beyond to enable entire analytics life cycle. This session will discuss the critical role of data virtualization in the four key phases of big data analytics: Discovery of raw and enriched data, Analytic Exploration, Real-time Operationalization, and Predictive Intervention.
In this session, you will learn:
• Design of advanced analytics with view towards business goal realization
• The role of data virtualization in enabling analytics through four key phases
• How to exploit product capabilities relevant to each stage
• Creating a system of governed self-service and collaborative analytics
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
Watch Paul's session from Fast Data Strategy on-demand here: https://goo.gl/3veKqw
"Through 2020, 50% of enterprises will implement some form of data virtualization as one enterprise production option for data integration" according to Gartner. It is clear that data virtualization has become a driving force for companies to implement an agile, real-time and flexible enterprise data architecture.
Attend this session to learn:
• What data virtualization actually means and how it differs from traditional data integration approaches
• The most important use cases and key patterns of data virtualization
• The benefits of data virtualization
Rapid changes in the technology lead to increased variety of data sources. These varied data sources
generating data in the large volume and with extremely high speed. To accommodate and use this data in decision
making systems is the big challenge. To make fullest use of the valuable data generated by different systems, target
users of the analysis systems need to be increased. In general knowledge discovery process using the tools which are
available requires the handsome expertise in the domain as well as in the technology. The project ITDA (Integrated
Tool for Data Analysis) focuses to provide the complete platform for multidimensional data analysis to enhance the
decision making process in every domain. This projects provides all the techniques required to perform
multidimensional data analysis and avoids the overheads occurred by the traditional cube architecture followed by
most of the analytics system. Modelling the available data in the multidimensional form is the basis and crucial step
for multidimensional analysis. This work describes the multidimensional modelling aspect and its implementation
using ITDA project.
Watch full webinar here: https://buff.ly/2XXbNB7
What started to evolve as the most agile and real-time enterprise data fabric, Data Virtualization is proving to go beyond its initial promise and is becoming one of the most important enterprise big data fabrics.
Attend this session to learn:
*What data virtualization really is
*How it differs from other enterprise data integration technologies
*Why data virtualization is finding enterprise wide deployment inside some of the largest organizations
Virtualisation de données : Enjeux, Usages & BénéficesDenodo
Watch full webinar here: https://bit.ly/3oah4ng
Gartner a récemment qualifié la Data Virtualisation comme étant une pièce maitresse des architectures d’intégration de données.
Découvrez :
- Les bénéfices d’une plateforme de virtualisation de données
- La multiplication des usages : Lakehouse, Data Science, Big Data, Data Service & IoT
- La création d’une vue unifiée de votre patrimoine de données sans transiger sur la performance
- La construction d’une architecture d’intégration Agile des données : on-premise, dans le cloud ou hybride
The advent of hybrid clouds, multi clouds and app driven business models hasnecessitated a network that has to be robust, secure and scalable to meet rapidly changing business expectations.
Watch full webinar here: https://bit.ly/2SaBj5l
You will often hear that "data is the new gold". In this context, data management is one of the areas that has received more attention by the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
In this webinar we will discuss the technology trends that will drive the enterprise data strategies in the years to come. Don't miss it if you want to keep yourself informed about how to convert your data to strategic assets in order to complete the data-driven transformation in your company.
Join us for an exciting session that will cover:
- The most interesting trends in data management
- How to build a logical data fabric architecture?
- How to manage your data integration strategy in the new hybrid world?
- Our predictions on how those trends will change the data management world
- How can companies monetize the data through data-as-a-service infrastructure?
- What is the role of the voice computing in the future of data analytics?
Integrated Analytics for IIoT Predictive Maintenance using IoT Big Data Cloud...Hong-Linh Truong
For predictive maintenance of equipment with In-
dustrial Internet of Things (IIoT) technologies, existing IoT Cloud
systems provide strong monitoring and data analysis capabilities
for detecting and predicting status of equipment. However, we
need to support complex interactions among different software
components and human activities to provide an integrated analyt-
ics, as software algorithms alone cannot deal with the complexity
and scale of data collection and analysis and the diversity of
equipment, due to the difficulties of capturing and modeling
uncertainties and domain knowledge in predictive maintenance.
In this paper, we describe how we design and augment complex
IoT big data cloud systems for integrated analytics of IIoT
predictive maintenance. Our approach is to identify various
complex interactions for solving system incidents together with
relevant critical analytics results about equipment. We incorpo-
rate humans into various parts of complex IoT Cloud systems
to enable situational data collection, services management, and
data analytics. We leverage serverless functions, cloud services,
and domain knowledge to support dynamic interactions between
human and software for maintaining equipment. We use a real-
world maintenance of Base Transceiver Stations to illustrate our
engineering approach which we have prototyped with state-of-
the art cloud and IoT technologies, such as Apache Nifi, Hadoop,
Spark and Google Cloud Functions.
Modeling and Provisioning IoT Cloud Systems for Testing UncertaintiesHong-Linh Truong
Modern Cyber-Physical Systems (CPS) and Internet of Things (IoT)
systems consist of both loosely and tightly interactions among
various resources in IoT networks, edge servers and cloud data
centers. These elements are being built atop virtualization layers
and deployed in both edge and cloud infrastructures. They also deal
with a lot of data through the interconnection of different types of
networks and services. Therefore, several new types of uncertainties
are emerging, such as data, actuation, and elasticity uncertainties.
This triggers several challenges for testing uncertainty in such
systems. However, there is a lack of novel ways to model and
prepare the right infrastructural elements covering requirements
for testing emerging uncertainties. In this paper, first we present
techniques for modeling CPS/IoT Systems and their uncertainties
to be tested. Second, we introduce techniques for determining and
generating deployment configuration for testing in different IoT
and cloud infrastructures. We illustrate our work with a real-world
use case for monitoring and analysis of Base Transceiver Stations.
Testing Uncertainty of Cyber-Physical Systems in IoT Cloud Infrastructures: C...Hong-Linh Truong
Today’s cyber-physical systems (CPS) span IoT and cloud-based
datacenter infrastructures, which are highly heterogeneous with
various types of uncertainty. Thus, testing uncertainties in these
CPS is a challenging and multidisciplinary activity. We need several
tools for modeling, deployment, control, and analytics to test and
evaluate uncertainties for different configurations of the same CPS.
In this paper, we explain why using state-of-the art model-driven
engineering (MDE) and model-based testing (MBT) tools is not
adequate for testing uncertainties of CPS in IoT Cloud infrastruc-
tures. We discus how to combine them with techniques for elastic
execution to dynamically provision both CPS under test and testing
utilities to perform tests in various IoT Cloud infrastructures.
Towards a Resource Slice Interoperability Hub for IoTHong-Linh Truong
Interoperability for IoT is a challenging problem
because it requires us to tackle (i) cross-system interoperability
issues at the IoT platform sides as well as relevant network
functions and clouds in the edge systems and data centers
and (ii) cross-layer interoperability, e.g., w.r.t. data formats,
communication protocols, data delivery mechanisms, and perfor-
mance. However, existing solutions are quite static w.r.t software
deployment and provisioning for interoperability. Many middle-
ware, services and platforms have been built and deployed as
interoperability bridges but they are not dynamically provisioned
and reconfigured for interoperability at runtime. Furthermore,
they are often not considered together with other services as a
whole in application-specific contexts. In this paper, we focus
on dynamic aspects by introducing the concept of Resource
Slice Interoperability Hub (rsiHub). Our approach leverages
existing software artifacts and services for interoperability to
create and provision dynamic resource slices, including IoT,
network functions and clouds, for addressing application-specific
interoperability requirements. We will present our key concepts,
architectures and examples toward the realization of rsiHub.
On Supporting Contract-aware IoT Dataspace ServicesHong-Linh Truong
Advances in the Internet of Things (IoT) enable a
huge number of connected devices that produce large amounts
of data. Such data is increasingly shared among various
stakeholders to support advanced (predictive) analytics and
precision decision making in different application domains like
smart cities and industrial internet. Currently there are several
platforms that facilitate sharing, buying and selling IoT data.
However, these platforms do not support the establishment and
monitoring of usage contracts for IoT data. In this paper we
address this research issue by introducing a new extensible
platform for enabling contract-aware IoT dataspace services,
which supports data contract specification and IoT data flow
monitoring based on established data contracts. We present
a general architecture of contract monitoring services for
IoT dataspaces and evaluate our platform through illustrative
examples with real-world datasets and through performance
analysis.
Towards the Realization of Multi-dimensional Elasticity for Distributed Cloud...Hong-Linh Truong
As multiple types of distributed, heterogeneous cloud computing environments have proliferated, cloud software can leverage
diverse types of infrastructural, platform and data resources with di
erent cost and quality models. This introduces a multi-
dimensional elasticity perspective for cloud software that would greatly meet changing demands from the user. However, we argue
that current techniques are not enough for dealing with multi-dimensional elasticity in distributed cloud environments. We present
our approach to the realization of multi-dimensional elasticity by introducing novel concepts and a roadmap to achieve them.
On Engineering Analytics of Elastic IoT Cloud SystemsHong-Linh Truong
Developing IoT cloud platforms is very challenging, as IoT
cloud platforms consist of a mix of cloud services and IoT elements, e.g.,
for sensor management, near-realtime events handling, and data analyt-
ics. Developers need several tools for deployment, control, governance
and analytics actions to test and evaluate designs of software compo-
nents and optimize the operation of di erent design con gurations. In
this paper, we describe requirements and our techniques on support-
ing the development and testing of IoT cloud platforms. We present our
choices of tools and engineering actions that help the developer to design,
test and evaluate IoT cloud platforms in multi-cloud environments.
HINC – Harmonizing Diverse Resource Information Across IoT, Network Functions...Hong-Linh Truong
Effective resource management in IoT systems must
represent IoT resources, edge-to-cloud network capabilities, and
cloud resources at a high-level, while being able to link to diverse
low-level types of IoT devices, network functions, and cloud
computing infrastructures. Hence resource management in such
a context demands a highly distributed and extensible approach,
which allows us to integrate and provision IoT, network functions,
and cloud resources from various providers. In this paper, we
address this crucial research issue. We first present a high-
level information model for virtualized IoT, network functions
and cloud resource modeling, which also incorporates software-
defined gateways, network slicing and data centers. This model
is used to glue various low-level resource models from different
types of infrastructures in a distributed manner to capture
sets of resources spanning across different sub-networks. We
then develop a set of utilities and a middleware to support
the integration of information about distributed resources from
various sources. We present a proof of concept prototype with
various experiments to illustrate how various tasks in IoT cloud
systems can be simplified as well as to evaluate the performance
of our framework.
SINC – An Information-Centric Approach for End-to-End IoT Cloud Resource Prov...Hong-Linh Truong
We present SINC –
Slicing IoT, Network Functions, and Clouds – which enables designers to dynamically create/update end-to-end slices of the overall IoT network in order to simultaneously meet multiple user needs.
Governing Elastic IoT Cloud Systems under UncertaintiesHong-Linh Truong
we introduce U-GovOps – a novel framework for
dynamic, on-demand governance of elastic IoT cloud systems under
uncertainty. We introduce a declarative policy language to simplify
the development of uncertainty- and elasticity-aware governance
strategies. Based on that we develop runtime mechanisms, which
enable mitigating the uncertainties by monitoring and governing
the IoT cloud systems through specified strategies.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
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.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
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.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
M2M Platform-as-a-Service for Sustainability Governance
1. M2M Platform-as-a-Service for
Sustainability Governance
Hong-Linh Truong and Schahram Dustdar
Distributed Systems Group
Vienna University of Technology
truong@dsg.tuwien.ac.at
http://pc3l.infosys.tuwien.ac.at
SOCA 2012, 18 Dec 2012, Taipei, 1
Taiwan
2. Outline
Context, motivation, and approach
Linking M2M data
Platform as a service
Prototype
Conclusions and future work
SOCA 2012, 18 Dec 2012, 2
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3. The context – sustainability governance
Infrastructure/Internet of Things Internet/public cloud Organization-specific
boundary boundary
Emergency
Management
Near Enterprise
realtime
analytics Resource
Planning
Predictive
data
analytics
Tracking/Log
istics
Visual
Analytics
Infrastructure
Monitoring
...
Cities, e.g. including:
10000+ buildings
1000000+ sensors
SOCA 2012, 18 Dec 2012, Taipei, 3
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4. Motivation (1)
Multiple phases, different data
gathering processes, different types
of data
Big and near-realtime data
Different types of analytics
Not a single programmig
language/model
Covering simple to complex
applications
Hong Linh Truong, Schahram Dustdar: A survey on cloud-based sustainability governance systems. IJWIS 8(3): 278-295 (2012)
SOCA 2012, 18 Dec 2012, 4
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5. Motivation (2)
A small example
Only a few cloud-based infrastructures are investigated for managing
low-level data for sustainability governance
(Open) e-science data or sensor Web platforms mainly support one type
of stakeholders
Low-level (big sensor-based) cloud-based data infrastructures and
analytics platforms for single type of stakeholders are not enough
SOCA 2012, 18 Dec 2012, Taipei, 5
Taiwan
6. Approach – Platform as a Service
Link near-realtime monitoring data with facility
monitored objects
Using linked data models and leveraging data services
for monitoring data and for monitored object information
Manual/automatic processes to establish the links
Develop data-as-a-service and platform-as-a-
service concepts for sustainabiltiy governance
Support near-realtime and predictive analytics
Different application models and bot-as-a-service
SOCA 2012, 18 Dec 2012, 6
Taipei, Taiwan
7. Linking cloud-based M2M data
Different situations in realistic systems:
Monitored object descriptions are/are not well-defined
Monitored object information might or might not
available
Sensor data can/cannot be annotated
SOCA 2012, 18 Dec 2012, Taipei, 7
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8. DaaS for sustainability governance
Monitoring data Data-as-a-Service
Facility information Data-as-a-Service
SOCA 2012, 18 Dec 2012, 8
Taipei, Taiwan
9. Platform-as-a-Service for
sustainability governance
Different analytics application models, such as
batch, workflow and stream applications and
intelligent bots
different programming models and languages
offline predictive analytics of large-scale data but also
near-realtime analytics and bot-as-a-service
SOCA 2012, 18 Dec 2012, 9
Taipei, Taiwan
10. Platform-as-a-Service and Bots
Hong Linh Truong, Phu H. Phung, Schahram Dustdar: Governing Bot-as-a-Service in Sustainability Platforms - Issues
and Approaches. Procedia CS 10: 561-568 (2012)
SOCA 2012, 18 Dec 2012, Taipei, 10
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12. Prototype
Near-realtime monitoring data are obtained from
Niagara AX gateways, part of the Pacific
Controls Galaxy Platform
http://www.pacificcontrols.net/products/galaxy.html
An RDF-based data service for buiding
concepts and links
SusGov Apps profiles are in RDF
Using Allergro Graph
(http://www.franz.com/agraph/allegrograph)
Java-based PaaS with RESTful APIs
SOCA 2012, 18 Dec 2012, 12
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13. Linking M2M Cloud data - example
SOCA 2012, 18 Dec 2012, Taipei, 13
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14. Cloud-based sustainability
governance analysis framework
Application discovery
Data dependencies
Results
Local execution environment
SOCA 2012, 18 Dec 2012, Taipei, 14
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15. Conclusions and Future Work
We present
Techniques to link monitoring data and monitored
objects in cloud-based M2M systems
Platform-as-a-Service and data services for different
types of data analytics required by different
stakeholders
Future plan
Large-scale tests
Dynamic near-realtime analytics by combining bots
and cloud predictive data analytics
SOCA 2012, 18 Dec 2012, 15
Taipei, Taiwan
16. Thanks for your attention
Hong-Linh Truong
Distributed Systems Group
Vienna University of Technology
truong@dsg.tuwien.ac.at
http://www.infosys.tuwien.ac.at/staff/truong
SOCA 2012, 18 Dec 2012, Taipei, 16
Taiwan