Nuclear power plants work by releasing huge amounts of energy when they cause atoms to break apart. This is fission. The sun and other stars combine atoms, making their mass smaller and releasing enormous amounts of heat and energy.
Microsoft Tests a Renewable Energy-Powered Data Center at the Bottom of the O...Abaram Network Solutions
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Microsoft estimates that more than half of the worlds population lives within about 120 miles of the coast. Therefore by placing data centers near coastal cities, data would have a shorter distance to travel to reach its destination.
This statement of completion certifies that Beethoven Adelson Plaisir successfully finished the online, non-credit course "Data Mining with Weka" provided by the University of Waikato on February 28, 2017, which covered machine learning algorithms, representing learned models, filtering data, classification methods, data visualization, and training, testing and evaluation. However, this statement does not represent or confer credit towards a University of Waikato qualification or verify the person's identity.
A lecture to the National University of Ireland, Galway honours year and masters students in oceanography (14th November 2016) on the basics of marine data management.
Using Erddap as a building block in Ireland's Integrated Digital OceanAdam Leadbetter
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The document discusses using Erddap as part of Ireland's Integrated Digital Ocean platform. Erddap is used to aggregate data from various sources and provide it to users through standardized APIs and web interfaces. This allows diverse data and applications to interoperate through common access points and data flows, minimizing the distances between different technologies and systems. The Marine Institute of Ireland has implemented this approach to integrate ocean observation data and provide open access through their Digital Ocean portal.
Where Linked Data meets Big Data: Applying standard data models to environmen...Adam Leadbetter
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This document discusses applying standard data models to environmental data streams from ocean observations. It presents examples of encoding oceanographic observation data using semantic web standards like the W3C Observation and Measurement ontology. These approaches aim to integrate live sensor data with linked open data to support interoperability across scientific domains.
Various industrial challenges in full scale data handling situations in shipping are considered in this study. These large scale data handling approaches are often categorized as "Big Data" challenges; therefore various solutions to overcome such situations are identified. The proposed approach consists of a marine engine centered data flow path with various data handling layers to address the same challenges. These layers are categorized as: sensor fault detection, data classification, data compression, data transmission and receiver, data expansion, integrity verification, and data regression. The functionalities of each data handling layer with respect to ship performance and navigation information of a selected vessel are discussed and additional challenges that are encountered during this process are also summarized. Hence, these results can be used to develop data analytics that are related to energy efficiency and system reliability applications of shipping.
Linked Ocean Data - Exploring connections between marine datasets in a Big Da...Adam Leadbetter
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Adam Leadbetter works for the Marine Institute in Ireland and is interested in data management, oceanography, and long-distance running. The document provides his contact information and describes his interests using RDF triples. It also includes several links to resources about ocean data, sensors, observations, and semantic web standards for observational data.
The document discusses the virtualization landscape in the financial industry. It addresses how virtualization relates to cloud computing, the challenges of regulations/standards, sustaining resiliency during flip/flops between production and DR sites, integrating legacy systems, and storage issues. The document also outlines where the industry currently stands with cloud services and active/active setups, and concludes by stating that financial institutions are embracing new technologies but fully embracing public cloud remains uncertain due to regulatory requirements.
Where did my layer come from? The semantics of data releaseAdam Leadbetter
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This document discusses the semantics of spatial data release and provenance metadata. It introduces Adam Leadbetter from the Marine Institute and provides several relevant links on topics like linked data, the PROV ontology, and information on data publication and citation. Several citations and the author's contact details are also included.
Nexergy CEO Darius Salgo's presentation from the All Energy conference, Oct 2017. In the presentation he outlines the shift from a one-way to two-way distributed energy future, and the value of new tools like local energy trading in better managing the grid.
How to Build Consistent and Scalable Workspaces for Data Science TeamsElaine K. Lee
油
This document discusses how to build consistent and scalable workspaces for data science teams. It recommends identifying system requirements, stabilizing dependencies, increasing test coverage, and using continuous integration to ensure resources are available. It also suggests creating a pool of worker machines and asynchronous task queue to scale workloads. This allows tasks to run in isolated, identical environments and provides flexible use of cloud computing resources. Benefits include guaranteed task environments, extensibility, and a reusable command line interface. Examples of use cases provided are quality assurance testing and parallelizable data and model tasks.
The session I conducted at the Pre-Bootcamp series of AI-Driven Sri Lanka.
The following topics were covered:
Growth engineering / Hacking
Dave McClures Pirate Metrics / Growth funnel
Growth Framework
Data architecture
Azure monitor logs
A/B testing
How to get into data science
Presentation talk: https://youtu.be/sxQxOlK5aGI
This document summarizes a presentation about Myria, a relational algorithmics-as-a-service platform developed by researchers at the University of Washington. Myria allows users to write queries and algorithms over large datasets using declarative languages like Datalog and SQL, and executes them efficiently in a parallel manner. It aims to make data analysis scalable and accessible for researchers across many domains by removing the need to handle low-level data management and integration tasks. The presentation provides an overview of the Myria architecture and compiler framework, and gives examples of how it has been used for projects in oceanography, astronomy, biology and medical informatics.
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The Merritt repository, operated by the University of California Curation Center (UC3) at the California Digital Library (CDL), functions as a curation core for several data sharing initiatives, including the eScholarship open access publishing platform, the DataONE network, and the Open Context archaeological portal. This presentation with highlight two recent examples of external integration for purposes of research data sharing: DataShare, an open portal for biomedical data at UC, San Francisco; and Research Hub, an Alfresco-based content management system at UC, Berkeley. They both significantly extend Merritts coverage of the full research data lifecycle and workflows, both upstream, with augmented capabilities for data description, packaging, and deposit; and downstream, with enhanced domain-specific discovery. These efforts showcase the catalyzing effect that coupled integration of curation repositories and well-known public disciplinary search environments can have on research data sharing and scientific advancement.
Webinar: How Microsoft is changing the game with Windows AzureCommon Sense
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The Windows Azure Common Sense Webinar! Solution Specialist with Microsoft Nate Shea-han will present How Microsoft is changing the game with Windows Azure.
Learn the difference between Azure (PAAS) and Infrastructure As A Service and standing up virtual machines; how the datacenter evolution is driving down the cost of enterprise computing and about the modular datacenter and containers.
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Practical solutions to implementing "Born Connected" data systems
1. Practical solutions to implementing
"Born Connected" data systems
Adam Leadbetter, Marine Institute
(adam.leadbetter@marine.ie)
Justin Buck, British Oceanographic Data Centre
Paul Stacey, Institute of Technology Blanchardstown
11. An example URI in SenseOcean:
http://linked.systems.ac.uk/System/
AanderaaOxygenOptode4531/XX34213/
ClassName for the
System
SerialNumber
Class for System or
SensingDevice
Host
A Unique identifier for the concept or data
A Unique reference for the concept
Computer readable
Looks familiar
24. Adam Leadbetter, Marine Institute, Ireland
adam.leadbetter@marine.ie
@AdamLeadbetter
https://github.com/IrishMarineInstitute/
sensor-observation-service
https://github.com/peterataylor/om-json
Editor's Notes
#2: Acknowledge:
Janet Fredericks @ WHOI
Damian Smyth & Rob Fuller @ MI
Alexandra Kokinakki @ BODC
Born Digital -> Born Semantic -> Born Connected
#3: Why?
Traditionally ocean data has been structured, and particularly, linked post fact
#4: Why?
Traditionally ocean data has been structured, and particularly, linked post fact
Gliders, Argo floats, ROVs, seafloor observatories break the sustainability of that model
#5: Why?
Traditionally ocean data has been structured, and particularly, linked post fact
Gliders, Argo floats, ROVs, seafloor observatories break the sustainability of that model
Shepherds metaphor
Do you have the time to go to Dagobah and take the training with the Big Data age
Lesley Wyborn Data needs to be Born Connected to enable Transdisciplinary Science and beginning at conceived connected!
So
#8: Extending the Born Semantic to ultra-constrained observation environments
Achieving Born Semantic data in an ultra-constrained environment presents more difficulties. Communications may be intermittent, very low bandwidth, data-logger must be highly power efficient etc..
Extending the Born Semantic to ultra-resource constrained environments
There has been a recently flurry of development activity around Internet of Things (IoT) technologies. This has lead to a drive for IoT enabling technologies that presents opportunities to further realise the concept of Born Semantic data, pushing the semantic annotation closer to the data capture point.
These technologies are all about squeezing the bits reducing storage, processing and communication overhead.
Low-power, highly efficient operating systems such as TinyOS and Contiki (among others) provide powerful enough capabilities to leverage semantic annotation efforts.
Fernandez et al. have recently addressed compression of RDF, with the Header-Dictionary-Triples approach that compresses tuple elements into a dictionary, followed by a compressed representation of triples of dictionary keys. This approach is only applicable to large data sets, which is not an option in a constrained environment. Wiselib TupleStore and RDF provider, provides a suitable solution here as it is a light weight flexible data storage solution.
The Constrained Application Protocol (CoAP) is a specialised web 油transfer protocol for use with constrained embedded systems and networks. CoAP is designed to easily interface with HTTP for integration with the Web with very low overhead, and simplicity for constrained environments. Although HTTP is the defacto standard for RESTful architectures CoAP.
CoAP specifies a minimal subset of REST requests (GET, POST, PUT, and DELETE) it also relies on UDP as a transport protocol while providing reliability with a simple built-in retransmission mechanism and so the communications overhead is small compared to HTTP.
#10: Ocean Data Interoperability Platform
52N plus others
Different encodings for SOS results
RESTful URLs for SOS access
#11: EGU 2014 prototypes in RDFa (CTD);
SensorML 1.0 (Qartod 2 OGC now re-funded as X-DOMES;
Direct embedding concept ids in file headers (Lake Ellswort Drilling Project) or SWE XML definitions (Q2O).
Funding from EU SenseOCEAN, BRIDGES, OpenGovIntelligence
Funding from SEAI
Onto SenseOCEAN slides from BODC
#12: First step is a sensor / instrument register
Built on Fuseki with custom Java API
Live in next few months
#13: SSN = has some issues with alignment later with O&M, which will be introduced in the next slide Simon Cox will go into details
#14: Ideally associated with something like an ORCiD not just the persons name
#15: We have created the models. But we are still gathering metadata from the manufacturers. So will be able to publish some example sensor descriptions soon enough (couple of months).
#19: Single machine
Distributed processing
One-to-one communication
Publish-subscribe pattern
No fault tolerance
Replication, auto-recovery
Fixed schema, encoding
Schema management, evolvable encoding
#22: Simon Cox & Peter Taylor presentation at OGC TC in September 2015
Work ongoing in Ocean Acidification community to use the proposed O&M JSON schema
Here is a snapshot from a SOS call to the Galway Bay Cable Observatory
#24: Adding a JSON-LD context to the output allows us to generate a triple-ified model of the SOS output