This document discusses potential enhancements to the EPA's Facility Registry System (FRS) Linked Open Data approach. It notes issues with the current data serialization, which treats data as flat tables without semantic structure. The document proposes improving data modeling, leveraging existing resources and metadata, and collaborating with others to enhance query capabilities and representational robustness. Short term needs include semantic enhancements to support faceted analysis and unique identification. Long term, the data model may need updates to better support Linked Open Data applications.
This patent describes a method for assigning importance ranks to nodes in a linked database, such as the world wide web. The rank assigned to a document is calculated based on the ranks of documents that cite it, and a constant representing the probability a user will randomly access the document. The method enhances the performance of search engine results for hypermedia databases like the web, whose documents have large variations in quality.
Linked Open Data Principles, Technologies and ExamplesOpen Data Support
Theoretical and practical introducton to linked data, focusing both on the value proposition, the theory/foundations, and on practical examples. The material is tailored to the context of the EU institutions.
The document discusses the Metadata Encoding and Transmission Standard (METS), which is an XML schema for encoding descriptive, administrative, and structural metadata regarding objects within a digital library. It describes the characteristics and sections of a METS file, including the header, descriptive and administrative metadata, file and structural map sections. Current users of METS are also listed, such as libraries and universities. The purpose of METS is to provide a flexible structure for linking metadata and content about digital objects.
The need of Interoperability in Office and GIS formatsMarkus Neteler
Free GIS and Interoperability: The need of Interoperability in Office and GIS formats
GIS Open Source, interoperabilità e cultura del dato nei SIAT della Pubblica Amministrazione
[GIS Open Source, interoperability and the 'culture of data' in the spatial data warehouses of the Public Administration]
This document discusses linked data and its use for publishing and connecting environmental data on the web. It describes how linked data allows data to work like web pages by using URIs and standards like RDF to connect related information. The document provides an overview of linked data basics including its underlying structure using triples, standards for formatting and sharing data, and techniques for querying linked data using SPARQL similar to SQL. It also discusses ongoing work by the EPA and other organizations to publish environmental and geospatial data as linked open data.
Presentation by Luiz Olavo Bonino about the current state of the developments on FAIR Data supporting tools at the Dutch Techcentre for Life Sciences Partners Event on November 3-4 2016.
Discussion Notes: Presentation to Ecoinformatics International Technical Collaboration Partnership
International Web Meeting - Linked Open Data and Environmental Information
Day 1 – December 6, 2010
Geospatial Topic – Dave Smith
This patent describes a method for assigning importance ranks to nodes in a linked database, such as the world wide web. The rank assigned to a document is calculated based on the ranks of documents that cite it, and a constant representing the probability a user will randomly access the document. The method enhances the performance of search engine results for hypermedia databases like the web, whose documents have large variations in quality.
Linked Open Data Principles, Technologies and ExamplesOpen Data Support
Theoretical and practical introducton to linked data, focusing both on the value proposition, the theory/foundations, and on practical examples. The material is tailored to the context of the EU institutions.
The document discusses the Metadata Encoding and Transmission Standard (METS), which is an XML schema for encoding descriptive, administrative, and structural metadata regarding objects within a digital library. It describes the characteristics and sections of a METS file, including the header, descriptive and administrative metadata, file and structural map sections. Current users of METS are also listed, such as libraries and universities. The purpose of METS is to provide a flexible structure for linking metadata and content about digital objects.
The need of Interoperability in Office and GIS formatsMarkus Neteler
Free GIS and Interoperability: The need of Interoperability in Office and GIS formats
GIS Open Source, interoperabilità e cultura del dato nei SIAT della Pubblica Amministrazione
[GIS Open Source, interoperability and the 'culture of data' in the spatial data warehouses of the Public Administration]
This document discusses linked data and its use for publishing and connecting environmental data on the web. It describes how linked data allows data to work like web pages by using URIs and standards like RDF to connect related information. The document provides an overview of linked data basics including its underlying structure using triples, standards for formatting and sharing data, and techniques for querying linked data using SPARQL similar to SQL. It also discusses ongoing work by the EPA and other organizations to publish environmental and geospatial data as linked open data.
Presentation by Luiz Olavo Bonino about the current state of the developments on FAIR Data supporting tools at the Dutch Techcentre for Life Sciences Partners Event on November 3-4 2016.
Discussion Notes: Presentation to Ecoinformatics International Technical Collaboration Partnership
International Web Meeting - Linked Open Data and Environmental Information
Day 1 – December 6, 2010
Geospatial Topic – Dave Smith
Big data processing using - Hadoop TechnologyShital Kat
This document summarizes a report on Hadoop technology as a solution to big data processing. It discusses the big data problem, including defining big data, its characteristics and challenges. It then introduces Hadoop as a solution, describing its components HDFS for storage and MapReduce for parallel processing. Examples of common friend lists and word counting are provided. Finally, it briefly mentions some Hadoop projects and companies that use Hadoop.
Secrets of Enterprise Data Mining: SQL Saturday Oregon 201411Mark Tabladillo
If you have a SQL Server license (Standard or higher) then you already have the ability to start data mining. In this new presentation, you will see how to scale up data mining from the free Excel 2013 add-in to production use. Aimed at beginning to intermediate data miners, this presentation will show how mining models move from development to production. We will use SQL Server 2014 tools including SSMS, SSIS, and SSDT.
EDF2014: Daniel Vila-Suero, Researcher, Ontology Engineering Group, Universid...European Data Forum
Selected Talk of Daniel Vila-Suero, Researcher, Ontology Engineering Group, Universidad Politecnica de Madrid, Spain at the European Data Forum 2014, 19 March 2014 in Athens, Greece: 3LD: Towards high quality, industry-ready Linguistic Linked Licensed Data
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham ALMark Tabladillo
This document discusses secrets of enterprise data mining. It begins by defining data mining as the automated or semi-automated process of discovering patterns in data. It then discusses how data mining can be applied in various industries like telecommunications, oil and gas, and Volkswagen Group. Finally, it discusses how Microsoft offers solutions for enterprise data mining through SQL Server Analysis Services and Microsoft Azure Machine Learning.
The document discusses database concepts including:
- What a database is and its components like data, hardware, software, and users.
- Database management systems (DBMS) that enable users to define, create and maintain databases.
- Data models like hierarchical, network, and relational models. Relational databases using SQL are now most common.
- Database design including logical design, physical implementation, and application development.
- Key concepts like data abstraction, instances and schemas, normalization, and integrity rules.
20th Athens Big Data Meetup - 1st Talk - Druid: the open source, performant, ...Athens Big Data
Title: Druid: the open source, performant, real-time, analytical datastore
Speaker: Peter Marshall (https://linkedin.com/in/amillionbytes/)
Date: Tuesday, January 28, 2020
Event: https://meetup.com/Athens-Big-Data/events/266900242/
DC-2008 Tutorial 3 - Dublin Core and other metadata schemasMikael Nilsson
The document discusses metadata standards and interoperability. It provides an overview of Dublin Core and other metadata schemas. It describes how Dublin Core terms are defined both for human understanding through textual definitions, as well as machine understanding through formal semantics expressed in RDF. This allows metadata using Dublin Core terms to be combined and processed in an interoperable way on the Semantic Web.
The document discusses challenges facing the semantic web as it tries to keep up with the growth of the regular web, including not having enough agreed upon vocabularies, data, and links between data. It also notes problems with reasoning over large amounts of noisy and inconsistent web data from different sources. Solutions proposed include cleverly injecting semantic web technologies into content management systems to extract and link more data, as well as developing lightweight vocabularies and simplified reasoning techniques.
The document summarizes key concepts about linked data and the semantic web. It discusses how linked data uses URIs and RDF to publish structured data on the web in a way that is machine-readable and interconnected. It provides examples of how linked data is being implemented in projects from the UK government and BBC to link disparate data sources on the web. While progress is being made, challenges remain around getting organizations to publish their data as linked open data and proving the business value of doing so.
This document provides an overview of XML and related technologies. It discusses how XML can be used to structure data to allow it to be passed between different systems. Some key benefits of using XML include its flexibility, ability to search and extract data, and compressibility. The document also outlines several common tasks involved in working with XML data, such as validation, editing, transformation, querying, and linking documents. It recommends using technologies like XML Schema, XSLT, and namespaces to accomplish these tasks.
The USA and UK governments have made significant progress with linked, open data in recent months. Several fundamental datasets from the Australian Government are on the cusp of being exposed as meaningful, reusable, machine-readable assets, further driving the adoption of linked data within and around government.
Making better use of online data offerings using a combination of top-down policy and guidance, together with bottom-up development efforts from agency web teams, would seem to describe a sustainable, organic growth in linked government data.
Learn about the path to the first release of data.gov.au; a draft roadmap to future releases; the barriers to linked data and open public sector information (PSI); and the real-world questions this technology aims to solve.
IRJET- Generate Distributed Metadata using Blockchain Technology within HDFS ...IRJET Journal
This document proposes a new HDFS architecture that eliminates the single point of failure of the NameNode by distributing metadata storage using blockchain technology. In the traditional HDFS, the NameNode stores all metadata, but in the new architecture this is replaced by blockchain miners that securely store encrypted metadata across data nodes. Blockchain links data blocks in a serial manner with cryptographic hashes to ensure integrity. The key components are HDFS clients, data nodes for storage, and specially designated miner nodes that help create and store metadata blocks in an encrypted and distributed fashion similar to how transactions are recorded in a blockchain. This architecture aims to provide reliable, secure and faster metadata access without a single point of failure.
The document discusses integrating government data from multiple sources using semantic web technologies. It describes various data formats used by government sources, including spreadsheets, XML, RSS, RDFa. It also discusses strategies for importing different types of "found data" into RDF, merging the data through schema mapping and tagging, and analyzing and displaying the integrated data using semantic web approaches. Controlled vocabularies play an important role in mapping schemas and enabling data integration and reuse.
"Big Data" is term heard more and more in industry – but what does it really mean? There is a vagueness to the term reminiscent of that experienced in the early days of cloud computing. This has led to a number of implications for various industries and enterprises. These range from identifying the actual skills needed to recruit talent to articulating the requirements of a "big data" project. Secondary implications include difficulties in finding solutions that are appropriate to the problems at hand – versus solutions looking for problems. This presentation will take a look at Big Data and offer the audience with some considerations they may use immediately to assess the use of analytics in solving their problems.
The talk begins with an idea of how big "Big Data" can be. This leads to an appreciation of how important "Management Questions" are to assessing analytic needs. The fields of data and analysis have become extremely important and impact nearly all facets of life and business. During the talk we will look at the two pillars of Big Data – Data Warehousing and Predictive Analytics. Then we will explore the open source tools and datasets available to NATO action officers to work in this domain. Use cases relevant to NATO will be explored with the purpose of show where analytics lies hidden within many of the day-to-day problems of enterprises. The presentation will close with a look at the future. Advances in the area of semantic technologies continue. The much acclaimed consultants at Gartner listed Big Data and Semantic Technologies as the first- and third-ranked top technology trends to modernize information management in the coming decade. They note there is an incredible value "locked inside all this ungoverned and underused information." HQ SACT can leverage this powerful analytic approach to capture requirement trends when establishing acquisition strategies, monitor Priority Shortfall Areas, prepare solicitations, and retrieve meaningful data from archives.
The document outlines Renault's big data initiatives from 2014-2016 which progressed from an initial sandbox to a full industrialized big data platform. Key steps included implementing a new Hadoop infrastructure in 2015, industrializing the platform in 2016 to host production projects and POCs, and designing for scalability, isolation, simplified operations, and data protection. The document also discusses deploying quality projects to the data lake, ingestion scenarios, interactive SQL analytics, security measures including tokenization, and the next steps of federation and dynamic data change management.
The document outlines Renault's big data initiatives from 2014-2016, including:
1. Starting with a big data sandbox in 2014 using an old HPC infrastructure for data exploration.
2. Implementing a DataLab in 2015 with a new HP infrastructure and establishing a first level of industrialization while improving data protection.
3. Creating a big data platform in 2016 to industrialize hosting both proofs of concept and production projects while ensuring data protection.
Logical Data Fabric: Architectural ComponentsDenodo
Watch full webinar here: https://bit.ly/39MWm7L
Is the Logical Data Fabric one monolithic technology or does it comprise of various components? If so, what are they? In this presentation, Denodo CTO Alberto Pan will elucidate what components make up the logical data fabric.
Presentation on EPA's Facility Registry Service API for the DC Web API Meetup. The API is being used in front end integration and master data management, delivering data quality improvements, better integration, and burden reduction to reporters.
Big data processing using - Hadoop TechnologyShital Kat
This document summarizes a report on Hadoop technology as a solution to big data processing. It discusses the big data problem, including defining big data, its characteristics and challenges. It then introduces Hadoop as a solution, describing its components HDFS for storage and MapReduce for parallel processing. Examples of common friend lists and word counting are provided. Finally, it briefly mentions some Hadoop projects and companies that use Hadoop.
Secrets of Enterprise Data Mining: SQL Saturday Oregon 201411Mark Tabladillo
If you have a SQL Server license (Standard or higher) then you already have the ability to start data mining. In this new presentation, you will see how to scale up data mining from the free Excel 2013 add-in to production use. Aimed at beginning to intermediate data miners, this presentation will show how mining models move from development to production. We will use SQL Server 2014 tools including SSMS, SSIS, and SSDT.
EDF2014: Daniel Vila-Suero, Researcher, Ontology Engineering Group, Universid...European Data Forum
Selected Talk of Daniel Vila-Suero, Researcher, Ontology Engineering Group, Universidad Politecnica de Madrid, Spain at the European Data Forum 2014, 19 March 2014 in Athens, Greece: 3LD: Towards high quality, industry-ready Linguistic Linked Licensed Data
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham ALMark Tabladillo
This document discusses secrets of enterprise data mining. It begins by defining data mining as the automated or semi-automated process of discovering patterns in data. It then discusses how data mining can be applied in various industries like telecommunications, oil and gas, and Volkswagen Group. Finally, it discusses how Microsoft offers solutions for enterprise data mining through SQL Server Analysis Services and Microsoft Azure Machine Learning.
The document discusses database concepts including:
- What a database is and its components like data, hardware, software, and users.
- Database management systems (DBMS) that enable users to define, create and maintain databases.
- Data models like hierarchical, network, and relational models. Relational databases using SQL are now most common.
- Database design including logical design, physical implementation, and application development.
- Key concepts like data abstraction, instances and schemas, normalization, and integrity rules.
20th Athens Big Data Meetup - 1st Talk - Druid: the open source, performant, ...Athens Big Data
Title: Druid: the open source, performant, real-time, analytical datastore
Speaker: Peter Marshall (https://linkedin.com/in/amillionbytes/)
Date: Tuesday, January 28, 2020
Event: https://meetup.com/Athens-Big-Data/events/266900242/
DC-2008 Tutorial 3 - Dublin Core and other metadata schemasMikael Nilsson
The document discusses metadata standards and interoperability. It provides an overview of Dublin Core and other metadata schemas. It describes how Dublin Core terms are defined both for human understanding through textual definitions, as well as machine understanding through formal semantics expressed in RDF. This allows metadata using Dublin Core terms to be combined and processed in an interoperable way on the Semantic Web.
The document discusses challenges facing the semantic web as it tries to keep up with the growth of the regular web, including not having enough agreed upon vocabularies, data, and links between data. It also notes problems with reasoning over large amounts of noisy and inconsistent web data from different sources. Solutions proposed include cleverly injecting semantic web technologies into content management systems to extract and link more data, as well as developing lightweight vocabularies and simplified reasoning techniques.
The document summarizes key concepts about linked data and the semantic web. It discusses how linked data uses URIs and RDF to publish structured data on the web in a way that is machine-readable and interconnected. It provides examples of how linked data is being implemented in projects from the UK government and BBC to link disparate data sources on the web. While progress is being made, challenges remain around getting organizations to publish their data as linked open data and proving the business value of doing so.
This document provides an overview of XML and related technologies. It discusses how XML can be used to structure data to allow it to be passed between different systems. Some key benefits of using XML include its flexibility, ability to search and extract data, and compressibility. The document also outlines several common tasks involved in working with XML data, such as validation, editing, transformation, querying, and linking documents. It recommends using technologies like XML Schema, XSLT, and namespaces to accomplish these tasks.
The USA and UK governments have made significant progress with linked, open data in recent months. Several fundamental datasets from the Australian Government are on the cusp of being exposed as meaningful, reusable, machine-readable assets, further driving the adoption of linked data within and around government.
Making better use of online data offerings using a combination of top-down policy and guidance, together with bottom-up development efforts from agency web teams, would seem to describe a sustainable, organic growth in linked government data.
Learn about the path to the first release of data.gov.au; a draft roadmap to future releases; the barriers to linked data and open public sector information (PSI); and the real-world questions this technology aims to solve.
IRJET- Generate Distributed Metadata using Blockchain Technology within HDFS ...IRJET Journal
This document proposes a new HDFS architecture that eliminates the single point of failure of the NameNode by distributing metadata storage using blockchain technology. In the traditional HDFS, the NameNode stores all metadata, but in the new architecture this is replaced by blockchain miners that securely store encrypted metadata across data nodes. Blockchain links data blocks in a serial manner with cryptographic hashes to ensure integrity. The key components are HDFS clients, data nodes for storage, and specially designated miner nodes that help create and store metadata blocks in an encrypted and distributed fashion similar to how transactions are recorded in a blockchain. This architecture aims to provide reliable, secure and faster metadata access without a single point of failure.
The document discusses integrating government data from multiple sources using semantic web technologies. It describes various data formats used by government sources, including spreadsheets, XML, RSS, RDFa. It also discusses strategies for importing different types of "found data" into RDF, merging the data through schema mapping and tagging, and analyzing and displaying the integrated data using semantic web approaches. Controlled vocabularies play an important role in mapping schemas and enabling data integration and reuse.
"Big Data" is term heard more and more in industry – but what does it really mean? There is a vagueness to the term reminiscent of that experienced in the early days of cloud computing. This has led to a number of implications for various industries and enterprises. These range from identifying the actual skills needed to recruit talent to articulating the requirements of a "big data" project. Secondary implications include difficulties in finding solutions that are appropriate to the problems at hand – versus solutions looking for problems. This presentation will take a look at Big Data and offer the audience with some considerations they may use immediately to assess the use of analytics in solving their problems.
The talk begins with an idea of how big "Big Data" can be. This leads to an appreciation of how important "Management Questions" are to assessing analytic needs. The fields of data and analysis have become extremely important and impact nearly all facets of life and business. During the talk we will look at the two pillars of Big Data – Data Warehousing and Predictive Analytics. Then we will explore the open source tools and datasets available to NATO action officers to work in this domain. Use cases relevant to NATO will be explored with the purpose of show where analytics lies hidden within many of the day-to-day problems of enterprises. The presentation will close with a look at the future. Advances in the area of semantic technologies continue. The much acclaimed consultants at Gartner listed Big Data and Semantic Technologies as the first- and third-ranked top technology trends to modernize information management in the coming decade. They note there is an incredible value "locked inside all this ungoverned and underused information." HQ SACT can leverage this powerful analytic approach to capture requirement trends when establishing acquisition strategies, monitor Priority Shortfall Areas, prepare solicitations, and retrieve meaningful data from archives.
The document outlines Renault's big data initiatives from 2014-2016 which progressed from an initial sandbox to a full industrialized big data platform. Key steps included implementing a new Hadoop infrastructure in 2015, industrializing the platform in 2016 to host production projects and POCs, and designing for scalability, isolation, simplified operations, and data protection. The document also discusses deploying quality projects to the data lake, ingestion scenarios, interactive SQL analytics, security measures including tokenization, and the next steps of federation and dynamic data change management.
The document outlines Renault's big data initiatives from 2014-2016, including:
1. Starting with a big data sandbox in 2014 using an old HPC infrastructure for data exploration.
2. Implementing a DataLab in 2015 with a new HP infrastructure and establishing a first level of industrialization while improving data protection.
3. Creating a big data platform in 2016 to industrialize hosting both proofs of concept and production projects while ensuring data protection.
Logical Data Fabric: Architectural ComponentsDenodo
Watch full webinar here: https://bit.ly/39MWm7L
Is the Logical Data Fabric one monolithic technology or does it comprise of various components? If so, what are they? In this presentation, Denodo CTO Alberto Pan will elucidate what components make up the logical data fabric.
Similar to FRS Linked Open Data Concept v1.3 20101130 (20)
Presentation on EPA's Facility Registry Service API for the DC Web API Meetup. The API is being used in front end integration and master data management, delivering data quality improvements, better integration, and burden reduction to reporters.
This document discusses initiatives by the Facilities Registry Service (FRS) to improve data quality for key environmental datasets relevant to emergency response. It identifies known data gaps for oil and hazardous waste facilities (ESF-10) and wastewater/drinking water infrastructure (ESF-3). The FRS will conduct thematically and geographically targeted reviews of high-risk facilities to address these gaps. Geographically, counties in Louisiana, Florida, Alabama and Mississippi with the most frequent hurricane disaster declarations will be prioritized. The FRS will also develop new GIS layers, including one for wastewater treatment plants integrating data from ICIS-NPDES and other sources.
The executive order establishes a cross-agency working group to improve coordination between federal, state, and local agencies on chemical facility safety. The working group is tasked with developing plans to modernize regulations and information sharing, identify best practices, and enhance emergency response coordination. Key objectives include reviewing coverage of existing risk management programs, identifying ways to improve ammonium nitrate safety, and convening stakeholders to discuss options for strengthening chemical safety and security.
This document discusses the EPA's use of infrastructure data for emergency response efforts. It notes that the EPA's emergency response has traditionally focused on oil and hazardous waste cleanup after disasters. However, the poor quality of drinking water and wastewater infrastructure data hampered the EPA's response to Hurricane Sandy. Address and location data for many facilities was missing, invalid, or in the wrong county. The EPA's Facility Registry Service helped fill some gaps by integrating data from other EPA programs and sources. The document calls for more reliable infrastructure data to better support emergency response, assessment of damage, and prioritization of aid.
The document discusses the EPA's efforts to publish environmental data as linked open data. It provides background on the Facility Registry System (FRS), which contains information on 2.8 million facilities. The EPA has begun publishing FRS data as linked data and is testing functionality to better represent the data. The EPA is also working to publish other data sets as linked open data, such as the Substance Registry and Toxic Release Inventory. It is collaborating with other organizations to develop standards and best practices for linked open data.
The document discusses the Facility Registry System (FRS) which aggregates and integrates facility data from over 30 federal and 50 state, local, and tribal databases. FRS contains information on nearly 2.8 million facilities, over 80% of which have latitude and longitude data. FRS improves the validity of facility program data from 40% to 95% by selecting the best contact and location information from multiple sources. It allows users to evaluate facility compliance and perform cross-media analyses. FRS incorporates several layers of quality control and utilizes EPA standards to determine the best pick location from possible location options for each facility.
The document summarizes data and services provided by the U.S. Environmental Protection Agency (EPA) to support health initiatives. It describes EPA's mission to protect human health and the environment. It then provides an overview of various EPA data assets and systems, including the EPA Data Finder, System of Registries, Environmental Dataset Gateway, Substance Registry, and the Facility Registry System. It also describes the National Environmental Information Exchange Network for exchanging data.
The Facility Registry System (FRS) is a data aggregator that integrates, validates and quality assures data from 32 federal and 57 state, tribal and territorial environmental databases containing information on over 2.6 million facilities, over 80% of which have latitude and longitude data. FRS currently publishes this geospatial and facility information as basic RDF on Data.gov but aims to develop a more robust, standards-driven and semantically enriched linked open data representation.
More from Dave Smith / USEPA Office of Environmental Information (9)
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
High performance Serverless Java on AWS- GoTo Amsterdam 2024Vadym Kazulkin
Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...DanBrown980551
This LF Energy webinar took place June 20, 2024. It featured:
-Alex Thornton, LF Energy
-Hallie Cramer, Google
-Daniel Roesler, UtilityAPI
-Henry Richardson, WattTime
In response to the urgency and scale required to effectively address climate change, open source solutions offer significant potential for driving innovation and progress. Currently, there is a growing demand for standardization and interoperability in energy data and modeling. Open source standards and specifications within the energy sector can also alleviate challenges associated with data fragmentation, transparency, and accessibility. At the same time, it is crucial to consider privacy and security concerns throughout the development of open source platforms.
This webinar will delve into the motivations behind establishing LF Energy’s Carbon Data Specification Consortium. It will provide an overview of the draft specifications and the ongoing progress made by the respective working groups.
Three primary specifications will be discussed:
-Discovery and client registration, emphasizing transparent processes and secure and private access
-Customer data, centering around customer tariffs, bills, energy usage, and full consumption disclosure
-Power systems data, focusing on grid data, inclusive of transmission and distribution networks, generation, intergrid power flows, and market settlement data
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
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FRS Linked Open Data Concept v1.3 20101130
1. FRS and Linked Open Data Potential –
Conceptual Discussion v 1.3
November 30, 2010
Dave Smith
USEPA/OEI/OIC/IESD/ISSB
smith.davidg@epa.gov
202-566-0797
Document Change History
Revision Date Author Description
1.0 11/12/2010 David G. Smith Initial Version
1.1 11/24/2010 David G. Smith Minor
updates/revisions
as followon to
11/23 discussion
1.2 11/29/2010 David G. Smith Collaborations,
potential pilots,
FOAF and other
models
1.3 11/30/2010 David G. Smith Additional
collaborations and
detail on facility
granularity
concept
2. FRS Data Model Initial Conceptual Discussion
November 11, 2010 November 30, 2010
Contents
Document Change History.......................................................................1
Introduction:............................................................................................2
Concept:...................................................................................................2
Current Situation:.....................................................................................3
Linked Open Data Issues:.........................................................................3
Data Model Issues: .................................................................................7
Linked Open Data Development:.............................................................7
Existing Resources....................................................................................7
Short-Term data needs:...........................................................................7
Potential Pilots.........................................................................................9
Longer-Range, Emergent data needs:....................................................10
Other Ongoing, Related Activities..........................................................11
Introduction:
The intent of this concept paper is to initially explore some conceptual, blue-sky, no-constraints for
potential improvements to the FRS Linked Open Data approach being published via data.gov, and to
stimulate additional ideas and brainstorming. Followon to this will be examination of alternatives,
prioritizations and finalization of thoughts toward implementation.
Concept:
Provide enhancements to FRS Linked Open Data approach to improve analysis, enhance facility
representation, improve robustness of LOD querying and analytics, integrate other existing metadata
capabilities and improve capabilities to support Semantic Web approaches, such as more-informed RDF
serialization.
2
3. FRS Data Model Initial Conceptual Discussion
November 11, 2010 November 30, 2010
Current Situation:
FRS data is currently being published via Data.gov, e.g. RDF button on Data.gov catalog pages (e.g.
http://www.data.gov/raw/1030 ) for FRS data.
Figure 1: Example of Current FRS RDF Offering (highlighted in red box)
The data returned is tied to a data.gov URL, e.g.
http://www.data.gov/semantic/data/alpha/1030/dataset-1030.rdf.gz
Linked Open Data Issues:
Currently, FRS and other datasets published via Data.gov are being serialized as RDF to support semantic
web and linked open data. A basic problem with the Data.gov RDF does not just apply to the FRS RDF
data, it likely applies across the board.
Firstly, in terms of access, the data is a gzipped download. Data must be downloaded and unzipped
before it can be accessed - more ideally, it would be good to see Data.gov serving the data up as a
SPARQL endpoint, or as a SESAME repository or other means of serving up a triple store. That
download/unzip paradigm does not lend itself to dynamic mashups.
3
4. FRS Data Model Initial Conceptual Discussion
November 11, 2010 November 30, 2010
With regard to the Data.gov RDF, it appears to be a brute-force serialization of data tables into RDF. It
doesn't really have the semantic depth to support analysis that it could use (See Fig. 1-3).
<rdf:Description rdf:about="#entry9985">
<hdatum_desc>NAD83</hdatum_desc>
<state_name>NEBRASKA</state_name>
<latitude83>40.944623</latitude83>
<interest_types>STATE MASTER</interest_types>
<city_name>GARLAND</city_name>
<create_date>01-MAR-00</create_date>
<frs_facility_detail_report_url rdf:resource="
http://iaspub.epa.gov/enviro/fii_query_detail.disp_program_facility?
p_registry_id=110006555085 "/>
<congressional_dist_num>01</congressional_dist_num>
<pgm_sys_acrnms>NE-IIS</pgm_sys_acrnms>
<epa_region_code>07</epa_region_code>
<country_name>USA</country_name>
<fips_code>31159</fips_code>
<huc_code>10200203</huc_code>
<collect_desc>ADDRESS MATCHING-HOUSE NUMBER</collect_desc>
<primary_name>TERRI KELLER RESIDENCE</primary_name>
<rdf:type rdf:resource=" http://data-gov.tw.rpi.edu/2009/data-gov-twc.rdf#DataEntry
"/>
<ref_point_desc>ENTRANCE POINT OF A FACILITY OR STATION</ref_point_desc>
<postal_code>683609338</postal_code>
<registry_id>110006555085</registry_id>
<location_address>1976 OLD MILL RD</location_address>
<accuracy_value>30</accuracy_value>
<update_date>06-AUG-01</update_date>
<county_name>SEWARD</county_name>
<conveyor>FRS</conveyor>
<longitude83>-96.990306</longitude83>
<state_code>NE</state_code>
<site_type_name>STATIONARY</site_type_name>
</rdf:Description>
Figure 1: Sample of current Data.gov FRS RDF/XML Representation
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6. FRS Data Model Initial Conceptual Discussion
November 11, 2010 November 30, 2010
http://www.data.gov/semantic/data/alpha/997/dataset-997.rdf#longitude83 > "-96.990306" .
< http://www.data.gov/semantic/data/alpha/997/dataset-997.rdf#entry9985 > <
http://www.data.gov/semantic/data/alpha/997/dataset-997.rdf#state_code > "NE" .
< http://www.data.gov/semantic/data/alpha/997/dataset-997.rdf#entry9985 > <
http://www.data.gov/semantic/data/alpha/997/dataset-997.rdf#site_type_name > "STATIONARY"
.
Figure 2: Sample of current Data.gov FRS Representation as Triples
The current RDF serialization is essentially just a brute force conversion - there is plenty of opportunity
to enhance and improve.
The properties are things that some EPA users might easily understand, but would others, e.g.
huc_code, pgm_sys_acrnms – are these uniquely identifiable and understood, within this dataset?
Thinking import reference to EPA data dictionary, perhaps EPA namespace or other means of defining
them more positively is needed. We have a lot of metadata that we can bring into the mix, toward
enhancing identifiability, understandability and usability of the RDF data.
There isn't really much structure or model, it's essentially a flat table. Everything is just treated as
alphanumeric data types. No temporal intelligence to dates, et cetera. It doesn't identify registry ID as
something unique or indexable. There are many things that can and should be defined better. There is
probably a semantic analogue to our data model that we can develop as an RDF/OWL/etc analogue and
then map to it.
One approach which may make more sense is to go back and look at the relational database model,
which can support more richness – essentially, individual tables and their relationships would be
generated as Linked Open Data, and the SPARQL queries would then have the flexibility of current SQL
queries.
Regarding the properties, are there in some cases other namespaces that we could/should be
leveraging? geo: as one example - our data is, however, NAD83, and geo: assumes WGS84. We could
reproject to WGS84 and provide geo: values to supplement what we have, as one possibility. Similarly,
maybe foaf: or other namespaces, which deal with addresses and points of contact. The RDF only
carries locations, but FRS also has contacts, if we should at some point incorporate those as well.
In summary, I think it could stand to be improved from a standpoint of accessibility (SPARQL, et cetera - I
think Data.gov needs to look at that from a services infrastructure standpoint), and then, improved
usability, by following more of a data model approach, as opposed to this flat mapping, and approaches
like mapping to existing namespaces and following existing models where appropriate, and we should
be able to leverage some of our metadata elements, data models and other artifacts toward a better
representation and mapping.
6
7. FRS Data Model Initial Conceptual Discussion
November 11, 2010 November 30, 2010
Data Model Issues:
Long range, some additional tweaks to FRS data model may be needed in order to enhance data
representation and better support Linked Open Data - some of these are described in brief below.
Linked Open Data Development:
Potential collaboration with
• Joshua Lieberman (OGC Geospatial Semantics SWG)
• Spatial Ontology Community of Practice
• Jim Hendler (RPI), George Thomas (HHS): CIO Council and Data.gov Geospatial Semantics
threads
• John Harman / Michael Pendleton (LOD, SRS)
• Steve Young / Zach Scott / Open Gov Team (LOD)
• Talis, pending contract (LOD)
• TRI Program (Potential Pilot)
• Kevin Kirby (Data Model)
• Tom Giffen (Data Model, Business Rules)
• Ken Blumberg (Business Rules)
• Cindy Dickinson (Standards, Business Rules)
• Others (program offices, regions, GISWG)
Existing Resources
• Leverage Data Modeling work that Kevin Kirby has been working on
• Drill into gist.owl and other potential resources
Short-Term data needs:
• Semantic Enhancements / Linked Open Data
Improvement of capabilities for supporting Linked Open Data applications –
Analysis of data structure toward supporting faceted, dimensional analyses (Figure 1)
Development of URI schemes, potentially namespaces, and mans and approaches for allowing
7
8. FRS Data Model Initial Conceptual Discussion
November 11, 2010 November 30, 2010
unique identification and linkage
Administrative POC
Site -level Organizational
Legal POC Affiliation
Operational POC
Ultimate Organizational
Parent
Lat/Long
People Physical USPS Address
Municipality
Organizational
Dimension HUC Code
Spatial
Temporal Dimension Site
Dimension
Regulatory Dimension
Program IDs
Activity
NAICS Code
SIC Code
Figure 3: Potential Facets / Dimensions for Analysis and Semantic
Enhancement
• Semantic Dimensions:
Explore various dimensions of facility:
• Spatial –
o GML representation of absolute location (lat/long, etc)
o Spatial representation framework for facility (building footprints, parcel boundary,
others for future)
o Facility data modeling granularity and relationships - get a better handle on what
the facility "thing" represents, and its' relation to other things - for example, a parcel
8
9. FRS Data Model Initial Conceptual Discussion
November 11, 2010 November 30, 2010
boundary, containing an industrial complex with manufacturing and storage
buildings (differing NAICS, possibly even different companies operating and
licensed/permitted), plus associated air stacks, SPCC measures, water outfalls, et
cetera. When we pull up "facility" it should ultimately reflect that bigger picture for
context, with the component of interest in highlight.
• Temporal
o Data currency
o Temporal aspects to regulation, enforcement, permitting, et cetera – future
• Corporate Dimension
o Corporate ownership – at facility level and at ultimate corporate parent level
• Function - Activity and Use
o NAICS/SIC Codes
o EPA Regulatory program
o EPA Interest Type
o Linkages / translation between interest type and other ontologies/vocabularies
o Linkages to regulatory programs and other components
• Interrelationships of facilities (future)
• Individuals
o Friend-of-a-friend (FOAF) and other existing RDF constructs
• Many other potential enhancements
Potential Pilots
A number of potential pilots for mashups can be considered. What may be “low hanging fruit” for OEI
build upon exploitation of known internal assets, i.e.
9
10. FRS Data Model Initial Conceptual Discussion
November 11, 2010 November 30, 2010
• FRS
• TRI (Toxic Release Quantities for Given Location)
• SRS (Substance)
Potentially, as one scenario, one could tie TRI discharges to reaches via OW web services and TRI
reported receiving waters, and then tie this to observed impacts downstream.
One caveat of using EPA data is that it is known to EPA users, but ideally needs to be more fully fleshed-
out to make it discoverable and uniquely identifiable for external users, perhaps via embedded EPA
identifiers (perhaps an epa: namespace or similar means of identifying our assets)
Other potential scenarios TBD… OECA targeted enforcement vs. OSHA, or OPP vs. USDA pesticides
application data.
Longer-Range, Emergent data needs:
These are not specific to LOD, but are instead emergent attributes of interest for FRS – LOD approaches
may help inform on how to structure these.
• HUC Codes
Completion of prepopulating of HUC Codes can support identification of facilities impacting
major watersheds, e.g. Chesapeake Bay (OECA need) – Other potential needs: Airsheds
• Municipality
Toward improving data quality – Physical street address may include ZIP Code for city which is
different than actual municipality where site resides – for example, Suburban Drive, State
College PA is actually Ferguson Township, PA – and local planning and building code officials and
emergency responders who either have or need information on the facility of interest would be
different than that of the one listed
• Relationship
Ability to relate facilities – relating individual components of a larger system of infrastructure,
such as relating a gas terminal to a compressor station – changes to one may impact others.
Ability to organize information in appropriate fashions, such as relating multiple individual oil
platforms with discrete permits to a lease boundary with another level of permitting.
• Indian Country
More robust identification/validation of facilities which may lie within tribal boundaries –
refinement of IND-3 boundaries with other source data, analysis of flows containing either tribal
flag (Y/N) and/or tribal identifier (tribe/reservation name) - (collaboration with Elizabeth
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11. FRS Data Model Initial Conceptual Discussion
November 11, 2010 November 30, 2010
Jackson / Ed Liu)
• Facility Definition
Potential broadening of scope and use of FRS to accomodate grant award locations and other
types of locations – 2005 NAPA Report recommendations for consistent agencywide site
identification. May be predicated on buildout of other capabilities, such as being able to relate
sites.
Other Ongoing, Related Activities
A number of activities, internal and external, can help to inform on direction and data model for FRS
data collection and publishing activities – some of these are listed below:
• Potential EPA Corporate ID Workgroup
Collaborate with TRI, TSCA, FRP, RMP, Others who collect corporate parent information, as well
as OECA and others who need corporate parent information to support analysis.
• White House Corporate ID Workgroup
Collaborate with emergent White House Corporate ID workgroup – Beth Noveck / Steve Croley,
SEC, Labor and other agencies to align, coordinate and collaborate on corporate identifiers
• OpenGov
Collaboration with EPA Open Gov initiatives to inform on how best to publish data for external
reuse.
• National Academy of Public Administration
Follow-through on 2005 NAPA Report recommendations
• Spatial Ontology Community of Practices (SOCOP)
Collaboration on vocabularies, standards and data modeling approaches
• Data.Gov Data Architecture Subgroup
Collaboration on vocabularies, standards and data modeling approaches
• EPA OEI/OIC/IESD Data Standards Branch
Collaboration on vocabularies, standards and data modeling approaches
• Others…
Anticipated Next Steps:
TBD, develop ideas for potential pilots, engage on “LOD Cookbook” and approaches for representing
and rendering our data as RDF.
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