1. The document discusses several conclusions about data formats and documentation. It finds that HDF is a reasonable choice for image data and many tools are available for it. However, tools for point data in HDF have lagged but are now being addressed.
2. Flexibility in data access is still needed within NOAA to accommodate different data types and users. The importance of standard formats is also decreasing over time as access methods improve.
3. Several sections discuss concepts for organizing data and documentation hierarchically and developing interoperable variables, properties, and metadata to facilitate understanding across communities. Input from communities on revisions is important for convergence.
Traditionally, data integration has meant compromise. No matter how rapidly data architects and developers could complete a project before its deadline, speed would always come at the expense of quality. On the other hand, if they focused on delivering a quality project, it would generally drag on for months thus exceeding its deadline. Finally, if the teams concentrated on both quality and rapid delivery, the costs would invariably exceed the budget. Regardless of which path you chose, the end result would be less than desirable. This led some experts to revisit the scope of data integration. This write up shall focus on the same issue.
Toward a System Building Agenda for Data Integration(and Dat.docxjuliennehar
Toward a System Building Agenda for Data Integration
(and Data Science)
AnHai Doan, Pradap Konda, Paul Suganthan G.C., Adel Ardalan, Jeffrey R. Ballard, Sanjib Das,
Yash Govind, Han Li, Philip Martinkus, Sidharth Mudgal, Erik Paulson, Haojun Zhang
University of Wisconsin-Madison
Abstract
We argue that the data integration (DI) community should devote far more effort to building systems,
in order to truly advance the field. We discuss the limitations of current DI systems, and point out that
there is already an existing popular DI “system” out there, which is PyData, the open-source ecosystem
of 138,000+ interoperable Python packages. We argue that rather than building isolated monolithic DI
systems, we should consider extending this PyData “system”, by developing more Python packages that
solve DI problems for the users of PyData. We discuss how extending PyData enables us to pursue an
integrated agenda of research, system development, education, and outreach in DI, which in turn can
position our community to become a key player in data science. Finally, we discuss ongoing work at
Wisconsin, which suggests that this agenda is highly promising and raises many interesting challenges.
1 Introduction
In this paper we focus on data integration (DI), broadly interpreted as covering all major data preparation steps
such as data extraction, exploration, profiling, cleaning, matching, and merging [10]. This topic is also known
as data wrangling, munging, curation, unification, fusion, preparation, and more. Over the past few decades, DI
has received much attention (e.g., [37, 29, 31, 20, 34, 33, 6, 17, 39, 22, 23, 5, 8, 36, 15, 35, 4, 25, 38, 26, 32, 19,
2, 12, 11, 16, 2, 3]). Today, as data science grows, DI is receiving even more attention. This is because many
data science applications must first perform DI to combine the raw data from multiple sources, before analysis
can be carried out to extract insights.
Yet despite all this attention, today we do not really know whether the field is making good progress. The
vast majority of DI works (with the exception of efforts such as Tamr and Trifacta [36, 15]) have focused on
developing algorithmic solutions. But we know very little about whether these (ever-more-complex) algorithms
are indeed useful in practice. The field has also built mostly isolated system prototypes, which are hard to use and
combine, and are often not powerful enough for real-world applications. This makes it difficult to decide what
to teach in DI classes. Teaching complex DI algorithms and asking students to do projects using our prototype
systems can train them well for doing DI research, but are not likely to train them well for solving real-world DI
problems in later jobs. Similarly, outreach to real users (e.g., domain scientists) is difficult. Given that we have
Copyright 0000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for
advertising or promotional purpose ...
Traditionally, data integration has meant compromise. No matter how rapidly data architects and developers could complete a project before its deadline, speed would always come at the expense of quality. On the other hand, if they focused on delivering a quality project, it would generally drag on for months thus exceeding its deadline. Finally, if the teams concentrated on both quality and rapid delivery, the costs would invariably exceed the budget. Regardless of which path you chose, the end result would be less than desirable. This led some experts to revisit the scope of data integration. This write up shall focus on the same issue.
Toward a System Building Agenda for Data Integration(and Dat.docxjuliennehar
Toward a System Building Agenda for Data Integration
(and Data Science)
AnHai Doan, Pradap Konda, Paul Suganthan G.C., Adel Ardalan, Jeffrey R. Ballard, Sanjib Das,
Yash Govind, Han Li, Philip Martinkus, Sidharth Mudgal, Erik Paulson, Haojun Zhang
University of Wisconsin-Madison
Abstract
We argue that the data integration (DI) community should devote far more effort to building systems,
in order to truly advance the field. We discuss the limitations of current DI systems, and point out that
there is already an existing popular DI “system” out there, which is PyData, the open-source ecosystem
of 138,000+ interoperable Python packages. We argue that rather than building isolated monolithic DI
systems, we should consider extending this PyData “system”, by developing more Python packages that
solve DI problems for the users of PyData. We discuss how extending PyData enables us to pursue an
integrated agenda of research, system development, education, and outreach in DI, which in turn can
position our community to become a key player in data science. Finally, we discuss ongoing work at
Wisconsin, which suggests that this agenda is highly promising and raises many interesting challenges.
1 Introduction
In this paper we focus on data integration (DI), broadly interpreted as covering all major data preparation steps
such as data extraction, exploration, profiling, cleaning, matching, and merging [10]. This topic is also known
as data wrangling, munging, curation, unification, fusion, preparation, and more. Over the past few decades, DI
has received much attention (e.g., [37, 29, 31, 20, 34, 33, 6, 17, 39, 22, 23, 5, 8, 36, 15, 35, 4, 25, 38, 26, 32, 19,
2, 12, 11, 16, 2, 3]). Today, as data science grows, DI is receiving even more attention. This is because many
data science applications must first perform DI to combine the raw data from multiple sources, before analysis
can be carried out to extract insights.
Yet despite all this attention, today we do not really know whether the field is making good progress. The
vast majority of DI works (with the exception of efforts such as Tamr and Trifacta [36, 15]) have focused on
developing algorithmic solutions. But we know very little about whether these (ever-more-complex) algorithms
are indeed useful in practice. The field has also built mostly isolated system prototypes, which are hard to use and
combine, and are often not powerful enough for real-world applications. This makes it difficult to decide what
to teach in DI classes. Teaching complex DI algorithms and asking students to do projects using our prototype
systems can train them well for doing DI research, but are not likely to train them well for solving real-world DI
problems in later jobs. Similarly, outreach to real users (e.g., domain scientists) is difficult. Given that we have
Copyright 0000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for
advertising or promotional purpose ...
Standard Safeguarding Dataset - overview for CSCDUG.pptxRocioMendez59
13 July, 2023 - CSCDUG Online Event
Presenting the Sector-led Standard Safeguarding Dataset
Colleagues from Data to Insight, the LA-led service for children’s safeguarding data professionals, are delivering a DfE-funded project in partnership with LAs to define a new “standard safeguarding dataset” which all LAs will be able to produce from their safeguarding information systems.
At this session, they shared what they’ve learned so far from user research with LA colleagues and discussed their early thinking about what a better standard dataset might look like. Participants shared their own thoughts about how to improve these systems and processes.
Presenters
Alistair Herbert
Alistair is the lead officer for Data to Insight, the LA-led service for children’s safeguarding data professionals. With a career focused on local authority children’s services data work, he knows about safeguarding data, information systems, and cross-organisation collaboration.
John Foster
John is a Data Manager for Data to Insight. He has supported a range of children’s services data work, most recently at Shropshire Council. He led Data to Insight’s project to introduce the first national benchmarking dataset for Early Help, and is the user research lead for Data to Insight’s Standard Safeguarding Dataset project.
Rob Harrison and Joe Cornford-Hutchings
Rob and Joe are new Data Managers joining Data to Insight from the private and public sector respectively. They bring between them a wealth of experience and technical expertise, and will be working together to support design and implementation of the new Standard Safeguarding Dataset through 2023-24.
Impact of DDOD on Data Quality - White House 2016David Portnoy
"The Impact of Demand-Driven Open Data (DDOD) on Data Quality" was presented on April 27, 2016 at Open Data Roundtable held at the White House Office of Science and Technology Policy.
It discusses the data quality problems prevalent in open data and their impact, the origins of the DDOD concept, how it works, progress towards its goals, several use case examples, and how to implement it at other organizations.
More information:
* DDOD http://ddod.healthdata.gov
* Open Data Roundtables https://www.data.gov/meta/open-data-roundtables/
* White House Office of Science and Technology Policy: https://www.whitehouse.gov/blog/2016/02/05/open-data-empowering-americans-make-data-driven-decisions
Linking HPC to Data Management - EUDAT Summer School (Giuseppe Fiameni, CINECA)EUDAT
EUDAT and PRACE joined forces to help research communities gain access to high quality managed e-Infrastructures whose resources can be connected together to enable cross-utilization use cases and make them accessible without any technical barrier. The capability to couple data and compute resources together is considered one of the key factors to accelerate scientific innovation and advance research frontiers. The goal of this session was to present the EUDAT services, the results of the collaboration activity achieved so far and delivers a hands-on on how to write a Data Management Plan or DMP. The DMP is a useful instrument for researchers to reflect on and communicate about the way they will deal with their data. It prompts them to think about how they will generate, analyse and share data during their research project and afterwards.
Visit: https://www.eudat.eu/eudat-summer-school
Opportunity Cost — How Cloud Changes the TCO of Data, AnalyticsInside Analysis
Hot Technologies with Lyndsay Wise, Dr. Robin Bloor and Teradata
Live Webcast Feb. 11, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=0c853144c649389f544bcca5dbde8caa
Most executives realize the promise of Cloud, but often underestimate the total cost of ownership for moving information systems off-premise. On the plus side, the Cloud offers agility, scalability, lower initial costs, ease of maintenance and minimal staffing needs. But control is diminished and overall complexity increases, sometimes significantly. If not managed carefully, any large-scale move to the Cloud can create a host of new, often intractable challenges.
Register for this episode of Hot Technologies to learn from veteran analysts Lyndsay Wise and Dr. Robin Bloor, along with Marc Clark of Teradata, as they discuss cost analysis and benefits of moving to the cloud. They will cover key components that organizations often face when considering Cloud deployments, framed within the context of TCO and designed to demonstrate the differences between on-premise and cloud environments.
Visit InsideAnalysis.com for more information.
Hadoop was born out of the need to process Big Data.Today data is being generated liked never before and it is becoming difficult to store and process this enormous volume and large variety of data, In order to cope this Big Data technology comes in.Today Hadoop software stack is go-to framework for large scale,data intensive storage and compute solution for Big Data Analytics Applications.The beauty of Hadoop is that it is designed to process large volume of data in clustered commodity computers work in parallel.Distributing the data that is too large across the nodes in clusters solves the problem of having too large data sets to be processed onto the single machine.
In this paper, we discuss security issues for cloud computing, Map Reduce and Hadoop
environment. We also discuss various possible solutions for the issues in cloud computing
security and Hadoop. Today, Cloud computing security is developing at a rapid pace which
includes computer security, network security and information security. Cloud computing plays a
very vital role in protecting data, applications and the related infrastructure with the help of
policies, technologies and controls.
HIGH LEVEL VIEW OF CLOUD SECURITY: ISSUES AND SOLUTIONScscpconf
In this paper, we discuss security issues for cloud computing, Map Reduce and Hadoop
environment. We also discuss various possible solutions for the issues in cloud computing
security and Hadoop. Today, Cloud computing security is developing at a rapid pace which
includes computer security, network security and information security. Cloud computing plays a
very vital role in protecting data, applications and the related infrastructure with the help of
policies, technologies and controls.
Enterprise Data Marketplace: A Centralized Portal for All Your Data AssetsDenodo
Watch full webinar here: https://bit.ly/3OLv0jY
Organizations continue to collect mounds of data and it is spread over different locations and in different formats. The challenge is navigating the vastness and complexity of the modern data ecosystem to find the right data to suit your specific business purpose. Data is an important corporate asset and it needs to be leveraged but also protected.
By adopting an alternate approach to data management and adapting a logical data architecture, data can be democratized while providing centralized control within a distributed data landscape. The web-based Data Catalog tool a single access point for secure enterprise-wide data access and governance. This corporate data marketplace provides visibility into your data ecosystem and allows data to be shared without compromising data security policies.
Catch this on-demand session to understand how this approach can transform how you leverage data across the business:
- Empower the knowledge worker with data and increase productivity
- Promote data accuracy and trust to encourage re-use of important data assets
- Apply consistent security and governance policies across the enterprise data landscape
Response needed 1The paper is well placed on the issues of the.docxaudeleypearl
Response needed 1
The paper is well placed on the issues of the yester years data integration methods and its drawbacks such as no ‘know-how’ guides, limited tools for the pain points, difficulty in customization etc. PyData as a integration tools come with added benefits and easy of doing over the traditional data integration and it is more cutting edge in present day data science field. One of the main reasons author gave for the success of the PyData is ease of interoperability. Which I accept as the user needs better tools for efficiency and affectivity. And PyData has also more varied packages available, more so better packages than many integration tools. All these lead to a close conclusion that PyData can be a possible tool for data integration.
With that being said, author also talked about making all data science community to focus on single system, i.e. PyData. Paper put forth the ideas building systems PyData software packages, foster PyDI as a part of PyData system and extend PyDI to cloud etc.
At this point, I think paper took a tangent forcing PyData as a monopoly is the field of data science. Paper didn’t have enough reasons as to why the systems of DI should be build in PyDI and PyData. It did mention the limitations of the Di systems of past and its packages but it has very less to say about the limitations of more recent and contemporary Data systems.
And coming to the second question if this integration approach helps and facilitate the adoption of ‘R’ programming model, paper is missing the subtle difference between PyData and R. Though both PyData and R functions to same ends it has specified distinctions in its users. That’s exactly the misconception. While programmers are more proficient with python, the data scientists use the R. And PyData to facilitate the adoption of R is trying to overlap the two similar entities with different usages. At the end both are tools are the need to replace one or other is not logical step. R programming is widely used and so is PyData. They can be complementing to each other and meet each other short comings.
Response needed 2
Based on the ideas and the concepts presented in the given article, I would say that pydata is one of the most appropriate data integration systems. One of the reasons why I would back up the use of pydata is because it is open-source and has thousands of other interoperable python packages. Most of these packages can be applied to solve some of the common user problems in python (Mattmann, 2014). It has also been proven that pydata offers users a chance to exploit different capabilities. Other common data integration methods use different capabilities to solve single data integration problems. It would be hard for a user to incorporate all the capabilities into one single data integration system. with the use of pydata it would be to utilize different packages and solve the challenge using an iteration process.
Equally, pydata offers solutions to other integ ...
The OMG DDS standard has recently received an incredible level of attention and press coverage due to its relevance for Consumer and Industrial IoT applications and its adoption as part of the Industrial Internet Consortium Reference Architecture. The main reason for the excitement in DDS stems from its data-centricity, efficiency, Internet-wide scalability, high-availability and configurability.
Although DDS provides a very feature rich platform for architecting distributed systems, it focuses on doing one thing well — namely data-sharing. As such it does not provide first-class support for abstractions such as distributed mutual exclusion, distributed barriers, leader election, consensus, atomic multicast, distributed queues, etc.
As a result, many architects tend to devise by themselves – assuming the DDS primitives as a foundation – the (hopefully correct) algorithms for classical problems such as fault-detection, leader election, consensus, distributed mutual exclusion, distributed barriers, atomic multicast, distributed queues, etc.
This Webcast explores DDS-based distributed algorithms for many classical, yet fundamental, problems in distributed systems. By attending the webcast you will learn how recurring problems arising in the design of distributed systems can be addressed using algorithm that are correct and perform well.
Big Data and Big Data Management (BDM) with current Technologies –ReviewIJERA Editor
The emerging phenomenon called ―Big Data‖ is pushing numerous changes in businesses and several other organizations, Domains, Fields, areas etc. Many of them are struggling just to manage the massive data sets. Big data management is about two things - ―Big data‖ and ―Data Management‖ and these terms work together to achieve business and technology goals as well. In previous few years data generation have tremendously enhanced due to digitization of data. Day by day new computer tools and technologies for transmission of data among several computers through Internet is been increasing. It‗s relevance and importance in the context of applicability, usefulness for decision making, performance improvement etc in all areas have emerged very fast to be relevant in today‗s era. Big data management also has numerous challenges and common complexities include low organizational maturity relative to big data, weak business support, and the need to learn new technology approaches. This paper will discuss the impacts of Big Data and issues related to data management using current technologies
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
2. Conclusions
1. HDF is a reasonable choice as a format for data which can be represented as images.
2. Many tools for working with images in HDF are available in the public domain and
from the private sector. These tools may provide enough increased capabilities for some
groups in NOAA to immediately justify translation of their data into HDF.
3. HDF presently has data structures for describing several types of point data, and is
developing others. Development of tools for this type of data has lagged behind the
image tools. This lag is presently being addressed by several government, university, and
commercial groups, making access to and analysis of point data in HDF a very exciting
and active field of research. NOAA will certainly benefit from this work in the near
future.
4. The diversity of NOAA data and NOAA data users suggests that, even with the
selection of HDF as a standard format, NOAA needs to incorporate format flexibility into
the foundation of its data access plans.
5. Recent developments in data access suggest that the importance of standard formats
is decreasing as a function of time.
3. Ferment in the Adoption Cycle
Disruption
Selection
A disruption is an event that destroys existing competence. It could be a new
Era of Ferment:
The Era of Ferment rate
innovation, a new technology or a new standard. The endsof progress
high risks, of (technical) experimentation begins increases because
with the agreement on
and a period
uncertainty,
a dominant design.
community energy is
Every disruption
considerableraises a series of questions:resources and processes?
waste
focused.
What impact will adoption have on existing
and
Is adoption consistent with existing culture and values?
If I interoperability
nodon't adopt, why not? What are the trade-offs and costs?
Developments are
What will be the cost to me if a competing technology/emerges?
supportive and
How many of the organizations that I coordinate with are going to adopt?
cumulative
How is adoption going to affect my legacy data/applications?
TIME
6. Ferment in the Adoption Cycle
Selection
The rate of progress
increases because
community energy is
focused.
Developments are
supportive and
cumulative
TIME
7. Leadership Model: Positive Deviance
Positive deviance says that if you want to create change, you must scale it down
to the lowest level of granularity and look for people within the social system
who are already manifesting the desired future state. Take only the arrows that
are already pointing toward the way you want to go, and ignore the others.
Identify and differentiate those people who are headed in the right direction.
Give them visibility and resources. Bring them together. Aggregate them.
Barbara Waugh
8. Data and Information: End-to-End Process
Producers
Data
Consumers
Information
Community
Knowledge
Wisdom
Many to Information Concept Mapping successfully implemented to achieve (or at least
Data concepts have been well developed and
improve) data Properties
Variables and interoperability.
Standards and Conventions
Hierarchical Organizations
Can the same concepts facilitate interoperable information?
Multiple Dialects
Spiral Development
Training
Persistence vs. Transport
Spatial/Temporal Data Systems
Evolution
15. Variables and Properties - Documentation
MD_Metadata
MD_CoverageDescription
+contentInfo 0..*
+ attributeDescription : RecordType
+ contentType [1.*]: MD_CoverageContentTypeCode
+ processingLevelCode [0..1]: MD_Identifier
+attribute 0..*
MD_RangeDimension
+ sequenceIdentifier [0..1] : MemberName
+ name[0..*]: MD_Identifier
+ description [0..1] : CharacterString
+rangeElementDescription 0..*
MD_SampleDimension
+ minValue [0..1] : Real
+ maxValue [0..1] : Real
+ units [0..1] : UnitOfMeasure
+ scaleFactor [0..1] : Real
+ offset [0..1] : Real
+ numberOfValues [0..1] : Integer
+ meanValue [0..1] : Real
+ standardDeviation [0..1] : Real
+ otherAttributeType [0..1] : RecordType
+ otherAttribute [0..1] : Record
MD_Band
+ peakResponse [0..1] : Real
+ bitsPerValue [0..1] : Integer
+ toneGradation [0..1] : Integer
MI_RangeElementDescription
+rangeElementDescription
0..*
+ name : CharacterString
+ definition : CharacterString
+ rangeElement[1..*] : Record
<<CodeList>>
MD_CoverageContentTypeCode
minValue, maxValue and units
must have units of length.
RangeElement,
otherAttributeType, and other
Attribute have cardinality [0..0]
+ image
+ thematicClassification
+ physicalMeasurement
+ referenceInformation
+ qualityInformation
+ auxilliaryData
+ modelResult
Community Input to Revisions
16. Convergence: Data
O&M 1.0
WXXM 1.0
O&M 1.0
WXXM 1.1
O&M 2.0
WXXM 2.X
Aligned with
WXXM Builds
on CSML 3.X
O&M 2.0
CSML 1.0
CSML 2.X
Aligned with
Unidata CDM
Unidata CDM
(XML encoding)
CSML 3.X
Aligned with
Unidata CDM
(Binary encoding)
17. Convergence: Data
The Open Geospatial Consortium
(OGC®) membership has approved the
OGC Network Common Data
Form(netCDF) Core Encoding Standard,
and netCDF Binary Encoding Extension
Standard - netCDF Classic and 64-bit
Offset Format as official OGC standards.
20. Metadata Types and Sharing
User
More documentation is
required for understanding
data than discovering or
using it.
Discovery Portal
Discovery
Use / Mashup
Understanding
22. Andy Grove: Communication Overcomes
Computing
The framework is changing now. The Internet is
redefining software. The Internet is redefining the
role of computing and communication and their
interaction with each other. I still don’t
understand the framework. I don’t think any of us
really do. But some aspects of it are pretty clear.
It’s proven not to be computing based but
communications based. In it computing is going
to be subordinated to the communications
task.
“Decisions
Don’t Wait”,
Harvard
Management
Update.
Many groups around the world have decided to adopt the ISO Metadata Standards. The World Meteorological Organization, the NextGen Project (FAA and NOAA/NWS), the GOES-R Project (NASA, NOAA/NESDIS, NOAA/NWS), the FGDC, the American Meteorology Society, the Federal Coordinator for Meteorology, ESDIS (NASA), the Group for High Resolution Sea Surface Temperature, the EU (INSPIRE), Australia and New Zealand have all adopted, or are considering adoption, of ISO documentation standards. All of these groups are beginning to work on guidance for application of these standards in their communities. This community effort is reflected in the revision process for the ISO documentation standards. The final picture in this set shows a segment of the model for the revisions to the ISO 19115 Standard that will coalesce into a new version of the standard during 2012. These revisions have benefitted significantly from input from the community of early adopters.