The rapid development of sensing technologies had led to the creation of large volumes of environmental observation data. Data quality control information informs users how it was gathered, processed, examined. Sensor Web is a web-centric framework that involves observations from various providers. It is essential to capture quality control information within the framework to ensure that observation data are of known and documented quality. In this paper, we present a quality control framework covering different environmental observation data, and show how it is implemented in the TERENO data infrastructure. The infrastructure is modeled after the OGC’s Sensor Web Enablement (SWE) standards.
Process and Regulated Processes Software Validation ElementsArta Doci
Medical device manufacturers operate in a competitive marketplace with increasing end-user demands for features and usability and in a highly regulated environment.
Regulatory bodies look for evidence that medical devices are developed under a structured, quality-oriented development process. By following software validation and verification best practices, one can not only increase the likelihood that they will meet their compliance goals, they can also enhance developer productivity.
Presentation describes the importance of IT validation from the perspectives of the FDA and our company. It explains GAMP 5, the Validation Life Cycle, good documentation practices, document naming conventions, Change Control, Problem Management, Periodic Evaluation, FDA 483 Warning Letters and 21 CFR Part 11 and a unique Validation Life Cycle.
Process and Regulated Processes Software Validation ElementsArta Doci
Medical device manufacturers operate in a competitive marketplace with increasing end-user demands for features and usability and in a highly regulated environment.
Regulatory bodies look for evidence that medical devices are developed under a structured, quality-oriented development process. By following software validation and verification best practices, one can not only increase the likelihood that they will meet their compliance goals, they can also enhance developer productivity.
Presentation describes the importance of IT validation from the perspectives of the FDA and our company. It explains GAMP 5, the Validation Life Cycle, good documentation practices, document naming conventions, Change Control, Problem Management, Periodic Evaluation, FDA 483 Warning Letters and 21 CFR Part 11 and a unique Validation Life Cycle.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
IEEE 2014 DOTNET DATA MINING PROJECTS Product aspect-ranking-and--its-applica...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
IEEE 2014 DOTNET DATA MINING PROJECTS Product aspect-ranking-and--its-applica...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Data Quality Challenges & Solution Approaches in Yahoo!’s Massive DataDATAVERSITY
Data is Yahoo!'s most strategic assets - from user engagement and insights data to revenue and billing data. Three years ago, Yahoo! invested in a Data Quality program.
By applying industry principles and techniques the Data Quality program has provided proactive and reactive system solutions to Audience data issues and root causes by addressing technical challenges of data quality at scale and engaging and leveraging the rest of the organization in the solution: from product teams all through the data stack (data sourcing, ETL, aggs and analytics) to analysts and sciences teams who consume the data. This methodology is now being scaled to the all data across Yahoo! including Search and Display Advertising.
Computers and the Internet in sensory quality control
Chris Findlay*
Compusense Inc., 111 Farquhar Street, Guelph, Ontario, Canada N1H 3N4
Accepted 8 February 2002
Platform Observability “is when you infer the internal state of a system only by observing the data it generates, such as logs, metrics, and traces”. When observability is implemented well, a system will not require operations teams to spend much effort on understanding its internal state.
Funders, publishers, and data service providers have strongly endorsed applying FAIR principles to maximize the reuse of research data since the principles were published in 2016. Much of existing work on FAIR assessment focuses on "what" needs to be measured, which led to the development of assessment metrics. However, the questions of "how" to measure the FAIRness of the research data and use the assessment results to improve data reuse haven't been fully demonstrated in practice yet. This presentation will cover some insights on these aspects derived from the development of a practical solution (F-UJI) to measure the progress of FAIR aspects of data programmatically.
Towards A Web-Enabled Geo-Sample Web: An Open Source Resource Registration an...Anusuriya Devaraju
Within the earth sciences the curation and sharing of geo-samples is crucial
to supporting reproducible research, in addition to extending the use of the samples in new
research, and saving costs by avoiding sample loss and duplicating sampling activities. In the
Commonwealth Scientic and Industrial Research Organisation (CSIRO), researchers gather
various geo-samples as part of their eld studies and collaborative projects. The diversity of the
samples and their unsystematic management led ambiguous sample numbers, incomplete sample
descriptions, and diculties in nding the samples and their related data. These problems are
also found in universities, research institutes and government agencies, which usually curate and
manage diverse samples. To address this problem, we developed an open source registration
and management system to identify geo-samples unambiguously and to manage their metadata
and data systematically. The system supports the linking of samples and sample collections to
the real world features from where they were collected, as well as to their data and reports on
the Web. This paper describes the implementation of the system including its underlying design
considerations, and its applications. The system was built upon the International Geo Sample
Number persistent identier system with Semantic Web technologies. It has been implemented
and tested with individual users and three sample repositories in the organization.
Data You May Like: A Recommender System for Research Data DiscoveryAnusuriya Devaraju
Various data portals been developed to facilitate access to research datasets from different sources. For example, the Data Publisher for Earth & Environmental Science (PANGAEA), the Registry of Research Data Repositories (re3data.org), and the National Geoscience Data Centre (NGDC). Due to data quantity and heterogeneity, finding relevant datasets on these portals may be difficult and tedious. Keyword searches based on specific metadata elements or multi-key indexes may return irrelevant results. Faceted searches may be unsatisfactory and time consuming, especially when facet values are exhaustive. We need a much more intelligent way to complement existing searching mechanisms in order to enhance user experiences of the data portals.
We developed a recommender system that helps users to find the most relevant research datasets on the CSIRO’s Data Access Portal (DAP). The system is based on content-based filtering. We computed the similarity of datasets based on data attributes (e.g., descriptions, fields of research, location, contributors, and provenance) and inference from transaction logs (e.g., the relations among datasets and between queries and datasets). We improved the recommendation quality by assigning weights to data similarities. The weight values are drawn from a survey involving data users. The recommender results for a given dataset are accessible programmatically via a web service. Taking both data attributes and user actions into account, the recommender system will make it easier for researchers to find and reuse data offered through the data portal.
The Implementation of the International Geo Sample Number in CSIRO: Experienc...Anusuriya Devaraju
In 2014 the Commonwealth Scientific and Industrial Research Organisation (CSIRO) began to implement the International Geo Sample Number (IGSN) to allow unambiguous identification of physical samples and data derived from these samples. In this paper we describe the requirements for the implementation of persistent identifiers for physical samples in the organisation and technical solutions we developed to meet these requirements.
Using Feedback from Data Consumers to Capture Quality Information on Environm...Anusuriya Devaraju
Data quality information is essential to facilitate reuse of Earth science data. Recorded quality information must be sufficient for other researchers to select suitable data sets for their analysis and confirm the results and conclusions. In the research data ecosystem, several entities are responsible for data quality. Data producers (researchers and agencies) play a major role in this aspect as they often include validation checks or data cleaning as part of their work. It is possible that the quality information is not supplied with published data sets; if it is available, the descriptions might be incomplete, ambiguous or address specific quality aspects. Data repositories have built infrastructures to share data, but not all of them assess data quality. They normally provide guidelines of documenting quality information. Some suggests that scholarly and data journals should take a role in ensuring data quality by involving reviewers to assess data sets used in articles, and incorporating data quality criteria in the author guidelines. However, this mechanism primarily addresses data sets submitted to journals. We believe that data consumers will complement existing entities to assess and document the quality of published data sets. This has been adopted in crowd-source platforms such as Zooniverse, OpenStreetMap, Wikipedia, Mechanical Turk and Tomnod. This paper presents a framework designed based on open source tools to capture and share data users’ feedback on the application and assessment of research data. The framework comprises a browser plug-in, a web service and a data model such that feedback can be easily reported, retrieved and searched. The feedback records are also made available as Linked Data to promote integration with other sources on the Web. Vocabularies from Dublin Core and PROV-O are used to clarify the source and attribution of feedback. The application of the framework is illustrated with the CSIRO’s Data Access Portal.
CAPTURING DATA PROVENANCE WITH A USER-DRIVEN FEEDBACK APPROACHAnusuriya Devaraju
Various portals have been developed to provide an easy way to discover and access public research data sets from various organizations. Data sets are made available with descriptive metadata based on common (e.g., OGC, CUAHSI, FGDC, INSPIRE, ISO, Dublin Core) or proprietary standards to facilitate better understanding and use of the data sets. Provenance descriptions may be included as part of the metadata and are
specified from a data provider’s perspective. These can include, for example, different entities and activities involved in a data creation flow, such as sensing platforms, personnel, and data calculation and transformation processes. Moving beyond the provider-centric descriptions, data provenance may be complemented with
forward provenance records supplied by data consumers. The records may be gathered via a user-driven feedback approach. The feedback information from data consumers gives valuable insights into application and assessment of published data sets. This might include descriptions about a scientific analysis in which the data
sets were used, the corrected version of an actual data set or any discovered issues and suggestions concerning the quality of the published data sets. Data providers might then use this information to handle erroneous data and improve existing metadata, their data collection and processing methods. Contributors can use the feedback channel to share their scientific analyses. Data consumers can learn more about data sets based on
other people’s experiences, and potentially save time by avoiding the need for interpreting or cleaning data sets. The goals of the study are to capture feedback from data users on published research data sets, link this to actual data sets, and finally support search and discovery of research data using feedback information. This
paper reports preliminary results addressing the goals. We provide a summary of current practices on gathering feedback from end-users on research data portals, and discuss their relevance and limitations. Examples from the Earth Science domain on how commentaries from data users might be useful in practice are also included.
Then, we present a data model representing key aspects of user feedback. We propose a system architecture to gather and manage feedback from end-users. We describe how the core PROV model may be used to represent the provenance of user feedback information. Technical solutions for linking feedback to existing data portals are also specified.
Representing and Reasoning about Geographic Occurrences in the Sensor WebAnusuriya Devaraju
Observations are fed into the Sensor Web through a growing number of environmental sensors, including technical and human observers. While a wealth of observations is now accessible, there is still a gap between low-level observations and the high-level descriptive information they reflect. For example, we may ask what the measurements mean when a weather buoy provides a temperature time series. The challenge is not to gather a vast number of observations, but rather to make sense of them in environmental monitoring and decision making.
In order to infer meaningful information about occurrences from observations, a description of how one gets from the former to information about the latter must be expressed. This thesis develops an ontology to formally capture the relationships between geographic occurrences and the properties observed by in situ sensors. Building upon the existing positions on experiential and historical perspectives, stimulus-centric sensing, event-process algebra and thematic roles, the ontology elucidates the key concepts associated with geographic occurrences that are particularly significant from a sensing point of view. A use case for reasoning about blizzards and their temporal parts from real time series supplied by the Environment Canada illustrates the ontological approach. This thesis evaluates its findings on the basis of a comparison with an alternative approach in the Sensor Web, a verification of the use case results using an official event report published by the weather agency and an analytical assessment approached from the system development perspective.
The theoretical contribution of the thesis lies in the development of a formal model, which constitutes common building blocks for constructing application ontologies that account for inferences of geographic events from observations. With regards to its practical contribution, the thesis has demonstrated how ontological vocabularies are exploited with reasoning mechanisms to infer information about events, and to formulate symbolic spatio-temporal queries.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
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/
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Enabling Quality Control of SensorWeb Observations
1. Mitglied der Helmholtz-Gemeinschaft
Enabling Quality Control of
Sensor Web Observations
7th January 2014 | 3rd International Conference on Sensor Networks (SENSORNETS 2014)
Anusuriya Devaraju, Ralf Kunkel, Juergen Sorg, Heye Bogena, Harry Vereecken
4. 1. Quality Control (QC)
“…. started with activities whose purpose is to control the quality
of products or services by finding problems and defects..”1
1http://www.iso9001consultant.com.au/QA.html
The goal of QC of observation data is to identify problems
Mitglied der Helmholtz-Gemeinschaft
within the data, fixing or eliminating them, and documenting
the details involved.
4
5. 2. Sensor Web
Mitglied der Helmholtz-Gemeinschaft
Common standards
for structuring sensor
information and its
exchange.
5
13. Key Aspect of QC Information
How are data
series quality
checked? Which
quality tests are
applied?
Mitglied der Helmholtz-Gemeinschaft
What leads to
problems
within data?
Where the quality
control is
performed?
Who checks the
data?
What are the
quality levels
of the data?
When the quality
control procedure
is performed?
13
14. Research Goals
Mitglied der Helmholtz-Gemeinschaft
The goals are to capture QC information of various observation
data systematically and make the information accessible via
the Sensor Web.
14
16. Research Questions
Q1. How are raw data gathered and processed into qualitycontrolled observation data?
Mitglied der Helmholtz-Gemeinschaft
Q2. How the key aspects of data quality control can be modeled
and be related to existing observational information? How can
QC information be made available via the Sensor Web?
16
17. Mitglied der Helmholtz-Gemeinschaft
Different Ways of Importing Data
1. Data series are quality
controlled externally via
proprietary tools and then imported
into the data infrastructure
2. Data series are imported
automatically from sensors and
then quality controlled internally
(within the TEODOOR data
infrastructure).
17
18. Data Processing Status (Level)
Level
Descriptions
QC
Data Editing
Availability
Raw Data
No
No
Internal*
2a
Externally quality controlled
data; approval is pending
Yes
No, flagging only
(except human
observations)
Internal*
2b
Internally quality controlled
data with automatic QC
procedures
Yes
No, flagging only
Internal*
2c
Externally quality controlled
data with approval
Yes
No, flagging only
Public
2d
Mitglied der Helmholtz-Gemeinschaft
1
Internally quality controlled
data with combined
QC procedures (automatic
and human)
Yes
No, flagging only
Public
3
Derived data
Yes
Allowed
Public
*on request
18
19. Quality Flags (Qualifiers)
Quality Flags
GENERIC FLAGS
unevaluated
ok
baddata
suspicious
gapfilled
SPECIFIC FLAGS
moderatequality
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goodquality
extrapolated
minerror
interpolated
badqualityquality
isolatedspike
19
20. Externally QC Data (from level 2a to 2c)
Start
Manually-uploaded, externally quality
controlled data
e.g., eddy-covariance series
fail
Send an email alert of
resubmission of data
Data importing
pass
Perform flags mapping
no
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Processing level: Level 2a (quality controlled data without approval)
Set processing level: Level 2c (externally quality controlled data with approval)
Update approver information
Publish data via
TEODOOR
Approval
yes
End
20
21. Internally QC Data (from level 2b to 2d)
Start
Automatically-uploaded data
e.g., air temperature series
fail
Send an email alert to the responsible
scientist / field technician
DATA IMPORT
Raw data processing
pass
fail
Set processing level: Level 2b
Set generic flag: e.g., suspicious
Set specific flag: e.g., minerror (value below detection)
Automatic quality checks
Visual Inspection
Mitglied der Helmholtz-Gemeinschaft
pass
Set processing level: Level 2b
Set generic flag: ok
Set specific flag: passedautochecks
Set processing level : Level 2d (quality controlled data with automated procedures and visual inspections)
Update specific flags and evaluator information
Publish data via TEODOOR
End
21
22. Research Questions
Q1. How are raw data gathered and processed into qualitycontrolled observation data?
Mitglied der Helmholtz-Gemeinschaft
Q2. How the key aspects of data quality control can be modeled
and be related to existing observational information? How can
QC information be made available via the Sensor Web?
22
23. Observational Data Model (ODM)
sites
PK
objectid
U2,U1
code
definition
elevation_m
foi
geom
latitude
localx
localy
longitude
name
posaccuracy_m
remarks
latlondatumid
localprojectiondatumid
verticaldatumid
sources
PK
U2,U1
qualifiers
variables
objectid
PK
objectid
PK
objectid
address
administrativearea
citation
city
code
country
definition
email
firstname
link
organization
phone
surname
zipcode
metadataid
U1
code
definition
U1
U2
abbreviation
code
definition
datatypeid
offeringid
samplemediumid
timeunitid
unitid
valuetypeid
propertyid
qualifiergroups
PK
objectid
FK1
FK2
groupid
qualifierid
processingstati
PK
PK
U1
code
definition
link
manufacturer
model
type
version
terenodata
objectid
FK1
FK7
FK3
I1
FK4
methods
objectid
U1
Mitglied der Helmholtz-Gemeinschaft
PK
code
definition
link
organization
FK6
I2
FK5
FK2
objectid
U1
sensors
code
definition
shortdesc
U2
timestampto
processingstatusid
siteid
variableid
The existing observational data model
has been modified to support quality
control descriptions
• Qualifiers (quality flags)
• Data processing status
• Source
• Method..etc.
objectid
datavalue
datavalueaccuracy
offsetvalue
timestampfrom
censorcodeid
importid
methodid
offsettypeid
qualifierid
sampleid
sourceid
validationsourceid
derivedfrom
binobject
binobjecttypeid
usersitevariablepermissions
PK
objectid
U1
U1
FK1,U1
groupsetid
siteid
sourceid
variableid
loggervariables
PK
sensorcomponents
PK
objectid
U1
code
definition
functionid
methodid
sensorid
sensortypeid
FK1,U1
FK2,U1
U1
FK1,U1
FK3
FK4,U1
FK2,U1
U1
logger
objectid
PK
objectid
allowedmaxvalue
allowedminvalue
importfactor
loggerfilecolumnname
loggerfilecolumnnumber
loggerid
processingstatusid
sampletypeid
sensorcomponentid
variableid
sensorinstanceid
U1
code
definition
technicalwarningdays
timestampfrom
timestampto
datatableclassid
filetypeid
sourceid
timezone
siteid
notify
U1
23
24. QC-Enabled SOS
Quality Flags
Observation Values
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Data Processing Status
Each value is accompanied with a reference
combining quality flag id and data processing
24
status id
28. Summary
A common quality control framework for processing and assessing
time series from various sensing applications of TERENO
infrastructure. The framework consists of:
A common QC workflow covering various sensor data
•
An extensible quality flag classification
•
Changes applied to existing observational data model
•
QC-Enabled SOS
•
Sensor Web Client(s) delivering quality controlled observation
data.
Mitglied der Helmholtz-Gemeinschaft
•
28
29. What’s Next?
Extend the
observation request
of the SOS with QCbased filters
1.
Mitglied der Helmholtz-Gemeinschaft
1.
Incorporate
descriptions about
operation and
maintenance
sensing systems in
the Sensor Web
29
30. Thank you.
Mitglied der Helmholtz-Gemeinschaft
For more information, please visit:
http://teodoor.icg.kfa-juelich.de
30