Presentation from the BigClean event in spring 2011 in Prague. Briefly introduces to data quality, cleansing and shows some examples from existing open data/open government projects.
Applying Data Quality Best Practices at Big Data ScalePrecisely
Global organizations are investing aggressively in data lake infrastructures in the pursuit of new, breakthrough business insights. At the same time, however, 2 out of 3 business executives are not highly confident in the accuracy and reliability of their own Big Data. Regaining that confidence requires utilizing proven data quality tools at Big Data scale.
In this on-demand webinar, discover how to ensure your data lake is a trusted source for advanced business insights that lead to new revenue, cost savings and competitiveness. You will have the opportunity to:
• Compare your organization’s data lake “readiness” against initial findings from our upcoming annual Big Data Trends survey
• Gain insight into where and how to leverage data quality best practices for Big Data use cases
• Explore how a ‘Develop Once, Deploy Anywhere’ approach, including to native Big Data infrastructures such as Hadoop and Spark, facilitates consistent data quality patterns
Applying Data Quality Best Practices at Big Data ScalePrecisely
Global organizations are investing aggressively in data lake infrastructures in the pursuit of new, breakthrough business insights. At the same time, however, 2 out of 3 business executives are not highly confident in the accuracy and reliability of their own Big Data. Regaining that confidence requires utilizing proven data quality tools at Big Data scale.
In this on-demand webinar, discover how to ensure your data lake is a trusted source for advanced business insights that lead to new revenue, cost savings and competitiveness. You will have the opportunity to:
• Compare your organization’s data lake “readiness” against initial findings from our upcoming annual Big Data Trends survey
• Gain insight into where and how to leverage data quality best practices for Big Data use cases
• Explore how a ‘Develop Once, Deploy Anywhere’ approach, including to native Big Data infrastructures such as Hadoop and Spark, facilitates consistent data quality patterns
Data-Ed: Best Practices with the Data Management Maturity ModelData Blueprint
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization's data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements.
Slides for the following paper: NLP Data Cleansing Based on Linguistic Ontology Constraints
Abstract: Linked Data comprises of an unprecedented volume of structured data on the Web and is adopted from an increasing number of domains. However, the varying quality of published data forms a barrier for further adoption, especially for Linked Data consumers. In this paper, we extend a previously developed methodology of Linked Data quality assessment, which is inspired by test-driven software development. Specifically, we enrich it with ontological support and different levels of result reporting and describe how the method is applied in the Natural Language Processing (NLP) area. NLP is – compared to other domains, such as biology – a late Linked Data adopter. However, it has seen a
steep rise of activity in the creation of data and ontologies. NLP data quality assessment has become an important need for NLP datasets. In our study, we analysed 11 datasets using the lemon and NIF vocabularies in 277 test cases and point out common quality issues.
Data cleansing approaches have usually focused on detecting and fixing errors with little attention to big data scaling. This presents a serious impediment since identifying and repairing dirty data often involves processing huge input datasets, handling sophisticated error discovery approaches and managing huge arbitrary errors. With large datasets, error detection becomes overly expensive and complicated especially when considering user-defined functions. Furthermore, a distinctive algorithm is desired to optimize sophisticated error discovery, that requires inequality joins, rather than naïvely parallelizing them. Also, when repairing large errors, their skewed distribution may obstruct effective error repairs. In this dissertation, I present solutions to overcome the above three problems in scaling data cleansing.
I built this presentation for Informatica World in 2006. It is all about Data Administration, Data Quality and Data Management. It is NOT about the Informatica product. This presentation was a hit, with standing room only full of about 150 people. The content is still useful and applicable today. If you want to use my material, please put (C) Dan Linstedt, all rights reserved, http://LearnDataVault.com
Wellness & Consumer Driven Health Careguest00dbec2
See how oer 12,000 other businesses across the U.S. areusng Wellness & Consumer Driven Health Plans as an effective business strategy. How does your company compare?
Wellness & Consumer Driven Health Careguest00dbec2
Learn from what over 12,000 other businesses are doing across the U.S. with Wellness and Consumer Driven Health Plans as a business strategy. How does your plan compare?
Weather Outlook - Dr. Elwynn Taylor, Climatologist, Ag Meteorologist, Iowa State University, from the 2012 World Pork Expo, June 6-8, Des Moines, Iowa, USA.
Forces and Threats in a Data Warehouse (and why metadata and architecture is ...Stefan Urbanek
This keynote looks at some very common forces and threats that are causing common suffering in a data warehouse. Shows examples why the concepts are still relevant despite having all high-end technology. Provides suggestions for starting with architecture and metadata.
New feature overview of Cubes 1.0 – lightweight Python OLAP and pluggable data warehouse. Video: https://www.youtube.com/watch?v=-FDTK80zsXc Github sources: https://github.com/databrewery/cubes
Data-Ed: Best Practices with the Data Management Maturity ModelData Blueprint
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization's data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements.
Slides for the following paper: NLP Data Cleansing Based on Linguistic Ontology Constraints
Abstract: Linked Data comprises of an unprecedented volume of structured data on the Web and is adopted from an increasing number of domains. However, the varying quality of published data forms a barrier for further adoption, especially for Linked Data consumers. In this paper, we extend a previously developed methodology of Linked Data quality assessment, which is inspired by test-driven software development. Specifically, we enrich it with ontological support and different levels of result reporting and describe how the method is applied in the Natural Language Processing (NLP) area. NLP is – compared to other domains, such as biology – a late Linked Data adopter. However, it has seen a
steep rise of activity in the creation of data and ontologies. NLP data quality assessment has become an important need for NLP datasets. In our study, we analysed 11 datasets using the lemon and NIF vocabularies in 277 test cases and point out common quality issues.
Data cleansing approaches have usually focused on detecting and fixing errors with little attention to big data scaling. This presents a serious impediment since identifying and repairing dirty data often involves processing huge input datasets, handling sophisticated error discovery approaches and managing huge arbitrary errors. With large datasets, error detection becomes overly expensive and complicated especially when considering user-defined functions. Furthermore, a distinctive algorithm is desired to optimize sophisticated error discovery, that requires inequality joins, rather than naïvely parallelizing them. Also, when repairing large errors, their skewed distribution may obstruct effective error repairs. In this dissertation, I present solutions to overcome the above three problems in scaling data cleansing.
I built this presentation for Informatica World in 2006. It is all about Data Administration, Data Quality and Data Management. It is NOT about the Informatica product. This presentation was a hit, with standing room only full of about 150 people. The content is still useful and applicable today. If you want to use my material, please put (C) Dan Linstedt, all rights reserved, http://LearnDataVault.com
Wellness & Consumer Driven Health Careguest00dbec2
See how oer 12,000 other businesses across the U.S. areusng Wellness & Consumer Driven Health Plans as an effective business strategy. How does your company compare?
Wellness & Consumer Driven Health Careguest00dbec2
Learn from what over 12,000 other businesses are doing across the U.S. with Wellness and Consumer Driven Health Plans as a business strategy. How does your plan compare?
Weather Outlook - Dr. Elwynn Taylor, Climatologist, Ag Meteorologist, Iowa State University, from the 2012 World Pork Expo, June 6-8, Des Moines, Iowa, USA.
Forces and Threats in a Data Warehouse (and why metadata and architecture is ...Stefan Urbanek
This keynote looks at some very common forces and threats that are causing common suffering in a data warehouse. Shows examples why the concepts are still relevant despite having all high-end technology. Provides suggestions for starting with architecture and metadata.
New feature overview of Cubes 1.0 – lightweight Python OLAP and pluggable data warehouse. Video: https://www.youtube.com/watch?v=-FDTK80zsXc Github sources: https://github.com/databrewery/cubes
Python business intelligence (PyData 2012 talk)Stefan Urbanek
What is the state of business intelligence tools in Python in 2012? How Python is used for data processing and analysis? Different approaches for business data and scientific data.
Video: https://vimeo.com/53063944
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
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.
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.
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/
20. ■ why to measure?
■ when to measure?
■ where to measure?
21. from staging to analytical data
from source to staging data analytical model
since 2009 description
Download Parse Load source Cleanse Create cube
staging clean data
raw sources HTML files YAML files contracts table
(staging)
from source to staging data
2005-2008
REGIS (SK "unknown" fact table
organisations) suppliers map dimension tables
Load source
Download Parse 08
08
YAML files
raw sources
2005-2008
search index
Pre-process
Create
search index
One HTML per
Large HTML files
Procurement dimension tables search index
(one per year)
Document
dimension index
keep intermediate results for auditability
22. from staging to analytical data
from source to staging data analytical model
since 2009 description
Download Parse Load source Cleanse Create cube
staging clean data
raw sources HTML files YAML files contracts table
(staging)
from source to staging data
2005-2008
REGIS (SK "unknown" fact table
organisations) suppliers map dimension tables
Load source
Download Parse 08
08
YAML files
raw sources
2005-2008
search index
Pre-process
Create
search index
One HTML per
Large HTML files
Procurement dimension tables search index
(one per year)
Document
dimension index
insert probes at appropriate places
23. like unit testing:
1. write probes
2. set data quality indicators
3. pass data through
31. html
body
div id=#page
div id=#page
div id=#container
div id=#main
div id=#innerMain
div (anonymous)
div (anonymous)
table tbody
tr td
tabletbody
tr td
table trtd
tbody
tabletd value
√tr
32.
33. Now: you parse!
3 seconds
*non-technical explanation follows
39. <SPAN class=podnazov
style="TEXT-TRANSFORM: uppercase">o
</SPAN>
<SPAN class=podnazov>dkaz na projekt
...
here is a subtitle
and it should be in upper-case:
o
And here is another subtitle:
dkaz na (non-breaking space) projekt
much better
here is a label: Odkaz na projekt
55. from staging to analytical data
from source to staging data analytical model
since 2009 description
Download Parse Load source Cleanse Create cube
staging clean data
raw sources HTML files YAML files contracts table
(staging)
from source to staging data
2005-2008
REGIS (SK "unknown" fact table
organisations) suppliers map dimension tables
Load source
Download Parse 08
08
YAML files
raw sources
2005-2008
search index
Pre-process
Create
search index
One HTML per
Large HTML files
Procurement dimension tables search index
(one per year)
Document
dimension index
57. Data Sources Data Targets
CSV file
relational database
data stream
processing
Google Spreadsheet
report
X
remote Excel Spreadsheet URL
processing streams
58. data row data row data row
data source data target
value value value value
id id id
item item item
class class class
amount amount amount
data source data target
data record data record data record
id value
item value
class value
amount value
59. Sources
X
SQL
CSV file XLS file SQL query mongo DB
yml
Google spreadsheet YAML directory row list record list
60. Targets
yml
SQL
CSV file SQL table mongo DB YAML directory
{x:.2%}
<html> 15.00%
HTML table formatted printer row list record list
61. Record Operations
+
!
append distinct aggregate merge (join)
!x
? ? n
sample select set select data audit numerical statistics*
62. Field Operations
A→B
re + +
field map text substitute value threshold* derive*
abc
+
string strip consolidate value histogram/bin* set to flag*
to type