The document discusses opportunities for automation in content management systems (CCMS) and content delivery systems (CDS) using a component-based approach. It describes how a CCMS can componentize content to reduce duplication across variants, languages, and formats. Updates only need to happen in the source component and are then automatically propagated everywhere. It also discusses how a CDS can automatically generate, distribute, and deliver large volumes of content based on rules and templates. The document outlines various stages in the content lifecycle where automation could streamline authoring, translation, approval, production, delivery, and feedback processes.
Gary Grider from Los Alamos National Laboratory presented this deck at the 2016 OpenFabrics Workshop.
"Trends in computer memory/storage technology are in flux perhaps more so now than in the last two decades. Economic analysis of HPC storage hierarchies has led to new tiers of storage being added to the next fleet of supercomputers including Burst Buffers or In-System Solid State Storage and Campaign Storage. This talk will cover the background that brought us these new storage tiers and postulate what the economic crystal ball looks like for the coming decade. Further it will suggest methods of leveraging HPC workflow studies to inform the continued evolution of the HPC storage hierarchy."
Watch the video presentation: https://www.youtube.com/watch?v=iDYLIpF-6Ew
See more talks from the Open Fabrics Workshop: http://insidehpc.com/2016-open-fabrics-workshop-video-gallery/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Creating Effective Visuals for Teaching and PresentationKristen Sosulski
Topics:
-Overview of presentation design and readability.
-Common presentation pitfalls.
-Best practices for using and delivering charts and graphs in your presentations.
-Examples of effective visual communication through the use of data visualization.
Chemistry Data Basics with KNIME Analytics PlatformKNIMESlides
A short slide deck introducing Chemistry Data Basics with KNIME Analytics Platform. The topics covered include: types of chemistry formats integrated in KNIME, conversion between different formats, standardization of chemical structures, chemical sketching, and reading and writing files with chemistry data in KNIME.
Parallelizing Large Simulations with Apache SparkR with Daniel Jeavons and Wa...Spark Summit
Across all assets globally, Shell carries a huge stock of spare part inventory which ties up large quantities of working capital. Over the past 2 years an interdisciplinary project team has produced a tool, Inventory Optimization Analytics solution (IOTA), based on advanced analytical methods, that helps assets optimise stock levels and purchase strategies. To calculate the recommended stocking inventory level requirement for a material the Data Science team have written a Markov Chain Monte Carlo (MCMC) bootstrapping statistical model in R. Cumulatively, the computational task is large but, fortunately, is one of an embarrassingly parallel nature because the model can be applied independently to each material. The original solution which utilised the R “parallel” package was deployed on a single 48 core PC and took 48 hours to run. In this presentation, we describe how we moved the original solution to a distributed cloud-based Apache Spark framework. Using the new R User Defined Functions API in Apache Spark and with only a minimal amount of code changes the computational run time was reduced to 4 hours. A restructuring of the architecture to “pipeline” the problem resulted in a run time of less than 1 hour. This use case is important because it verifies the scalability and performance of SparkR.
This presentation was provided by Lesley West of ASTM during the NISO event, XML for Standards Publishers, held on Monday, April 24, 2017 in Washington DC.
Gary Grider from Los Alamos National Laboratory presented this deck at the 2016 OpenFabrics Workshop.
"Trends in computer memory/storage technology are in flux perhaps more so now than in the last two decades. Economic analysis of HPC storage hierarchies has led to new tiers of storage being added to the next fleet of supercomputers including Burst Buffers or In-System Solid State Storage and Campaign Storage. This talk will cover the background that brought us these new storage tiers and postulate what the economic crystal ball looks like for the coming decade. Further it will suggest methods of leveraging HPC workflow studies to inform the continued evolution of the HPC storage hierarchy."
Watch the video presentation: https://www.youtube.com/watch?v=iDYLIpF-6Ew
See more talks from the Open Fabrics Workshop: http://insidehpc.com/2016-open-fabrics-workshop-video-gallery/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Creating Effective Visuals for Teaching and PresentationKristen Sosulski
Topics:
-Overview of presentation design and readability.
-Common presentation pitfalls.
-Best practices for using and delivering charts and graphs in your presentations.
-Examples of effective visual communication through the use of data visualization.
Chemistry Data Basics with KNIME Analytics PlatformKNIMESlides
A short slide deck introducing Chemistry Data Basics with KNIME Analytics Platform. The topics covered include: types of chemistry formats integrated in KNIME, conversion between different formats, standardization of chemical structures, chemical sketching, and reading and writing files with chemistry data in KNIME.
Parallelizing Large Simulations with Apache SparkR with Daniel Jeavons and Wa...Spark Summit
Across all assets globally, Shell carries a huge stock of spare part inventory which ties up large quantities of working capital. Over the past 2 years an interdisciplinary project team has produced a tool, Inventory Optimization Analytics solution (IOTA), based on advanced analytical methods, that helps assets optimise stock levels and purchase strategies. To calculate the recommended stocking inventory level requirement for a material the Data Science team have written a Markov Chain Monte Carlo (MCMC) bootstrapping statistical model in R. Cumulatively, the computational task is large but, fortunately, is one of an embarrassingly parallel nature because the model can be applied independently to each material. The original solution which utilised the R “parallel” package was deployed on a single 48 core PC and took 48 hours to run. In this presentation, we describe how we moved the original solution to a distributed cloud-based Apache Spark framework. Using the new R User Defined Functions API in Apache Spark and with only a minimal amount of code changes the computational run time was reduced to 4 hours. A restructuring of the architecture to “pipeline” the problem resulted in a run time of less than 1 hour. This use case is important because it verifies the scalability and performance of SparkR.
This presentation was provided by Lesley West of ASTM during the NISO event, XML for Standards Publishers, held on Monday, April 24, 2017 in Washington DC.
2013 Perforce Collaboration Tour - MathWorksPerforce
By Marc Ullman, Senior Systems Architect at MathWorks
See how MathWorks is using version management to increase the speed and predictability of their product releases.
Data Day Seattle 2017: Scaling Data Science at Stitch FixStefan Krawczyk
At Stitch Fix we have a lot of Data Scientists. Around eighty at last count. One reason why I think we have so many, is that we do things differently. To get their work done, Data Scientists have access to whatever resources they need (within reason), because they’re end to end responsible for their work; they collaborate with their business partners on objectives and then prototype, iterate, productionize, monitor and debug everything and anything required to get the output desired. They’re full data-stack data scientists!
The teams in the organization do a variety of different tasks:
- Clothing recommendations for clients.
- Clothes reordering recommendations.
- Time series analysis & forecasting of inventory, client segments, etc.
- Warehouse worker path routing.
- NLP.
… and more!
They’re also quite prolific at what they do -- we are approaching 4500 job definitions at last count. So one might be wondering now, how have we enabled them to get their jobs done without getting in the way of each other?
This is where the Data Platform teams comes into play. With the goal of lowering the cognitive overhead and engineering effort required on part of the Data Scientist, the Data Platform team tries to provide abstractions and infrastructure to help the Data Scientists. The relationship is a collaborative partnership, where the Data Scientist is free to make their own decisions and thus choose they way they do their work, and the onus then falls on the Data Platform team to convince Data Scientists to use their tools; the easiest way to do that is by designing the tools well.
In regard to scaling Data Science, the Data Platform team has helped establish some patterns and infrastructure that help alleviate contention. Contention on:
Access to Data
Access to Compute Resources:
Ad-hoc compute (think prototype, iterate, workspace)
Production compute (think where things are executed once they’re needed regularly)
For the talk (and this post) I only focused on how we reduced contention on Access to Data, & Access to Ad-hoc Compute to enable Data Science to scale at Stitch Fix. With that I invite you to take a look through the slides.
This session will share large scale architectures from the author's experiences with various companies like Cisco, Symantec, and EMC and compare and contrast the architecture across : Infrastructure Architecture Scaling, Ecommerce integrations and migration approach from legacy into AEM, Digital Marketing Cloud Integrations such as personalization, analytics, and DMP.
This short paper discusses the work happening in the Fibre Channel Industry Association's T-11 committee to develop a new low latency protocol for a flash drive world. This paper is an excellent introduction to it.
Skutil - H2O meets Sklearn - Taylor SmithSri Ambati
Skutil brings the best of both worlds to H2O and sklearn, delivering an easy transition into the world of distributed computing that H2O offers, while providing the same, familiar interface that sklearn users have come to know and love.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
NEO4EMF, a Neo4j-based model repository and persistence framework allowing on-demand loading, storage, and unloading of large-scale EMF models.
Check us at : https://neo4emf.com
Fork us at : https://github.com/neo4emf/Neo4EMF
SplunkLive! Stockholm 2017 - EasyPark Customer PresentationSplunk
EasyPark's customer presentation at SplunkLive! Stockholm, presented by Niklas Magnusson, CTO.
Attendees of SplunkLive! Stockholm learnt how more than 13,000 enterprises, government agencies, universities and service providers in over 110 countries use Splunk software to deepen business and customer understanding, mitigate cybersecurity risk, prevent fraud, improve service performance and reduce cost.
Formaldehyde occurs in nature and it is formed from organic material by
photochemical processes in the atmosphere. Formaldehyde is an important metabolic
product in plants and animals (including humans), where it occurs in low but
measurable concentrations. It has a pungent odour and is an irritant to the eye, nose
and throat even at low concentrations.
However, Formaldehyde does not cause any chronic damage to human health.
Formaldehyde is also formed when organic material is incompletely combusted.
Formaldehyde is an important industrial chemical and is employed in the manufacture
of many industrial products and consumer articles.
Formaldehyde was first synthesized in 1859, when BUTLEROV hydrolyzed
methylene acetate and noted the characteristic odour of the resulting solution. In
1867,HOFMANN conclusively identified formaldehyde, which he prepared by
passing methanol vapour and air over a heated platinum spiral. This method, but with
other catalyst, still constitutes the principal method of manufacture.
2013 Perforce Collaboration Tour - MathWorksPerforce
By Marc Ullman, Senior Systems Architect at MathWorks
See how MathWorks is using version management to increase the speed and predictability of their product releases.
Data Day Seattle 2017: Scaling Data Science at Stitch FixStefan Krawczyk
At Stitch Fix we have a lot of Data Scientists. Around eighty at last count. One reason why I think we have so many, is that we do things differently. To get their work done, Data Scientists have access to whatever resources they need (within reason), because they’re end to end responsible for their work; they collaborate with their business partners on objectives and then prototype, iterate, productionize, monitor and debug everything and anything required to get the output desired. They’re full data-stack data scientists!
The teams in the organization do a variety of different tasks:
- Clothing recommendations for clients.
- Clothes reordering recommendations.
- Time series analysis & forecasting of inventory, client segments, etc.
- Warehouse worker path routing.
- NLP.
… and more!
They’re also quite prolific at what they do -- we are approaching 4500 job definitions at last count. So one might be wondering now, how have we enabled them to get their jobs done without getting in the way of each other?
This is where the Data Platform teams comes into play. With the goal of lowering the cognitive overhead and engineering effort required on part of the Data Scientist, the Data Platform team tries to provide abstractions and infrastructure to help the Data Scientists. The relationship is a collaborative partnership, where the Data Scientist is free to make their own decisions and thus choose they way they do their work, and the onus then falls on the Data Platform team to convince Data Scientists to use their tools; the easiest way to do that is by designing the tools well.
In regard to scaling Data Science, the Data Platform team has helped establish some patterns and infrastructure that help alleviate contention. Contention on:
Access to Data
Access to Compute Resources:
Ad-hoc compute (think prototype, iterate, workspace)
Production compute (think where things are executed once they’re needed regularly)
For the talk (and this post) I only focused on how we reduced contention on Access to Data, & Access to Ad-hoc Compute to enable Data Science to scale at Stitch Fix. With that I invite you to take a look through the slides.
This session will share large scale architectures from the author's experiences with various companies like Cisco, Symantec, and EMC and compare and contrast the architecture across : Infrastructure Architecture Scaling, Ecommerce integrations and migration approach from legacy into AEM, Digital Marketing Cloud Integrations such as personalization, analytics, and DMP.
This short paper discusses the work happening in the Fibre Channel Industry Association's T-11 committee to develop a new low latency protocol for a flash drive world. This paper is an excellent introduction to it.
Skutil - H2O meets Sklearn - Taylor SmithSri Ambati
Skutil brings the best of both worlds to H2O and sklearn, delivering an easy transition into the world of distributed computing that H2O offers, while providing the same, familiar interface that sklearn users have come to know and love.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
NEO4EMF, a Neo4j-based model repository and persistence framework allowing on-demand loading, storage, and unloading of large-scale EMF models.
Check us at : https://neo4emf.com
Fork us at : https://github.com/neo4emf/Neo4EMF
SplunkLive! Stockholm 2017 - EasyPark Customer PresentationSplunk
EasyPark's customer presentation at SplunkLive! Stockholm, presented by Niklas Magnusson, CTO.
Attendees of SplunkLive! Stockholm learnt how more than 13,000 enterprises, government agencies, universities and service providers in over 110 countries use Splunk software to deepen business and customer understanding, mitigate cybersecurity risk, prevent fraud, improve service performance and reduce cost.
Formaldehyde occurs in nature and it is formed from organic material by
photochemical processes in the atmosphere. Formaldehyde is an important metabolic
product in plants and animals (including humans), where it occurs in low but
measurable concentrations. It has a pungent odour and is an irritant to the eye, nose
and throat even at low concentrations.
However, Formaldehyde does not cause any chronic damage to human health.
Formaldehyde is also formed when organic material is incompletely combusted.
Formaldehyde is an important industrial chemical and is employed in the manufacture
of many industrial products and consumer articles.
Formaldehyde was first synthesized in 1859, when BUTLEROV hydrolyzed
methylene acetate and noted the characteristic odour of the resulting solution. In
1867,HOFMANN conclusively identified formaldehyde, which he prepared by
passing methanol vapour and air over a heated platinum spiral. This method, but with
other catalyst, still constitutes the principal method of manufacture.
Acorn Recovery: Restore IT infra within minutesIP ServerOne
Introducing Acorn Recovery as a Service, a simple, fast, and secure managed disaster recovery (DRaaS) by IP ServerOne. A DR solution that helps restore your IT infra within minutes.
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
Getting started with Amazon Bedrock Studio and Control Tower
Kesseler - New Ways for Automation in CCMS
1. Content 4.0 is Not Only Great for End Users:
New Ways for Automation in CCMS
Marcus Kesseler, Founder & Managing Director
Lavacon 2017, Dublin, 2017-05-22
3. SCHEMAGruppe2017–AlleRechtevorbehalten
▪ SCHEMA is a software company
▪ Headquarters in Nürnberg, Germany
▪ 120 employees
▪ About 500 customers
▪ Projects in Germany, Austria, Switzerland, USA, France, Belgium, The
Netherlands, Denmark, Sweden, Italy, India, Australia, Japan, …
SCHEMA develops and markets two products:
▪ The Component Content Management System (CCMS) SCHEMA ST4
▪ The Content Delivery Infrastructure SCHEMA CDS
About SCHEMA Group
3
10. SCHEMAGruppe2017–AlleRechtevorbehalten
Cool! But we need the same
content in four different formats!
High quality
PDF for print 1
Low resolution
PDF for the Web
Special XML for
on-board display
in the car
HTML5 for our
Web Portal and
tablets (iPad etc)
4
12. SCHEMAGruppe2017–AlleRechtevorbehalten
Wow, finally got all variants,
languages and formats done!
Variant
Gasol.
Diesel
Hybrid
jp
en
ch
sp
de
60!
From just 3 variants, 5 languages and 4 formats
we already get a set of 60 (= 3 x 5 x 4) documents
to manage.
And this for just one product!
18. Other names for a component
There are many synonyms in English for component:
▪ Topic (as in DITA)
▪ Module
▪ Chunk
▪ Unit
19. What is a Component?
A component is a part of a
document.
It can be
▪ a whole chapter,
▪ a section,
▪ a subsection,
▪ a table,
▪ an image,
▪ a warning or
▪ a single paragraph
and depends on the intended
use-case.
This is called the size or the
granularity of the component.
In some extreme cases it may be
useful to use single sentences or
even single words as components.
20. SCHEMAGruppe2017–AlleRechtevorbehalten
Sounds complicated!
How does this help?
Variant
Gasol.
Diesel
Hybrid
Many of these
components will be
identical between all
variants!
For example
entertainment
seats
and tires
documentation is very
likely to be exactly
equal in all versions of
the car.
21. SCHEMAGruppe2017–AlleRechtevorbehalten
In variants of the same product,
there will always be re-used components!
Variant
Gasol.
Diesel
Hybrid
This means, we can
consolidate the unique
components into a
special source pool
with no redundancies
Source pool
24. SCHEMAGruppe2017–AlleRechtevorbehalten
OK, but what about the
languages problem?
Ces variations d'obliquité induisent des
variations climatiques très
significatives à la surface de la planète,
affectant notamment la répartition de la
glace d'eau en fonction des latitudes.
Ainsi, la glace tend à s'accumuler aux
pôles en période de faible obliquité
comme actuellement, tandis qu'elle
tend à migrer aux basses latitudes en
période de forte obliquité.
Les données recueillies depuis le début
du siècle tendent à montrer que Mars
sortirait en ce moment même d'un
« âge glaciaire, » notamment en raison
de l'observation de structures
glaciaires (glaciers, fragments de
banquise et pergélisol notamment).
Jusqu'à des latitudes aussi basses que
30° et qui semblent connaître une
érosion active.
Until the first successful flyby of Mars
occurred in 1965, by Mariner 4, many
speculated about the presence of
liquid water on the planet's surface.
This was based on observed periodic
variations in light and dark patches,
particularly in the polar latitudes, which
appeared to be seas and continents;
long, dark striations were interpreted by
some as irrigation channels for liquid
water.
These straight line features were later
explained as optical illusions, though
geological evidence gathered by
unmanned missions suggest that Mars
once had large-scale water coverage
on its surface.
In 2005, radar data revealed the
presence of large quantities of water
ice at the poles, and at mid-latitudes.
のマントルは深さ約2,900kmまで存在し
、地球の体積の83%を占めている。マン
トル全体の化学組成は、必ずしもわか
っているわけではない。上部マントル
は、または仮想的な岩石であるから成
るとする考えが主流であるが、下部マ
ントルについては輝石に近い組成であ
るとする説もあり、定まっていない。
マントルは核によって暖められ、また
自らの内部にも熱源を持つ。
そのため固相のマントルはゆっくりと
対流(プルームテ)をしながら熱を地
殻に運んでいる。
地殻に近い位置ではこのマントル対流
は起こらず、地殻と一体化するような
ふるまいをしておりと いう水平運動を
起こす。
Современные модели внутреннего
строения Марса предполагают, что
Марс состоит из со средней толщиной
50 км (и максимальной до 130 км),
радиусом 1480 км. Плотность в
центре планеты должна достигать 8,5
г/см³. Ядро частично жидкое и
состоит в основном из железа.
Согласно современным оценкам
формирование ядра совпало с
периодом раннего вулканизма и
продолжалось около миллиарда лет.
Примерно то же время заняло
частичное плавление мантийных
силикатов.
Из-за меньшей силы тяжести на Марсе
диапазон давлений в мантии Марса
гораздо меньше, чем на Земле, а
значит в ней меньше фазовых
переходов.
Der Äquatordurchmesser des Mars von
6792 km ist etwa doppelt so groß und
halb so groß wie der der Erde. Seine
Oberfläche beträgt etwa ein Viertel der
Erdoberfläche, seine Masse ein Zehntel
der Erdmasse. Die Oberfläche des Mars
entspricht mit 144 Mio. km2 ungefähr
der Gesamtoberfläche aller Kontinente
der Erde (149 Mio. km2).
Die Fallbeschleunigung auf seiner
Oberfläche beträgt 3,69 m/s², dies
entspricht etwa 38 % der irdischen. Mit
einer Dichte von 3,9 g/cm³ weist der
Mars den geringsten Wert der
terrestrischen Planeten auf.
Deshalb ist die Schwerkraft auf ihm
sogar geringfügig niedriger als auf dem
kleineren, jedoch dichteren Merkur.
EnglishFrenchJapaneseRussianGerman
CCMS
components contain
multiple languages!
25. SCHEMAGruppe2017–AlleRechtevorbehalten
How does this help?
First: We translate less!
Variant
Gasol.
Diesel
Hybrid
Source pool
Instead of
translating
three
documents
with
redundant
copies of
content…
… we translate
directly from the
source pool.
Every multiply
re-used
component will
be translated
only once!
26. SCHEMAGruppe2017–AlleRechtevorbehalten
Second: Updates and change
management is much easier
jp
en
ch
sp
de
Once a component
is translated in the
source pool, the
translation is
automatically
available in all reuse
positions!
Instead of 3 x 4 = 12
complete document
translations, you
only translate 4
components. The
rest is automatic!
Variant
Gasol.
Diesel
Hybrid
27. SCHEMAGruppe2017–AlleRechtevorbehalten
One last detail:
Representation of m × n topics in a CCMS
EN FR JA PT
TOPIC
Metadata for
this version in
this language
Metadata for
all versions in
this language
Metadata for
all versions in
all languages
Topic
container
Language
container
XML
container XML
V1
XML
V2
XML
Vn
XML
V1
XML
V2
XML
Vn
XML
V1
XML
V2
XML
Vn
XML
V1
XML
V2
XML
Vn
XML
content
30. SCHEMAGruppe2017–AlleRechtevorbehalten
Demand is growing in many markets, but
the early adopters are mostly:
▪ Maintenance documentation for field service engineers
▪ User manuals for medical devices / laboratory equipment
And from a more research oriented viewpoint:
▪ Complete the Industry 4.0 and the Internet of Things visions
Content Delivery:
Market Demand Drivers
30
31. SCHEMAGruppe2017–AlleRechtevorbehalten
▪ Wide content format range: From rasterized scans in PDF to standard PDF,
to sophisticated HTML5 multimedia content including videos
▪ Works on all modern platforms (Android, iOS, Windows) and devices
▪ Scalable to very large volumes of content modules (millions of topics)
▪ State-of-the-art navigation and search (faceted search)
▪ User and roles management (access control)
▪ Exact audience targeting for convenience and security (subscriptions)
▪ Implements analytics & feedback
▪ Also works offline [optional, but crucial in some
environments like mines, jungles and hospitals]
Content Delivery:
Core Requirements
31
35. SCHEMAGruppe2017–AlleRechtevorbehalten
▪ Reuse extant content instead of writing anew (excellent search capabilities!)
▪ Authoring Assistance (= suggestions based on all current sentences in the CCMS)
▪ Use of variables to minimize work and impact of changes to technical data
▪ Composition maps are isomorphic across all languages (needs ability to handle
exceptions to this rule)
▪ Rule-based composition maps based on taxonomic topic classifications
(= product configurator)
Automation Opportunities in Authoring
35
36. SCHEMAGruppe2017–AlleRechtevorbehalten
▪ Automatic computation of delta translation packages
▪ Automatic export and import of translation packages using
Language Service Provider Portals
▪ Automatic (or suggested) updates to all reuse positions of
any new topic translation
Automation Opportunities in Translation
36
37. SCHEMAGruppe2017–AlleRechtevorbehalten
▪ Trigger new approval workflow directly from the CCMS
▪ Visualize content with tracked changes
▪ Comments & feedback directly in the CCMS
▪ Single click integration of change suggestions
Automation Opportunities in Approval
37
38. SCHEMAGruppe2017–AlleRechtevorbehalten
▪ Content should be generated by using templates
that work across all needed languages
▪ Generation of multiple documents, in multiple variants,
in multiple languages with a single click
▪ Automated generation triggered by CCMS approval workflows
▪ Automated generation triggered by external workflows (e.g. SAP order)
▪ Automated export of generated documents to
external target systems like DMSes, Web Portals, CDSes, etc
Automation Opportunities in Production
38
39. SCHEMAGruppe2017–AlleRechtevorbehalten
▪ Automated update propagation
▪ Automatic audience targeting based on taxonomic topic classification and metadata
▪ Automatic security screening based on taxonomic topic classification and metadata
▪ Automated content display in track changes mode
▪ Analytics (e.g. the most popular topics, which topics are were never accessed)
▪ Automated integration with Digital Parts Catalogs or Service Billing Systems
Automation Opportunities in Controlled Delivery
39
40. SCHEMAGruppe2017–AlleRechtevorbehalten
▪ Automatic relay of feedback to the original author within
minutes (even across languages/translations)
enables very fast correction/update cycles
▪ Quality assurance based on analytics and feedback
▪ Content authors can get directly into touch with field service
engineers or customers
Automation Opportunities in Feedback
40