This document provides an overview of building BI data models with DeepSee. It discusses DeepSee's integration with Caché and Ensemble as an embedded real-time BI component. It outlines key concepts of DeepSee data modeling including cubes, dimensions, measures, hierarchies and levels. The document also provides guidance on accessing DeepSee and includes a demonstration of what a cube can do.
Intersystems DeepSee Mobile approach.
Rendering DeepSee Dashboards on mobile devices.
Concept, implementation, usage.
InterSystems DeepSee mobile.
Воспроизведение DeepSee дашбордов на мобильных устройствах iPhone, Android, Winphone
Концепция, реализация, использование
Автор Шваров Евгений
This is a short introduction to BPM (Business Process Management) for a project we had to do for a course in Operations and Information Management as part of an MBA at the University of Stellenbosch Business School.
References:
Garimella K. & Lees M. & Williams B. 2008. BPM Basics For Dummies. Indianapolis: Wiley.
Jeston, J. & Nelis, J. 2006. Business Process Management - Practical Guidelines to Successful Implementations. Oxford: Butterworth-Heinemann.
InterSystems Healthshare +DeepSee. BI solution for hospitalization queue monitoring Krasnoyarsk Region
InterSystems Healthshare +DeepSee. BI решение для мониторинга очереди госпитализации на примере Красноярского Крас
2. BPM Praxistag: Keynote von Prof. Dr. Ayelt KomusIOZ AG
Erfolgsfaktoren und neue Perspektiven:
- Trends & Status-quo im BPM
- Erfolgsfaktoren BPM
- Mega Trends für das BPM (Cloud)
- Neue Perspektiven: Social Media, lose Kopplung, Scrum
www.ioz.ch
www.bpmpraxistag.ch
www.komus.de
Estimating your Process Projects presented at FSOkx BPM ForumProlifics
Estimating always presents challenges. This is especially true when estimating the intangibles of process improvement efforts. This presentation will focus on how to reduce the guesswork associated with the estimation process by considering the following questions: What is it that you are estimating? How big is the thing you are estimating? What baselines are you using for your estimates? Should you be estimating top down, bottom up or somewhere in between? How do your estimates tie to your project plan? Do your estimates reflect ROI and business value?
Intersystems DeepSee Mobile approach.
Rendering DeepSee Dashboards on mobile devices.
Concept, implementation, usage.
InterSystems DeepSee mobile.
Воспроизведение DeepSee дашбордов на мобильных устройствах iPhone, Android, Winphone
Концепция, реализация, использование
Автор Шваров Евгений
This is a short introduction to BPM (Business Process Management) for a project we had to do for a course in Operations and Information Management as part of an MBA at the University of Stellenbosch Business School.
References:
Garimella K. & Lees M. & Williams B. 2008. BPM Basics For Dummies. Indianapolis: Wiley.
Jeston, J. & Nelis, J. 2006. Business Process Management - Practical Guidelines to Successful Implementations. Oxford: Butterworth-Heinemann.
InterSystems Healthshare +DeepSee. BI solution for hospitalization queue monitoring Krasnoyarsk Region
InterSystems Healthshare +DeepSee. BI решение для мониторинга очереди госпитализации на примере Красноярского Крас
2. BPM Praxistag: Keynote von Prof. Dr. Ayelt KomusIOZ AG
Erfolgsfaktoren und neue Perspektiven:
- Trends & Status-quo im BPM
- Erfolgsfaktoren BPM
- Mega Trends für das BPM (Cloud)
- Neue Perspektiven: Social Media, lose Kopplung, Scrum
www.ioz.ch
www.bpmpraxistag.ch
www.komus.de
Estimating your Process Projects presented at FSOkx BPM ForumProlifics
Estimating always presents challenges. This is especially true when estimating the intangibles of process improvement efforts. This presentation will focus on how to reduce the guesswork associated with the estimation process by considering the following questions: What is it that you are estimating? How big is the thing you are estimating? What baselines are you using for your estimates? Should you be estimating top down, bottom up or somewhere in between? How do your estimates tie to your project plan? Do your estimates reflect ROI and business value?
The attached narrated power point presentation explains the popular methods to list down and analyse design objectives. The material will be useful for KTU second year students who prepare for the subject EST 200, Design and Engineering.
Data Con LA 2022 - Real world consumer segmentationData Con LA
Jaysen Gillespie, Head of Analytics and Data Science at RTB House
1. Shopkick has over 30M downloads, but the userbase is very heterogeneous. Anecdotal evidence indicated a wide variety of users for whom the app holds long-term appeal.
2. Marketing and other teams challenged Analytics to get beyond basic summary statistics and develop a holistic segmentation of the userbase.
3. Shopkick's data science team used SQL and python to gather data, clean data, and then perform a data-driven segmentation using a k-means algorithm.
4. Interpreting the results is more work -- and more fun -- than running the algo itself. We'll discuss how we transform from ""segment 1"", ""segment 2"", etc. to something that non-analytics users (Marketing, Operations, etc.) could actually benefit from.
5. So what? How did team across Shopkick change their approach given what Analytics had discovered.
الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة
و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA
رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Chapter8Hakky St
This is the documentation of the study-meeting in lab.
Tha book title is "Hands-On Machine Learning with Scikit-Learn and TensorFlow" and this is the chapter 8.
This presentation will give a simple overview of image classification technique using difference type software focusing on object-based image classification and segmentation.
Improving the Model’s Predictive Power with Ensemble ApproachesSAS Asia Pacific
Bagus Sartono, Lecture at Department of Statistics, Institut Pertanian Bogor (IPB) University,
New Trends in Research Methodoloy & Analytics Technology Update, Nov 28, 2012, Jakarta Indonesia
The attached narrated power point presentation explains the popular methods to list down and analyse design objectives. The material will be useful for KTU second year students who prepare for the subject EST 200, Design and Engineering.
Data Con LA 2022 - Real world consumer segmentationData Con LA
Jaysen Gillespie, Head of Analytics and Data Science at RTB House
1. Shopkick has over 30M downloads, but the userbase is very heterogeneous. Anecdotal evidence indicated a wide variety of users for whom the app holds long-term appeal.
2. Marketing and other teams challenged Analytics to get beyond basic summary statistics and develop a holistic segmentation of the userbase.
3. Shopkick's data science team used SQL and python to gather data, clean data, and then perform a data-driven segmentation using a k-means algorithm.
4. Interpreting the results is more work -- and more fun -- than running the algo itself. We'll discuss how we transform from ""segment 1"", ""segment 2"", etc. to something that non-analytics users (Marketing, Operations, etc.) could actually benefit from.
5. So what? How did team across Shopkick change their approach given what Analytics had discovered.
الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة
و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA
رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Chapter8Hakky St
This is the documentation of the study-meeting in lab.
Tha book title is "Hands-On Machine Learning with Scikit-Learn and TensorFlow" and this is the chapter 8.
This presentation will give a simple overview of image classification technique using difference type software focusing on object-based image classification and segmentation.
Improving the Model’s Predictive Power with Ensemble ApproachesSAS Asia Pacific
Bagus Sartono, Lecture at Department of Statistics, Institut Pertanian Bogor (IPB) University,
New Trends in Research Methodoloy & Analytics Technology Update, Nov 28, 2012, Jakarta Indonesia
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...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.
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:
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
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• Why is it important?
• How can it help today’s business and the benefits
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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!
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
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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.
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1. Building BI Data Models with DeepSee
Kenneth Poindexter, Michael Braam
March 2011
2. DeepSee
• BI component of Caché and Ensemble
– Embedded Real-Time Business Intelligence
• Available since Caché 2009.1
– New release available with Caché 2011.1
– All academies based on the 2011.1 version
• Integrated User Interface and API
– Light-weight browser-based user interface
– Standardized API for BI data access
3. Academy Outline
• DeepSee Data Modeling
– Cubes
– Subject Areas
– KPI’s
4. Accessing DeepSee
• Use Firefox as the browser
– DeepSee can be accessed from different
browsers.
• Firefox, Internet Explorer and Chrome
– Within Firefox, click one of the three shortcuts
in the browser’s toolbar
• From your Desktop
– Click on one of the shortcuts provided.
6. Exercises (Shortcuts and Hints)
C:Academy FilesDSMODEL Exercises.pdf
Contains a pdf copy of the exercise guide. For those of us
that are visually challenged.
C:Academy FilesCode.txt
Anyplace in the exercise guide where you find code that
you have to type in, you can find that code in this file for
easy copying and pasting.
C:Academy FilesCompleted Exercises
When all else fails…..
8. Cube
A data structure that allows fast analysis of
data from different perspectives.
Normal Two Dimensional View
Region Product Year Units
Europe Candy 2011 12
Europe Candy 2010 6
Europe Candy 2009 2
9. Cube
A data structure that allows fast analysis of
data from different perspectives.
Normal Two Dimensional View View using a Cube
Region Product Year Units
Europe Candy 2011 12
Fruit
Europe Candy 2010 6
Candy 12 6 2
Europe Candy 2009 2 Asia
N America
Chips Europe
2011 2010 2009
10. Cube
A data structure that allows fast analysis of
data from different perspectives.
View using a Cube
Fruit
Candy 12 6 2 Asia
N America
Chips Europe
2011 2010 2009
11. Cube: Dimensions and Measures
A data structure that allows fast analysis of
data from different perspectives.
View using a Cube
Dimensions Measures
Defines the Defines what we
perspective Fruit are analyzing
Product Candy 12 6 2 Asia Units Sold
Year Sold N America Sale Amount
Chips Europe
Location Average
2011 2010 2009 Discount
13. Exercise 1: Create a New Cube
Key Concepts
• Every cube requires a unique name
• The Source Class is where DeepSee will get
data for your cube. A cube is based on a single
source class or its related data
• DeepSee cubes are stored in Cache classes.
14. Dimension
A Dimension can be a value from our Source
Class which we add to our cube in order to slice
and dice, or view our data in different ways or
from different perspectives.
Dimensions
Defines the
Fruit perspective
Candy 12 6 2 Asia Product
N America
Chips Europe Year Sold
2011 2010 2009 Location
16. Exercise 2: Dimensions from Properties
Key Concepts
• Dimensions must have unique names within the cube
• Dimensions are values used to analyze our data from
different perspectives
• A dimension can be based upon a source property
• Any change made to a dimension requires you to compile
and rebuild your cube.
17. Measure
A Measure is a numerical value that defines what
we are analyzing. Measures can be aggregated
in different ways, such as Sum, Average,
Minimum and Maximum
Measures
Defines what we
Fruit are analyzing
Candy 12 6 2 Asia Units Sold
N America
Chips Europe Sale Amount
2011 2010 2009 Average
Discount
19. Exercise 3: Measures from Properties
Key Concepts
• Measures are numerical values associated with
the records from our source class that we want
to analyze
• It is perfectly normal to use a source property as
both a measure and a dimension
• Measures can be aggregated in different ways,
such as Sum, Min, Max and Avg.
20. Null Values
A null is a property within your database that does
not have a value. DeepSee indexes null values.
21. Null Values
A null is a property within the database that does
not contain a value. DeepSee indexes null
values.
<null> Meaning
23. Exercise 4: Null Replacement
Key Concepts
• Null values are properties within our source
class that do not have a value.
• By default, DeepSee displays null values as
<null>
• Null Value does not necessarily = no meaning
• A default null replacement string can be set at
the cube level
• Null replacement strings on levels override the
null replacement string from the cube.
24. Ranges
What is a range
• A range is a grouping of one or more values into
buckets of lesser number.
25. Ranges
What is a range
• A range is a grouping of one or more values into
buckets of lesser number.
No Discount 1-19% 20-49% 50%+
• 0% • 5% • 21.5% • 51.2%
Discounts • 6.5% • 22% • 68%
• 8% • 25% • 72.1%
• 10% • 31% • 80%
Grouped Into • 14.5% • 36.5% • 81%
• 15% • 40% • 82%
• 18% • 42% • 91%
Meaningful • 18.5% • 45.2% • 92.5%
Ranges • 49%
26. Ranges
What is a range
• A range is a grouping of one or more values into
buckets of lesser number.
Pediatric • 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17
Age
18-30 • 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30
Grouped Into
31-50 • 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
45, 46, 47, 48, 49, 50
Age Groups
51+ • 51, 52, 53, 54. 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,
65, 66, 67, 68, 69, 70…
28. Exercise 5: Range Expressions
Key Concepts
• Range Expressions allow us to group values that
would otherwise be difficult to manage into
smaller more manageable range sets
• Each Range has a From value, a To value and a
Replacement Value
• The From and To values can be exclusive or
inclusive.
29. Dimension Hierarchies and Levels
Multiple levels define their members in order of
detail, with each level becoming more detail than
the level before.
30. Dimension Hierarchies and Levels
Multiple levels define their members in order of
detail, with each level becoming more detail than
the level before.
31. Dimension Hierarchies and Levels
Hierarchy
Outlet Region Country City Each dimension level
contains Members that
are tree-like organized.
Asia China Beijing
Shanghai
India Bangalore
Mumbai
Japan Osaka
Tokyo
Europe Belgium Antwerp
Brussels
England Mancheste
London
France Nice
r
Paris
Germany Berlin
Frankfurt
Netherland Amsterda
Munich
Spain
s Barcelona
m
Madrid
N. America Canada Montreal
Toronto
USA Vancouver
Atlanta
Boston
Chicago
Los
Houston
New York
Angelesde
Seattle
S. America Brazil Rio
Brasilia
Chile Santiago
Janeiro
Sao Paolo
33. Exercise 6: Multiple Levels and Hierarchies
Key Concepts
• DeepSee dimensions can contain multiple
hierarchies and multiple levels within each
hierarchy
• Levels must be organized into a tree structure
where each level contains all of the members of
the next lower level.
34. Time Dimensions
Time dimensions are dimensions which give us
information from the perspective of time or date.
36. Exercise 7: Time Dimensions
Key Concepts
• Time dimensions follow the same rules as other
multi-level dimensions. They must be organized
in a way that presents a parent-child
relationship.
• DeepSee provides Functions which allow us to
extract specific portions of date and time fields.
• Time isn’t always on our side.
37. Changing our Thought Process…
Most of the time when we begin building a data
model, it’s habit to look at our source data and
say, “What data do I have available to analyze.”
• Unit Sold
• Amount of Sale
Measures •
•
Avg Sales Amount
Min Sales Amount
• Max Sales Amount
• Date of Sale
• Channel
Dimensions •
•
Outlet
Product
• Category
• name
38. Changing our Thought Process…
I propose a new way of thinking. Instead of
saying, “What do I have to analyze?” Say to
yourself, “How do I want to analyze?” And then
say, “How can I get it?” • Unit Sold
• Amount of Sale
• Avg Sales Amount
Measures
• Min Sales Amount
• Max Sales Amount
• Avg Unit Amount
• Max Unit Amount
• Min Unit Amount
• Cases sold
• Date of Sale
• Channel
• Outlet
• Product
Dimensions
• Category
• Name
• Comment
• Type
• Comment
• SKU Category
• Sales Person’s Age
41. Review: Expressions
Key Concepts
• Change the way we think about the elements
that will go into our data model.
• We can use source expressions to create new
measures and dimensions that are not contained
directly in the properties of our source class.
• We can use any Cache ObjectScript expression,
which includes basic expressions as well as calls
to external class methods which extend our
possibilities endlessly.
42. Level Properties
Are values that are associated with the level to
which they belong.
City Possible
Properties
Population
Mayor
Registered Dogs
Or any other
value that might
be associated
with a particular
city
43. Level Properties
Are values that are associated with the level to
which they belong.
Within DeepSee, they can be used in different
ways:
• As the name of the members of the level they
belong
• To sort the members of the level they belong.
• Much like a measure in the Columns box within
Analyzer
45. Exercise 10: Level Properties
Key Concepts
• Level properties are used to provide additional
information about the member of a level to which
it belongs.
• They can be used to sort the members, or as the
name of the members of the level to which it
belongs.
• Level properties can be both source property
and source expression based.
46. Detail List
A Detail List allows us to drill to the detail
information about the records which make up a
given aggregated value.
47. Detail List
A Detail List allows us to drill to the detail
information about the records which make up a
given aggregated value.
49. Exercise 11: Detail Lists
Key Concepts
• Allows drill-down to the detail records which
make up a given aggregated value or set of
values
• Is based on a SQL statement, executed at run-
time
50. Subject Area
A subset of a cube.
• Filters the records returned by queries without having to
explicitly define the filter within the query
• Filters the cube elements available to the end user
• Allows cube elements to be modified and renamed.
Fruit
Candy 12 6 2 Asia
N America
Chips Europe
2011 2010 2009
51. Subject Area
A subset of a cube.
• Filters the records returned by queries without having to
explicitly define the filter within the query
• Allows cube elements to be hidden from the end user
• Allows you change the display name.
Candy 6 2
Orlando
Chips Los Angeles
2010 2009
53. Exercise 12: Subject Areas
Key Concepts
• Provide the ability to filter the records available
to the user
• Provider the ability to limit access to measures,
dimensions and listings as well as modify their
display names.
• Enables us to secure our data by requiring a
specific resource to access the subject area.
55. Review: More Information
• Exploring and Presenting Data with DeepSee
– Tuesday 4:00PM
– Wednesday 9:00AM
• Building BI Data Models with DeepSee
– Monday 2:00PM
– Tuesday 1:30PM
• Using MDX in DeepSee
– Tuesday 10:00AM
– Wednesday 11:15AM
56. Review: More Information
• Developers Room
• Cache Documentation
– Defining and Building DeepSee II Models
– Getting Started with DeepSee Introduction
– DeepSee II Implementation Guide
Implementation
– Developer Tutorial Step-by-Step
57. Building BI Data Models with DeepSee
Kenneth Poindexter, Michael Braam
March 2011