Business Information Systems covers dimensional analysis and dimensional models. Dimensional models organize data into fact and dimension tables for understandability and ease of reporting rather than update efficiency. Facts are measures associated with business processes, while dimensions provide context. Dimensional modeling involves selecting fact tables, determining granularity, adding surrogate keys, date dimensions, and other necessary dimensions. This provides a standardized framework that responds well to changing reporting needs.
Even though exploring data visually is an integral part of the data analytic pipeline, we struggle to visually explore data once the number of dimensions go beyond three. This talk will focus on showcasing techniques to visually explore multi dimensional data p 3. The aim would be show examples of each of following techniques, potentially using one exemplar dataset. This talk was given at the Strata + Hadoop World Conference @ Singapore 2015 and at Fifth Elephant conference @ Bangalore, 2015
Introduction to the Structured Query Language SQLHarmony Kwawu
Our world depends on data in order to thrive. There are many different methods for storing data but the idea of relational database technology has proved the most advantageous. At the heart of all major relational database approach is the SQL, standing for Structured Query Language. SQL is based on set theory or relational principles.
Consists of the explanations of the basics of SQL and commands of SQL.Helpful for II PU NCERT students and also degree studeents to understand some basic things.
The seminar is about Data warehousing, in here we are gonna discuss about what is data warehousing, comparison b/w database and data warehouse, different data warehouse models.about Data mart, and disadvantages of data warehousing.
Retail Store Locations - Retail Management'Nipun Jain'
About, how-to, processes, decision criteria, etc on picking a Retail Store Location.
Useful for students and professionals, with inclination towards Retail.
Even though exploring data visually is an integral part of the data analytic pipeline, we struggle to visually explore data once the number of dimensions go beyond three. This talk will focus on showcasing techniques to visually explore multi dimensional data p 3. The aim would be show examples of each of following techniques, potentially using one exemplar dataset. This talk was given at the Strata + Hadoop World Conference @ Singapore 2015 and at Fifth Elephant conference @ Bangalore, 2015
Introduction to the Structured Query Language SQLHarmony Kwawu
Our world depends on data in order to thrive. There are many different methods for storing data but the idea of relational database technology has proved the most advantageous. At the heart of all major relational database approach is the SQL, standing for Structured Query Language. SQL is based on set theory or relational principles.
Consists of the explanations of the basics of SQL and commands of SQL.Helpful for II PU NCERT students and also degree studeents to understand some basic things.
The seminar is about Data warehousing, in here we are gonna discuss about what is data warehousing, comparison b/w database and data warehouse, different data warehouse models.about Data mart, and disadvantages of data warehousing.
Retail Store Locations - Retail Management'Nipun Jain'
About, how-to, processes, decision criteria, etc on picking a Retail Store Location.
Useful for students and professionals, with inclination towards Retail.
Why BI ?
Performance management
Identify trends
Cash flow trend
Fine-tune operations
Sales pipeline analysis
Future projections
business Forecasting
Decision Making Tools
Convert data into information
How to Think ?
What happened?
What is happening?
Why did it happen?
What will happen?
What do I want to happen?
Analyzed sales data using market analysis, SWOT analysis, GAP analysis and implemented matrices
Represented graphical charts for sales prediction(Current and Future) using Tableau and identified growth with future prediction
Designed data models, logical models, data mart, data warehouse, relational database, star schema, extended star schema, tables, columns, attributes, relationship (primary, foreign , composite keys), sorting
Future of Horizontal Services by Harrick Vin, VP & Chief Scientist, TCS. The two functions of enterprise IT -- run the business (RTB) and change the business (CTB) -- are undergoing significant changes because of automation. In this presentation, we talked about what is fueling this change, and some of the challenges in realizing automation benefits in enterprises.
Presentation
Database description (very short version)
Background
LeadDesk is the industry-leading platform for call center, inside sales and telemarketing operations. More than 1 million calls handled each week. LeadDesk platform includes (A) All-in-one software for call centers and telesales team, (B) Control & communication solution for product owners with outsourced call centers, (C) Database of B2B and B2C contact information.Contact sales@leaddesk.com
Bitcoin, Blockchain and the Crypto Contracts - Part 2Prithwis Mukerjee
Where we explain how the cryptographic ideas are used to create a crypto asset on the block chain. This one part of a three part slide deck. For the full deck and the context please visit http://bit.ly/pm-bbc
Where we explain how the concept of a crypto currency can lead to the creation of a new kind of autonomous corporation. This one part of a three part slide deck. For the full deck and the context please visit http://bit.ly/pm-bbc
Presentation made at Engage 2013, the annual event of the Public Relations Society of India on the topic of how to create your own personal radio and TV channel
Can a mind control a machine ? Can a machine control a mind ? Can a mind control another mind through a machine ? Explore all these fascinating possibilities in a slidedeck that I had presented at the PricewaterhouseCoopers Technology Forecast in Calcutta
Please click on the embedded Videos to see them in YouTube
One of the earliest presentation made in Bangla to a group of school students in Nabadwip in the year 2000. The original Powerpoint presentation is no more usable because the fonts used are not available any more. However the screen shots have been preserved here.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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.
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
2. Dimensional Models
A denormalized relational model
Made up of tables with attributes
Relationships defined by keys and foreign keys
Organized for understandability and ease of reporting
rather than update
Queried and maintained by SQL or special purpose
management tools.
2
3. From Relational to Dimensional
Relational Model
Designed from the
perspective of process
efficiency
Dimensional Model
Sales
Marketing
Sales
Customers
“Normalised” data
structures
Entity Relationship Model
Used for transactional, or
operational systems
Based on data that is
Current
Non Redundant
“De-normalised” data
structures in blatant
violation of normalisation
Used for analysis of
aggregated data
OLAP : OnLine Analytical
Processing
OLTP : OnLine Transaction
Processing
Designed from the
perspective of subject
Based on data that is
Historical
May be redundant
3
4. ER vs. Dimensional Models
One table per entity
Minimize data
redundancy
Optimize update
The Transaction
Processing Model
One fact table for
data organization
Maximize
understandability
Optimized for
retrieval
The data
warehousing model
4
5. Strengths of the Dimensional Model
Predictable, standard
framework
Respond well to changes in
user reporting needs
Relatively easy to add data
without reloading tables
Standard design approaches
have been developed
There exist a number of
products supporting the
dimensional model
“The Data Warehouse
Toolkit” by Ralph
Kimball & Margy Ross
“The Data Warehouse
Lifecycle Toolkit” by
Ralph Kimball & Margy
Ross
5
9. Facts & Dimensions
• There are two main types of objects in a dimensional
model
– Facts are quantitative measures that we wish to analyse and
report on.
– Dimensions contain textual descriptors of the business. They
provide context for the facts.
9
10. Fact & Dimension Tables
FACTS
DIMENSIONS
Contains two or more
foreign keys
Contain text and
descriptive information
Tend to have huge
numbers of records
1 in a 1-M relationship
Useful facts tend to be
numeric and additive
Generally the source of
interesting constraints
Typically contain the
attributes for the SQL
answer set.
10
11. Fact Table
Measurements associated with a specific business
process
Grain: level of detail of the table
Process events produce fact records
Facts (attributes) are usually
Numeric
Additive
Derived facts included
Foreign (surrogate) keys refer to dimension tables
(entities)
Classification values help define subsets
11
12. Dimension Tables
Entities describing the objects of the process
Conformed dimensions cross processes
Attributes are descriptive
Text
Numeric
Surrogate keys
Less volatile than facts (1:m with the fact table)
Null entries
Date dimensions
Produce “by” questions
12
14. Business Model
As always in life, there are some disadvantages
to 3NF:
Performance can be truly awful. Most of the
work that is performed on denormalizing a data
model is an attempt to reach performance
objectives.
The structure can be overwhelmingly complex.
We may wind up creating many small relations
which the user might think of as a single
relation or group of data.
14
15. The 4 Step Design Process
Choose the Data Mart
Declare the Grain
Choose the Dimensions
Choose the Facts
15
16. Structural Dimensions
The first step is the development of the
structural dimensions. This step corresponds
very closely to what we normally do in a
relational database.
The star architecture that we will develop here
depends upon taking the central intersection
entities as the fact tables and building the
foreign key => primary key relations as
dimensions.
16
17. Steps in dimensional modeling
Select an associative entity for a fact table
Determine granularity
Replace operational keys with surrogate keys
Promote the keys from all hierarchies to the fact table
Add date dimension
Split all compound attributes
Add necessary categorical dimensions
Fact (varies with time) / Attribute (constant)
17
18. The Big Picture
Customer ID
Cust Name
Cust Address
Order ID
Customer ID (FK)
Date
Order ID (FK)
Item ID
Product ID (FK)
Quantity
Value
Product ID
Product Name
Product Desc
Unit Price
OLTP
OLAP
Customer ID
Cust Name
Cust Address
Transaction ID
Product ID (FK)
Client ID (FK)
Date
Quantity
Value
Product ID
Product Name
Product Desc
Unit Price
18
19. Converting an E-R Diagram
Determine the purpose of the mart
Identify an association table as the central fact
table
Determine facts to be included
Replace all keys with surrogate keys
Promote foreign keys in related tables to the
fact table
Add time dimension
Refine the dimension tables
19
20. Fact Tables
Represent a process or reporting environment that is of
value to the organization
It is important to determine the identity of the fact table
and specify exactly what it represents.
Typically correspond to an associative entity in the E-R
model
20
21. Grain (unit of analysis)
The grain determines what each fact record represents:
the level of detail.
For example
Individual transactions
Snapshots (points in time)
Line items on a document
Generally better to focus on the smallest grain
21
22. Facts
Measurements associated with fact table records at fact
table granularity
Normally numeric and additive
Non-key attributes in the fact table
Attributes in dimension tables are constants. Facts vary with
the granularity of the fact table
22
23. Dimensions
A table (or hierarchy of tables) connected with the
fact table with keys and foreign keys
Preferably single valued for each fact record
(1:m)
Connected with surrogate (generated) keys, not
operational keys
Dimension tables contain text or numeric
attributes
23
26. Date Dimensions
Fiscal Year
Calendar Year
Fiscal Quarter
Calendar
Quarter
Fiscal Month
Calendar
Month
Fiscal Week
Calendar
Week
Type of Day
Day of Week
Day
Holiday
26
27. Attribute Name
Attribute Description
Day
The specific day that an activity took
place.
Day of Week
The specific name of the day.
Holiday
Identifies that this day is a holiday.
Type of Day
Indicates whether or not this day is
a weekday or a weekend day.
Calendar Week
The week ending date, always a
Saturday. Note that WE denotes
Calendar Month
The calendar month.
Calendar Quarter
Calendar Year
Fiscal Week
Fiscal Month
Fiscal Quarter
Fiscal Year
Sample Values
06/04/1998; 06/05/1998
Monday; Tuesday
Easter; Thanksgiving
Weekend; Weekday
WE 06/06/1998;
WE 06/13/1998
January,1998; February,
1998
The calendar quarter.
1998Q1; 1998Q4
The calendar year.
1998
The week that represents the
F Week 1 1998;
corporate calendar. Note that the F F Week 46 1998
The fiscal period comprised of 4 or 5 F January, 1998;
weeks. Note that the F in the data
F February, 1998
The grouping of 3 fiscal months.
F 1998Q1; F1998Q2
The grouping of 52 fiscal weeks / 12 F 1998; F 1999
fiscal months that comprise the
financial year.
27
31. Slowly Changing Dimensions
(Addresses, Managers, etc.)
Type 1: Store only the current value, overwrite
previous value
Type 2: Create a dimension record for each value (with
or without date stamps)
Type 3: Create an attribute in the dimension record for
previous value
31
33. Type 1 Slowly Changing Dimension
The simplest form
Only updates existing records
Overwrites history
33
34. Type 1 Slowly Changing Dimension
CustomerID
Code
Name
State Gender
1
K001
Miranda Kerr
VIC
NSW
F
34
35. Type 2 Slowly Changing Dimension
Allows the recording of changes of state over time
Generates a new record each time the state changes
Usually requires the use of effective dates when joining
to facts.
35
36. Type 2 Slowly Changing Dimension
CustomerID
Code
Name
State Gender Start
End
1
K001
Miranda Kerr NSW
F
1/1/09
23/2/09
<NULL>
2
K001
Miranda Kerr VIC
F
24/2/09
<NULL>
36
37. Type 3 Slowly Changing Dimension
De-normalized change tracking
Only keeps a limited history
Stores changes in separate columns
37
38. Type 3 Slowly Changing Dimension
CustomerID Code Name
1
K001
Miranda Kerr
Current Gender Prev
State
State
NSW
F
<NULL>
VIC
38
Editor's Notes
A simplistic transactional schema showing 7 tables relating to sales orders
This is a star schema, (later on we will discuss snowflake schemas.) showing 4 tables that relate to the previous transactional schema
State and Country have been denormalized under Customer
Dimensions are in Blue
These are the things that we analyse “by” (eg. By Time, By Customer, By Region)
Fact is yellow
These are ususally quantitative things that we are interested in
We already have the data in a data model – why create another data model…? Well…
What is currently called “Data Warehousing” or “Business Intelligence” was originally often called “Decision Support Systems”
We already have all the data in the OLTP system, why replicate it in a dimensional model?
Atomic - Summary
Supports Transaction throughput – Supports Aggregate queries
Current - Historic
Facts work best if they are additive
Dimensions allow us to “slice & dice” the facts into meaningful groups. The provide context
There are some changes where it is valid to overwrite history. When someone gets married and changes their name, they may want to carry the history of their previous purchases over to their new name rather than see a split history.
This makes inserts into your fact table more expensive as you always need to match on the effective dates as well as the business key. Sometimes people kept a “Current” flag. Another approach rather than putting nulls in the End date is to put an arbitrary date well in the future, this can make the join logic a bit simpler.
This type of change tracking is more useful when there is a once off change like a change in sales regions where you want to see history re-cast into the new regions, but may also want to compare the old and new regions.