Part III.
Presentation Style
How you do it also matters
Overview
How to say things
How NOT to say things
Slides to use
Slides NOT to use
Preparation
First: Narrative
Next:
Slides
Mechanics
•
•
•
•
•
•
Detrmine what you will say
Figure what type of visual would help most
Create your visual
Test-drive in your head
Test-drive on others
Make improvements
Dos and Don’ts
How much to put on a single slide?
• Not too much
Applying Normal Maps to the Implicit
Surface
y
y
z
x
z
x
 2 x  ab sin(bx) 

f ( x, y , z )  2 y  ab sin(by)
 2 z  ab sin(bz) 
y
z
y
x
x
Mark Barry
VS.
Dual Contouring With Normal Map Extraction
• Same process as just described
– Generate polygons, project vertices, etc.
• Simple “search” space for finest-level contour
vertices
• Only difference:
– Polygons generated: quads & triangles
– Quads span four cubes
– Only have to collect finest-level contour vertices
from the four cubes
Mark Barry
Avoid full sentences
Future Work
• Pre-process the DOM rather than re-evaluating
the indices each time
• Efficient algorithms to store and retrieve
intermediate results.
• Comparisons can be performed with other
proposed solutions and it would be helpful in
finding the different areas for improvement.
Karthikeyan S.
VS.
Future work / Conclusions
•
•
•
•
•
•
Three or more join relations
Non numeric data
Real mobile environment
Levels of abstraction (signatures)
Multiple Join attributes
Promising results
Saad Ijad
Avoid full sentences
 Noone reads them
 Clutter
 Notes to self
 Stops you from reading
Avoid reading your slides
BAYESIAN NETWORK
• A Bayesian network for a set of variables
X = {X1…Xn} consists of (1) a network
structure S that encodes a set of
conditional independence assertions about
variables in X, and (2) a set P of local
probability distributions associated with
each variable.
Bayesian Network
B = <G, P>
P(x2|x1)
G = <X, E> - Directed Acyclic Graph
x1 A
x1
A
B
C
P(x1)
0.1
x1
0.3
0.6
x3
x3
A
B
C
P(x3)
0.4
0.3
0.3
C
A 0.1 0.4 0.5
B 0.2 0.7 0.1
C 0.3 0.3 0.4
X = {x1,…,xN} – Discrete random Variables
P: conditional probability tables
B
x2
x5
x4
Avoid reading your slides
Corollary:
A picture is worth a thousand words
• What is an ontology?
–describes basic concepts in a domain and
defines relations among them.
–provides the basic blocks in its structure
–provides a common vocabulary for
researchers who need to share information in
a specific domain
Charles Wei
• Goals of using an ontology
–share common understanding of the structure
of information among people or software
agents
–enable reuse of domain knowledge
–make domain assumptions explicit
–separate domain knowledge from operational
knowledge
–analyze domain knowledge
Charles Wei
• The experience of using an ontology
–Easier to understand, but creating an
ontology is…
–Easier to reuse, but creating an ontology is …
–Easier to implement, but creating an
ontology is …
•So, is there anyway to improve the
process of creating an ontology?
Charles Wei
• Ontology creation – related works
–generating an ontology from text-based
documents
–extracting the concepts and relationships from
large quantities of data
–making a model-based ontology, which
extracts the concepts and relationships from
specifications, formalizations and computergenerated artifacts
Charles Wei
• Generating an ontology from textbased documents
–from a given collection of textual
resources by applying natural language
processing and machine learning
techniques.
–requires significant computational effort on
natural language processing
–is still difficult to working on the knowledge
Charles Wei
which resides in different languages

Extracting the concepts and relationships
from large quantities of data
–Data mining and Formal Concept Analysis
–The original concepts exist in human’s mind.
–The transformation from ideas to formal
knowledge is necessary
–Same problems as generating an ontology
from text-based documents
Charles Wei

Making a model-based ontology
–Adjustment: forming instead of extracting
–form the concepts and relationships from
specifications, formalizations and computergenerated artifacts
–Manually input instead information
extraction from existing documents
Charles Wei
• Seamless integration of new input interface
–More intuitive and simplified information input
process
–Working with model-based ontology with a
better input interface
• Categorize classes and instances automatically
–Implement bottom-up approach and
demonstrate the ability to help on creating an
ontology
Charles Wei
Nine slides describing
ontologies
… without a picture!!!
Ontologies
Media
Action
•
•
•
•
Movies
Describe basic concepts
Define relations among them
basic blocks
common vocabulary for a
specific domain
Horror
Comedy
Music
Classical
Books
Fiction
Non-Fictionl
Jazz
Modern
DON’T
Quote verbatim from your thesis
DON’T
Quote verbatim from your thesis
Exception: Formal definitions that
need to be read
DON’T
Copy-and-paste diagrams from thesis
DO
Create diagrams for presentations
• Before insertion
Slot 1
Slot 2
Slot 3
:
:
Slot N
Class B
Class A
Slot 1
Slot 2
:
:
Slot N
Slot N+ 1
:
:
Slot M
Instance A
Slot 1
Slot 2
:
:
Slot N
Slot N+ 1
:
:
Slot M
Slot P
:
:
Slot Q
Class C
Class D
Charles Wei
Slot 1
Slot 2
:
:
Slot N
Slot N+ 1
:
:
Slot M
Slot R
:
:
Slot S
KyGODDAG
Swati Tata
Characteristics of an ODS
• Star Schema
Chad Smith
Star Schema
Store
-------------------
Product
Orders
Customer
-------------------
product
store
customer
shipment
-------------------------
-------------------
Shipment
-------------------
• Fact tables --- hold the “measured” data of the
business (i.e. sales transactions); contain the majority of
ODS data
• Dimension tables --- pre-joined to the fact table(s) via
FK relationships; usually contain a fixed # of records
(i.e. store locations)
Fact table(s) are de-normalized to reduce table joins
and improve query performance.
Characteristics of an ODS
• Extract/Transform/Load (ETL)
Chad Smith
Extract/Transform/Load (ETL)
ETL
source
applications
application
databases
ETL
ODS
data mart /
data warehouse
target
applications
• E --- extract data from the primary data source(s)
• T --- transform source data into a format compliant
with the destination
• L --- load the transformed source data
ETL steps are often combined into a single process.