# Part III. Presentation Style How you do it also matters.

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Part III. Presentation Style How you do it also matters

Overview How to say things NOT How NOT to say things Slides to use NOT Slides NOT to use

Preparation First: Narrative Next:Slides

Mechanics sayDetrmine what you will say Figure what type of visual would help mostFigure what type of visual would help most Create your visualCreate your visual Test-drive in your headTest-drive in your head Test-drive on othersTest-drive on others Make improvementsMake improvements

DoDon’t Do s and Don’t s

How much to put on a single slide? Not too much

Applying Normal Maps to the Implicit Surface y z x y z x y z x y 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

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 = - Directed Acyclic Graph X = {x1,…,xN} – Discrete random Variables P: conditional probability tables x1 x5 x2 x3x4 x3 P(x3) A 0.4 B 0.3 C 0.3 x1 P(x1) A 0.1 B 0.3 C 0.6 x1 P(x2|x1) A B C A 0.1 0.4 0.5 B 0.2 0.7 0.1 C 0.3 0.3 0.4

Avoid reading your slides A picture is worth a thousand words Corollary:

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 computer- generated artifacts Charles Wei

Generating an ontology from text- based 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 which resides in different languages Charles Wei

 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 computer- generated 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 Movies Books Music Action Horror Comedy ClassicalJazzModern Fiction Non-Fictionl Describe basic concepts Define relations among them basic blocks common vocabulary for a specific domain

DON’T Quote verbatim from your thesis

DON’T Exception: Formal definitions that need to be read

DON’T Copy-and-paste diagrams from thesis DO Create diagrams for presentations

Before insertion Instance A Class A Class B Class C Class D Slot 1 Slot 2 Slot 3 : Slot N Slot 1 Slot 2 : Slot N Slot N+ 1 : Slot M Slot 1 Slot 2 : Slot N Slot N+ 1 : Slot M Slot P : Slot Q Slot 1 Slot 2 : Slot N Slot N+ 1 : Slot M Slot R : Slot S Charles Wei

KyGODDAG Swati Tata

Characteristics of an ODS Star Schema Chad Smith

Star Schema 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. Orders product store customer shipment ------- Product ------- Store ------- Shipment ------- Customer -------