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Part III. Presentation Style

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

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Part III. Presentation Style

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

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

  3. Preparation First: Narrative Next: Slides

  4. 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

  5. Dos and Don’ts

  6. How much to put on a single slide? • Not too much

  7. y y y y x x x z z z x Applying Normal Maps to the Implicit Surface Mark Barry

  8. VS.

  9. 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

  10. Avoid full sentences

  11. Future Work • Pre-process the DOMrather 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.

  12. VS.

  13. 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

  14. Avoid full sentences • Noone reads them • Clutter • Notes to self • Stops you from reading

  15. Avoid reading your slides

  16. 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.

  17. P(x2|x1) A B C x1 A 0.1 0.4 0.5 B 0.2 0.7 0.1 C 0.3 0.3 0.4 x3 P(x3) A 0.4 B 0.3 C 0.3 x1 P(x1) A 0.1 B 0.3 C 0.6 Bayesian Network B = <G, P> G = <X, E> - Directed Acyclic Graph X = {x1,…,xN} – Discrete random Variables P: conditional probability tables x2 x1 x5 x3 x4

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

  19. Background Charles Wei • 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

  20. Background 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

  21. Background 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?

  22. Background 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

  23. Background 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

  24. Background 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

  25. Background 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

  26. Background 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

  27. Nine slides describing ontologies … without a picture!!!

  28. Ontologies • Describe basic concepts • Define relations among them • basic blocks • common vocabulary for a specific domain Media Action Movies Horror Comedy Music Classical Jazz Modern Fiction Books Non-Fictionl

  29. DON’T Quote verbatim from your thesis

  30. DON’T Quote verbatim from your thesis Exception: Formal definitions that need to be read

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

  32. Approach Overview Slot 1 Slot 2 Slot 3 : : Slot N Slot 1 Slot 2 : : Slot N Slot N+ 1 : : Slot M Class B Class A Instance A 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 Class C Class D Charles Wei Before insertion

  33. KyGODDAG Swati Tata

  34. Characteristics of an ODS Star Schema Chad Smith

  35. 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. Product ------- ------- ------- Orders product store customer shipment ------- ------- ------- ------- Customer ------- ------- ------- Shipment ------- ------- ------- Store ------- ------- -------

  36. Characteristics of an ODS Extract/Transform/Load (ETL) Chad Smith

  37. Extract/Transform/Load (ETL) 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. ETL ETL ODS source applications application databases data mart / data warehouse target applications

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