Chapter 5 Analysis: Four Level for Validation Vis/Visual Analytics, Chap 5 Validation 1 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Contents • Why Validate? • Validation Approaches – Domain • Why is Validation – Abstraction difficult? – I diom • Four Levels of Design – Algorithm – Domain Situation – Mismatches – Task & Data Abstraction • Examples – Visual Encoding & I nteraction – Genealogical Graphs – Algorithm – MatrixExplorer – … and 4 more • Angles of Attack • Threats to Validity – Different for what, why, how Vis/Visual Analytics, Chap 5 Validation 2 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Why Validate? • Why? – The vis design space is huge, and most designs are ineffective – Think about how you might validate your choices from the very beginning of the design space, rather than leaving it at the end Vis/Visual Analytics, Chap 5 Validation 3 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Four Nested Levels of Design • Why four nested levels? – Splitting the complex vis design into four cascading levels provides an analysis framework that lets you to address different concerns separately Vis/Visual Analytics, Chap 5 Validation 4 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Four Nested Levels of Design • Four nested levels – Consider the details of a particular application domain – The what-why abstraction • Map domain- specification problems and data into forms that are independent of the domain Vis/Visual Analytics, Chap 5 Validation 5 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Four Nested Levels of Design • Four nested levels – How level: design of idioms that specify the visual encoding and interaction – Design of algorithms to instantiate those idioms computationally Vis/Visual Analytics, Chap 5 Validation 6 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Four Nested Levels of Design • Four nested levels – The output from an upstream level is input to the downstream below – Choosing a poor choice at an upstream level inevitably cascades to all downstream levels Vis/Visual Analytics, Chap 5 Validation 7 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Four Nested Levels of Design • Domain situation – A domain situation includes a group of target users • e.g. computational biologists – domain of interest • – comparative genomics • Questions e.g. genetic source of – adaptivity in a species • Data e.g. genomic sequence data – – Methods to identify domain I nterviews • observations • careful research • User introspection is insufficient! Vis/Visual Analytics, Chap 5 Validation 8 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Four Nested Levels of Design • Domain situation – Output • A detailed set of questions asked or actions carried out by the target uses, about a possibly heterogeneous collection of data Two of the questions asked by the computational biologist working on the comparative genomics: 1. What’s the difference between individual nucleotides ( 核甘酸 ) of feature pairs 2. Where are the gaps across a chromosome ( 染色體 ) ? Other: What is the genetic basis of a disease? (X, not specific enough) Vis/Visual Analytics, Chap 5 Validation 9 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Four Nested Levels of Design • Data/ Task Abstraction – Domain-specific into generic representation • I dentify abstract tasks – Ex. Browsing, comparing, summarizing • Design abstract data forms – Are designed, a creative design step » Often choose to transform the original data to something quite different Vis/Visual Analytics, Chap 5 Validation 10 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Four Nested Levels of Design • Data/ Task Abstraction – Explicitly considering the choices made in abstracting tasks and data can be very useful in design process. – Bad alternative: to do this implicitly and w/ o justification » Solving the “lost in hyperspace” problem done by showing the searcher a website hyperlink connectivity graph? » Wrong, too much cognitive load. Vis/Visual Analytics, Chap 5 Validation 11 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Four Nested Levels of Design • Visual encoding and interaction idiom – Decide on the specific way to create and manipulate the visual rep. of the abstract data block, guided by the abstract tasks – I diom: each distinct possible approach • Visual encoding – How to represent data visually (what users see) Vis/Visual Analytics, Chap 5 Validation 12 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Four Nested Levels of Design – I diom • Visual encoding – How to represent data visually (what users see) • I nteraction – How to manipulate that representation dynamically (how users change what they see) • Possible to analyze encoding and interaction as separate decision. I n some cases, need to be considered as a combined idiom Vis/Visual Analytics, Chap 5 Validation 13 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Four Nested Levels of Design – I diom blocks are designed • A big design space… • Abstracting data and task can be used to rule out many bad options • Should make decisions about good and bad matches based on understanding human abilities, especially – Visual perception – memory Vis/Visual Analytics, Chap 5 Validation 14 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Four Nested Levels of Design Word Tree combines the visual encoding idiom of a hierarchical tree of keywords laid out horizontally and the interaction idiom of navigation based on keyword selection. Vis/Visual Analytics, Chap 5 Validation 15 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Four Nested Levels of Design • Algorithm – A detailed procedure that allows a computer to automatically carry out the desired goal • To efficiently handle visual encoding and interaction – Are designed • Could have many algorithms for the same idiom. Ex. Many algorithms Visual/interaction: for direct volume rendering primary concerns are human – Primary concerns: perceptual issues computational issues Vis/Visual Analytics, Chap 5 Validation 16 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Four Levels of Design • Dependency: – Wrong block upstream cascades downstream choices • poor t as ask, perfect id idio iom = > X • I terative process: – Consider each level separately – A better understanding of one block will refine other levels Vis/Visual Analytics, Chap 5 Validation 17 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Angles of Attack for Vis Design • Two angles of attack for vis design – Top down (problem-driven) or bottom up (technique driven) Vis/Visual Analytics, Chap 5 Validation 18 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Angles of Attack for Vis Design • Problem-driven (Top-down) – Start with the problems of real-world user and attempt to design solution that that helps them work more effectively – Often the problem can be solved using existing visual coding and interaction idioms • Much of the challenge lies at the abstraction level – Sometimes the problem motivates the design of new idioms, if no existing ones will adequately solve the abstracted design problem Vis/Visual Analytics, Chap 5 Validation 19 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Angles of Attack for Vis Design • Problem-driven (Top-down) – Considering the four levels of nested model explicitly can help you avoid the pitfall of skipping important steps in problem-driven approach • Some designers skip over the domain situation level completely, short-circuit the abstraction level by assuming that the first abstraction is right and jump immediately into the third level • THE ABSTRACTI ON LEVEL I S OFTEN THE HARDEST TO GET RI GHT!! • The design process for problem-driven work involves iterative refinement at all levels. Vis/Visual Analytics, Chap 5 Validation 20 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Angles of Attack for Vis Design • Technique driven (Bottom-Up) – Start with idiom or algorithm design • Goal is to invent new idioms that better support existing abstractions, or new algorithms that better support existing idioms – Considering the four nested model can help you articulate your assumptions at the level just above your focus • Articulate the abstraction requirement for new idiom, or articulate the idiom requirement for new algorithm Vis/Visual Analytics, Chap 5 Validation 21 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Treats to Validity • Validating the effectiveness of a vis design is difficult because there are so many possible questions – Considering the validity questions at each level separately helps – Each level has a different set of treats to validity • Different fundamental reasons why you might have made wrong choices Vis/Visual Analytics, Chap 5 Validation 22 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
Threats to Validity Vis/Visual Analytics, Chap 5 Validation 23 CGGM Lab., CS Dept., NCTU Jung Hong Chuang
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