LECTURE 12: ANALYTIC PROVENANCE November 16, 2015 SDS235: Visual Analytics Note: slide deck adapted from R. Chang.

Slides:



Advertisements
Similar presentations
Introduction to Multimedia Adeyemi Adeniyi Bsc, MCP MCTS
Advertisements

ProvenanceIntroLOCCog StateDist FuncWrap-up 1/52 User-Centric Visual Analytics Remco Chang Tufts University.
Test Automation Success: Choosing the Right People & Process
LECTURE 10: ANALYTIC PROVENANCE April 6, 2015 COMP Topics in Visual Analytics Note: slide deck adapted from R. Chang.
EvaluationIntroVis/GfxInteractionWrap-up Thinking Interactively with Visualizations Remco Chang UNC Charlotte Charlotte Visualization Center.
VALTChessVA IntroAppsWrap-up 1/25 User-Centric Visual Analytics Remco Chang Tufts University Department of Computer Science.
Dist FuncIntroVAAppsATGWrap-up 1/25 Visual Analytics Research at Tufts Remco Chang Assistant Professor Tufts University.
ProvenanceIntroApplicationPersonalityDist FuncWrap-up 1/36 User-Centric Visual Analytics Remco Chang Tufts University Department of Computer Science.
Knowledge Acquisition and Modelling Concept Mapping.
Data Visualization STAT 890, STAT 442, CM 462
User and Task Analysis Howell Istance Department of Computer Science De Montfort University.
Research to Reality William Ribarsky Remco Chang University of North Carolina at Charlotte.
1 SYS366 Week 1 - Lecture 2 How Businesses Work. 2 Today How Businesses Work What is a System Types of Systems The Role of the Systems Analyst The Programmer/Analyst.
Live Re-orderable Accordion Drawing (LiveRAC) Peter McLachlan, Tamara Munzner Eleftherios Koutsofios, Stephen North AT&T Research Symposium August, 2007.
Lecture 13 Revision IMS Systems Analysis and Design.
Wednesday, 24 June rd UKIBNET Workshop1 Distributing Cognition in the design of ubiquitous computers Chris Baber Pervasive Computing Group The University.
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall Chapter Chapter 1: Introduction to Decision Support Systems Decision Support.
Models of Human Performance Dr. Chris Baber. 2 Objectives Introduce theory-based models for predicting human performance Introduce competence-based models.
1 Info 1409 Systems Analysis & Design Module Lecture 8 – Modelling tools and techniques HND Year /9 De Montfort University.
Copyright 2004 Prentice-Hall, Inc. Essentials of Systems Analysis and Design Second Edition Joseph S. Valacich Joey F. George Jeffrey A. Hoffer Chapter.
Course Instructor: Aisha Azeem
©Ian Sommerville 2004Software Engineering, 7th edition. Chapter 6 Slide 1 Software Requirements 2.
Spreadsheet Management. Sarbanes-Oxley Act (SOX, 2002) Requires “an effective system of internal control” for financial reporting in publicly- held companies.
A Semantic Workflow Mechanism to Realise Experimental Goals and Constraints Edoardo Pignotti, Peter Edwards, Alun Preece, Nick Gotts and Gary Polhill School.
Copyright 2001 Prentice-Hall, Inc. Essentials of Systems Analysis and Design Joseph S. Valacich Joey F. George Jeffrey A. Hoffer Chapter 1 The Systems.
Overview of the Database Development Process
1 BTEC HNC Systems Support Castle College 2007/8 Systems Analysis Lecture 9 Introduction to Design.
Lecture 01: Introduction September 5, 2012 COMP Visual Analytics and Provenance.
Dist FuncIntroPersonalityProvenanceGroupWrap-up 1/40 User-Centric Visual Analytics Remco Chang Tufts University.
ITEC224 Database Programming
VALTVA IntroAppsWrap-up 1/16 Interactive Data Analysis and Model Exploration: A Visual Analytics Approach Remco Chang Tufts University Department of Computer.
 Knowledge Acquisition  Machine Learning. The transfer and transformation of potential problem solving expertise from some knowledge source to a program.
LECTURE 09: INTERACTION PT. 2: COST AND COLLABORATION April 1, 2015 COMP Topics in Visual Analytics Note: slide deck adapted from R. Chang.
What are your interactions doing for your visualization? Remco Chang UNC Charlotte Charlotte Visualization Center.
1/20 (Big Data Analytics for Everyone) Remco Chang Assistant Professor Department of Computer Science Tufts University Big Data Visual Analytics: A User-Centric.
Relationships: A Kindergarten Literacy Unit Kate Wills, Carlinville Unit School District #1
Visual Perspectives iPLANT Visual Analytics Workshop November 5-6, 2009 ;lk Visual Analytics Bernice Rogowitz Greg Abram.
VISUAL ANALYTICS: VISUAL EXPLORATION, ANALYSIS, AND PRESENTATION OF LARGE COMPLEX DATA Remco Chang, PhD (Charlotte Visualization Center) (Tufts University)
VALTVA IntroAppsWrap-up 1/34 User-Centric Visual Analytics Remco Chang Tufts University Department of Computer Science.
Abstract We present two Model Driven Engineering (MDE) tools, namely the Eclipse Modeling Framework (EMF) and Umple. We identify the structure and characteristic.
Exploring Universal Design for Learning (UDL):
COMPSCI 705 / SOFTENG 702 Exam Review Lecture Jim Warren Professor of Health Informatics Course coordinator CS705/SE702.
Understanding Users Cognition & Cognitive Frameworks
ProvenanceIntroPersonalityPrimingDist FuncWrap-up 1/52 User-Centric Visual Analytics Remco Chang Tufts University.
Human Abilities 2 How do people think? 1. Agenda Memory Cognitive Processes – Implications Recap 2.
Human Computer Interaction
ProvenanceIntroPersonalityPrimingDist FuncWrap-up 1/40 User-Centric Visual Analytics Remco Chang Tufts University.
LECTURE 16: (EVEN MORE) OPEN QUESTIONS IN VISUAL ANALYTICS December 9, 2015 SDS 235 Visual Analytics.
LECTURE 09: INTERACTION PT. 2: COST October 19, 2015 SDS235: Visual Analytics Note: slide deck adapted from R. Chang.
1 Advanced Software Architecture Muhammad Bilal Bashir PhD Scholar (Computer Science) Mohammad Ali Jinnah University.
1 Visual Analytics Techniques that Enable Knowledge Discovery: Detect the Expected and Discover the Unexpected Jim J. Thomas Director, National Visualization.
Evaluating the Relationships between User Interaction and Financial Visual Analysis Dong Hyun Jeong, Wenwen Dou, Felesia Stukes, William Ribarsky, Heather.
Slide no 1 Cognitive Systems in FP6 scope and focus Colette Maloney DG Information Society.
CognitiveViews of Learning Chapter 7. Overview n n The Cognitive Perspective n n Information Processing n n Metacognition n n Becoming Knowledgeable.
Introduction to Multimedia. What is Multimedia? Derived from the word “Multi” and “Media” Multi Many, Multiple, Media Tools that is used to represent.
PERCEPTION & MAP DESIGN Ntshate Athenkosi Gregory Crichton
IntroGoalCrowdPredictionWrap-up 1/26 Learning Debugging and Hacking the User Remco Chang Assistant Professor Tufts University.
#1 Make sense of problems and persevere in solving them How would you describe the problem in your own words? How would you describe what you are trying.
Interaction Frameworks COMPSCI 345 S1 C and SoftEng 350 S1 C Lecture 3 Chapter (Heim)
DOCUMENTATION REF: Essentials of IT (Hamilton et al) Chapter 1.
1 Visual Computing Institute | Prof. Dr. Torsten W. Kuhlen Virtual Reality & Immersive Visualization Till Petersen-Krauß | GUI Testing | GUI.
Lecture 09: Interaction pt. 2: Cost
Lecture 15: Analytic Provenance
Ubiquitous Computing and Augmented Realities
Kenneth Baclawski et. al. PSB /11/7 Sa-Im Shin
Lecture 18: (even more) Open Problems
CSc4730/6730 Scientific Visualization
CSc4730/6730 Scientific Visualization
Introduction to Systems Analysis and Design Stefano Moshi Memorial University College System Analysis & Design BIT
Chapter 5 Architectural Design.
Presentation transcript:

LECTURE 12: ANALYTIC PROVENANCE November 16, 2015 SDS235: Visual Analytics Note: slide deck adapted from R. Chang

Announcements Next FP deliverable: Needs Assessment: “personas” Who are you designing for, and what do they need? Piazza post due Wednesday before class SDS Launch Party: Tuesday, November 17th 12 pm in Ford 240 Free (non-pizza) food!!! Guest speaker on Wednesday: Georges Grinstein Professor Emeritus at the Institute for Visualization & Perception Research at UML Founder of Weave

Provenance Definition: “origin, source” “the history of ownership of a valued object or work of art of literature” Term has been adapted: Data provenance Information provenance Insight provenance Analytic provenance

Analytic Provenance Goal: To understand a user’s analytic reasoning process when using a (visual) analytical system for task-solving. Benefits: Training Validation Verification Recall Repeated procedures Etc.

Types of Human-Visualization Interactions Word editing (input heavy, little output) Browsing, watching a movie (output heavy, little input) Visual analysis (closer to 50-50?)

Recap: Van Wijk’s model of visualization D = Data V = visualization S = specification (params) I = image P = perception K = knowledge E = exploration (1) (2) (3) (4) (5)

Discussion: interaction as a data source What drives user interaction? What gets encoded during the interaction? What might it tell us about their reasoning process?

Case study: Detecting Financial Fraud

The WireVis Interface Heatmap View (Accounts to Keywords Relationship) Strings and Beads (Relationships over Time) Search by Example (Find Similar Accounts) Keyword Network (Keyword Relationships) Chang, Remco, et al. "Scalable and interactive visual analysis of financial wire transactions for fraud detection." Information visualization 7.1 (2008):

But what if there’s more? What if a user’s reasoning and intent are reflected in their interactions? How could we find out?

Experiment Analysts Grad Students (Coders) Logged (semantic) Interactions Compare! (manually) Strategies Methods Findings Guesses of Analysts’ thinking WireVis Interaction-Log Vis

Interaction Visualizer

What’s in a user’s interactions? From this experiment, we find that interactions contains at least: 60% of the (high level) strategies 60% of the (mid level) methods 79% of the (low level) findings R. Chang et al., Recovering Reasoning Process From User Interactions. IEEE Computer Graphics and Applications, R. Chang et al., Evaluating the Relationship Between User Interaction and Financial Visual Analysis. IEEE Symposium on VAST, 2009.

What’s in a user’s interactions? Why are these two so much lower than others? (recovering “methods” at about 15%) R. Chang et al., Recovering Reasoning Process From User Interactions. IEEE Computer Graphics and Applications, R. Chang et al., Evaluating the Relationship Between User Interaction and Financial Visual Analysis. IEEE Symposium on VAST, 2009.

What’s in a user’s interactions? In this case, only recording a user’s explicit interaction is insufficient.

Questions?

Five Stages of Provenance Perceive: what does the user see? Capture: which interactions to record, and how? Encode: how do we want to store the interactions Recover: how do we translate to something meaningful Reuse: how can we reapply the interaction to a different problem or dataset?

Five Stages of Provenance Perceive: what does the user see? Capture: which interactions to record, and how? Encode: how do we want to store the interactions Recover: how do we translate to something meaningful Reuse: how can we reapply the interaction to a different problem or dataset?

Perceive What did the user see that prompted the subsequent actions? Johansson et al. Perceiving patterns in parallel coordinates: determining thresholds for identification of relationships. InfoVis 2008.

Perceive - Uncertainty Correa et al. A Framework for Uncertainty-Aware Visual Analytics. VAST 2009.

Perceive – Visual Quality Sipps et al. Selecting good views of high-dimensional data using class consistency. Eurovis 2009.

Perceive – Visual Quality Dasgupta and Kosara. Pargnostics: Screen-Space Metrics for Parallel Coordinates. InfoVis 2010.

Discussion What other types of visual perceptual characteristics should we (as designers and developers) be aware of? As a developer, if you know these characteristics, how can you control them in an open exploratory visualization system?

Capture The “bread and butter” of analytic provenance Need to choose carefully about “what” to capture - Capturing at too low level  cannot decipher the intent - Capturing at too high level  not usable for other applications

Manual Capturing When in doubt, ask the user: Annotations: directly edited text Structured diagrams: illustrating analytical steps Reasoning graph: reasoning artifacts and relationships

Annotations

Shrinivasan and van Wijk. Supporting the Analytical Reasoning Process in Information Visualization. CHI Structured diagrams

Reasoning graphs

Automatic capturing Option 1: capture the mouse and key strokes Option 2: capture the state of the visualization

Capturing interaction in a single application Groth and Streefkerk. Provenance and Annotation for Visual Exploration Systems. TVCG 2006.

Interaction across multiple platforms Cowley PJ, JN Haack, RJ Littlefield, and E Hampson "Glass Box: Capturing, Archiving, and Retrieving Workstation Activities." In The 3rd ACM Workshop on Capture, Archival and Retrieval of Personal Experiences, CARPE 2006, October 27, 2006, Santa Barbara, California, USA, pp ACM, New York, NY.

Capturing visualization state (periodic) Marks et al. Design Gallaries. Siggraph 1997.

Capturing visualization state (transitions) Heer et al. Graphical Histories for Visualization: Supporting Analysis, Communication, and Evaluation. InfovVis 2008.

Discussion How many different levels are there between low level interactions (e.g. mouse x, y) to high level interactions? What are the pros and cons of manual capturing vs. automatic capturing? Single application vs. multiple?

Encode How do we store the captured interactions or visualization states? Encoding manually captured interactions: could be issues with different “languages” Encoding automatically captured interactions: more robust description of event sequences and patterns

Encoding manual captures Xiao et al. Enhancing Visual Analysis of Network Traffic Using a Knowledge Representation. VAST 2007.

Encoding manual captures

Encoding automatic captures Kadivar et al. Capturing and Supporting the Analysis Process. VAST 2009.

Encoding automatic captures Jankun-Kelly et al. A Model and Framework for Visualization Exploration. TVCG 2006.

Encoding automatic captures Shrinivasan et al. Connecting the Dots in Visual Analysis. VAST 2009.

Discussions Is the use of predicates or inductive logic programming generalizable? Does it scale? How could we integrate interaction logging and perceptual logging?

Recover Given all the stored interactions, derive meaning, reasoning processes, and intent Manually: ask other humans to interpret a user’s interactions Automatically: ask a computer to interpret a human’s interactions

Manual recovery From this experiment, we find that interactions contains at least: 60% of the (high level) strategies 60% of the (mid level) methods 79% of the (low level) findings

Automatic recovery Perry et al. Supporting Cognitive Models of Sensemaking in Analytics Systems DIMACS Technical Report 2009.

Automatic recovery Perry et al. Supporting Cognitive Models of Sensemaking in Analytics Systems DIMACS Technical Report 2009.

Automatic recovery Shrinivasan et al. Connecting the Dots in Visual Analysis. VAST 2009.

Discussion Could we integrate a manually constructed model with automated learning? What would that entail?

Reuse Reapply the recovered user interactions, intent, reasoning process, etc. to a different dataset or problem Reuse user interactions: reapply the recorded interactions with some ability to recover from failures Reuse analysis patterns: reapply the “rules” learned from previous analysis

Reuse user interactions

Reuse analysis patterns

Discussion Reuse is only applicable when some combinations of the previous stage(s) are successful More broadly speaking, does it make sense? (Familiar) example of reuse

Generating tutorials Grabler et al. Generating Photo Manipulation Tutorials by Demonstration. SIGGRAPH 2009.

Generating tutorials

Ongoing research So far: interaction as window into what a user does (when faced with a specific problem) Recent work: can interaction patterns also be a window into who a user is?

Learning about users from interaction Brown, Eli T., et al. "Finding Waldo: Learning about Users from their Interactions." (2014).

Learning about users from interaction Brown, Eli T., et al. "Finding Waldo: Learning about Users from their Interactions." (2014).

Thoughts/Questions?