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“Big Picture”: Mixed-Initiative Visual Analytics of Big Data (VINCI 2013 Keynote)
 

“Big Picture”: Mixed-Initiative Visual Analytics of Big Data (VINCI 2013 Keynote)

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Information graphics have been used for thousands of years to help illustrate ideas and communicate information. However, it requires skills and time to hand craft high-quality, customized information ...

Information graphics have been used for thousands of years to help illustrate ideas and communicate information. However, it requires skills and time to hand craft high-quality, customized information graphics for specific situations (e.g., data characteristics and user tasks). The problem becomes more acute when we must deal with big data. To address this problem, we are researching and developing mixed-initiative visual analytic systems that leverage both the intelligence of humans and machines to aid users in deriving insights from massive data. On the one hand, such a system automatically guides users to perform their data analytic tasks by recommending suitable visualization and discovery paths in context. On the other hand, users interactively explore, verify, and improve visual analytic results, which in turn helps the system to learn from users' behavior and improve its quality over time. In this talk, I will present key technologies that we have developed in building mixed-initiative visual analytic systems, including feature-based visualization recommendation and optimization-based approaches to dynamic data transformation for more effective visualization. I will also use concrete applications to demonstrate the use and value of mixed-initiative visual analytic systems, and discuss existing challenges and future directions in this area.

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    “Big Picture”: Mixed-Initiative Visual Analytics of Big Data (VINCI 2013 Keynote) “Big Picture”: Mixed-Initiative Visual Analytics of Big Data (VINCI 2013 Keynote) Presentation Transcript

    • 1 “Big Picture” Mixed-Initiative Visual Analytics of Big Data Michelle Zhou IBM Research, Almaden mzhou@us.ibm.com http://blog.threestory.com/
    • Outline Definitions – Big data – Mixed-initiative visual analytics Challenges and Goals Our Approaches – Key technologies – Use cases Future Directions
    • Variety Definitions “Big Picture” Mixed-Initiative Visual Analytics of Big Data Volume Velocity Veracity 210-million customers 10-billion transactions 850 TB of data … rumors Incomplete data … 100,000 tweets 684,478 FB shares 204 million emails … Per Minute www.domo.com
    • Definitions “Big Picture” Mixed-Initiative Visual Analytics of Big Data Here is your customer summary. I also suggest … Here is your customer summary. I also suggest … Tell me more about my customers dougblakely.com user-initiative system-initiative
    • Key Challenges “Tell me about … ” – How to visually summarize large volumes of heterogeneous data to quickly discover meaningful insights “What do they mean?” – How to visually explain discovered insights (complex + abstract) and guide exploration “This does not look right, I want to … do it again” – How to allow users to correct analytic results and adopt previous analytic steps www.gfi.com big data
    • Combine advanced data analytics and interactive visualization to help end users Our Goals Derive and consume insights Explore various analytic paths and trust derived insights Discover opportunities to compensate for and improve insights and analytic processes www.gfi.com
    • Analytic Requests Output: Interactive Visualization Input: User Actions Data Analytic RecipesUser Models Analytic Engines Alternative Visualization Visualization Examples “Big Picture”: Overview Expressive UI & Action Interpreter Expressive UI & Action Interpreter Visual Analytics “Concierge” Visual Analytics “Concierge”
    • Analytic Requests Output: Interactive Visualization Input: User Actions Data Analytic RecipesUser Models Analytic Engines Alternative Visualization Visualization Examples “Big Picture”: Our Focus Expressive UI & Action Interpreter Expressive UI & Action Interpreter Visual Analytics “Concierge” Visual Analytics “Concierge”
    • Analytic Requests Output: Interactive Visualization Data Analytic RecipesUser Models Analytic Engines Alternative Visualization Visualization Examples Visual Analytics Concierge “Big Picture”: Our Focus Visualization Recommender Visualization Recommender Data Transformer Data Transformer Insight Revision & Provenance Insight Revision & Provenance
    • Analytic Requests Output: Interactive Visualization Data Analytic RecipesUser Models Analytic Engines Alternative Visualization Visualization Examples “Big Picture”: Our Focus Visualization Recommender Visualization Recommender Data Transformer Data Transformer Insight Revision & Provenance Insight Revision & Provenance Visual Analytics Concierge
    • www.gfi.com Data Transformation: Motivation “Dirty”, noisy data Large data variance “Plain” raw data Distorted, illegible visualization “Messy” visualization without insights Quality of data to be visualized affects the quality of visualization
    • Example 1: “Dirty” Noisy Data Original visualization After separating noise Task: Show houses on a map
    • Example 2: Large Data Variance Task: Summarize houses by styles and towns Original visualization After normalization
    • Example 3: “Plain” Raw Data Task: Correlate house price and towns under $1.5M Original visualization After ordering towns Price Town Ordered
    • Example 4: “Plain” Raw Data Task: What is my emotional style? After semantic-temporal segmentation [Pan et al. IUI 2013] Original visualization
    • Technical Challenges Determine proper data transformation for different visualization situations – Difficult to predict visualization situations involving multiple factors: data, user, and types of visualization Certain situations require multiple data transformations Balance multiple, potentially conflicting factors – Quality of visualization and performance
    • Our Approach Optimization-based approach to automatically derive data transformations that maximize visualization quality Original Data (D) Data RetrievalData Retrieval Visualization Generation Visualization Generation Data Transformer DataData TransformerTransformer Transformed Data Visualization Type (Vt) Input: Original data D, Visualization type Vt Output: A set of transformation operators Op = {…, op[i], …} where reward ∑ desirability(D, Vt , Op) is maximized Visualization Recommender Visualization Recommender [Wen and Zhou IUI 2008, InfoVis 2008]
    • Measuring Visualization Desirability ∑ Desirability (D, V, Op) ∑ Visual_Quality (D, Vt , Op) ∑ Cost (Op) Visual quality metrics Time cost of data transformations –– visual legibility visual pattern recognizability visual fidelity visual continuity ))(/)()((1),( 21 tt VDdensityDcomplexityVD βλλχ ×+×−= [Wen and Zhou IUI 2008, InfoVis 2008]
    • Data Transformation: What’s Next What additional desirability metrics should we consider? How to perform data transformation in context (incremental transformation)? How to scale out to support exabytes of data for different user tasks and situations?
    • Analytic Requests Output: Interactive Visualization Data Analytic RecipesUser Models Analytic Engines Alternative Visualization Visualization Examples Visual Analytics Concierge “Big Picture”: Our Focus Visualization Recommender Visualization Recommender Data Transformer Data Transformer Insight Revision & Provenance Insight Revision & Provenance
    • Visualization Recommendation: Motivation www.gfi.com – Characteristics of data – User tasks – Device – User interaction behavior Visually encoding data requires skills and time and is influenced by a number of factors
    • Visualization Recommendation: Types www.gfi.com Data-driven recommendation – Dynamically recommend suitable visualizations based on data, display, and user tasks Behavior-driven recommendation – Dynamically track user interactions and detect behavior patterns to recommend suitable visualizations in context DisplayDisplay + new data + task DisplayDisplay + user behavior
    • Two situations – Single display – Multiple, consecutive displays Multiple methods – Rule based – Planning based – Machine learning based Visualization Recommendation Visualization Recommendation Adopted from [Roth et al., CHI 94] [Mackinlay ’86; Roth & Mattis CHI ’94; Zhou & Feiner InfoVis 96; Zhou & Feiner CHI 98; Zhou IJCAI 99; Zhou & Chen InfoVis 02; Zhou & Chen IJCAI03; Wen & Zhou InfoVis 05] Data-Driven Visualization Recommendation
    • Behavior-Driven Visualization Recommendation Observation – Users tend to stay with unsuitable visualization or compensate for with large number of interactions instead of changing visualization Goal – Detect user interaction patterns and make pattern-based visualizations recommendations
    • Behavior-Driven Visualization Recommendation: Example 1 Display: Bridge problems by State by year User interactions: click on each state to examine the structural bridge problems
    • Behavior-Driven Visualization Recommendation: Example 1 Pattern: Scan Visualization Recommendation: line charts for direct comparison [Gotz and Wen IUI 2009]
    • Behavior-Driven Visualization Recommendation: Example 2 Display: Map of the Market User interactions: repeatedly change time windows for two industries Time Window: 26 weeks 52 weeks … Time Window: 26 weeks 52 weeks …
    • Behavior-Driven Visualization Recommendation: Example 2 Pattern: Flip Visualization Recommendation: line chart for direct trend comparison -10 -5 0 5 10 15 20 10 20 30 40 50 Utility Netw orking [Gotz and Wen IUI 2009]
    • Behavior-Driven Visualization Recommendation: Pattern-Based Approach Pattern Detection Pattern Detection Pattern-Task Matching Pattern-Task Matching Pattern-Data Matching Pattern-Data Matching Example Match Example Match Visualization Recommendations Task Features Data Features User Interactions [Gotz and Wen IUI 2009] Scan Flip Swap …
    • Recommending Visual Interactions Automatically annotate and suggest follow-on user interactions based on displayed visual features Original display Annotated display A B [Kandogan VAST’2012]
    • Recommending Visual Interactions Grid-based approach to detect salient visual features in a display, and then annotate Clusters Outliers Trends [Kandogan VAST’2012]
    • Visualization Recommendation: What’s Next Recommend a suitable heterogeneous visualization as a consecutive display Recommend the composition of two or more existing visualizations + = ? Vehicle Group Vehicle Age Cost ? ? [Wen, Zhou & Aggarwal, InfoVis 05; Heer & Robertson, InfoVis07] [Yang , Li, & Zhou 2013]
    • Visualization Recommendation: What’s Next “Individualized” (hyper- personalized), adpative visualization – By cognitive style and personality [Gardner 1983] – By one’s emotional/affective states inventive/curious vs. consistent/cautious friendly/compassionate vs. cold/unkind outgoing/energetic vs. solitary/reserved efficient/organized vs. easy-going/careless Sadness Optimism Trust sensitive/nervous vs. secure/confident O C E A N Big 5 Personality Model
    • Analytic Requests Output: Interactive Visualization Data Analytic RecipesUser Models Analytic Engines Alternative Visualization Visualization Examples Visual Analytics Concierge “Big Picture”: Our Focus Visualization Recommender Visualization Recommender Data Transformer Data Transformer Insight Revision & Provenance Insight Revision & Provenance
    • Insight Revision and Provenance Insight revision – Users amend derived insights to correct analytic mistakes or make personalized adjustments Insight provenance – Users record interactions and insight for continuation and reuse Neuroticism (high low) Extroversion (low high) Zoom In2 Edit2 Query Filter User Actions
    • Insight Revision A crowd-powered approach to insight revision – Users amend various types of text analytics mistakes – Adopting multi-user consistent inputs Correcting sentiment classification error [Hu et al. INTERACT 2013]
    • Insight Revision A crowd-powered approach to insight revision – Users amend various types of text analytics mistakes – Adopting multi-user consistent inputs Correcting summarization label error [Hu et al. INTERACT 2013]
    • Insight Provenance [Gotz and Zhou InfoVis 2009] An action-based approach to insight provenance – “Actions” captures observable and semantically meaningful user interactions • Three types of actions: Exploration | Insight | Meta – “Action trails” captures sequence of actions leading to an insight for insight provenance InsightnExploratio AA o+ = ])([ ττ
    • Insight Provenance [Gotz and Zhou InfoVis 2009]
    • Insight Revision and Provenance: What’s Next Balance crowd input and personalized adjustments – Reconcile diverse user amendments vs. prevent potential system abuse Detect and learn different types of logical structures from user interactions – Automatically infer and predict user interaction patterns to better support and anticipate user tasks ?
    • Summary Tell me what’s in my data Tell me what’s in my data Here is the “big picture” of your data. I also suggest you look into … Here is the “big picture” of your data. I also suggest you look into … dougblakely.com “Big data” is of high volume, heterogeneous, and often “dirty” It requires both users and computers to take initiatives for effective visual analytics of big data Something is wrong… should be… Remember what I have done so far Something is wrong… should be… Remember what I have done so far I incorporated your and others’ feedback. Please continue … I incorporated your and others’ feedback. Please continue … Data Transformation Data Transformation Visualization Recommendation Visualization Recommendation Insight Revision & Provenance Insight Revision & Provenance
    • Acknowledgements IBM Research, Almadn – Eser Kandogan, Fei Wang, Huahai Yang, Liang Gou, Ying Xuan, Eben Haber, Yunyao Li IBM T. J. Watson – David Gotz, Zhen Wen, Shimei Pan, Jie Lu, Min Chen*, Sheng Ma*, Peter Kissa*, Vikram Aggarwal* IBM Research, China – Shixia Liu*, Nan Cao, Yangqiu Song* Weihong Qian Summer interns – Ying Feng (Indiana University) – Basak Alper (UC Santa Barbara) – Mengdie Hu (Georgia Tech) – Jian Zhao (University of Toronto)
    • References of Our Work David Gotz and Michelle X. Zhou: Characterizing users' visual analytic activity for insight provenance. Information Visualization 8(1): 42-55, 2009. David Gotz and Zhen Wen: Behavior-driven visualization recommendation. IUI 2009: 315-324, 2008. Eser Kandogan: Just-in-time annotation of clusters, outliers, and trends in point-based data visualizations. IEEE VAST 2012: 73-82. Mengdie Hu, Huahai Yang, Michelle X. Zhou, Liang Gou, Yunyao Li, and Eben Haber: OpinionBlocks: A Crowd-Powered, Self-Improving Interactive Visual Analytic System for Understanding Opinion Text. To appear in Proc. INTERACT 2013. Zhen Wen and Michelle X. Zhou: Evaluating the Use of Data Transformation for Information Visualization. IEEE Trans. Vis. Comp. Graph. 14(6): 1309-1316, 2008. Zhen Wen and Michelle X. Zhou: An optimization-based approach to dynamic data transformation for smart visualization. IUI 2008: 70-79 Zhen Wen, Michelle X. Zhou, and Vikram Aggarwal: An Optimization-based Approach to Dynamic Visual Context Management. INFOVIS 2005: 25-32. Huahai Yang, Yunyao Li, and Michelle X. Zhou: A Crowd-sourced Study: Understanding Users’ Comprehension and Preferences for Composing Information Graphics. In Submission to TOCHI 2013. Michelle X. Zhou and Min Chen: Automated Generation of Graphic Sketches by Example. IJCAI 2003: 65-74 Michelle X. Zhou, Min Chen, and Ying Feng: Building a Visual Database for Example-based Graphics Generation. INFOVIS 2002: 23-30. Michelle X. Zhou, Sheng Ma, and Ying Feng: Applying machine learning to automated information graphics generation. IBM Systems Journal 41(3): 504-523 (2002)