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Big and Useful : What’s in the Data for Me? (VLDB’13 Panel)

Big and Useful : What’s in the Data for Me? (VLDB’13 Panel). Yannis Ioannidis MaDgIK Lab University of Athens & ATHENA Research Center. Traditional DBer. Stay away from work that touches the user! “Why should I care about what is important to users?” [VLDB ‘03 (or similar)]

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Big and Useful : What’s in the Data for Me? (VLDB’13 Panel)

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  1. Big and Useful:What’s in the Data for Me?(VLDB’13 Panel) Yannis Ioannidis MaDgIK Lab University of Athens & ATHENA Research Center

  2. Traditional DBer • Stay away from work that touches the user! • “Why should I care about what is important to users?” [VLDB ‘03 (or similar)] • “Even though we are database people, user interfaces matter!” [VLDB ‘13] • Data management is about management and not about use, usefulness, usability, usX (forall X)

  3. Data presentation • Narratives, visualizations: dynamic generation? • Interactivity: what are the right primitives? • Stories, games: data-based authoring? Preprocessing Preparation Processing Postprocessing Presentation Narratives, Stories Games,Interaction

  4. Data analysis • Holistic analysis: relationships, impact, … • Task as a Service: Just specify requirements • Function • External resources (time/money) and demands (quality) Preprocessing Preparation Processing Postprocessing Presentation Hoiistic analysis Task as a Service

  5. Open Access • Pubs, talks, Videos, Sci data, Public data, Hardware, Instruments, Learning objects • Data Infrastructures: policy implementation, optimization, … Preprocessing Preparation Processing Postprocessing Presentation Open Access

  6. End users Citizen, Scientist Citizen scientist • Emerging “citizen scientist” (aka “crowd scientist”) • Special data reqs? What about "scientist”, "citizen”, …? • Customization, personalization, adaptation • “Scientists”: They thinkthey are data management experts because they understand the nature of data Preprocessing Preparation Processing Postprocessing Presentation

  7. Data Scientist • Wide variety of skillsets and competencies • Find, interpret, manage, merge, ensure, create, understand, build, enable, … • How do we educate them? Data Scientist Data Scientist Data Scientist Preprocessing Preparation Processing Postprocessing Presentation

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