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How Will Applications Drive Future Data-Intensive Systems?

Explore how various applications, such as social networking, biomedical sensor networks, finance analytics, and more, are driving the future of data-intensive systems. Discover the common application structures, big data challenges, and the need for data security and integrity in this evolving landscape.

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How Will Applications Drive Future Data-Intensive Systems?

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  1. How Will Applications Drive Future Data-Intensive Systems? Data-Intensive Computing Workshop Applications Break-Out Session

  2. Google-style Search Social Networking (Facebook/Twitter) Data warehouse mining Biomedical Sensor networks (e.g., video, radar) Cosmology Astro Climate Fusion Machine translation National security Disaster preparedness Financial analytics GIS Some Driving Applications Many Domains benefit from Data-Intensive Computing

  3. Common Application Structures Derived Data Query • Big Data • Big Data background live Query Derived Data Anticipated vs. ad hoc analysis/queries

  4. Application Trends: Scale E.g., Climate Change Studies need: • 5 orders of magnitude data scale • 5 orders of magnitude speed scale (including algorithmic improvements) But More than That…

  5. SW as service, pervasive mobile clients P2P interaction Built-in verifiability/ provenance of answers Too much raw data; must decide what (derived) data to retain Dealing with privacy controls, role-based authentication Multi-resolution, Multi-D visualization (multi-sensory presentation) at scale Queries expressed using multimedia Heterogeneity, Cross data sources Increased value of data=>increased demand for data security/integrity Application Trends: Features Big Data Challenges: Around the Corner for All of Us

  6. Reducing App Development Time Key issues: • Effective workflow tools: need for convergence to open, standard tools (Multi-user: Tasks are collaborative) • Effective big data libraries & frameworks • Avoid recoding when scale changes • Use familiar APIs (C.S. stuff just works)

  7. Some Lessons Learned • Curriculum mismatch between domain scientists and computer science courses • Hard to determine the resource needs of an app a priori • Cross-disciplinary work is challenging • More cross-disciplinary possibilities in sharing Big Data • Typically not a big data cliff: can make do with less data, but improve with more data • Although some apps need min data size to be useful • Meet needs of those already feeling the pinch vs. Trying to leap ahead • Economics: data is free, networking is free • Payment may not be money: what demand of users

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