Trained Systems: The Future of Database Research
Explore the emergence of trained systems combining data management and statistical processing, shaping the future of database research. Discover the impact, challenges, and opportunities in this evolving field.
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Presentation Transcript
Trained systems are coming. Christopher R Stanford University
Database research is exciting, relevant, and healthy. But what s next? 1. Trained systems are coming, 2. DB is the community to do it, & 3. It will be interesting. 2
What: Trained Systems are Coming Trained Systems combine data management with statistical processing to answer rich queries on less structured data. Recently, a quality inflection point. Problem: not a commodity, yet. 3
Why: Commodity-level Interest Trained systems have captured attention across communities Healthcare Academics Insurance, Finance Retail Energy Making a commodity system is not a machine-learning problem; it is a database systems problem. 4
How: Old & New Tradeoffs DB skills to make into a commodity: querying, maintenance, 1. extraction, 2. integration, & 3. BI. Argue: next step of all three 1. Alters tradeoff space, SAMPLINGASJOINEVALUATION 2. Opens new tradeoff spaces, and ROBUST: REORDERCOMPUTATIONFORLOCALITY, 3. Entirely new design points. TWO OK NLP TOOLS = BETTER NLP TOOL! New Challenges 5
Conclusion Trained Systems offer us: (1) a way to attack pressing problems, (2) an area with competitiveadvantage, (3) and technically interesting problems. http://youtube.com/HazyResearch Example for Climate: PaleoDeepDive 6
