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Evacuating the Comfort Zone: (Via Curriculum Reform…)

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Presentation on theme: "Evacuating the Comfort Zone: (Via Curriculum Reform…)"— Presentation transcript:

1 Evacuating the Comfort Zone: (Via Curriculum Reform…)

2 Comfy Topics Logical data models and languages Query optimization/execution Consistency models and mechanisms Storage architectures Enterprise IT applications

3 Moving Outside the Zone Rethinking system architectures –Deep memory hierarchies, componentization, adaptive algorithms, extreme scales (nano to global) Embracing probabilistic reasoning –In data analysis, adaptive algorithms (again!), online user interactions, data modeling and integration, lossy compression

4 Course 1: Data Systems Not so radical: infect the OS course with the RSS –Traditional OS material (scheduling, protection, resource management) –File & Record storage –Transactions, Concurrency, Recovery –Storage Hierarchies –Dataflow architectures: query plans, NW support Big pedagogical benefit to merging this material –Two design targets (OS/FS vs. DBMS) –Leads to instructive architectural tradeoffs –Illustrates 2 design philosophies (bottom-up vs. top- down)

5 Course 2: Modeling & Analysis Relational + IR + Statistics + Information Theory Review of basic math –1st-order logic –Central Limit Theorem, Chernoff/Hoeffding bounds –Simple information theory: entropy, error metrics Data Modeling –Logical: Relational normalization, ontologies, IR bag-of-words –Probabilistic: simple graphical models (Bayes nets), IR vector space Data Analysis –Relational-style query optimization/execution, OLAP –Sampling and summarization –Boolean IR, ranked retrieval, link analysis, info extraction –Predictive analyses: classification, clustering –Data Visualization Pragmatics, Exercises: –Decision-support systems & tasks: queries, mining tools, etc.

6 Assertions Course 1 is a Good Idea –4 years at the grad level at Berkeley, it works great Course 2 is The Future –It’s in demand Think of what Business, BioEng, etc. really want! Do our systems students even know how to manage, exploit experimental data? –A curriculum, or a research agenda? KDD is a piece of this But fragmentary Opportunity here! –The DB textbook market is saturated :-)


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