1 / 4

Key Observation

Corollary: must give top priority to eliminating human bottleneck or at least alleviating human bottleneck. autonomic computing. intelligent information organization & search. Key Observation. Theorem: the one resource where demand always exceeds supply

marianhicks
Download Presentation

Key Observation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Corollary: • must give top priority to • eliminating human bottleneck • or at least • alleviating human bottleneck autonomic computing intelligent information organization & search Key Observation Theorem: the one resource where demand always exceeds supply is human intelligence (smart people) Proof: by looking around and induction

  2. Problem 1: Self-tuning DBMS - From Visionary Buzzwords to Practical Tools - Common practice: KIWI (kill it with iron) Problem: When is KIWI applicable, and how much will it cost? • Problem (technical variant): • given: • perfect info about DB and previous week‘s multi-class workload • throughput & response time goals (QoS / SLA / WS Policies) •  predict accurately performance improvement by • adding x GB memory • adding y MB/s disk bandwidth • adding or replacing processors • with all software tuning knobs left invariant or • with tuning knobs automatically adjusted Challenge: How to automate introspective bottleneck analysis and asap alerting about (modest) HW upgrades?

  3. query logs, bookmarks, etc. provide • human assessments & endorsements • correlations among words & concepts • and among documents Problem 2: Exploit Collective Human Input for Collaborative Web Search - Beyond Relevance Feedback and Beyond Google - • href links are human endorsements  PageRank, etc. • Opportunity: online analysis of human input & behavior • may compensate deficiencies of search engine Typical scenario for 3-keyword user query: a & b & c  top 10 results: user clicks on ranks 2, 5, 7  top 10 results: user modifies query into a & b & c & d user modifies query into a & b & e user modifies query into a & b & NOT c  top 10 results: user selects URL from bookmarks user jumps to portal user asks friend for tips Challenge: How can we use knowledge about the collective input of all users in a large community?

  4. DB schemas, instances & • data usage in apps provide • human annotations • correlations among tables, attr‘s, etc. Problem 2‘: Exploit Collective Human Input for Automated Data Integration - Inspired by Alon Halevy - • „semantic“ data integration is hoping for ontologies • Opportunity: all existing DBs & apps already provide • a large set of subjective mini-ontologies Typical scenario for analyzing if A and B mean the same entity  compare their attributes, relationships, etc. • consider attributes and relationships of all similar tables/docs in all known DBs • consider instances of A and B in comparison to instances of similar tables/docs in all known DBs • compare usage patterns of A and B in queries & apps in comparison to similar tables/docs of all known DBs Challenge: How can we use knowledge about the collective designs of all DB apps in a large community?

More Related