1. Telegraph: A Universal System for Information

2. Telegraph History & Plans Initial Vision –Carey, Hellerstein, Stonebraker –“Regres”, “B-1” Sweat, ideas and further vision –4 of my grads committed –Brewer + 2 grads committed –Franklin will play –obvious tie-ins with other projects

3. Telegraph Architecture Query/Browse/Mine Global Agoric Federation Continuously Reoptimizing Query Processor Adaptive Data Placement Storage Manager (FS, DB, Web) Ninja, GiST, IStore River, Ninja, Aetherstore, Control,STIX Mariposa, Millenium, Control Control, DigLib & synergies!

4. Storage Manager Historic chance to start over! –new hardware realities variable-length segments, not blocks big main memories extra CPUs at the devices (IStore) –revisit and clean up infrastructure for transactions clean API supporting both log-based & version-based schemes; version-based runs today! big SW Eng. challenge –unify DB/FS/Web server! Clients: Ninja’s persistent hash table, query processing, web server, Linux (NT?) filesystem. –(Mohan Lakhamraju, Rob von Behren, Steve Gribble)

5. Query Engine Shared-nothing (cluster) –all data flow (no blocking ops) auto load-balance to micro/macro changes in environment adaptivity more important than raw performance!! CONTROL! || ripple join, online reordering (Shankar Raman) –continuously reoptimizing query plans tie-ins with STIX (Christos/Sinclair/Russell/Hellerstein) (Ron Avnur) –first steps in handling streaming sources

6. Cluster Data Layout –issues: fragmentation, placement, replication on 10^6 disks. For DB/FS/Web. –goals: availability, efficiency, consistency, manageability. –Adaptivity: cooperative vs. competitive ($$) techniques? –(Mehul Shah)

7. Global Federation Global distribution –federated DBMS layer a la Mariposa/Cohera address all the hard stuff they dropped! –Global data placement as in cluster, but must be competitive. (Mehul Shah) –Global query processing (Amol Deshpande) Agoric query optimization distributed query processing –Global metadata yellow pages both for services & datasets Millenium/Ninja tie-ins?

8. Applications Really finding stuff in all the world’s data? –UI meets AI meets Logic (browse/mine/query) CONTROL is key: seamless, non-blocking interaction multi-res output and feedback during browse/query hints, wizards, training (AI mining, user in the loop) build on existing “scalable spreadsheet”/xform tools (Shankar Raman)