1. Intermittently Connected Query Processing Yang Zhang, Bret Hull, Hari Balakrishnan, and Samuel Madden MIT Computer Science and AI Lab April 17, 2007

8. ICEDB Server (portal) Example Query Show me photos of traffic jams. No duplicates. Example Query Show me photos of traffic jams. No duplicates.

9. ICEDB Server (portal) wireless connection sensors + #!/usr/bin/perl while (true) { raw = read(serial); tuple = convert(raw); send(icedb, tuple); } #!/usr/bin/perl while (true) { raw = read(serial); tuple = convert(raw); send(icedb, tuple); } adapter schema data source

10. ADAPTERADAPTER ADAPTERADAPTER DB CQ Ad-hoc Query Processor Ad-hoc Query Processor Output Buffers Output Buffers CAFNETCAFNET CAFNETCAFNET

11. SELECT... EVERY n [SECONDS] BUFFER IN buffername

13. tuplesbuffer query DB

15.  PRIORITY rank,weight : inter-query (local)  DELIVERY ORDER BY : intra-query (local)  SUMMARIZE AS : global tuplesbuffer

16. FIFO Bisect

17. ICEDB Server

19. 46 3 2 1 5 from central server to central server ICEDB Server latlonins_time 31.41527.1827:30pm 31.42327.1797:35pm ……… latlonins_timerank 31.42327.1797:35pm1 31.41527.1827:30pm2 …………

21.  232 days of normal driving (07/05 – 07/06)  Boston and Seattle areas  260 distinct km of roads, 50% from 15 km  32,000 APs discovered, 2,000 open  Mean time between APs: 23 seconds  Mean association duration: 24 seconds  Median TCP upload: ~200 kbytes  Connectivity is equi-probable in [0,60] km/h

22.  Query workloads: uniform, hotspot  Camera data: 50KB  Metric: fraction query points satisfied  Prioritization schemes: FIFO, bisect, random, global  Cars: one, many query point

24.  FIFO: zero success  Random/bisect: ~0.25x success of global  Bottleneck: not query count, but total network capacity  Global: remote nodes and central server share data

26. Intermittently Connected Query Processing Yang Zhang, Bret Hull, Hari Balakrishnan, and Samuel Madden MIT Computer Science and AI Lab http://cartel.csail.mit.edu/

27. SELECT... EVERY n [SECONDS] BUFFER IN buffername

28. ICEDB Server latlonins_time 31.41527.1827:30pm 31.42327.1797:35pm ……… latlonins_timerank 31.42327.1797:35pm1 31.41527.1827:30pm2 …………

29. Trace: 1 trip all sensor data collected between ignition on to ignition off Trace: 1 trip all sensor data collected between ignition on to ignition off

32.  Trace-driven simulation

33. ICEDB Server

35. data source = ++ #!/usr/bin/perl while (true) { raw = read(serial); tuple = convert(raw); send(icedb, tuple); } #!/usr/bin/perl while (true) { raw = read(serial); tuple = convert(raw); send(icedb, tuple); }

36.  Trace-driven simulation  Query workloads: uniform, hotspot  Camera data: 50 KB/img  Metric: success ratio  Prioritization schemes: FIFO, bisect, random, global  Cars: one, many

39.  CarTel: mobile sensor platform  Opportunistic data delivery  >1 year, Boston/Seattle, 260 km