1 Querying the Physical World Son, In Keun Lim, Yong Hun

2 Outline  Introduction  Kinds of Queries  Warehousing vs. Distributed approach  Device Database System  Query Execution Plans  Conclusion

3 Future Computing Environments  The widespread deployments of devices  sensors, actuators, and mobile devices  with power, memory, and communication capabilities  devices are interconnected and accessible from local and wide area networks  What is a device?  physical object with computing and communication capabilities

4 Kinds of Queries  Historical Queries  typically aggregate queries over historical data  “For each rainfall sensor, display the average level of rainfall for 1999”  Snapshot queries  concerns the device network at a given point in time  “Retrieve the current rainfall level for all sensors in Tompkins County”  Long-running queries  concern the device network over a time interval  “For the next five hors, retrieve every 30 seconds the rainfall level for all sensors in Tompkins County”

5 Warehousing approach  Traditional query processing  A warehousing approach – data are extracted in a predefined way and store in a centralized DB system DBMS Query requesters Query Result Raw data -ATM Machine -Airplane/Ship/Train -Etc NETWORK

6 Warehousing approach(cont’d)  Dissociates access to devices from the query workload  Transfer large amounts of raw data from devices to the database server  Wastes valuable resources (network resources, processing power)  Modern devices include processing capabilities that could be used to process data locally

7 Distributed Approach  New way for query processing  Small-scale mobile devices will have self-computing power, memory, and communication capabilities Meta- Information Query requesters Query Result Sub query -ATM Machine -Airplane/Ship/Train -Etc NETWORK Sub result Query processor

8 Distributed Approach(cont’d)  The query determines the data that are extracted from remote sites  The query determines where possibly portions of queries are executed on devices  Device Database System  access devices directly when processing queries

9 Device Database System  What is a device?  A mini-server supporting functions  Functions like acquiring data  Functions like actuating (Triggers)  Device types  Synchronous devices returns results immediately  Asynchronous devices may move, may be unconnected, therefore returns results after an arbitrary period of time

10 Representation of Functions  User Representation : ADT  modeling each type of device in the network as an ADT  the public interface of the ADT corresponds to the specific functions supported by the device  Internal Representation : Virtual Relation  tabular relation of a function

11 Virtual Relation  Attributes  Inputs to the function  Arguments to the function  Output of the function  Timestamp of the function  Properties  records are never updated or deleted  is naturally partitioned over the network => a distributed DB

12 Query Execution Plan  Performance metrics  Throughput and response time  Resource usage and reaction time  Notation  Joins are between R and VRs  Rs are centrally located tables (like a list of sensors)  VRs are distributed (like a VR representing a device function)

13 Traditional Query Execution Plan  Materialize VR  Do Join at Front End  Centralized  Bottleneck, not scalable

14 Query Execution Plan A  Virtual records are produced once on each site for a snapshot query  Virtual records are produced repetitively for a long-running query  Whenever you get a new records, do a join

15 Query Execution Plan B  Semi-join : send joining attribute to devices  Use processing power of devices more  let them do the join  Send back only valid records  Reduced network traffic

16 Query Execution Plan C  Just send the condition  Again do the join at the center

17 Experiments 1  Nodes outside the target region  Plan C predictably shows no consumption (the transfer of the condition is negligible)  Plan B amortizes  Plan A accumulates

18 Experiment 2  Nodes inside the target region  Plan C shows growth, similar to Plan A  Plan B grows much more  The difference is the cost of performing a selection – low compared to cost of sending data.

19 Conclusions  Flexible  Scalable  Optimizer assume global knowledge  Query plans must adapt dynamically and be robustness

20 Future Expectations  A lot of self-computing devices which have their own memory, communication capability will exist in the future world (already?)  Refrigerator, Television, Vending Machine…  Hot issues are going to be  How to maintain device meta-information with a d ecentralized approach (P2P?)  Which information should be stored carefully at the data warehouse and which device should be accessed  Information retrieval will be more important  How to make query execution plans