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Aurora

Aurora. Proponent Team Wei, Mingrui Liu, Mo Rebuttal Team Joshua M Lee Raghavan, Venkatesh. AURORA Proponent. Introduction Motivation Aurora System Architecture Aurora System Query model Conclusion. Introduction:Traditional DBMSs.

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Aurora

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  1. Aurora Proponent Team Wei, Mingrui Liu, Mo Rebuttal Team Joshua M Lee Raghavan, Venkatesh

  2. AURORA Proponent • Introduction • Motivation • Aurora System Architecture • Aurora System Query model • Conclusion

  3. Introduction:Traditional DBMSs • Passive reporitory: Human-Active, DBMS-Passive(HADP) model • The current of state of the data is important: Previous data needs to be extracted form the log • Triggers and alerts as second-class citizens • Perfect synchronization of data elements and exact query answers • No real-time services from applications

  4. Introduction: Monitoring apps • Monitoring applications are applications that monitor continuous streams of data • Active repository: DBMS-Active, Human-Passive (DAHP) model • History of the data is important: Not only the current state but also the previous history • Triggers and alerts as the first-class citizens • Missing or imprecise data, and approximate query answers • Real-time services required by applications

  5. Introducation: Monitoring apps • Target Applications :military financial analysis tracking other real-time applications

  6. Car Navigation System • Data( e.g., the location of the car) comes from external sources • History of the data is required( e.g., display a trajectory of your car in the past 20 minutes) • Trigger and alert oriented: an alert for the driver when the car is approaching to an intersection • The location of the car is not always perfectly transmitted due to interferences etc..

  7. Motivation-DSMS • Data Stream Management Systems Streams: continuous data feeds from sources. ect. sensors and satellites Monitoring applications track the data from numerous streams, filtering them for signs of abnormal activity and processing them for purposes of aggregation, reduction and correlation

  8. Management Requirements DBMS 1.Data processing results issuing transactions and queries 2.Manages data in its tables 3.Provide exact answers to exact queries and is blind to real-time deadlines 4.Optimizations of all queries in the same way 5.The norm is pull-based queries DSMS 1.Monitoring and alerting humans for abnormal activities 2.Processing of data that is bounded by finite window of values and not over unbounded past 3.Respond to real-time deadlines and provide reasonable approximations to queries 4.Benefits from Application specific optimization criteria (QoS) 5.The norm is push-based data processing

  9. What is Aurora • Deals with large numbers of data streams • Users built queries out of a small set of operators (boxes) • Supports combination of (boxes) for better answers • Aurora-Better support monitoring application; • Stream processing • QoS functions • Operators: filtering, mapping, windowed aggregate, join • Timeout

  10. Aurora –Architecture • Continuous stream data comes • Flow through a set of operators • Output to application or materialized • Multiple streams can be merged

  11. Aurora System: Query model Supports continuous queries Supports Ad-hoc queries

  12. Aurora QoS Model • 􀂄Each output is supplied with a QoS specification • 􀂄QoS is captured by three functions A latency graph 􀂅 A loss-tolerance graph 􀂅 A value-based graph

  13. QoS

  14. Aurora optimization Dynamic continuous query optimization optimizer selects a portion of the network to optimize by: • Inserting projections. • Combining boxes. • Reordering boxes.

  15. QoS data structures • Response times – output tuples should be produced in a timely fashion, as otherwise QoS will degrade as delays get longer. • Tuple drops – if tuples are dropped to shed load, then the QoS of the affected outputs will deteriorate. • Values produced – QoS clearly depends on whether or not important values are being produced.

  16. QoS

  17. Load Shedding • Load shedding by dropping tuples Minimizing the overall performance degradation as a result of static analysis • Semantic load shedding by filtering tuples Semantic load shedding based on value-based QoS information if available

  18. Conclusion • Aurora is a new rising star in DBMS • More demand for monitoring applications • Future directions: Aurora* for distributed processing More efficient data handing algorithm for missing and/or imprecise data that is common in sensor network

  19. AURORA Rebuttal • User Issues • The graphical workflow style specification could be cumbersome in real life use • An extended SQL may be easier for users

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