1. Yanlei Diao, University of Massachusetts Amherst Future Directions in Sensor Data Management Yanlei Diao University of Massachusetts, Amherst

2. Yanlei Diao, University of Massachusetts Amherst Overview of Sensor Data Management  Infrastructural work  Deploying a network of wireless sensing nodes  Optimizing energy efficiency  Communicative vs. storage intensive paradigms  Sensor data management research  Diverse types of sensor data, e.g., temperature, light, GPS, RFID, radar data, astronomical data, …  Query processing on stored data and data streams  Much work lies ahead  Supporting scientific applications: high-volume data, complex data types, data uncertainty, user-defined functions…  Building a smart planet: platform, data integration, scale…

3. Yanlei Diao, University of Massachusetts Amherst Data Streams from Sensing Applications TV Data: incomplete, imprecise, misleading Results: unknown quality

4. Yanlei Diao, University of Massachusetts Amherst CASA: Severe Weather Monitoring High-Volume Raw Signal Data: 1.66 million data items, 200Mb per sec High-Volume Raw Signal Data: 1.66 million data items, 200Mb per sec Highly Noisy Data: Environmental noise Device noise Transmit frequency System clock Positioner Antenna… Highly Noisy Data: Environmental noise Device noise Transmit frequency System clock Positioner Antenna… Sensing

5. Yanlei Diao, University of Massachusetts Amherst Uncertain Data Processing Sensing Transformation & Averaging Transformation & Averaging Transformation & Averaging Transformation & Averaging Uncertainty: What is the data quality of those tuples? What is the effect of averaging over uncertain data? Uncertainty: What is the data quality of those tuples? What is the effect of averaging over uncertain data? Transform raw data to tuples (time, area, velocity, reflectivity, …) Average tuples for reduced volume and smoothing Transform raw data to tuples (time, area, velocity, reflectivity, …) Average tuples for reduced volume and smoothing

6. Yanlei Diao, University of Massachusetts Amherst Final Tornado Detection Sensing Detection/ Predication Detection/ Predication wireless transmission Sensing Transformation & Averaging Transformation & Averaging Transformation & Averaging Transformation & Averaging Quality of the final detection result? location gradespeedexistence prob. Tornado Detection

7. Yanlei Diao, University of Massachusetts Amherst SELECT group_id, max(O.luminosity) FROM Observations O [RANGE 1 hour] GROUP BY area_id(O.(x,y), AREA_DEF) as group_id HAVING max(O.luminosity) > 20 Computational Astrophysics (o_id, time, (x,y) p, luminosity p, color p ) 10 8 stars, galaxies 0.5TB – 20 TB nightly data rates Noisy observations from images Quality of alert? Query answer group_id max_luminosityexistence prob. max_luminosity p :, ε group_idexistence prob.

8. Yanlei Diao, University of Massachusetts Amherst Object Tracking and Monitoring using RFID  Incomplete, noisy RFID data streams Electronic devices Metal objects Orientations of reading Raw data Raw data Data needed for querying Data needed for querying vs.  Not directly queriable  Fire monitoring Alert when a flammable object is exposed to a high temperature.

9. Yanlei Diao, University of Massachusetts Amherst Scope of our Project  Uncertain data modeled using continuous random variables  High-volume data streams  An end-to-end solution 1.From raw streams to queriable probabilistic tuple streams 2.Relational processing of probabilistic tuple streams Query answers with bounded errors (existence prob., attribute dist.) Stream-speed processing  Objectives:

10. Yanlei Diao, University of Massachusetts Amherst System Overview T1 T2 T3 A1 A2 A3 A4 J1 tuples w. lineage Archived tuples

11.  Aggregates (SUM, COUNT, AVG)  Joins (  ): Equijoin using a probabilistic view Non-equijoin using a cross-product  Projections (  )  Selections (  )  Linear arithmetic operations  Selection - Aggregation  Group By - Aggregation (  G,Aggr )  Arbitrary arithmetic operations Yanlei Diao, University of Massachusetts Amherst Relational Processing under the GMM Model Closed-form Distributions in GMMs! Approximation w. bounded errors! Truncated GMMs No commutativity Major result : Relational algebra under GMMs

12. Yanlei Diao, University of Massachusetts Amherst © KSWO TV © Patrick Marsh May 8, 2007 Series of low-level circulations. NWS Tornado Warnings: 7:16pm, 7:39pm, 8:29pm 7:21pm 8:15 pm 9:54pm 11:00pm

13. Yanlei Diao, University of Massachusetts Amherst Velocity Maps Boduo Li, Liping Peng, University of Massachusetts Amherst

14. Yanlei Diao, University of Massachusetts Amherst Processing Speed and Accuracy Data trace of 84 scans in 947 seconds Data Analysis Time (sec) Detection Time False Positives CASA 19844862137 PODS 5793929 More analysis time, still stream speed Faster detection due to improved data quality Much fewer false alarms due to better data quality Signal strength filter or smoothing improves detection time. GMM fitting & noise removal finally cuts down # false positives.

15. Yanlei Diao, University of Massachusetts Amherst Future Work  User-defined functions  e.g., fuzzy joins, tornado detection algorithms…  Array model for scientific applications  Correlation  between derived attributes; between tuples  Query optimization  cheapest plan that meets a query accuracy requirement  Large-scale data analysis for scientific applications  leveraging cluster computing…