U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science SPIRE: Scalable Processing of RFID Event Streams Yanlei Diao University of Massachusetts, Amherst Joint work with Richard Cocci and Prashant Shenoy

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 2 UMass RFID Research Center Kevin Fu RFID security & privacy Yanlei Diao RFID data management Wayne Burleson Secure RFID Hardware Mark Corner RFID locationing and mobile readers Prashant Shenoy RFID software systems UMass Center for Advanced RFID Research 5 faculty + 9 students

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 3 Research Thrusts Embedded devices & hardware thrust Data management middleware thrust Application thrust Security & Privacy Robustness & Scalability Contact-less payments Contact-less payments Precise locationing at home/office Precise locationing at home/office Healthcare process management Healthcare process management Trace-&-trace in supply chain Trace-&-trace in supply chain Monitoring & anomaly detection Monitoring & anomaly detection

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 4 Real-time Visibility using Event Processing Monitor, Alert, Correct, Control, Improve Data management middleware thrust Data Management Middleware Tracking Individual Objects on a Large Scale Track-and-Trace

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 5 Challenges Data-information mismatch  RFID data  Meaningful, actionable information Incomplete, insufficient, misleading data  Missing data  Overlapping read ranges  Location unclear  Containment unclear

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 6 Challenges Scalability  Large-scale deployment:  Dozens of locations  Thousands of readers  Millions of objects  Unprecedented volume of data Low-latency  Anomaly detection: up-to-the-second information  Online data warehousing: frequent updates

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 7 Historical Data 4. Event DB Track & trace Overview of SPIRE RFID Devices Continuous queriesResultsSQL queriesResults Data/Queries 1. Data Cleaning Incomplete data, misleading data Live Data 3. Complex Event Processor Monitoring, anomaly detection 2. Data Interpretation, Compression Insufficient data, redundant data

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 8 Data Cleaning Raw RFID Stream Anomaly Filtering Temporal Smoothing Time Conversion Deduplication Removes bad data Handles missing data Adds logical timestamp Resolves misleading data For details see [CocciDS07, GyllstromWC+07, JefferyGM06].

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 9 Location-Containment View A physical world  Objects, locations, object containment at time t RFID readings  Isolated, fragmented views of the physical world An integrated location-containment view  View evolves as new readings arrive  View is used to interpret (give meanings to) the readings

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 10 View Maintenance View(time T, location A) 1, 6, 2, 4, 5 + Stream(T+1, A) 7, 3, 8 + Stream(T+2, B) View(T+2, B) ? X View(T+1, A) ?

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 11 Data Interpretation & Compression Interpretation of low-quality, insufficient data  Location unclear  Containment unclear Data Compression - Location compression - Containment compression - Compression vs. real-time anomaly detection Archival to event database for track-and-trace  Querying over both history and current state

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 12 Complex Event Processing Real-time translation from data to actionable information  Filtering  Correlation  Aggregation  Transformation  Predication Proactive, adaptive systems  Monitor, alert, correct, control, improve

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 13 SASE Event Processor Computation Complexity Misplaced Inventory EVENTSEQ(Shelf_Reading x, Shelf_Reading y, !(ANY(Register_Reading, Shelf_Reading) z) ) WHERE [TagId] AND x.AreaId != y.AreaId AND x.AreaId = z.AreaId WITHIN1 minute RETURNx.TagId, x.ProdId, x.AreaId, y.AreaId, retrieveHistOfMvmt(x.TagId) Stock Market Analysis Event SEQ(Stock+ a[], Stock b) Where skip_till_next_match(a[]!,b) { [symbol] AND a[1].volume > 1000 AND a[i].price > avg(a[…i-1].price)) AND b.volume < 80%*a[a.LEN].volume } Within 1 hour Return a[1].symbol, a[].(price,volume), b.(price,volume) Medical Compliance Event seq(MEDICINE-TAKEN x, MEDICINE-TAKEN y) Where[name=‘John’]  [medicine=‘Antibiotics’]  (x.amount + y.amount) > 1000 Within 4 hours Event Language * 2 * AB D Implementation Mechanisms

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 14 Monitoring & Anomaly Detection Alert if a bottle of drug with an unknown manufacturer appeared in the supply chain. Alert if a case of vaccines has not been seen at three consecutive points in the supply chain. Alert if a nominally peanut-free bottle was filled with food containing peanuts. Alert if two credit cards with the same number have been found in different locations. Alert if a patient has taken overdoses of medicine in past 12 hours. ……

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 15 Receiving Area Shelf 1 Shelf 2 Shipping Area RFID Simulator: Supply Chain

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science 16 Questions