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  • 1. RFID Data Management Kamlesh Laddhad (05329014) Karthik B.(05329021) Guide: Prof. Bernard Menezes
  • 2. Outline
    • Introduction to RFID Technology.
    • Issues with RFID Technology.
    • RFID Data Characteristics.
    • Data Warehousing.
      • Expressive Temporal Model: Dynamic Relationship ER Model
      • RFID - Cuboids.
      • Use of Bitmap Datatype.
    • Data Cleaning.
      • Extensible Sensor stream Processing (ESP)
      • Statistical sMoothing for Unreliable RFid data.(SMURF)
    • Future Plans.
  • 3. Introduction
    • Radio Frequency Identification:
      • It is an Automatic Identification and Data Capture Technology.
      • Fast
      • No contact or line of sight.
      • Uses radio-frequency waves to transfer data
    • Components
      • Tag: small, low-cost device that can hold a limited amount of data.
        • Associated with objects, such as pallets, cases, and even individual items.
      • Reader: Recognize presence of tag and read info stored on it.
    • Unique electronic product code (EPC) associated with a tag.
    • By placing RFID tag readers at various locations, one can track the movement of objects through supply chain networks.
  • 4. Applications and Adoptions
    • Supply Chain Management: real-time inventory tracking.
      • US Department Of Defense: shipments to armed forces
    • Retail: Active shelves monitor product availability
      • Wal-Mart, Albertson: Major Retails stores
    • Access control: toll collection, transportation.
      • Airline luggage management:
        • British airways:20 million bags a year
        • Implemented to reduce lost/misplaced luggage
    • Anti-counterfeiting and security:
      • Food and Drug Administration: To reduce counterfeit in pharmaceutical supply chain
  • 5. Prospective for RFID research
    • The physics of building tags and readers
      • Tags have few gates: Apart from basic operation, very less computing power.
      • Radio-frequency has some issues with operating in certain physical mediums.
    • The privacy and safety issues:
      • Complex encryption schemes are not possible on RFID tags.
      • Counterfeiting by means of either illegitimate readers or spoofed tags are possible
      • Reader-tag communication is wireless: Third parties can eavesdrop on signals.
    • Software Architecture to collect, filter, organize, and answer online queries:
      • No. of tags are proportional to No of items being serviced/tracked.
      • No. of readers are proportional to traceable strategic locations/areas
        • Each Reader picks up tag signals on continuous basis.
        • Data generated by RFID systems is enormous:
        • E.g. Wal-Mart is expected to generate 7 terabytes of RFID data per day.
    • Our Focus: Third Stream.
  • 6. Data Warehousing Techniques
  • 7. Data Management Challenges
    • Data Explosion : Example
      • A retailer with 3,000 stores, selling 10,000 items a day per store.
      • Each item moves 10 times on average before being sold
        • Movement recorded as (EPC, location, second)
      • Data volume: 300 million tuples per day.
      • Example OLAP Query: “Average time for items to move from warehouse to checkout counter in March 2006?”.
        • Costly to answer if there are a billion tuples for March 2006.
  • 8. Data Characteristics
    • Temporal and history oriented
      • Applications dynamically generate observations (readings).
      • Objects location and containment relationship among objects changes
      • Need: Expressive data model.
    • Inaccurate data and implicit semantics
      • False positive: Non-existing tag incorrectly read.
      • False Negative: Reader missed a tag which was in its vicinity.
      • Noisy data & duplicate readings (redundancy): Same tag read more than once.
      • Need: Automated data filtering and transformation.
    • Streaming and large volume
      • Object stay in place for longer duration: Readers records them periodically. Large data keeps generating.
      • We need to preserve this data for tracking and monitoring.
      • Need: Scalable storage scheme, compression techniques to reduce data.
    • Data Granularity
      • Data collection granularity needs to be decided
      • Differs across applications.
  • 9. Warehousing Helps!!
    • Lossless compression
      • Remove redundancy: (r 1 ,l 1 ,t 1 ) (r 1 ,l 1 ,t 2 ) ... (r 1 ,l 1 ,t 10 ) => (r 1 ,l 1 ,t 1 ,t 10 )
      • Group objects that move and stay together.
    • Data cleaning: Multi-reading, missed-reading, error-reading, bulky movement.
    • Data mining: Find trends, outliers, frequent, sequential, flow patterns.
    • Multi-dimensional summary: product, location, time, …
      • Store manager: Check item movements from the backroom to different shelves in his store
      • Region manager: Collapse intra-store movements and look at distribution centers, warehouses, and stores
    • Query Processing
      • Support for OLAP: roll-up, drill-down, slice, and dice
      • Path query: New to RFID-Warehouses, about the structure of paths
        • What products that go through quality control have shorter paths?
        • What locations are common to the paths of a set of defective auto-parts?
        • Identify containers at a port that have deviated from their historic paths
  • 10. Dynamic Relationship ER Model
    • Proposed by Wang and Liu from Siemens.
    • RFID entities are static and are not altered.
    • RFID relationships: dynamic and change all the time.
    • Two types of dynamic relationships added:
      • Event-based dynamic relationship. A timestamp attribute added to represent the occurring timestamp of the event.
      • State-based dynamic relationship. tstart and tend attributes added to represent the lifespan of a state.
  • 11.
    • Static entity table
      • OBJECT (object_epc, name, description)
      • LOCATION (location_id, name, owner)
    • Dynamic relationship tables
      • OBSERVATION(sensor_epc, value, timestamp)
      • OBJECTLOCATION(epc, location_id, tstart, tend)
      • TRANSACTIONITEM(transaction_id, epc, timestamp)
      • SENSOR (sensor_epc, name, description)
      • TRANSACTION (transaction_id, transaction_type)
      • CONTAINMENT(epc, parent_epc, tstart, tend)
      • SENSORLOCATION(sensor epc, location id,position, tstart, tend)
  • 12. Monitoring.
    • Missing RFID Object Detection:
      • Find when and where object holding EPC= `MEPC’ was lost.
        • select location_id, tstart, tend from objectlocaiton where epc='MEPC' and tstart = ( select max(o.tstart) from objectlocation o where o.epc='MEPC' )
      • Check if there are missing objects at current location C, knowing that all objects were complete at previous location L at time T.
        • select l.epc from objectlocation l where l.location_id = 'L' and l.tstart <= 'T' and l.tend >= 'T' and l.epc not in ( select c.epc from objectlocation c where c.location_id = 'C' )
  • 13. Tracking
    • RFID Object Moving Time Inquiry:
      • Time it takes to supply ‘OEPC’ from location S to location E?
        • select (e.tstart-s.tstart) as supplying_time from objectlocation e, objectlocation s where e.epc = 'OEPC' and s.epc='OEPC' and s.location_id ='S' and e.locaiton_id='E'
  • 14. Compression Idea
    • Bulky object movements
      • Objects often move and stay together through the supply chain.
      • If 1000 packs of product P stay together at the distribution center, register a single record.
      • (GID, distribution center, time_in, time_out).
      • GID is a generalized identifier that represents the 1000 packs that stayed together at the distribution center
    • Analysis usually takes place at a much higher level of abstraction than the one present in raw RFID data
    Factory Dist. Center 1 Dist. Center2 … 10 pallets (1000 cases) store 1 store 2 … 20 cases (1000 packs) shelf 1 shelf 2 … 10 packs (12 sodas)
  • 15. RFID Cuboids
    • Fact Table: (EPC, location, time_in, time_out).
    • In supply chain: Items travel through a series of locations.
    • Query: what is the average time that product P stays at store in Location A?
    • Traditional cubes miss the path structure of the data
    • Stay Table: (GIDs, location, time_in, time_out: measures):
      • Records information on items that stay together at a given location
      • If using record transitions: difficult to answer queries, lots of intersections needed
    • Map Table: (GID, <GID1,..,GIDn>)
      • Links together stages that belong to the same path. Provides additional: compression and query processing efficiency
      • High level GID points to lower level GIDs
      • If saving complete EPC Lists: high costs of IO to retrieve long lists, costly query processing
    • Information Table: (EPC list, attribute 1,...,attribute n)
      • Records path-independent attributes of the items, e.g., color, manufacturer, price..
  • 16. EPC Overview
    • Electronic product code
      • Standard naming scheme, proposed by Auto-Id Center.
      • An EPC uniquely identifies an item.
      • Format: <Header, Manager_No., Object Class, Serial No.>
        • Header: Identifies the length, type, structure, version and generation of EPC.
        • Manager Number: Identifies an organizational entity.
        • Object Class: Identifies a “class”, or type of thing.
        • Serial Number: Specific instance of the Object Class being tagged.
      • We will refer to
        • <Header, Manager No, Object Class>: Prefix
        • <Serial No.>: Suffix
  • 17. Use of Bitmap Datatype
    • Observation: Items move together.
      • Groups of items in the same proximity - e.g. on a shelf, on a shipment
      • Groups of items with same property - e.g. Same product
    • Use a bitmap type for modeling a collection of EPCs that can occur in item tracking applications.
      • Instead of storing a tuple per item store a tuple for all the items having same prefix.
      • New extra fields instead of epc:
        • <Len, Suffix_length, Prefix, suffix_start, Suffix_end, bitmap>
  • 18. Example: Product Inventory
    • With EPC Collections
    • With epc_bitmaps
    … p2 p1 Prod_id … t2 t1 Time … s1 s1 Store_id … epc21, epc22, epc23, … epc11, epc12, epc13, … Item_collection … p2 p1 Prod_id … t2 t1 Time … s1 s1 Store_id … bmap2 bmap1 Item_bmap
  • 19. Use of Bitmap Datatype
    • Header EPC_Manager Object_Class Serial_Number
    • 2-bits 21-bits 17-bits 24-bits
    • 0x 4AA890001F 62C160
    • …………………………
    • 0x 4AA890001F A0B38E
    101001…00010 0xA0B38E 0x62C160 0x4AA890001F 24 64 bitmap Suff_end Suff_start Prefix Suff_len Len
  • 20. Bitmap Operations
    • To use this with such datatype in SQL, we need operations on such bitmaps.
    • Conversion and couting Operations: epc2Bmap, bmap2Epc and bmap2Count
    • Pairwise Logical Operations: bmapAnd, bmapOr, bmapMinus, and bmapXor
    • Maintenance Operations: bmapInsert and bmapDelete
    • Membership Testing Operation: bmapExists
    • Comparison Operation: bmapEqual
  • 21. Use of these operations in SQL
    • Items added to a given shelf between time t1 and t2.
      • SELECT bmap2Epc(bmapMinus(s2.item_bmap, s1.item_bmap)) FROM Shelf_Inventory s1, Shelf_Inventory s2 WHERE s1.shelf_id = <sid1> AND s1.shelf_id = s2.shelf_id AND s1.time = <t1> AND s2.time = <t2>;
    • Book store categorizes books in various categories.
      • Following query determines the shelves where the books with property ’Adventure’ and ’Romance’, are currently present in the store.
      • SELECT s.shelf_id FROM Shelf_Inventory s WHERE bmap2Count(bmapAnd( s.item_bmap, SELECT bmapAnd(p.Adventure, p.Romance) FROM Propery_Inventory p) ) > 0; AND s.time=<current_date>;
  • 22. Road Ahead
    • Extension to bitmap proposal:
      • Bitmap datatype is more appropriate for initial bulk-load & batch updates.
      • It performs badly for incremental updates.
      • A ‘hybrid Scheme’ for incremental Updates:
        • Maintain inventories periodic checkpoints using bitmaps.
        • For changes occurring between checkpoints, Maintain a traditional item-level table.
        • Answer queries by merging the latest checkpoint bitmap with the corresponding duration’s item-level data.
    • The epc_suffix in the collection may not be contiguous
      • The bitmap will be sparse- Lot of zeros.
      • Compress this using some encoding scheme
        • Good for initial bulk loading and batch updates
        • May reduce efficiency of bitmap operations.
  • 23. Open Problems
    • Efficient methods data mining problems
      • Trend analysis
      • Outlier detection
      • Path clustering
    • We will try exploring data mining applications to RFID data.
  • 24. RFID Data Cleaning
  • 25. Issues in Data Cleaning
    • Lack of Completeness
      • RFID readers capture only 60-70% of all tags that are in the vicinity
      • Smoothing of data is done to rectify the loss of intermediate messages
    • Temporal Nature of data or tag dynamics
      • RFID tags are in motion and that is what makes them more difficult to handle
      • But motion of a tag causes dropping of messages
    • RFID data streams are very fast and are huge in number
      • Hence filtering is important before sending them to database
  • 26. Current Strategies
    • Temporal Granule:
      • Based on the fact that tag data do not differ much over a small time period
      • Data can be clubbed on a small time frame
    • Spatial Granule:
      • Similarly, data from physically close readers are also homogeneous
  • 27. Stages of ESP
    • Point: operates over a single value in a sensor stream, filtered by a predicate in the WHERE clause
    • Smooth: granularity defined by applications to correct for missed readings temporally (over one input only); uses aggregate function over the input.
    • Merge: granularity specified by the application to correct for missed readings spatially; grouped by the specified spatial granule.
  • 28. Stages of ESP (contd.)
    • Arbitrate: deals with conflicts between different spatial granules; grouped by spatial granule first and then uses HAVING construct to determine those conflicts
    • Virtualize: used for combining data streams from different sources, could also be different devices; join construct is used to combine the different data streams and then filtered using some predicate
  • 29. Smooth stage
    • False Positives: (erroneous readings) reporting objects that are not actually present
    • False Negatives: (missed readings) not reporting objects that actually are present
    False positives and False Negatives [Jeff06]
  • 30. Tag List
    • The reader has an internal table called the Tag List .
    • An epoch is the smallest unit of interaction between the reader and the middleware.
    • Every epoch consists of certain number of Interrogation cycles
    • Interrogation Cycle is one run of the reader protocol to determine all tags
    • At every epoch the reader sends the tag list to the middleware.
    t2 1 12347890 t1 6 12341234 Timestamp Responses Tag ID
  • 31. SMURF – Per tag Cleaning
    • SMURF uses statistical methods to reduce the false negative and false positives happening in the RFID stream.
    • The goal here is two fold: one is to determine the statistical window size, and secondly, ensuring that the transition of the tags is determined.
    • To determine the window size we need to fit a probability distribution to the sample size
    • And to determine the transition of the tag out of the reader's vicinity, we define a 98% confidence interval within that probability distribution function on the sample size |S i | .
  • 32. SMURF – Per tag Cleaning (contd.)
    • Using the tag list, per-epoch sampling probability, p i,t is determined, p i,t = number of times tag was read in a epoch / interrogation cycles per epoch
    • We average this over the sample size |S i | to get the average read rate ( p i avg ) for a tag i .
    • If same probability of p i is assumed for each epoch throughout the window then each successful observation is like a Bernoulli trail.
  • 33. SMURF – Per tag Cleaning (contd.)
    • So, |S i | is the binomial random variable for a sample S i with mean = w i . p i avg and variance = w i . p i avg . (1-p i avg )
    • Now using this we can express the window size as a limit,
    • If the current window size is less than the calculated one then the window size is adjusted accordingly.
    • Similarly using the Central limit theorem for transition detection we get ||S i | - μ | > 2 σ
  • 34. Normal Sliding window….
    • Epoch based mid-point sliding window
    • Emits a reading with an epoch value corresponding to the middle of the window
  • 35. Ensuring Completeness
    • In the first window, p i avg demands a larger window
    • Thus window size is increased
  • 36. Transition Detection
    • In the first window the number of readings decreases significantly (and statistically)
    • Thus a transition is likely to have occurred; so window is halved
    [Fraklin06]
  • 37. SMURF – Multi-tag aggregate Cleaning
    • Similar to per-tag cleaning, the window for multi-tag cleaning is determined by: Here, p avg is the average per-epoch sampling probability over all observed tags.
    • To detect the transition in population count, we estimate the population count of two windows [ t – w i , t ] and [ t – w i /2 , t ]; with true populations: N w & N w’
    • Thus, for a transition to have happened, we need the difference between the two estimates to be within the limit: 2( σ w + σ w’ )
  • 38. SMURF – Multi-tag aggregate Cleaning
    • To calculate the estimate of population count, we use π -estimators; The estimated population count is given by:
    • Similarly by π -estimators, and assuming independence across different tags, the variance of the estimate is estimated as:
    • Here π i is probability of reading the tag i at least once during the whole window, given by 1 – (1 – p i avg ) w
  • 39. The Road ahead…
    • Applications in RFID do not accept any delays in the data delivery
    • Data is either present in the cache or the database; data in the database increases processing time and data in cache does not understand SQL like queries
    • Anomaly detection in object tracking is also an important part of object tracking
    • Issues like untraceability, forward security, and database desynchronization are still not completely resolved.
    • One more serious problem with RFID is counterfeiting
    • In the next stage we expect to look into some of these issues
  • 40.
    • ????
  • 41. Thank You.
  • 42. References
    • Xiaolei Li, Hector Gonzalez, Jiawei Han and Diego Klabjan. Warehousing and analyzing massive RFID data sets. ICDE, 2006.
    • Fusheng Wang and Peiya Liu. Temporal management of RFID data. VLDB , 2005.
    • Timothy Chorma, Ying Hu, Seema Sundara and Jagannathan Srinivasan. Supporting RFID-based item tracking applications in oracle DBMS using a bitmap datatype. VLDB , 2005.
  • 43. References
    • Minos Garofalakis, Shawn R. Jeffery and Michael J. Franklin. Adaptive cleaning for RFID data streams. VLDB , 2006.
    • J. Franklin, Wei Hong, Shawn R. Jeffery, Gustavo Alonso and Jennifer Widom. Declarative support for sensor data cleaning. In Pervasive , 2006.
    • Sridhar Ramachandran Sudarshan S. Chawathe, Venkat Krishnamurthy and Sanjay E. Sarma. Managing RFID data. VLDB , 2004.