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Supporting RFID-based Item Tracking Applications in Oracle DBMS Using a Bitmap Datatype

Supporting RFID-based Item Tracking Applications in Oracle DBMS Using a Bitmap Datatype. Ying Hu, Seema Sundara, Timothy Chorma, Jagannathan Srinivasan Oracle New England Development Center One Oracle Drive, Nashua, NH 03062. Talk Outline. Background A Bitmap Datatype Applications

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Supporting RFID-based Item Tracking Applications in Oracle DBMS Using a Bitmap Datatype

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  1. Supporting RFID-based Item Tracking Applications in Oracle DBMS Using a Bitmap Datatype Ying Hu, Seema Sundara, Timothy Chorma, Jagannathan Srinivasan Oracle New England Development Center One Oracle Drive, Nashua, NH 03062

  2. Talk Outline • Background • A Bitmap Datatype • Applications • Implementation • Performance Study • Conclusions

  3. Background

  4. Radio Frequency Identification • RFID uses radio frequency to automatically detect and identify individual items, which have RFID tags associated with them. • RFID technology is increasingly being used in applications such as • Asset Tracking, • Supply Chain Management, and • Retail Management.

  5. Electronic Product Codes (EPCs) • EPC is a standard naming scheme proposed by Auto-Id Center for RFID applications. • An EPC uniquely identifies an item. It contains <Header, Manager No., Object Class, Serial No.> • The header identifies the format of the EPCtype, the next two components identify the product, whereas the last component uniquely identifies the item within the product.

  6. Item Tracking • RFID Technology enables item tracking applications • However, there is a need for efficient mechanism for managing the high volume of item tracking information.

  7. A Bitmap Type for Item Tracking • Key Observation: RFID Items can be efficiently tracked by tracking the groups to which an item belongs. For example • Groups of items in the same proximity: e.g. on a shelf, on a shipment • Groups of items with same property: e.g. items of a single product, items of a single product with same expiry date • Our Solution: Use a bitmap type for modeling a collection of EPCs that can occur in item tracking applications.

  8. A Bitmap Datatype

  9. With EPC Collections With epc_bitmaps Example: Product Inventory

  10. A new type to represent a collection of EPCs with a common prefix CREATE TYPE epc_bitmap_segment ( epc_length NUMBER, epc_suffix_length NUMBER, epc_prefix RAW, epc_suffix_start RAW, epc_suffix_end RAW, epc_suffix_bitmap RAW ); epc_bitmap type to represent a collection of EPCs CREATE TYPE epc_bitmap IS TABLE of epc_bitmap_segment; epc_bitmap Datatype

  11. epc_bitmap_segment Datatype Header EPC_Manager Object_Class Serial_Number 2-bits 21-bits 17-bits 24-bits 0x4AA890001F62C160 ………………………… 0x4AA890001FA0B38E

  12. epc_bitmap Operations • Conversion 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

  13. Applications

  14. Applications: Shelf Analytics • Determine the items added to a 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>; Shelf_Inventory

  15. Applications: Product Recall • Identify the stores that currently have recalled items SELECT Store_id FROM Product_Inventory P WHERE bmap2Count(bmapAnd(P.Item_bmap, epc2Bmap(<recall_items>))) > 0 AND P.Product_id=<recall_product_id> AND P.Time = <current_time>; Product_Inventory

  16. Implementation

  17. Implementation • Leverages Oracle’s Bitmap Index Implementation • Uses G. Antoshenkov’s “Byte Aligned Data Compression” for creation and operation of bitmaps for rowid collections. • A collection of EPCs is grouped into different epc_bitmap_segments, • When the previous epc_suffix_bitmap (maximum size of RAW = 2000) can not hold any more EPCs • When epc_prefixes are different

  18. Persistent epc_bitmap • Stored in a primary B+-tree structure primary B+-tree structure primary key columns non-key columns

  19. Query Processing & Optimization • epc_bitmap function is executed per row • Queries with predicates on bitmap columns can be speeded up • Using Function-based B-tree index: On pre-computed value of bmap2Count(bmap_col) SELECT S.shelf_id FROM Shelf_inventory S WHERE bmap2Count(S.item_bmap) > 0 ; • Using Summary Bitmap Index

  20. Summary Bitmap • Specialized index structure to speed up query to detect the presence of an epc identifier SELECT s.shelf_id, s.time FROM Shelf_Inventory s WHERE bmapExists(s.item_bmap, <epc1>); • Create a summary bitmapsbmapusing OR operation on{bmap1, …, bmapk}: bmapExists(sbmap)  i  1…k: bmapExists(bmapi)

  21. Summary Bitmap Index Structure EPC Bitmap column A Summary Bitmap Tree root node Bmp1 Bmp2 … branch nodes … leaf nodes … <Bmp1,rowid1> … <Bmpn,rowidn> Bmpn Table

  22. Summary Bitmap Index Algorithm DFS(X, epc): X: a node in the tree index; epc: a given epc; bmp(X): the epc_bitmap associated with node X; if ( bmapExists(bmp(X), epc)== TRUE) { if (X == leaf node) report bmp(X); else for each child node V of X do DFS(V, epc); } Complexity:O(1) , no candidate O(m log N), m: fan-out, N: no. of rows

  23. Performance Study

  24. Experimental Setup • Data represents EPC-64 collections, which are randomly generated with a uniform distribution • Data sampled every hour for 30 days(720 obs.), 300 days (7200 obs) and 3000 days (72000 obs). • EPC collections of size 100000, 10000 and 1000 represent typical number of items in department and/or shelf

  25. Storage Comparison

  26. Bulk Load Performance

  27. Query 1: Enumerate Removed Items • Enumerate the items removed from a shelf between 2 time intervals Collection Type Query SELECT b.epc_value FROM epc_coll a, TABLE(a.epcs) b WHERE a.time = '2004-03-04 10:00 AM' MINUS SELECT b2.epc_value FROM epc_coll a2, TABLE(a2.epcs) b2 WHERE a2.time = '2004-03-04 11:00 AM'; epc_bitmap Type Query SELECT * FROM TABLE( SELECT bmap2Epc(bmapMinus(p1.epcs,p2.epcs))  FROM epc_bmp p1, epc_bmp p2 WHERE p1.time = '2004-03-04 10:00 AM' AND p2.time = '2004-03-04 11:00 AM');

  28. Query 1: Enumerate Removed Items

  29. Query 1 with Variant Datasets

  30. Query 2: Report All Observations • Report all observations when a given EPC was present Collection Type Query SELECT a.time FROM epc_coll a WHERE EXISTS(SELECT 1 FROM TABLE(a.epcs) b WHERE b.epc_value = ‘400003000300052A’); epc_bitmap Type Query SELECT time FROM epc_bmp WHERE bmapExists(epcs, ‘400003000300052A’);

  31. Query 2: Report All Observations

  32. Performance Discussion • Storage savings of 2 to 8 times due to compressed bitmaps • Bulk load performance faster due to smaller storage needs • For collections of size > 1000, query performance gains of 10% to 1200% • For smaller collection sizes, performance is same as native collections

  33. Conclusions • epc_bitmap can model transient and persistent EPC collections • epc_bitmap can be used to support RFID-based item tracking applications • An efficient primary B+-tree based storage and access mechanism • Performance experiments validate the feasibility and benefits of the epc_bitmap • Support can be extended to Row Identifier (rowid) and Life Science Identifier (LSID) collections

  34. Q & Q U E S T I O N S A N S W E R S A

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