Statistical estimators for aggregate relational algebra queries
ACM Transactions on Database Systems (TODS)
Towards estimation error guarantees for distinct values
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient Object Identification with Passive RFID Tags
Pervasive '02 Proceedings of the First International Conference on Pervasive Computing
Revealing the Retail Black Box by Interaction Sensing
ICDCSW '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
RFID Handbook: Fundamentals and Applications in Contactless Smart Cards and Identification
RFID Handbook: Fundamentals and Applications in Contactless Smart Cards and Identification
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Effective use of block-level sampling in statistics estimation
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Working Models for Uncertain Data
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Towards correcting input data errors probabilistically using integrity constraints
MobiDE '06 Proceedings of the 5th ACM international workshop on Data engineering for wireless and mobile access
Fast and reliable estimation schemes in RFID systems
Proceedings of the 12th annual international conference on Mobile computing and networking
Adaptive cleaning for RFID data streams
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
ULDBs: databases with uncertainty and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A Sampling-Based Approach to Information Recovery
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Collaborative sensing in a retail store using synchronous distributed jam signalling
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Towards materialized view selection for distributed databases
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Finding misplaced items in retail by clustering RFID data
Proceedings of the 13th International Conference on Extending Database Technology
Collective communication for dense sensing environments
Journal of Ambient Intelligence and Smart Environments
Maximum Likelihood Approach for RFID Tag Set Cardinality Estimation with Detection Errors
Wireless Personal Communications: An International Journal
| Hi-index | 0.00 |
Radio Frequency Identification (RFID) promises optimization of commodity flows in all industry segments. But due to physical constraints, RFID technology cannot detect all RFID tags from an assembly of items. This poses problems when integrating RFID data with enterprise-backend systems for tasks like inventory management or shelf replenishment. In this paper we propose the TagMark method to accomplish this integration. TagMark targets at a retailer scenario, where it estimates the number of tagged items from samples like the sales history or the tags read by smart shelves. The problem is challenging because most existing estimation methods depend on assumptions that do not hold in typical RFID applications, e.g., static item sets, simple random samples, or the availability of samples with user-defined sizes. TagMark adapts mark-recapture-methods in order to provide guarantees for the accuracy of the estimation and bounds for the sample sizes. It can be implemented as a database extension, allowing seamless integration into existing enterprise backend systems. A study with RFID-equipped goods acknowledges that our approach is effective in realistic scenarios, and database experiments with up to 1,000,000 items confirm that it can be efficiently implemented. Finally, we explore a broad range of extreme conditions that might stress TagMark, including a thief who knows the location of unread items.