Temporal management of RFID data

TLDR

RFID technology enhances business process efficiency through automatic identification and data capture, but its time‑dependent, large‑volume, and semantically rich data pose significant challenges for existing data management systems. The study aims to develop RFID data management systems that support large‑scale temporal data by providing an explicit temporal model and automated transformation of raw reader observations. The authors implement an integrated RFID middleware that employs an expressive temporal data model and automated rule‑based transformation of reader observations. The resulting system supports semantic filtering, rule‑based data transformation, and robust tracking and monitoring queries, and is adaptable to various RFID applications.

Abstract

RFID technology can be used to significantly improve the efficiency of business processes by providing the capability of automatic identification and data capture. This technology poses many new challenges on current data management systems. RFID data are time-dependent, dynamically changing, in large volumes, and carry implicit semantics. RFID data management systems need to effectively support such large scale temporal data created by RFID applications. These systems need to have an explicit temporal data model for RFID data to support tracking and monitoring queries. In addition, they need to have an automatic method to transform the primitive observations from RFID readers into derived data used in RFID-enabled applications. In this paper, we present an integrated RFID data management system -- Siemens RFID Middleware -- based on an expressive temporal data model for RFID data. Our system enables semantic RFID data filtering and automatic data transformation based on declarative rules, provides powerful query support of RFID object tracking and monitoring, and can be adapted to different RFID-enabled applications.