GSN '09 Proceedings of the 3rd International Conference on GeoSensor Networks
Optimization of continuous query processing for RFID sensor tag data stream
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Enhancing Business Process Automation by Integrating RFID Data and Events
OTM '09 Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on On the Move to Meaningful Internet Systems: Part I
Supporting real-time supply chain decisions based on RFID data streams
Journal of Systems and Software
Incorporating business logics into RFID-enabled applications
Information Processing and Management: an International Journal
| Hi-index | 0.00 |
RFID middleware systems collect and filter RFID streaming data gathered continuously by numerous readers to process requests from applications. These requests are called continuous queries because they are executed continuously during tag movement. To enhance the performance of the middleware, an index must be built to process these continuous queries efficiently. Several approaches to building an index on queries rather than data records, called query index, have been proposed and are widely used to evaluate continuous queries over streaming data. EPCglobal proposed an Event Cycle Specification (ECSpec) model, which is a de facto standard query interface for RFID applications. Continuous queries based on ECSpec consist of a large number of segments that represent the query conditions. The problem when using any of the existing query indexes on these continuous queries is that it takes a long time to build the index because it is necessary to insert a large number of segments into the index. To solve this problem, we propose an aggregate transformation that converts a group of segments into compressed data. We also propose an efficient query index scheme for the transformed space. We compare the performance of the proposed index with existing query indexes. Our experiments show that the proposed index outperforms the others on various datasets.