RFID Handbook: Fundamentals and Applications in Contactless Smart Cards and Identification
RFID Handbook: Fundamentals and Applications in Contactless Smart Cards and Identification
Warehousing and Analyzing Massive RFID Data Sets
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Towards Traceability across Sovereign, Distributed RFID Databases
IDEAS '06 Proceedings of the 10th International Database Engineering and Applications Symposium
The end of an architectural era: (it's time for a complete rewrite)
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient storage scheme and query processing for supply chain management using RFID
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A common database approach for OLTP and OLAP using an in-memory column database
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
The RDF-3X engine for scalable management of RDF data
The VLDB Journal — The International Journal on Very Large Data Bases
| Hi-index | 0.00 |
Accurate tracking and tracing of moving objects is an emerging trend in vertical industries like retail, logistics, and manufacturing. In order to monitor objects in business processes, more and more companies are deploying upcoming technologies like Radio Frequency Identification (RFID). Therefore, modern databases have to be able to cope with the challenges originating from the specifics of traceability data: efficient incremental update as well as efficient transactional and analytic ad-hoc querying and efficient storage of the data. Another requirement of business intelligence applications is to provide "real world awareness"[7] by using the latest information in the descision-making process. We therefore present an approach for efficient storing and managing of traceability data (on the example of RFID data), where the OLAP and OLTP components reside in one database and which meets the defined challenges. We discuss and analyze the experimental results and lessons learned and take them as a basis for our future research direction.