Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Fundamentals of Database Systems
Fundamentals of Database Systems
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
RFID: A Technical Overview and Its Application to the Enterprise
IT Professional
Temporal management of RFID data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Warehousing and Analyzing Massive RFID Data Sets
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Managing RFID data in supply chains
International Journal of Internet Protocol Technology
SmartDrawer: RFID-based smart medicine drawer for assistive environments
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Leveraging RFID in hospitals: Patient life cycle and mobility perspectives
IEEE Communications Magazine
Space-time roll-up and drill-down into geo-trend stream cubes
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
TTL: a transformation, transference and loading approach for active monitoring
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
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Radio Frequency Identification (RFID) is an automatic identification (Auto-ID) Technology, which is most commonly used now days in healthcare for tracking and identifying objects. In the context of assistive environment, statistical query analysis over the history of Data generated from RFID Applications as well as real time monitoring of the patients or the elderly people (people who need assistance) are really important. But Data generated from these types of healthcare applications can be very large, if each individual object becomes RFID-Tagged. As a result, the RFID technology is also imposing a greater challenge to provide efficient query responses over these Data. In this paper, we show how to apply traditional Data Warehousing techniques to model these massive amounts of RFID Data. In short, we describe how to construct an RFID Warehouse so that important query analyses can be performed very efficiently. We also show how to process a continuous stream of RFID Data to answer real time queries using Sliding Window techniques. By the help of using synthetic Datasets, we conclude that querying over Data Warehouse is much faster than traditional Relational DBMS. We also find that the aforesaid performance improvement is expected to be much higher as the size of the Dataset increases.