Data origin tracking method on sensor data stream complex query results
A technology of query results and data origin, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of staying, meaningless, unable to adapt to sensor data streams, etc., to achieve low cost, good scalability, Data Provenance Tracking Result Set Accurate Effects
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Embodiment 1
[0211] Embodiment 1: Considering the application scenario of the whole process quality tracking in the production workshop, a series of sensor readers with detection sensors are installed near the completion position of each process on the conveyor belt to track the passing products, and the system obtains the monitoring flow and operation Stream and perform a series of operations, and finally obtain an export stream (ie audit stream) with the help of continuous query technology on sensor data. The sequence of operations is as Figure 4 shown.
[0212] The monitoring data flow contains four attributes: reader number, location number, time stamp, and a list of products detected at the same time (composed of product numbers and detection probabilities), that is, Monitor (ReaderId, LocationId, MTimeStamp, Productid-list), and at a certain time The actual content of the Monitor stream is shown in Table 1.
[0213] Table 1 Sensor monitoring data stream Monitor instance
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Embodiment 2
[0231] Embodiment 2: To verify what kind of performance improvement can be obtained by the dynamic sliding window compared with the origin tracking of the static fixed sliding window. We choose the existing Cui inverse query method [Y.W.Cui, J.Widom, J.L.Wiener. Tracing the Lineage of View Data in a Warehousing Environment. ACMTransactions on Database Systems, 2000, 25(2): 179-227] and Zhang slice The tracking method [M. Zhang, X. Zhang, X. Zhang, and S. Prabhakar. Tracing lineage beyond relational operators. Technical report, Purdue University, 2007] was compared with the method described in this patent. The experimental data comes from the TPC-D standard test set. The input tables are LineItem, Order, and PartSupp. The content of the table is generated by the standard dbgen program. The TPC-D scaling factor of 1.0 means that the size of the entire data warehouse is 1GB.
[0232] Experiment 2 compares the cost performance of the specified tuple origin obtained by these m...
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