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

Inactive Publication Date: 2011-07-06
NANJING UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0009] The existing provenance tracking technology has the following four problems when applied to sensor data streams: (1) Most of the existing research on data provenance is only for scientific databases, and does not consider the rapid response of sensor data provenance tracking characterized by data stream processing. This makes it difficult to directly apply the existing origin tracking methods to sensor data management, and it is necessary to solve the problem of tracking the origin of sensor data from the height of creating a new origin tracking model; (2) the existing research on data origin is still in the In terms of qualitative analysis and description of relatively slow-changing static data sets, it cannot adapt to changing sensor data flows; (3) Reversibility is not a common attribute of data processing queries or functions. If the data items cannot be accurately determined, even if a weak inverse function is found It doesn't make much sense for the application
(4) In order to design an inverse function or inverse query, it is necessary to understand the complex process of data processing in advance, which makes the solution only for specific applications and difficult to automate
Also coding the inverse lookup or inverse function must take an enormous amount of effort, hindering the application of this technique

Method used

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  • Data origin tracking method on sensor data stream complex query results
  • Data origin tracking method on sensor data stream complex query results
  • Data origin tracking method on sensor data stream complex query results

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Experimental program
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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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Abstract

The invention discloses a data origin tracking method on sensor data stream complex query results. The method comprises the following steps of determining the size of an origin tracking query sliding window; conducting standardized description on the origin query; judging the class of the origin tracking query and designing corresponding algorithm; designing the frame of origin tracking; and implementing the whole origin tracking algorithm so as to realize tracking the data origin of the sensor data stream complex query results. The method breaks through the technical limitation that an existing sensor data management system cannot support complex query backtrack, firstly introduces a data origin tracking concept to the complex query field of sensor data stream, and provides a feasible solution for the new online tracking application.

Description

technical field [0001] The invention belongs to the reverse tracking technology of iceberg query results in sensor data warehouses, in particular to a data origin tracking method for complex query results of sensor data streams. Background technique [0002] A new generation of sensors and sensor (radio frequency identification) technology provides people with a powerful ability to perceive, understand and manage the world. Many new sensor-based applications urgently need a capability that existing data management systems do not have - retroactive events and The origin of query results, that is, the ability to trace the origin of data that supports reverse query from high-level applications to low-level data. Iceberg query returns very few query results on a large number of input data tuples, which is a typical and frequently applied type of query in sensor data warehouses. Since the iceberg query involves an aggregate function on an attribute or attribute set, and the sens...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 王永利时真旺徐佳彭甫镕
Owner NANJING UNIV OF SCI & TECH
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