Data source quality evaluation method in sensing cloud environment

Abstract

The embodiment of the invention discloses a data source quality evaluation method in a sensing cloud environment. The method comprises the following steps: acquiring current and historical monitoringdata of a sensing cloud storage data source, the sensing cloud being a combination of cloud computing and a wireless sensor network and used for collecting and processing monitoring data from a plurality of sensor nodes or sensor sub-networks; integrating the monitoring data of the data source based on the spatial correlation and the time correlation, and determining a data true value; generatingan initial quality evaluation vector of a data source based on the data true value, and adjusting the initial quality evaluation vector of the data source according to a quality rule; and calculatinga final quality evaluation result of the data source according to the adjusted initial quality evaluation vector of the data source. By adopting the method and the device, the data source quality canbe described from multiple angles, and the description of the data source quality is more comprehensive.

Description

technical field [0001] The invention relates to the field of quality assessment, in particular to a data source quality assessment method in a sensor cloud environment. Background technique [0002] At present, some data source quality assessment methods have been proposed, most of which are based on rules, such as conditional functional dependency, conditional inclusion dependency, currency constraint, matching rule ), etc., to assess the quality of data in the database and monitor poor quality data. Data source quality can be further assessed based on the proportion of data sources that produce poor quality data. Data quality rules generally have the form of A=a→B=b, and its semantics is "if the value of attribute set A is a, then the value of attribute set B must be b". By screening the data in the database that satisfies the antecedent A=a of the rule and checking whether the consequent B=b of the rule is satisfied, it can be judged whether there is an error in the dat...

AI Technical Summary

Problems solved by technology

By screening the data in the database that satisfies the antecedent A=a of the rule and checking whether the consequent B=b of the rule is satisfied, it can be judged whether there is an error in the data, and the data that does not satisfy the rule is considered as inferior data (or wrong data), The data quality of some data sources may be affected by their own or external factors.

If these negatively affected low-quality data sources cannot be discovered in time, it will further affect the quality of services based on this data source

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