Intelligent Information and Database Systems by Paweł Sitek & Marcin Pietranik & Marek Krótkiewicz & Chutimet Srinilta

Author:Paweł Sitek & Marcin Pietranik & Marek Krótkiewicz & Chutimet Srinilta
Language: eng
Format: epub
ISBN: 9789811533808
Publisher: Springer Singapore

Pull reading.

Reading tasks user definition.

Reading tasks start up definition.

Reading task prioritization possibilities.

Reading task settings possibilities.

Reading task status monitoring possibilities.

Smart reading function definition and support.

Data control possibilities:

During data reading.

During communication with neighbouring systems.

2.3 Validation

The aim of the validation is to verify gained data intergrity. In AMM we understand the data validation as their quality improvement which can be reached by removing gross errors of reading, compensation of the system reading errors, and minimalization of random errors influence during reading. Methods of similarity with exactly valid mathematical and physical models are used for this approach for AMM data validation. The aforementioned results suggest that all data in the system must be validated so that their use is relevant and usable within next processes using the measured data. This method can be called the technical validation. Currently individual occurrences are mostly validated in the isolated way. Within validation it is relevant to demand extension of validation usage with connected processes and services, which would allow hidden connections to be found or e.g. possibly to decide data validity based on different values or sets within cooperation processes.

Regarding the data life cycle and defined methodology, it is evaluated whether implemented data centre allows some form of validation. From the methodological control perspective, we can differentiate two types of validation. The first type is so called technical validation that detects whether the measured values are not influenced by a technical error coming e.g. from the electrometer malfunction or if the measured value corresponds to the format. Another type of validation is the user validation which is based on the existence of possibility to specify set of validation rules that are set for the measured data. Typical example is control of the measured values that are not zero of electric power production registers on the consumer offtake site. The last validation parameter is context validation that can work with context of other connected parameters. As a typical example we can provide the validation whether the measured value of production from the electrometer installed on a photovoltaic power station is labelled as invalid in the case when they are read at night and therefore additional information of the processed data sentence about time context is validated. Within methodology it is necessary to evaluate:Determination of possibilities to start data validation.

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