DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data

Abstract

The Internet-of-Things (IoT) introduces several technical and managerial challenges when it comes to the use of data generated and exchanged by and between various Smart, Connected Products (SCPs) that are part of an IoT system (i.e., physical, intelligent devices with sensors and actuators). Added to the volume and the heterogeneous exchange and consumption of data, it is paramount to assure that data quality levels are maintained in every step of the data chain/lifecycle. Otherwise, the system may fail to meet its expected function. While Data Quality (DQ) is a mature field, existing solutions are highly heterogeneous. Therefore, we propose that companies, developers and vendors should align their data quality management mechanisms and artefacts with well-known best practices and standards, as for example, those provided by ISO 8000-61. This standard enables a process-approach to data quality management, overcoming the difficulties of isolated data quality activities. This paper introduces DAQUA-MASS, a methodology based on ISO 8000-61 for data quality management in sensor networks. The methodology consists of four steps according to the Plan-Do-Check-Act cycle by Deming.

Data Quality Management in the Internet of Things

“…Kim et al [ 33 ] divide the data generated and used in the IoT into six categories, including sensor data (sensor-generated data); observed metadata (describe sensor data behavior); device metadata (describe the characteristics of the device or sensor); business data (for business purposes); external data (provide additional information for product capabilities, such as weather) and technical metadata (data standards and physical data storage structures). Perez-Castillo et al consider the dependence on various data sources and classify data involved in the IoT into four categories [ 34 ]: sensor data, which is generated by sensors and digitized into machine-readable data (For example, the reading of temperature sensors); device data: metadata of sensor observations and IoT devices (for example, the timestamp of the observation and the device manufacturer); general data: IoT-device-generated or device-related data (for example, sensor observations stored in a database); IoT data: in an IoT system, all data other than the raw data generated by sensors are collectively referred to as IoT data, which is a collection of general data and device data. Many studies have been published on sensor DQ [ 12 , 20 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ] and streaming data DQ management [ 20 , 21 , 36 , 43 ].…”

“…To solve the shortcomings of ISO/TS 8000-61 and generic DQ management methodology, Perez et al [ 34 ] proposed “an ISO 8000-61 Based Data Quality Management Methodology for Sensor Data” (Daqua-Mass) for SCP-based environments. It is built according to the PDCA cycle of continuous improvement and proposes the DAQUA-model, which is the core of the PDCA cycle.…”

“…Perez-Castillo et al [ 34 ] characterize accuracy as the level to which data have the property of correctly representing the true value of the expected properties of a concept or event in a given context. If the DHT11 humidity sensor reads 30% and the actual value is 50%, the accuracy of the humidity sensor may be low.…”

“…Perez-Castillo et al [ 34 ] considered completeness as the level to which the data have values of all intended properties and the associated entity instances in the specified use environment. The lower the completeness, the more data are lost from the device.…”

“…Perez-Castillo et al [ 34 ] argue that in a particular context of use, consistency primarily describes data whose properties are consistent with other data without contradiction. It can be a comparison of multiple data from a single device or a comparison of multiple devices that produce similar data.…”