Data Management within mHealth Environments

Abstract

Pervasive environments generate large quantities of data, originating from backend servers, portable devices, and wireless mobile sensors. Pervasive sensing devices that monitor properties of the environment (including human beings) can be a large data source. Unprocessed datasets may include data that is faulty and irrelevant, and data that is important and useful. If not managed correctly the large amount of data from a data-rich pervasive environment may result in information overload or delivery of incorrect information. Context-sensitive quality data management aims to gather, verify, process, and manage the multiple data sources in a pervasive environment in order to deliver high quality, relevant information to the end-user. Managing the quality of data from different sources, correlating related data, and making use of context, are all essential in providing end users with accurate and meaningful data in real time. This requirement is especially true for critical applications such as in a medical environment. This article presents the Data Management System (DMS) architecture. It is designed to deliver quality data service to its users. The DMS architecture employs an agent-based middleware to intelligently and effectively manage all pervasive data sources, and to make use of context to deliver relevant information to the end-user. Two of the DMS components are presented: (1) data validation and (2) data consistency. The DMS components have been rigorously evaluated using various medical-based test cases. This article demonstrates a careful, precise approach to data based on the quality of the data and the context of its use. It emphasises the DMS architecture and the role of software agents in providing quality data management.