Data management

About: Data management is a research topic. Over the lifetime, 31574 publications have been published within this topic receiving 424326 citations.

Challenges of data integration and interoperability in big data

Abstract: The enormous volumes of data created and maintained by industries, research institutions are on the verge of outgrowing its infrastructure. The advancements in the organization's work flow include data storage, data management, data maintenance, data integration, and data interoperability. Among these levels, data integration and data interoperability can be the two major focus areas for the organizations which tend to implement advancements in their workflow. Overall, data integration and data interoperability influence the organization's performance. The data integration and data interoperability are complex challenges for the organizations deploying big data architectures due to the heterogeneous nature of data used by them. Therefore, it requires a comprehensive approach to negotiate the challenges in integration and interoperability. This paper focuses on the challenges of data integration and data interoperability in big data.

A scheme of data management in the Internet of Things

Abstract: The Internet of Things is a large network which integrates the current devices, such as RFID devices, sensors and other equipment and services. It extends the form of interaction between people to the interaction between people and things as well as things and things, and then, establishes a new ecological environment. In the Internet of Things, the heterogeneity of devices and technologies brings some challenges, especially, in the management of heterogeneity data with different characteristics, which is generated by different devices. The SOA approach is an effective solution. In this case, the structure of the Internet of Things is analyzed, and a data management framework of the Internet of Things based on web services is designed.

Reconfigurable algorithmic networks for aircraft data management

Abstract: An aircraft data management system providing management of a variety of flight data generated by multiple data sources. The flight data may include a wide variety of operational, maintenance, and flight safety data. The data management system is compatible with a variety of computers employing a variety of operating systems. The data management system provides flight data sources, a computer, and means for transmitting the flight data from the various flight data sources to the computer. In order to provide the flight data in a useful form to different users having a variety of needs, the data management system provides a reconfigurable algorithmic network, resident on the computer, which defines a set of predetermined operations performed on a predetermined set of the flight data. The data management system also provides a means, resident on the computer, for interpreting the flight data in accordance with the reprogrammable algorithmic network.

A survey of smart water quality monitoring system

Abstract: The smart water quality monitoring, regarded as the future water quality monitoring technology, catalyzes progress in the capabilities of data collection, communication, data analysis, and early warning. In this article, we survey the literature till 2014 on the enabling technologies for the Smart Water Quality Monitoring System. We explore three major subsystems, namely the data collection subsystem, the data transmission subsystem, and the data management subsystem from the view of data acquiring, data transmission, and data analysis. Specifically, for the data collection subsystem, we explore selection of water quality parameters, existing technology of online water quality monitoring, identification of the locations of sampling stations, and determination of the sampling frequencies. For the data transmission system, we explore data transmission network architecture and data communication management. For the data management subsystem, we explore water quality analysis and prediction, water quality evaluation, and water quality data storage. We also propose possible challenges and future directions for each subsystem.

Managing RFID Data: Challenges, Opportunities and Solutions

Abstract: The advances of Radio-Frequency Identification (RFID) technology have significantly enhanced the capability of capturing data from pervasive space. It becomes a great challenge in the information era to effectively understand human behavior, mobility and activity through the perceived RFID data. Focusing on RFID data management, this article provides an overview of current challenges, emerging opportunities and recent progresses in RFID. In particular, this article has described and analyzed the research work on three aspects: algorithm, protocol and performance evaluation. We investigate the research progress in RFID with anti-collision algorithms, authentication and privacy protection protocols, localization and activity sensing, as well as performance tuning in realistic settings. We emphasize the basic principles of RFID data management to understand the state-of-the-art and to address directions of future research in RFID.