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Toward the use of big data in smart ships
Published in Pentti Kujala, Liangliang Lu, Marine Design XIII, 2018
D.G. Belanger, M. Furth, K. Jansen, L. Reichard
Metadata is data that describes other data. It includes schemas for structured data, usually in relational data structures. It also includes logs of activity, and quality, integrity, timing, semantics, type (i.e. alphanumeric, audio, text, image, and video), access, security/privacy risk, and many other traits of the data. It is essential to effective management of the data asset, but also to the effective use of the data in applications. As a simple example, it is often the case that large, high velocity datasets must be integrated with much smaller datasets most of which are updated by transaction and only occasionally. These activities must be effectively synchronized, hence update timing is essential to correct use of the data. A specific example is the analysis, and setting off alerts/ alarms when necessary, on the health or security of a network, including that of a ships network. Today many of these networks use Internet Protocol (IP), hence health analysis will include very large flows of IP Header or Session Data. In addition, however, there are databases which evolve on completely different timescales. For example, the owners of communicating IP Addresses (e. g. for White or Black Lists), or expected Ports for various communication types (e.g. Port 80 for http web traffic), or if using Network Address Translation (NAT) to increase IP Addresses for security or network management.
Big Data in healthcare in China: Applications, obstacles, and suggestions
Published in Matthias Dehmer, Frank Emmert-Streib, Frontiers in Data Science, 2017
Metadata are sensitive. Metadata are data of characterization, which are applied to describe the contents, features, and properties. Through managing and structuring, metadata help people better understand, realize, and describe the contents, features, properties, and development of data. Metadata are an important vehicle for data management and control widely existing in daily life, for example, the file metadata that include author, company, time, length, number and size, and so on. In the age of Big Data, due to the even more sophisticated dataset, the related metadata become more and more complicated. Metadata maybe meaningless on its own, but the collection of a large number of metadata combined with the analytical techniques of Big Data may trace personal sensitive information and behavior fully.
Data Integrative Studies in Hydroinformatics
Published in Praveen Kumar, Jay Alameda, Peter Bajcsy, Mike Folk, Momcilo Markus, Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and Modeling, 2005
Information about the data, often referred to as the metadata, is very important for making the data useful. It contains information regarding as to where the data was collected, what instruments were used for their characteristics, what errors are associated with the measurements, what variables were measured, etc. Metadata may also be associated with results of numerical simulations in order to describe the problem, domain, variables, time frame, etc. When looking for a certain set of data, we often search the metadata to identify the relevant data. Searching a metadata catalog is an important component of the digital library technology. In these regard, metadata is an extremely important component of the data management process, particularly as the number of records stored in a database grows significantly large. However, for metadata to be effective certain standards and protocols must be followed during its creation. Recently, there has been significant effort in establishing such a standard. Chapter 4 describes these issues in detail.
Predicting water quality in Canada: mind the (data) gap
Published in Canadian Water Resources Journal / Revue canadienne des ressources hydriques, 2022
C.B. Miller, A. Cleaver, P. Huntsman, A. Asemaninejad, K. Rutledge, R. Bouwhuis, C.J. Rickwood
A good first step in coordinating water quality monitoring data would be the establishment of national metadata. Metadata is data about data, it provides basic information that is essential for users to understand and interpret the collected data (Plana et al. 2019). Without appropriate metadata, we are unable to understand basic aspects (e.g., the methods used for collection and analysis and the spatial and temporal description) of the data. It should provide essential information, not only about the data, but also how to access it. Environment and Climate Change Canada is currently compiling an inventory of Canadian freshwater data sources and placing these into four categories: water quality, quantity, demand and use, and water ecosystems. We hope that this process, alongside the findings highlighted in this paper, will help advance discussions on how to establish best practices for the collection, management and use of water quality data.
An integrated GIS, BIM and facilities infrastructure information platform designed for city management
Published in Journal of the Chinese Institute of Engineers, 2021
Yu-Shun Huang, Shen-Guan Shih, Kuo- Hsiung Yen
The use of metadata is important to ensure that the platform data structure has clear data classification, clear exchange standards, file formats, data attributes and properties, graphic units, images, etc. To integrate text, graphics, and images at the same spatial positioning, the metadata framework defines the exchange format, item, and property with CityGML, IFC, and COBie in the parameters database (see Figure 9). The main results are described in the following.
iSDS: a self-configurable software-defined storage system for enterprise
Published in Enterprise Information Systems, 2018
Wen-Shyen Eric Chen, Chun-Fang Huang, Ming-Jen Huang
One of its services is the monitoring service. It is a storage service that collects metadata of the iSDS cluster. There are two types of metadata. One is the configuration information of hardware and software. The other is performance metrics of each node. The types of performance metrics collected by iSDS include IOPS (storage I/O per second), throughput, latency, CPU load, memory usage, available storage capacity and network bandwidth.