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Data Lakes: A Panacea for Big Data Problems, Cyber Safety Issues, and Enterprise Security
Published in Mohiuddin Ahmed, Nour Moustafa, Abu Barkat, Paul Haskell-Dowland, Next-Generation Enterprise Security and Governance, 2022
A. N. M. Bazlur Rashid, Mohiuddin Ahmed, Abu Barkat Ullah
Data lakes usually do not accompany descriptive and complete data catalogs, common to data warehouse or database management systems. A data lake becomes only a data swamp without any metadata information. On-demand data discovery, data integration, and raw data cleaning in a data lake require data catalogs. Metadata management systems provide efficient metadata storage and querying over metadata to extract metadata from data sources and enrich data with meaningful metadata. Google Dataset Search (GOODS) is an example of a metadata management system to extract and collect metadata for datasets generated and used by Google. Metadata consists of dataset-specific information, such as schema, timestamps, and owners, for identifying the relationship (e.g., similarity or provenance) between multiple datasets. GOODS uses metadata to make datasets accessible and searchable. Metadata in a data lake can be categorized into two types – functional metadata and structural metadata.
BI and Financial Management
Published in Bert Brijs, Business Analysis for Business Intelligence, 2016
But for business analysts, things are a lot trickier. Let us sum up the issues and see how we can deal with them: Data lineage consists of tracing the entire life cycle of each data element from its start to its final destination, the data warehouse, its change history, and its consistency in definitions through metadata management.Ensure mutual adjustment between IT and finance produce maximum reporting performance and reduce latency to the minimum.Understand the major business process flows to make sure data integrity is if not guaranteed, then it is at least monitored.
An Introduction to Business Intelligence
Published in Deepmala Singh, Anurag Singh, Amizan Omar, S.B. Goyal, Business Intelligence and Human Resource Management, 2023
The crucial feature of BI is Metadata management. Metadata management denotes activities involved in the efficient management of data and outcomes related to it. It is mainly categorized into three activities, namely technical, business, and operational. Metadata management includes policies, processes, responsibilities, and roles that ensure that data-based information is obtainable, reachable, supportable, and sharable across an organization. Metadata management mainly concentrates on indicators, organization, measures, and other aspects of data desired for business analysis. The use of BI in metadata ensures the quality, completeness, and consistency of the data in use.
Management of local multi-sensors applied to SHM and long-term infrared monitoring: Cloud2IR implementation
Published in Quantitative InfraRed Thermography Journal, 2019
Antoine Crinière, Jean Dumoulin, Laurent Mevel
In fact it is often time-consuming to think about the right metadata and the right scheme to use. Presented developments are geared towards the use of the Hdf5 and OGC O&M standards which are built to support geographical metadata scheme [26]. For now the system produces autonomously basic metadata thanks to the definitions of the used standards (Name, type, size, time, position, etc.). Future developments will have to partly focus on the metadata management, for example with the support of the dublin-core scheme [24], in order to propose, as part of the sensor interface a dynamic and autonomous management of the metadata able to help and guide users.
Understanding data governance requirements in IoT adoption for smart ports – a gap analysis
Published in Maritime Policy & Management, 2022
Jing Gao, Yuhui Sun, Rameez Rameezdeen, Christopher Chow
It feels that IoT data modelling and data standards have not attracted sufficient attention yet. Many current IoT initiatives are still considered as the discovery phase for exploring how IoT devices can help business activities. The focus has yet moved to how to use IoT devices well. Unlike the well-established global consortiums such as W3C and IEEE, global IoT standards are still under-developing. This early-stage adoption can also be evident in the metadata management and data lifecycle management requirements. Adequate metadata management and data lifecycle management will lift organisations to a higher data governance maturity level.
Launching an unmanned aerial vehicle remote sensing data carrier: concept, key components and prospects
Published in International Journal of Digital Earth, 2020
Xiaohan Liao, Huanyin Yue, Ronggao Liu, Xiangyong Luo, Bin Luo, Ming Lu, Barbara Ryan, Huping Ye
The prototype system for metadata management of UAV RS data is also tested online (Figure 10). The testing covers the standardization of multiband and multimode remote sensors, adaptability transformation of multiclass UAV platforms, establishment of regional and national networking systems, development of data aggregation and large data processing platforms, resource scheduling, flight control, and product generation.