Engine fleet reliability data management optimization

Chapter 19 and Chapter 20 are on the emerging approaches to data management for a new geospatial science research. This chapter gives the essences and the methodologies of data matter perspective, and it has two sections; Section A highlights the subject theme; the essences of geospatial science data matter; while Section B expands that into the geospatial science data methodologies. Chapter 20 is the about the data management optimization perspective. It has only one section; Section C, that develops further the essence and methodologies of geospatial data cultivated in these two previous sections. The whole analytical discussion is in the emerging fields and how they had optimized and totally changed the geospatial science data management panorama.

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Data Management Optimization in a Real-Time Big Data Analysis System for Intensive Care

Communications in Computer and Information Science - Cloud Computing, Big Data & Emerging Topics ◽

10.1007/978-3-030-61218-4_7 ◽

2020 ◽

pp. 93-107

Author(s):

Rodrigo Cañibano ◽

Claudia Rozas ◽

Cristina Orlandi ◽

Javier Balladini

Keyword(s):

Big Data ◽

Intensive Care ◽

Data Analysis ◽

Data Management ◽

Real Time ◽

Big Data Analysis ◽

Management Optimization ◽

Analysis System ◽

Data Analysis System

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RDBMS Based Hadoop Metadata and Log Data Management Optimization

2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP) ◽

10.1109/icsidp47821.2019.9173057 ◽

2019 ◽

Author(s):

Haiying Che ◽

Octave Iradukunda ◽

Khalilov Shahin

Keyword(s):

Data Management ◽

Log Data ◽

Management Optimization

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Emerging Approaches to Data Management for a New Geospatial Science Research

Advances in Geospatial Technologies - Handbook of Research on Geospatial Science and Technologies ◽

10.4018/978-1-5225-3440-2.ch020 ◽

2018 ◽

pp. 337-353

Author(s):

Joyce Gosata Maphanyane ◽

Read Brown Mthanganyika Mapeo ◽

Sethunya Simela

Keyword(s):

Data Management ◽

Communication Networks ◽

Data Science ◽

Technological Development ◽

Science Research ◽

Hyperspectral Data ◽

Software Algorithms ◽

Quantum Leap ◽

Management Optimization ◽

Increase In Accuracy

This chapter is a continuation of themes discussed in Chapter 19. It draws attention to the newly emerging fields and the growth they injected in geospatial science research procedures. It analytically examines the new fields' role in data management optimization perspectives that emanate from the history of their developments and applications. A robust and rigorous data science methodology framework necessary for the success of a geospatial science research has been submitted, its components and challenges thereof are scrutinized. The overall analyses indicate increased growth in the collaborative efforts and a quantum leap in geospatial science technological development. The superior ICT tools: the Internet, communication networks; high performance computer infrastructure and sophisticated software algorithms: Big Data and cloud computing; increase in accuracy for geo-referencing tools: the GPS and other systems like CORS; and lastly increase in availability of geospatial data including the satellite images and hyperspectral data.

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