Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Table 6

Mapping between methods/models/techniques and other data-intensive domains.

		Earth science	Geometry	Image analysis	Internet of things	Manufacturing	Medicine	Nuclear domain	Scientific research	Social sciences	Spatial modelling
General software application							[115]
Artificial intelligence	Deep learning			[75, 76]
	Fuzzy logic
	General techniques		[77]
	Gene Programming	[78]				[73]
	Neural networks	[79]	[80]	[81]					[82]	[83]
	Reasoning techniques
	Planning	[84]	[85]				[74]
Computational architecture	Infrastructure								[90–93]		[94]
	Scheduling techniques
	Workflow								[95]
Computation model	Event calculus
	MPI
	Parallel programming	[102]
	Query distribution
	Stream processing										[103]
Computational science	Euler models
	Scientific computation								[107]
	Statistical methods
Graph theory	Automata
	Graph/complex network analysis		[118]							[119]
Engineering methods	Finite elements
	Simulation
Machine learning	Bayesian machine learning						[128]
	Data mining
	Information fusion						[129]
	Pattern recognition
	Predictive models		[130]
	Support vector machines
	Regression model
Mathematics and applied mathematics	Gradient descent search			[144]
	Integer linear programming
	Linear algebra/solvers								[141]
	Linear programming				[142]
	Matrix calculation							[143]
	Nonlinear programming
	Numerical methods
	Symbolic execution
Programming techniques	Constraint programming
	Cube computation
	Dynamic programming
	Domain specific languages	[149]
	Stochastic programming