Summary from: http://hdl.handle.net/10589/101044
Please, cite as part of:[1] de Rigo, D., 2015. Study of a collaborative repository of semantic metadata and models for regional environmental datasets' multivariate transformations. Ph.D. thesis, Politecnico di Milano, Milano, Italy.
This is a simplified summary of some core concepts from [1]. Please, refer to it for any detail.
Logics vs. practice of computational science: the case of transdisciplinary research. If you are a computational scientist, this may sound only too true: a common experience is to codify even short algorithms – if no out-of-the-box solutions are available – with remarkably longer implementations, whose intricacy may imply unmaintainability, if not even hidden fallacy.
A new modelling paradigm suggests how in some "desperately" complex cases this may be mitigated by making code modular and (partly) aware of the semantics of the actual problem. Computational-science algorithms not rarely deal with large amounts of data with a clear (despite at times nontrivial) semantic structure. If so, data may be organised in multiple groups with similar semantics. Examples are matrices, time series, tuples, graphs or more generic multi-dimensional arrays. Geospatial problems often associate geographic information to particular arrays – e.g. spatial grids of data represented as georeferenced matrices. Domain-specific frameworks may offer a convenient option for standard problems within a given discipline, while object oriented approaches may easily support structured information to be more broadly transferred along with default behaviours/assumptions.However, this communication is more difficult to achieve for "wide research": non-monolithic models held together using several programming languages and tools, with multiple teams involved and possibly no single expert able to cope with the overall integration complexity. The scale of this challenge may easily explode as the required transdisciplinary interactions increase between experienced and diverse research teams. This is a reality sometime underestimated even from a research management perspective, with unhappy consequences where a fragile integration strategy proves unable to scale and fulfil the accelerating needs of a healthy transdisciplinary endeavour. Ultimately, failing to realise on time the importance of scalable integration in transdisciplinary research – and its design impact – mig...
Please, cite as part of:[1] de Rigo, D., 2015. Study of a collaborative repository of semantic metadata and models for regional environmental datasets' multivariate transformations. Ph.D. thesis, Politecnico di Milano, Milano, Italy.
This is a simplified summary of some core concepts from [1]. Please, refer to it for any detail.
Logics vs. practice of computational science: the case of transdisciplinary research. If you are a computational scientist, this may sound only too true: a common experience is to codify even short algorithms – if no out-of-the-box solutions are available – with remarkably longer implementations, whose intricacy may imply unmaintainability, if not even hidden fallacy.
A new modelling paradigm suggests how in some "desperately" complex cases this may be mitigated by making code modular and (partly) aware of the semantics of the actual problem. Computational-science algorithms not rarely deal with large amounts of data with a clear (despite at times nontrivial) semantic structure. If so, data may be organised in multiple groups with similar semantics. Examples are matrices, time series, tuples, graphs or more generic multi-dimensional arrays. Geospatial problems often associate geographic information to particular arrays – e.g. spatial grids of data represented as georeferenced matrices. Domain-specific frameworks may offer a convenient option for standard problems within a given discipline, while object oriented approaches may easily support structured information to be more broadly transferred along with default behaviours/assumptions.However, this communication is more difficult to achieve for "wide research": non-monolithic models held together using several programming languages and tools, with multiple teams involved and possibly no single expert able to cope with the overall integration complexity. The scale of this challenge may easily explode as the required transdisciplinary interactions increase between experienced and diverse research teams. This is a reality sometime underestimated even from a research management perspective, with unhappy consequences where a fragile integration strategy proves unable to scale and fulfil the accelerating needs of a healthy transdisciplinary endeavour. Ultimately, failing to realise on time the importance of scalable integration in transdisciplinary research – and its design impact – mig...