Relationship analysis : improving the systems analysis process

A significant aspect of systems analysis involves discovering and representing entities and their inter-relationships. Guidelines exist to identify entities but do not provide a rigorous and comprehensive process to explicitly capture the relationship structure of the problem domain. Whereas, other analysis techniques lightly address the relationship discovery process, Relationship Analysis is the only systematic, domain-independent analysis technique focusing exclusively on a domain\u27s relationship structure. The quality of design artifacts, such as class diagrams, and development time necessary to generate these artifacts can be improved by first representing the complete relationship structure of the problem domain. The Relationship Analysis Model is the first theory-based taxonomy to classify relationships. A rigorous evaluation was conducted, including a formal experiment comparing novice and experienced analysts with and without Relationship Analysis. It was shown that the Relationship Analysis Process based on the model does provide a fuller and richer systems analysis, resulting in improved quality of and reduced time in generating class diagrams. It also was shown that Relationship Analysis enables analysts of varying experience levels to achieve a similar level of quality of class diagrams. Relationship Analysis significantly enhances the systems analyst\u27s effectiveness, especially in the area of relationship discovery and documentation resulting in improved analysis and design artifacts

Role-based Adaptation of Business Reference Models to Application Models: An Enterprise Modeling Methodology for Software Construction

Large software systems are in need of a construction plan to determine and define every concept and element used in order to not end up in complex, unusable, and cost-intensive systems. Different modeling languages, like UML, support the development of these construction plans and visualize them for the system’s stakeholders. Reference models are a specific kind of construction plan, used as templates for information systems and already capture business domain knowledge for reuse and tailoring. By adaptation, reference models are tailored to enterprise-specific application models, which can be used for software construction and maintenance. However, current adaptation methods suffer from the limitations of pure object-oriented development (e.g., identity issues, large inheritance trees, and inflexibility). In this thesis, the usage of roles as the sole adaptation mechanism is proposed to solve these challenges. With the help of conceptual roles, it is possible to create rich model variations and adaptations from existing (industry standard) reference models, and it is simpler to react to model evolution and changing business logic. Adaptations can be specified with more precision by maintaining or even increasing the model’s expressiveness. As a consequence, the role-enriched final application model can be used to describe software systems in more detail, with different perspectives, and, if available, can be implemented with a role supporting programming language. However, even without this step, the application model itself will provide valuable insights into the overall construction plan of a software system by the combination of structure and behavior and a clear separation of relatively stable domain knowledge from its use case specific adaptation

Enabling modeling framework with surrogate modeling capabilities and complex networks

Conceptual and physically based environmental simulation models as products of research environments efforts became complex software over time in order to allow describing the behaviour of natural phenomena more accurately. Results from these models are considered accurate but often require to operate an entire system of modeling resources with dedicated knowledge, an extensive set up, and sometimes significant computational time. Model complexity limits wide model adaptation among consultants because of lower available technical resources and capabilities. However, models should be ubiquitous to use in both research and consulting environments. This dissertation aims to address and alleviate two aspects of research model complexity: 1) for researchers, the model design complexity with respect to its internal software structure and 2) for consultants, the model application complexity with respect to data and parameter setup, runtime requirements, and proper model infrastructure setup. The first contribution provides modeling design and implementation support by managing interacting modeling solutions as “Directed Acyclic Graph”, while the second one helps to create surrogate models of complex physical models as a streamlined process. Both contributions are implemented within the OMS/CSIP modeling framework and infrastructure and were applied in various studies. First, a machine learning (ML)-based surrogate model approach is presented to respond to field application requirements to get quick but “accurate enough” model results with limited input and limited a-priori knowledge of the internal physical processes involved. The surrogate model aims to capture the behaviour of a physical model as an ensemble system of artificial neural networks (ANN). Here, the NeuroEvolution of Augmenting Topology (NEAT) technique has been leveraged because of its integration of a genetic approach to build and evolve its ANNs during supervised training. Throughout this phase, the thorough design of the services facilitate seamless monitoring of structural mutations of the artificial neural network and its performances with respect to behavioural emulation of the original model response. This results in a streamlined surrogate model generation. Furthermore, the stochasticity inherent to the evolutionary genetic algorithm combined with a specially designed cross-validation approach allows for straightforward use of the ensemble application. Several, slightly different artificial neural networks are concurrently trained. The ensemble system is built upon the selection of the utmost performant surrogate models and is used collectively to provide uncertainty quantified results when applied against new data. Secondly, a Directed Acyclic Graph (DAG) modeling structure NET3 was developed. NET3 provides appropriate data structures to represent modeling states interactions as relationships based on network topologies. The inherent structure of the DAG commands the execution of modeling tasks. NET3 implicitly manages the parallel computation depending on the network topology. A node of a NET3 modeling structure encapsulates any sort of modeling solution such as a system of ordinary differential equations, a set of statistical rules, or a system of partial differential equations. Each link connects these modeling solutions by handling their data flow. As a result, NET3 simplifies 1) the translation of physical mathematical concepts into model components, and 2) the management of complex interactions of modeling solutions. NET3 also pushes forward the idea of separating concerns between software architecture and scientific model codebase. It manages aspects that relate to the architectural design of the graph modeling structure and lets research scientist focus on their model’s domain. NET3 improves encapsulation and reusability of scientific/mathematical concepts. It avoids code duplication by allowing the same modeling solution to be adopted in different nodes and finely adapted to specific requirements. In summary, NET3 enables a new level of modeling flexibility by allowing to quickly change model representations to explore new modeling solutions. The two presented contributions were integrated into the Object Modeling System/Cloud Services Integrated Platform (OMS/CSIP) environmental modeling framework (EMF). EMFs are standard practice in environmental modeling because they represent a software solution of separating the burden of software architectural design management from scientific research. Here, OMS/CSIP has been identified “advanced” in terms of EMFs design. It offers high flexibility, low language invasiveness, fine and thorough architectural design, and innovative cloud computing deployment infrastructure. These aspects make OMS/CSIP infrastructure the suitable platform to host NEAT based surrogate modeling and NET3 extensions. Framework-enabled NEAT based Surrogate modeling (FeNS) results from the full integration of NEAT based surrogate modeling approach with OMS/CSIP platform. Here, the surrogate model approach was developed as CSIP services to help transitioning from research models to “field models” by enabling the modeling framework to interact with CSIP services, ML libraries, and a NoSQL database to emerge model surrogates for a(ny) modelling solution. OMS/CSIP was extended to harvest data from each model run and automatically derive the surrogate model at the modeling framework level. NET3 extends OMS modeling simulations to run as a graph network of interconnected modeling solutions. Furthermore, it enhances available OMS calibration algorithms to become multi-site calibration procedures. OMS already provided implicit parallel computation of independent components in a modeling solution. NET3 now adds a further layer of implicit parallelism by concurrently running independent modeling solutions. Two studies were carried out to develop and test FeSN while three applications supported the development and testing of NET3. Surrogate models of the Revised Universal Soil Loss Equation, Version 2 (R2) were generated to scale up from simple test cases with a constrained input space to more generic applications including a larger variety of input parameters. The main goal of the surrogate model was to streamline and simplify access to the R2 model behaviour. We performed sensitivity analysis of R2 to limit the input space to only relevant parameters (e.g. soil properties, climate parameter, field geometries, crop rotation description). The main study area was the State of Iowa starting from a single county (Clay county) ending up to four counties (Buena Vista, Cherokee, Clay, and Wright). Clustering methodologies were applied to improve surrogate model accuracy and to accelerate the training process by reducing the dataset size. The overall “goodness-of-fit” against the testing dataset estimated on the median of the uncertainty quantified result of the surrogate models ensemble was always above 0.95 Nash-Sutcliffe (NS), root mean squared error (RMSE) between 0.13 and 0.36, and bias between -0.07 and 0.02. In many cases, accuracy of the surrogate model with respect to testing dataset was above 0.98 NS. Surrogate models of the AgroEcoSystem (AgES) were generated to apply and test FeNS methodology to a semi-distributed hydrologic model. The main goal of the surrogate model was to streamline and simplify access to the AgES model behaviour. Only relevant lumped parameters on watershed centroid were used to train the surrogate models and limit the input space to only relevant parameters (e.g. precipitation, groundwater level, LAI, and potential evapotranspiration). The main study area was the South Fork Iowa River (SFIR) watershed in the State of Iowa across Wright, Franklin, Hamilton, and Hardin counties. The overall “goodness-of-fit” against the testing dataset estimated on the median of the uncertainty quantified result of the surrogate models ensemble was above 0.97 Nash-Sutcliffe (NS), root mean squared error (RMSE) of 2.24, and bias of -0.0794. With respect to NET3, the first application is the real-time modeling of flood forecasting through GEOframe system for the Civil Protection of Regione Basilicata implemented by PhD Bancheri. To scale the computation and finely tune calibration parameters, the Basilicata river basins were split into subcatchments where each was represented by a different NET3 node. The second application was part of Mr. Dalla Torre’s master thesis where the computational core of the rainfall-runoff model of Storm Water Management Model (SWMM by EPA) was componentized. NET3 now allows for reimplementing a concise and lightweight SWMM modeling core and highly parallel model runs. Software architectural design of rainfall-runoff, routing and sewer pipe design components targeted separation of concerns, single responsibility, and encapsulation principles. It resulted in clean and minimized code base. NET3 manages component connections and scalable computation by hosting rainfall-runoff modeling solution into separated nodes from routing and sewer pipe design modeling solution. It also enables each node of the modeling structure to 1) access a shared data structure to fetch input data from and push results to (SWMMobject), and 2) internally analyze the upstream subtree in order to adjust sewer pipe design parameters. The third test case is the application of a “system of systems” of urban models where each node of the graph modeling structure encapsulates a single responsibility system of models. Because of the stochasticity involved in each system of models, the entire graph modeling solution was required to run several times and generate independent realizations. Hence, NET3 was enabled to run a “graph of graphs” modeling structure

Smart rogaining for computer science orientation

In this paper, we address the problem of designing new formats of computer science orientation activities to be offered during high school students internships in Computer Science Bachelor degrees. In order to cover a wide range of computer science topics as well to deal with soft skills and gender gap issues, we propose a teamwork format, called smart rogaining, that offer engaging introductory activities to prospective students in a series of checkpoints dislocated along the different stages of a rogaine. The format is supported by a smart mobile and web application. Our proposal is aimed at stimulating the interest of participants in different areas of computer science and at improving digital and soft skills of participants and, as a side effect, of staff members (instructors and university students). In the paper, we introduce the proposed format and discuss our experience in the editions organized at the University of Genoa before the COVID-19 pandemic (2019 and 2020 waves)

Smart rogaining for computer science orientation

In this paper, we address the problem of designing new formats of computer science orientation activities to be offered during high school students internships in Computer Science Bachelor degrees. In order to cover a wide range of computer science topics as well to deal with soft skills and gender gap issues, we propose a teamwork format, called smart rogaining, that offer engaging introductory activities to prospective students in a series of checkpoints dislocated along the different stages of a rogaine. The format is supported by a smart mobile and web application. Our proposal is aimed at stimulating the interest of participants in different areas of computer science and at improving digital and soft skills of participants and, as a side effect, of staff members (instructors and university students). In the paper, we introduce the proposed format and discuss our experience in the editions organized at the University of Genoa before the COVID-19 pandemic (2019 and 2020 waves)

A Realistic Data Cleansing and Preparation Project

Although data cleansing and preparation are significant tasks in many real-world data projects, they are rarely found in project assignments in IS database courses. This paper describes a pilot study of a relatively open-ended project assignment in a graduate database course. The project required the students to cleanse and prepare five datasets on educational statistics from United Nations Data before storing them in relations that they designed. To gauge the level of students’ prior knowledge on data preparation, the instructor deliberately provided no prior lecture on the topic. A follow-up assignment was a PHP/MySQL Web database application to display educational statistics for a user-specified country. Submitted works and post assignment surveys were studied and analyzed. The result indicated that both assignments were well received and generally beneficial. Although our students appeared not to be well trained in data preparation in their undergraduate studies, they were able to learn quickly enough to produce acceptable products. This approach also appeared to encourage more creativity and better diversity in students’ database designs. Our experience suggested that while it was not difficult to identify interesting realworld datasets of appropriate complexity, the instructors will need to put in extra effort on project evaluation. We believe that this kind of assignment can be adapted in many ways to satisfy different educational objectives and it fits well in a wellrounded IS curriculum. Thus, the goal of the paper is to foster interests in real-world data cleansing projects in database courses with a well-examined case study