Research Findings on Empirical Evaluation of Requirements Specifications Approaches

Numerous software requirements specification (SRS) approaches have been proposed in software engineering. However, there has been little empirical evaluation of the use of these approaches in specific contexts. This paper describes the results of a mapping study, a key instrument of the evidence-based paradigm, in an effort to understand what aspects of SRS are evaluated, in which context, and by using which research method. On the basis of 46 identified and categorized primary studies, we found that understandability is the most commonly evaluated aspect of SRS, experiments are the most commonly used research method, and the academic environment is where most empirical evaluation takes place

Requirement and analysis: where is the boundary if any?

Even if it seems to be at a first glance a simple question, this is really a very interesting one. For many years, Analysis has been the generic term to refer to those initial Software Production Process steps where design is not yet taken into account. In some way, the analysis phase has been considered as the natural step where Conceptual Schemas of an Information System are conceived and represented. (Párrafo extraído del texto a modo de resumen)Facultad de Informátic

Conceptual Modeling of Proteins Based on UniProt

Clinical disease states reflect the interaction of a myriad of genetic and environ-mental contributions. In this context, a major challenge is to develop information systems and algorithms that can describe this complexity to facilitate an under-standing of the disease mechanisms as well as to guide the development and ap-plication of therapies. This work focuses on describing how a shared understand-ing of the domain can be achieved by analyzing the conceptual precision of the main concepts that should constitute the ontological commitment that is strictly required when studying an important area of research: the role that proteins play in the different functions carried out within the cell of any living systems. The contribution of this paper is to show the conceptual complexity of the UniProtKB database, and to let users face and manage that complexity by providing a sound and well-grounded conceptual background to achieve the shared understanding of the domain, a crucial aspect to allow the design of any fruitful data analytics-based strategy. A conceptual model for proteins is carefully developed taking the UniProtKB database as data source, explaining in detail the problems that have been faced together with their corresponding solutions.León Palacio, A.; Pastor López, O. (2020). Conceptual Modeling of Proteins Based on UniProt. http://hdl.handle.net/10251/14561

Conceptual Model of Proteins

The following conceptual model represents the knowledge associated to the protein domain, including protein-protein interactions, pathways, functionality, post translational modifications, and association with disease.León Palacio, A.; Pastor López, O. (2020). Conceptual Model of Proteins. http://hdl.handle.net/10251/14788

Towards a Shared, Conceptual Model-Based Understanding of Proteins and Their Interactions

[EN] Understanding the human genome is a big research challenge. The huge complexity and amount of genome data require extremely effective and efficient data management policies. A first crucial point is to obtain a shared understanding of the domain, which becomes a very hard task considering the number of different genome data sources. To make things more complicated, those data sources deal with different parts of genome-based information: we not only need to understand them well, but also to integrate and intercommunicate all the relevant information. The protein perspective is a good example: rich, well-known repositories such as UniProt provide a lot of valuable information that it is not easy to interpret and manage when we want to generate useful results. Proteomes and basic information, protein-protein interaction, protein structure, protein processing events, protein function, etc. provide a lot of information is that needs to be conceptually characterized and delimited. To facilitate the essential common understanding of the domain, this paper uses the case of proteins to analyze the data provided by Uniprot in order to make a sound conceptualization work for identifying the relevant domain concepts. A conceptual model of proteins is the result of this conceptualization process, explained in detail in this work. This holistic conceptual model of proteins presented in this paper is the result of achieving a precise ontological commitment. It establishes concepts and their relationships that are significant in order to have a solid basis to efficiently manage relevant genome data related to proteins.This work was supported in part by the Spanish State Research Agency under Grant TIN2016-80811-P, and in part by the Generalidad Valenciana under Grant PROMETEO/2018/176, co-financed with ERDF.León-Palacio, A.; Pastor López, O. (2021). Towards a Shared, Conceptual Model-Based Understanding of Proteins and Their Interactions. IEEE Access. 9:73608-73623. https://doi.org/10.1109/ACCESS.2021.3080040S7360873623

CSCG: Conceptual Schema of the Citrus Genome

We describe our proposed Conceptual Schema (CS) to work with Citrus genome information (CSCG). The presented CS is being used in a real-world industrial case to validate it and gather expert domain feedbackGarcía Simón, A.; Pastor López, O. (2020). CSCG: Conceptual Schema of the Citrus Genome. http://hdl.handle.net/10251/14423

Enhancing Precision Medicine: A Big Data-Driven Approach for the Management of Genomic Data

[EN] The management of the exponential growth of data that Next Generation Sequencing techniques produce has become a challenge for researchers that are forced to delve into an ocean of complex data in order to extract new insights to unravel the secrets of human diseases. Initially, this can be faced as a Big Data-related problem, but the genomic data have particular and relevant challenges that make them different from other Big Data working domains. Genomic data are much more heterogeneous; they are spread in hundreds of repositories, represented in multiple formats, and have different levels of quality. In addition, getting meaningful conclusions from genomic data requires considering all of the relevant surrounding knowledge that is under continuous evolution. In this scenario, the precise identification of what makes Genome Data Management so different is essential in order to provide effective Big Data-based solutions. Genomic projects require dealing with the technological problems associated with data management, nomenclature standards, and quality issues that only robust Information Systems that use Big Data techniques can provide. The main contribution of this paper is to present a Big Data-driven approach for managing genomic data, that is adapted to the particularities of the domain and to show its applicability to improve genetic diagnoses, which is the core of the development of accurate Precision Medicine.This work was supported by the Spanish State Research Agency (grant number TIN2016-80811-P) and the Generalitat Valenciana (grant number PROMETEO/2018/176), and co-financed with ERDF.León-Palacio, A.; Pastor López, O. (2021). Enhancing Precision Medicine: A Big Data-Driven Approach for the Management of Genomic Data. Big Data Research. 26:1-11. https://doi.org/10.1016/j.bdr.2021.100253S1112