arXiv subjects

Genoveva Vargas-Solar

Publications and source records attributed to Genoveva Vargas-Solar.

At least 19 recordsLinked to original sources

arXiv2025

Bridging the Divide: Gender, Diversity, and Inclusion Gaps in Data Science and Artificial Intelligence Across Academia and Industry in the majority and minority worlds

As Artificial Intelligence (AI) and Data Science (DS) become pervasive, addressing gender disparities and diversity gaps in their workforce is urgent. These rapidly evolving fields have been further impacted by the COVID-19 pandemic, which disproportionately affected women and minorities, exposing deep-seated inequalities. Both academia and industry shape these disciplines, making it essential to map disparities across sectors, occupations, and skill levels. The dominance of men in AI and DS reinforces gender biases in machine learning systems, creating a feedback loop of inequality. This imbalance is a matter of social and economic justice and an ethical challenge, demanding value-driven diversity. Root causes include unequal access to education, disparities in academic programs, limited government investments, and underrepresented communities' perceptions of elite opportunities. This chapter examines the participation of women and minorities in AI and DS, focusing on their representation in both industry and academia. Analyzing the existing dynamics seeks to uncover the collective and individual impacts on the lives of women and minority groups within these fields. Additionally, the chapter aims to propose actionable strategies to promote equity, diversity, and inclusion (DEI), fostering a more representative and supportive environment for all.

cs.CY↗

arXiv2025

Experiversum: an Ecosystem for Curating and Enhancing Data-Driven Experimental Science

This paper introduces Experiversum, a lakehouse-based ecosystem that supports the curation, documentation and reproducibility of exploratory experiments. Experiversum enables structured research through iterative data cycles, while capturing metadata and collaborative decisions. Demonstrated through case studies in Earth, Life and Political Sciences, Experiversum promotes transparent workflows and multi-perspective result interpretation. Experiversum bridges exploratory and reproducible research, encouraging accountable and robust data-driven practices across disciplines.

cs.DB↗

arXiv2025

Towards Responsible and Fair Data Science: Resource Allocation for Inclusive and Sustainable Analytics

This project addresses the challenges of responsible and fair resource allocation in data science (DS), focusing on DS queries evaluation. Current DS practices often overlook the broader socio-economic, environmental, and ethical implications, including data sovereignty, fairness, and inclusivity. By integrating a decolonial perspective, the project aims to establish innovative fairness metrics that respect cultural and contextual diversity, optimise computational and energy efficiency, and ensure equitable participation of underrepresented communities. The research includes developing algorithms to align resource allocation with fairness constraints, incorporating ethical and sustainability considerations, and fostering interdisciplinary collaborations to bridge technical advancements and societal impact gaps. This work aims to reshape into an equitable, transparent, and community-empowering practice challenging the technological power developed by the Big Tech.

cs.DB↗

arXiv2024

Dataversifying Natural Sciences: Pioneering a Data Lake Architecture for Curated Data-Centric Experiments in Life \& Earth Sciences

This vision paper introduces a pioneering data lake architecture designed to meet Life \& Earth sciences' burgeoning data management needs. As the data landscape evolves, the imperative to navigate and maximize scientific opportunities has never been greater. Our vision paper outlines a strategic approach to unify and integrate diverse datasets, aiming to cultivate a collaborative space conducive to scientific discovery.The core of the design and construction of a data lake is the development of formal and semi-automatic tools, enabling the meticulous curation of quantitative and qualitative data from experiments. Our unique ''research-in-the-loop'' methodology ensures that scientists across various disciplines are integrally involved in the curation process, combining automated, mathematical, and manual tasks to address complex problems, from seismic detection to biodiversity studies. By fostering reproducibility and applicability of research, our approach enhances the integrity and impact of scientific experiments. This initiative is set to improve data management practices, strengthening the capacity of Life \& Earth sciences to solve some of our time's most critical environmental and biological challenges.

cs.DB↗

arXiv2023

A Novel Method for Curating Quanti-Qualitative Content

This paper proposes a Researcher-in-the-Loop (RITL) guided content curation approach for quanti-qualitative research methods that uses a version control system based on consensus. The paper introduces a workflow for quanti-qualitative research processes that produces and consumes content versions through collaborative phases validated through consensus protocols performed by research teams. We argue that content versioning is a critical component that supports the research process's reproducibility, traceability, and rationale. We propose a curation framework that provides methods, protocols, and tools for supporting the RITL approach to managing the content produced by quanti-qualitative methods. The paper reports a validation experiment using a use case about the study on disseminating political statements in graffiti.

cs.DB↗

arXiv2023

MATILDA: Inclusive Data Science Pipelines Design through Computational Creativity

We argue for the need for a new generation of data science solutions that can democratize recent advances in data engineering and artificial intelligence for non-technical users from various disciplines, enabling them to unlock the full potential of these solutions. To do so, we adopt an approach whereby computational creativity and conversational computing are combined to guide non-specialists intuitively to explore and extract knowledge from data collections. The paper introduces MATILDA, a creativity-based data science design platform, showing how it can support the design process of data science pipelines guided by human and computational creativity.

cs.DB↗

arXiv2023

Conversational Data Exploration: A Game-Changer for Designing Data Science Pipelines

This paper proposes a conversational approach implemented by the system Chatin for driving an intuitive data exploration experience. Our work aims to unlock the full potential of data analytics and artificial intelligence with a new generation of data science solutions. Chatin is a cutting-edge tool that democratises access to AI-driven solutions, empowering non-technical users from various disciplines to explore data and extract knowledge from it.

cs.HC↗

arXiv2023

From Text to Knowledge with Graphs: modelling, querying and exploiting textual content

This paper highlights the challenges, current trends, and open issues related to the representation, querying and analytics of content extracted from texts. The internet contains vast text-based information on various subjects, including commercial documents, medical records, scientific experiments, engineering tests, and events that impact urban and natural environments. Extracting knowledge from this text involves understanding the nuances of natural language and accurately representing the content without losing information. This allows knowledge to be accessed, inferred, or discovered. To achieve this, combining results from various fields, such as linguistics, natural language processing, knowledge representation, data storage, querying, and analytics, is necessary. The vision in this paper is that graphs can be a well-suited text content representation once annotated and the right querying and analytics techniques are applied. This paper discusses this hypothesis from the perspective of linguistics, natural language processing, graph models and databases and artificial intelligence provided by the panellists of the DOING session in the MADICS Symposium 2022.

cs.DB↗

arXiv2022

Calling for a feminist revolt to decolonise data and algorithms in the age of Datification

Feminist and women groups, indigenous communities and scholars in the global south/north refusing to adhere to hegemonic datafication programs have started to organise and fight back from the inside. The first essential step is to show and problematise technological progress exhibiting the poverty, violence, exclusion, and cultural erase promoted by this "progress". The second step is to promote technology, algorithmic and artificial literacy. Education is critical to learn how to revert and revoke the datified digital twin already colonising all Earth's societies silently and with impunity. It is not the colonisation of body-territories; it goes beyond and occupies humanity's mind's essence, i.e., imagination and imaginary. Against the colonisation of the imaginary, militant groups are imagining and designing alternative algorithms, datasets collection strategies and appropriation methods. The paper discusses their actions and alternative thinking.

cs.CY↗

arXiv2022

NLDS-QL: From natural language data science questions to queries on graphs: analysing patients conditions & treatments

This paper introduces NLDS-QL, a translator of data science questions expressed in natural language (NL) into data science queries on graph databases. Our translator is based on a simplified NL described by a grammar that specifies sentences combining keywords to refer to operations on graphs with the vocabulary of the graph schema. The demonstration proposed in this paper shows NLDS-QL in action within a scenario to explore and analyse a graph base on patient diagnoses generated with the open-source Synthea.

cs.DB↗

arXiv2022

Comparing graph data science libraries for querying and analysing datasets: towards data science queries on graphs

This paper presents an experimental study to compare analysis tools with management systems for querying and analysing graphs. Our experiment compares classic graph navigational operations queries where analytics tools and management systems adopt different execution strategies. Then, our experiment addresses data science pipelines with clustering and prediction models applied to graphs. In this kind of experiment, we underline the interest in combining both approaches and the interest of relying on a parallel execution platform for executing queries.

cs.DB↗

arXiv2022

Graph analytics workflows enactment on just in time data centres, Position Paper

This paper discusses our vision of multirole-capable decision-making systems across a broad range of Data Science (DS) workflows working on graphs through disaggregated data centres. Our vision is that an alternative is possible to work on a disaggregated solution for the provision of computational services under the notion of a disaggregated data centre. We define this alternative as a virtual entity that dynamically provides resources crosscutting the layers of edge, fog and data centre according to the workloads submitted by the workflows and their Service Level Objectives.

cs.DB↗

arXiv2022

Data Centred Intelligent Geosciences: Research Agenda and Opportunities, Position Paper

This paper describes and discusses our vision to develop and reason about best practices and novel ways of curating data-centric geosciences knowledge (data, experiments, models, methods, conclusions, and interpretations). This knowledge is produced from applying statistical modelling, Machine Learning, and modern data analytics methods on geo-data collections. The problems address open methodological questions in model building, models' assessment, prediction, and forecasting workflows.

physics.geo-ph↗

arXiv2021

QoS-based Trust Evaluation for Data Services as a Black Box

This paper proposes a QoS-based trust evaluation model for black box data services. Under the black-box model, data services neither export (meta)-data about conditions in which they are deployed and collect and process data nor the quality of data they deliver. Therefore, the black-box model creates blind spots about the extent to which data providers can be trusted to be used to build target applications. The trust evaluation model for black box data services introduced in this paper originally combines QoS indicators, like service performance and data quality, to determine services trustworthiness. The paper also introduces DETECT: a Data sErvice as a black box Trust Evaluation arChitecTure, that validates our model. The trust model and its associated monitoring strategies have been assessed in experiments with representative case studies. The results demonstrate the feasibility and effectiveness of our solution.

cs.DB↗

arXiv2021

Building Analytics Pipelines for Querying Big Streams and Data Histories with H-STREAM

This paper introduces H-STREAM, a big stream/data processing pipelines evaluation engine that proposes stream processing operators as micro-services to support the analysis and visualisation of Big Data streams stemming from IoT (Internet of Things) environments. H-STREAM micro-services combine stream processing and data storage techniques tuned depending on the number of things producing streams, the pace at which they produce them, and the physical computing resources available for processing them online and delivering them to consumers. H-STREAM delivers stream processing and visualisation micro-services installed in a cloud environment. Micro-services can be composed for implementing specific stream aggregation analysis pipelines as queries. The paper presents an experimental validation using Microsoft Azure as a deployment environment for testing the capacity of H-STREAM for dealing with velocity and volume challenges in an (i) a neuroscience experiment and (in) a social connectivity analysis scenario running on IoT farms.

cs.DC↗

arXiv2021

JITA4DS: Disaggregated execution of Data Science Pipelines between the Edge and the Data Centre

This paper targets the execution of data science (DS) pipelines supported by data processing, transmission and sharing across several resources executing greedy processes. Current data science pipelines environments provide various infrastructure services with computing resources such as general-purpose processors (GPP), Graphics Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs) and Tensor Processing Unit (TPU) coupled with platform and software services to design, run and maintain DS pipelines. These one-fits-all solutions impose the complete externalization of data pipeline tasks. However, some tasks can be executed in the edge, and the backend can provide just in time resources to ensure ad-hoc and elastic execution environments. This paper introduces an innovative composable "Just in Time Architecture" for configuring DCs for Data Science Pipelines (JITA-4DS) and associated resource management techniques. JITA-4DS is a cross-layer management system that is aware of both the application characteristics and the underlying infrastructures to break the barriers between applications, middleware/operating system, and hardware layers. Vertical integration of these layers is needed for building a customizable Virtual Data Center (VDC) to meet the dynamically changing data science pipelines' requirements such as performance, availability, and energy consumption. Accordingly, the paper shows an experimental simulation devoted to run data science workloads and determine the best strategies for scheduling the allocation of resources implemented by JITA-4DS.

cs.DC↗

arXiv2021

An Abstract View of Big Data Processing Programs

This paper proposes a model for specifying data flow based parallel data processing programs agnostic of target Big Data processing frameworks. The paper focuses on the formal abstract specification of non-iterative and iterative programs, generalizing the strategies adopted by data flow Big Data processing frameworks. The proposed model relies on monoid AlgebraandPetri Netstoabstract Big Data processing programs in two levels: a high level representing the program data flow and a lower level representing data transformation operations (e.g., filtering, aggregation, join). We extend the model for data processing programs proposed in [1], to enable the use of iterative programs. The general specification of iterative data processing programs implemented by data flow-based parallel programming models is essential given the democratization of iterative and greedy Big Data analytics algorithms. Indeed, these algorithms call for revisiting parallel programming models to express iterations. The paper gives a comparative analysis of the iteration strategies proposed byApache Spark, DryadLINQ, Apache Beam and Apache Flink. It discusses how the model achieves to generalize these strategies.

cs.SE↗

arXiv2021

TRANSMUT-SPARK: Transformation Mutation for Apache Spark

We propose TRANSMUT-Spark, a tool that automates the mutation testing process of Big Data processing code within Spark programs. Apache Spark is an engine for Big Data Processing. It hides the complexity inherent to Big Data parallel and distributed programming and processing through built-in functions, underlying parallel processes, and data management strategies. Nonetheless, programmers must cleverly combine these functions within programs and guide the engine to use the right data management strategies to exploit the large number of computational resources required by Big Data processing and avoid substantial production losses. Many programming details in data processing code within Spark programs are prone to false statements that need to be correctly and automatically tested. This paper explores the application of mutation testing in Spark programs, a fault-based testing technique that relies on fault simulation to evaluate and design test sets. The paper introduces the TRANSMUT-Spark solution for testing Spark programs. TRANSMUT-Spark automates the most laborious steps of the process and fully executes the mutation testing process. The paper describes how the tool automates the mutants generation, test execution, and adequacy analysis phases of mutation testing with TRANSMUT-Spark. It also discusses the results of experiments that were carried out to validate the tool to argue its scope and limitations.

cs.SE↗