arXiv subjects

Oscar Romero

Publications and source records attributed to Oscar Romero.

14 recordsLinked to original sources

arXiv2026

Identifying Cost-Favorable Locations for Cosmic Explorer

Cosmic Explorer (CE) is a proposed next-generation gravitational-wave observatory that aims to extend our gravitational-wave vision to the edge of the observable universe. With a foundation of technology proven by the National Science Foundation's Laser Interferometer Gravitational-Wave Observatory (LIGO), CE will observe black holes and neutron stars across cosmic time, explore the nature of extreme matter with high fidelity, and probe the nature of gravity and fundamental physics. CE's reference design consists of two widely separated L-shaped detectors to be located in the conterminous United States, one with 20 km arms and one with 40 km arms. As of 2026, CE is in its design and site evaluation phase, with plans to begin observing in the early 2040s together with the Einstein Telescope in Europe. The size of CE observatories---up to an order of magnitude larger than the 4 km LIGO observatories---presents a significant challenge for identifying suitable candidate sites where CE will achieve its science goals, be built within cost boundaries, attract and retain a workforce, and align with community values. In this paper, we report on the design and use of a Python package, the Cosmic Explorer Location Search (CELS) package, to identify cost-favorable sites for CE. For a specified detector location and L-shaped geometry in the conterminous United States, CELS estimates site-preparation costs associated with excavation, land clearing, and land acquisition, while accounting for the scientific effects of detector tilt, arm length, and arm opening angle. After describing the package's methods, we present results for a national-level cost and positioning analysis that complements a recent national suitability analysis. We also discuss how future improvements to CELS will allow deeper, more local studies as the Cosmic Explorer team narrows its list of potential locations.

physics.ins-det↗

arXiv2026

Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric Images

This work introduces Structured 3D-SVD as a practical framework for the reconstruction, compression, and analysis of biological volumetric data. Inspired by the logic of matrix singular value decomposition (SVD), the proposed approach represents third-order volumetric data in the spatial domain and supports progressive reconstruction through ordered quasi-singular coeffients. The experimental evaluation was carried out on two biological volumetric datasets: one full-volume scan of a fish and another of a brain. The results show that Structured 3D-SVD achieves reconstruction quality close to that of Tucker decomposition while requiring shorter computation times and outperforms canonical polyadic decomposition (CPD) in both accuracy and runtime. In addition, a progressive reconstruction analysis shows that relatively low truncation levels are sufficient to preserve the main volumetric structures, while higher truncation levels lead to more detailed reconstructions.

eess.IV↗

arXiv2026

Driving Assistance System for Ambulances to Minimise the Vibrations in Patient Cabin

The ambulance service is the main transport for diseased or injured people which suffers the same acceleration forces as regular vehicles. These accelerations, caused by the movement of the vehicle, impact the performance of tasks executed by sanitary personnel, which can affect patient survival or recovery time. In this paper, we have trained, validated, and tested a system to assess driving in ambulance services. The proposed system is composed of a sensor node which measures the vehicle vibrations using an accelerometer. It also includes a GPS sensor, a battery, a display, and a speaker. When two possible routes reach the same destination point, the system compares the two routes based on previously classified data and calculates an index and a score. Thus, the index balances the possible routes in terms of time to reach the destination and the vibrations suffered in the patient cabin to recommend the route that minimises those vibrations. Three datasets are used to train, validate, and test the system. Based on an Artificial Neural network (ANN), the classification model is trained with tagged data classified as low, medium, and high vibrations, and 97% accuracy is achieved. Then, the obtained model is validated using data from three routes of another region. Finally, the system is tested in two new scenarios with two possible routes to reach the destination. The results indicate that the route with less vibration is preferred when there are low time differences (less than 6%) between the two possible routes. Nonetheless, with the current weighting factors, the shortest route is preferred when time differences between routes are higher than 20%, regardless of the higher vibrations in the shortest route.

cs.HC↗

arXiv2026

MLDAS: Machine Learning Dynamic Algorithm Selection for Software-Defined Networking Security

Network security is a critical concern in the digital landscape of today, with users demanding secure browsing experiences and protection of their personal data. This study explores the dynamic integration of Machine Learning (ML) algorithms with Software-Defined Networking (SDN) controllers to enhance network security through adaptive decision mechanisms. The proposed approach enables the system to dynamically choose the most suitable ML algorithm based on the characteristics of the observed network traffic. This work examines the role of Intrusion Detection Systems (IDS) as a fundamental component of secure communication networks and discusses the limitations of SDN-based attack detection mechanisms. The proposed framework uses adaptive model selection to maintain reliable intrusion detection under varying network conditions. The study highlights the importance of analyzing traffic-type-based metrics to define effective classification rules and enhance the performance of ML models. Additionally, it addresses the risks of overfitting and underfitting, underscoring the critical role of hyperparameter tuning in optimizing model accuracy and generalization. The central contribution of this work is an automated mechanism that adaptively selects the most suitable ML algorithm according to real-time network conditions, prioritizing detection robustness and operational feasibility within SDN environments.

cs.NI↗

arXiv2026

Low-Cost System for Automatic Recognition of Driving Pattern in Assessing Interurban Mobility using Geo-Information

Mobility in urban and interurban areas, mainly by cars, is a day-to-day activity of many people. However, some of its main drawbacks are traffic jams and accidents. Newly made vehicles have pre-installed driving evaluation systems, which can prevent accidents. However, most cars on our roads do not have driver assessment systems. In this paper, we propose an approach for recognising driving styles and enabling drivers to reach safer and more efficient driving. The system consists of two physical sensors connected to a device node with a display and a speaker. An artificial neural network (ANN) is included in the node, which analyses the data from the sensors, and then recognises the driving style. When an abnormal driving pattern is detected, the speaker will play a warning message. The prototype was assembled and tested using an interurban road, in particular on a conventional road with three driving styles. The gathered data were used to train and validate the ANN. Results, in terms of accuracy, indicate that better accuracy is obtained when the velocity, position (latitude and longitude), time, and turning speed for the 3-axis are used, offering an average accuracy of 83%. If the classification is performed considering just two driving styles, normal and aggressive, then the accuracy reaches 92%. When the geo-information and time data are included, the main novelty of this paper, the classification accuracy is improved by 13%.

cs.HC↗

arXiv2026

Text Data Integration

Data comes in many forms. From a shallow perspective, they can be viewed as being either in structured (e.g., as a relation, as key-value pairs) or unstructured (e.g., text, image) formats. So far, machines have been fairly good at processing and reasoning over structured data that follows a precise schema. However, the heterogeneity of data poses a significant challenge on how well diverse categories of data can be meaningfully stored and processed. Data Integration, a crucial part of the data engineering pipeline, addresses this by combining disparate data sources and providing unified data access to end-users. Until now, most data integration systems have leaned on only combining structured data sources. Nevertheless, unstructured data (a.k.a. free text) also contains a plethora of knowledge waiting to be utilized. Thus, in this chapter, we firstly make the case for the integration of textual data, to later present its challenges, state of the art and open problems.

cs.CL↗

arXiv2026

FREYJA: Efficient Join Discovery in Data Lakes

Data lakes are massive repositories of raw and heterogeneous data, designed to meet the requirements of modern data storage. Nonetheless, this same philosophy increases the complexity of performing discovery tasks to find relevant data for subsequent processing. As a response to these growing challenges, we present FREYJA, a modern data discovery system capable of effectively exploring data lakes, aimed at finding candidates to perform joins and increase the number of attributes for downstream tasks. More precisely, we want to compute rankings that sort potential joins by their relevance. Modern mechanisms apply advanced table representation learning (TRL) techniques to yield accurate joins. Yet, this incurs high computational costs when dealing with elevated volumes of data. In contrast to the state-of-the-art, we adopt a novel notion of join quality tailored to data lakes, which leverages syntactic measurements while achieving accuracy comparable to that of TRL approaches. To obtain this metric in a scalable manner we train a general purpose predictive model. Predictions are based, rather than on large-scale datasets, on data profiles, succinct representations that capture the underlying characteristics of the data. Our experiments show that our system, FREYJA, matches the results of the state-of-the-art whilst reducing the execution times by several orders of magnitude.

cs.DB↗

arXiv2025

Applications of Singular Entropy to Signals and Singular Smoothness to Images

This paper explores signal and image analysis by using the Singular Value Decomposition (SVD) and its extension, the Generalized Singular Value Decomposition (GSVD). A key strength of SVD lies in its ability to separate information into orthogonal subspaces. While SVD is a well-established tool in ECG analysis, particularly for source separation, this work proposes a refined method for selecting a threshold to distinguish between maternal and fetal components more effectively. In the first part of the paper, the focus is onmedical signal analysis,where the concepts of Energy Gap Variation (EGV) and Singular Energy are introduced to isolate fetal and maternal ECG signals, improving the known ones. Furthermore, the approach is significantly enhanced by the application of GSVD, which provides additional discriminative power for more accurate signal separation. The second part introduces a novel technique called Singular Smoothness, developed for image analysis. This method incorporates Singular Entropy and the Frobenius normto evaluate information density, and is applied to the detection of natural anomalies such asmountain fractures and burned forest regions. Numerical experiments are presented to demonstrate the effectiveness of the proposed approaches.

eess.SP↗

arXiv2023

Measuring and Predicting the Quality of a Join for Data Discovery

We study the problem of discovering joinable datasets at scale. We approach the problem from a learning perspective relying on profiles. These are succinct representations that capture the underlying characteristics of the schemata and data values of datasets, which can be efficiently extracted in a distributed and parallel fashion. Profiles are then compared, to predict the quality of a join operation among a pair of attributes from different datasets. In contrast to the state-of-the-art, we define a novel notion of join quality that relies on a metric considering both the containment and cardinality proportion between join candidate attributes. We implement our approach in a system called NextiaJD, and present experiments to show the predictive performance and computational efficiency of our method. Our experiments show that NextiaJD obtains greater predictive performance to that of hash-based methods while we are able to scale-up to larger volumes of data.

cs.DB↗

arXiv2020

Scalable Data Discovery Using Profiles

We study the problem of discovering joinable datasets at scale. This is, how to automatically discover pairs of attributes in a massive collection of independent, heterogeneous datasets that can be joined. Exact (e.g., based on distinct values) and hash-based (e.g., based on locality-sensitive hashing) techniques require indexing the entire dataset, which is unattainable at scale. To overcome this issue, we approach the problem from a learning perspective relying on profiles. These are succinct representations that capture the underlying characteristics of the schemata and data values of datasets, which can be efficiently extracted in a distributed and parallel fashion. Profiles are then compared, to predict the quality of a join operation among a pair of attributes from different datasets. In contrast to the state-of-the-art, we define a novel notion of join quality that relies on a metric considering both the containment and cardinality proportions between candidate attributes. We implement our approach in a system called NextiaJD, and present extensive experiments to show the predictive performance and computational efficiency of our method. Our experiments show that NextiaJD obtains similar predictive performance to that of hash-based methods, yet we are able to scale-up to larger volumes of data. Also, NextiaJD generates a considerably less amount of false positives, which is a desirable feature at scale.

cs.DB↗

arXiv2020

High-Level ETL for Semantic Data Warehouses -- Full Version

The popularity of the Semantic Web (SW) encourages organizations to organize and publish semantic data using the RDF model. This growth poses new requirements to Business Intelligence (BI) technologies to enable On-Line Analytical Processing (OLAP)-like analysis over semantic data. The incorporation of semantic data into a Data Warehouse (DW) is not supported by the traditional Extract-Transform-Load (ETL) tools because they do not consider semantic issues in the integration process. In this paper, we propose a layer-based integration process and a set of high-level RDF-based ETL constructs required to define, map, extract, process, transform, integrate, update, and load (multidimensional) semantic data. Different to other ETL tools, we automate the ETL data flows by creating metadata at the schema level. Therefore, it relieves ETL developers from the burden of manual mapping at the ETL operation level. We create a prototype, named Semantic ETL Construct (SETLCONSTRUCT), based on the innovative ETL constructs proposed here. To evaluate SETLCONSTRUCT, we create a multidimensional semantic DW by integrating a Danish Business dataset and an EU Subsidy dataset using it and compare it with the previous programmable framework SETLPROG in terms of productivity, development time and performance. The evaluation shows that 1) SETLCONSTRUCT uses 92% fewer Number of Typed Characters (NOTC) than SETLPROG, and SETLAUTO (the extension of SETLCONSTRUCT for generating ETL execution flow automatically) further reduces the Number of Used Concepts (NOUC) by another 25%; 2) using SETLCONSTRUCT, the development time is almost cut in half compared to SETLPROG, and is cut by another 27% using SETLAUTO; 3) SETLCONSTRUCT is scalable and has similar performance compared to SETLPROG.

cs.DB↗

arXiv2018

A Cost-based Storage Format Selector for Materialization in Big Data Frameworks

Modern big data frameworks (such as Hadoop and Spark) allow multiple users to do large-scale analysis simultaneously. Typically, users deploy Data-Intensive Workflows (DIWs) for their analytical tasks. These DIWs of different users share many common parts (i.e, 50-80%), which can be materialized to reuse them in future executions. The materialization improves the overall processing time of DIWs and also saves computational resources. Current solutions for materialization store data on Distributed File Systems (DFS) by using a fixed data format. However, a fixed choice might not be the optimal one for every situation. For example, it is well-known that different data fragmentation strategies (i.e., horizontal, vertical or hybrid) behave better or worse according to the access patterns of the subsequent operations. In this paper, we present a cost-based approach which helps deciding the most appropriate storage format in every situation. A generic cost-based storage format selector framework considering the three fragmentation strategies is presented. Then, we use our framework to instantiate cost models for specific Hadoop data formats (namely SequenceFile, Avro and Parquet), and test it with realistic use cases. Our solution gives on average 33% speedup over SequenceFile, 11% speedup over Avro, 32% speedup over Parquet, and overall, it provides upto 25% performance gain.

cs.DC↗

arXiv2018

An Integration-Oriented Ontology to Govern Evolution in Big Data Ecosystems

Big Data architectures allow to flexibly store and process heterogeneous data, from multiple sources, in their original format. The structure of those data, commonly supplied by means of REST APIs, is continuously evolving. Thus data analysts need to adapt their analytical processes after each API release. This gets more challenging when performing an integrated or historical analysis. To cope with such complexity, in this paper, we present the Big Data Integration ontology, the core construct to govern the data integration process under schema evolution by systematically annotating it with information regarding the schema of the sources. We present a query rewriting algorithm that, using the annotated ontology, converts queries posed over the ontology to queries over the sources. To cope with syntactic evolution in the sources, we present an algorithm that semi-automatically adapts the ontology upon new releases. This guarantees ontology-mediated queries to correctly retrieve data from the most recent schema version as well as correctness in historical queries. A functional and performance evaluation on real-world APIs is performed to validate our approach.

cs.DB↗

arXiv2016

GRAD: On Graph Database Modeling

Graph databases have emerged as the fundamental technology underpinning trendy application domains where traditional databases are not well-equipped to handle complex graph data. However, current graph databases support basic graph structures and integrity constraints with no standard algebra. In this paper, we introduce GRAD, a native and generic graph database model. GRAD goes beyond traditional graph database models, which support simple graph structures and constraints. Instead, GRAD presents a complete graph database model supporting advanced graph structures, a set of well-defined constraints over these structures and a powerful graph analysis-oriented algebra.

cs.DB↗