Person:
Martín Martín, Enrique

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First Name
Enrique
Last Name
Martín Martín
URI
https://hdl.handle.net/20.500.14352/78704
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Informática
Department
Sistemas Informáticos y Computación
Area
Lenguajes y Sistemas Informáticos
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2014 - 201922020 - 20222
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Author: Correas Fernández, Jesús × Has files: true ×

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Now showing 1 - 4 of 4
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    Project number: 18
    Juez automático para el aprendizaje de bases de datos
    (2021) Martín Martín, Enrique; Burgoa Muñoz, Iker; Rubio Cuéllar, Rubén Rafael; Cerro Cañizares, Pablo; Correas Fernández, Jesús; Montenegro Montes, Manuel; Riesco Rodríguez, Adrián
    Proponemos desarrollar un juez automático para el aprendizaje de bases de datos. Este sistema web permitirá a los estudiantes practicar con diferentes ejercicios de bases de datos desde casa y recibir retroalimentación inmediata sobre los fallos.
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    Project number: 387
    Evaluación del impacto sobre el aprendizaje de bases de datos del juez automático LearnSQL
    (2022) Martín Martín, Enrique; Correas Fernández, Jesús; Garmendia Salvador, Luis; Montenegro Montes, Manuel; Riesco Rodríguez, Adrián; Rubio Cuéllar, Rubén Rafael; Sáenz Pérez, Fernando
    Utilizamos el juez automático "LearnSQL" en la docencia de varios grupos de la asignatura "Bases de Datos" y medimos de manera rigurosa y extensiva el impacto que este tiene sobre el aprendizaje de los estudiantes.
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    Static Inference of Transmission Data Sizes in Distributed Systems
    (2014) Albert Albiol, Elvira María; Correas Fernández, Jesús; Martín Martín, Enrique; Román Díez, Guillermo
    We present a static analysis to infer the amount of data that a distributed system may ransmit. The different locations of a distributed system communicate and coordinate their actions by posting tasks among them. A task is posted by building a message with the task name and the data on which such task has to be executed. When the task completes, the result can be retrieved by means of another message from which the result of the computation can be obtained. Thus, the transmission data size of a distributed system mainly depends on the amount of messages posted among the locations of the system, and the sizes of the data transferred in the messages. Our static analysis has two main parts: (1) we over-approximate the sizes of the data at the program points where tasks are spawned and where the results are received, and (2) we over-approximate the total number of messages. Knowledge of the transmission data sizes is essential, among other things, to predict the bandwidth required to achieve a certain response time, or conversely, to estimate the response time for a given bandwidth. A prototype implementation in the SACO system demonstrates the accuracy and feasibility of the proposed analysis.
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    Resource Analysis: From Sequential to Concurrent and Distributed Programs
    (2015) Albert Albiol, Elvira María; Arenas Sánchez, Purificación; Correas Fernández, Jesús; Genaim, Samir; Gómez Zamalloa, Miguel; Martín Martín, Enrique; Puebla, Germán; Román Díez, Guillermo
    Resource analysis aims at automatically inferring upper/lower bounds on the worst/best-case cost of executing programs. Ideally, a resource analyzer should be parametric on the cost model, i.e., the type of cost that the user wants infer (e.g., number of steps, amount of memory allocated, amount of data transmitted, etc.). The inferred upper bounds have important applications in the fields of program optimization, verification and certification. In this talk, we will review the basic techniques used in resource analysis of sequential programs and the new extensions needed to handle concurrent and distributed systems.