Ponències/Comunicacions de congressos
http://hdl.handle.net/2117/3573
2024-03-28T11:31:41Z
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Using simulation to estimate critical paths and survival functions in aircraft turnaround processes
http://hdl.handle.net/2117/115397
Using simulation to estimate critical paths and survival functions in aircraft turnaround processes
Fonseca Casas, Pau; Guimarans Serrano, Daniel
In the context of aircraft turnaround processes, this paper illustrates how simulation can be used not only to analyze critical activities and paths, but also to generate the associated survival functions –thus providing the probabilities that the turnaround can be completed before a series of target times. After motivating the relevance of the topic for both airlines and airports, the paper reviews some related work and proposes the use of Monte Carlo simulation to obtain the critical paths of the turnaround process and generate the associated survival function. This analysis is performed assuming stochastic completion times for each activity in the process –which contrast with current practices in which deterministic times are usually assumed. A series of numerical experiments contribute to illustrate these ideas. These experiments are based on a realistic environment considering the Boeing 737-800 aircraft, although the analysis can be easily extended to any other configuration. Different levels of passengers’ occupancy are analyzed, as well as two alternative designs for the turnaround stage.
2018-03-19T12:23:09Z
Fonseca Casas, Pau
Guimarans Serrano, Daniel
In the context of aircraft turnaround processes, this paper illustrates how simulation can be used not only to analyze critical activities and paths, but also to generate the associated survival functions –thus providing the probabilities that the turnaround can be completed before a series of target times. After motivating the relevance of the topic for both airlines and airports, the paper reviews some related work and proposes the use of Monte Carlo simulation to obtain the critical paths of the turnaround process and generate the associated survival function. This analysis is performed assuming stochastic completion times for each activity in the process –which contrast with current practices in which deterministic times are usually assumed. A series of numerical experiments contribute to illustrate these ideas. These experiments are based on a realistic environment considering the Boeing 737-800 aircraft, although the analysis can be easily extended to any other configuration. Different levels of passengers’ occupancy are analyzed, as well as two alternative designs for the turnaround stage.
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An integration-oriented ontology to govern evolution in big data ecosystems
http://hdl.handle.net/2117/113387
An integration-oriented ontology to govern evolution in big data ecosystems
Nadal Francesch, Sergi; Romero Moral, Óscar; Abelló Gamazo, Alberto; Vassiliadis, Panos; Vansummeren, Stijn
Big Data architectures allow to flexibly store and process heterogeneous data, from multiple sources, in its original format. The structure of those data, commonly supplied by means of REST APIs, is continuously evolving, forcing data analysts using it need to adapt their analytical processes after each release. This gets more challenging when aiming to perform an integrated or historical analysis of multiple sources. To cope with such complexity, in this paper we present the Big Data Integration ontology, the core construct for a data governance protocol that systematically annotates and integrates data from multiple sources in its original format. To cope with syntactic evolution in the sources, we present an algorithm that semi-automatically adapts the ontology upon new releases. A functional evaluation on real world APIs is performed in order to validate our approach.
2018-01-30T12:21:39Z
Nadal Francesch, Sergi
Romero Moral, Óscar
Abelló Gamazo, Alberto
Vassiliadis, Panos
Vansummeren, Stijn
Big Data architectures allow to flexibly store and process heterogeneous data, from multiple sources, in its original format. The structure of those data, commonly supplied by means of REST APIs, is continuously evolving, forcing data analysts using it need to adapt their analytical processes after each release. This gets more challenging when aiming to perform an integrated or historical analysis of multiple sources. To cope with such complexity, in this paper we present the Big Data Integration ontology, the core construct for a data governance protocol that systematically annotates and integrates data from multiple sources in its original format. To cope with syntactic evolution in the sources, we present an algorithm that semi-automatically adapts the ontology upon new releases. A functional evaluation on real world APIs is performed in order to validate our approach.
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Validation of Service Blueprint models by means of formal simulation techniques
http://hdl.handle.net/2117/113267
Validation of Service Blueprint models by means of formal simulation techniques
Estañol Lamarca, Montserrat; Marcos, Esperanza; Oriol Hilari, Xavier; Pérez, Francisco J.; Teniente López, Ernest; Vara, Juan M.
As service design has gained interest in the last years, so has gained one of its primary tools: the Service Blueprint. In essence, a service blueprint is a graphical tool for the design of business models, specifically for the design of business service operations. Despite its level of adoption, tool support for service design tasks is still on its early days and available tools for service blueprint modeling are mainly focused on enhancing usability and enabling collaborative edition, disregarding the formal aspects of modeling. In this paper we present a way to support the validation of service blueprint models by simulation. This approach is based on annotating the models with formal semantics, so that each task can be translated into formal logics, and from them, to executable SQL statements. This works opens a new direction in the way to bridge formal techniques and creative service design processes.
2018-01-26T13:25:52Z
Estañol Lamarca, Montserrat
Marcos, Esperanza
Oriol Hilari, Xavier
Pérez, Francisco J.
Teniente López, Ernest
Vara, Juan M.
As service design has gained interest in the last years, so has gained one of its primary tools: the Service Blueprint. In essence, a service blueprint is a graphical tool for the design of business models, specifically for the design of business service operations. Despite its level of adoption, tool support for service design tasks is still on its early days and available tools for service blueprint modeling are mainly focused on enhancing usability and enabling collaborative edition, disregarding the formal aspects of modeling. In this paper we present a way to support the validation of service blueprint models by simulation. This approach is based on annotating the models with formal semantics, so that each task can be translated into formal logics, and from them, to executable SQL statements. This works opens a new direction in the way to bridge formal techniques and creative service design processes.
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Table identification and reconstruction in spreadsheets
http://hdl.handle.net/2117/113249
Table identification and reconstruction in spreadsheets
Koci, Elvis; Thiele, Maik; Romero Moral, Óscar; Lehner, Wolfgang
Spreadsheets are one of the most successful content generation tools, used in almost every enterprise to perform data transformation, visualization, and analysis. The high degree of freedom provided by these tools results in very complex sheets, intermingling the actual data with formatting, formulas, layout artifacts, and textual metadata. To unlock the wealth of data contained in spreadsheets, a human analyst will often have to understand and transform the data manually. To overcome this cumbersome process, we propose a framework that is able to automatically infer the structure and extract the data from these documents in a canonical form. In this paper, we describe our heuristics-based method for discovering tables in spreadsheets, given that each cell is classified as either header, attribute, metadata, data, or derived. Experimental results on a real-world dataset of 439 worksheets (858 tables) show that our approach is feasible and effectively identifies tables within partially structured spreadsheets.
2018-01-26T10:34:46Z
Koci, Elvis
Thiele, Maik
Romero Moral, Óscar
Lehner, Wolfgang
Spreadsheets are one of the most successful content generation tools, used in almost every enterprise to perform data transformation, visualization, and analysis. The high degree of freedom provided by these tools results in very complex sheets, intermingling the actual data with formatting, formulas, layout artifacts, and textual metadata. To unlock the wealth of data contained in spreadsheets, a human analyst will often have to understand and transform the data manually. To overcome this cumbersome process, we propose a framework that is able to automatically infer the structure and extract the data from these documents in a canonical form. In this paper, we describe our heuristics-based method for discovering tables in spreadsheets, given that each cell is classified as either header, attribute, metadata, data, or derived. Experimental results on a real-world dataset of 439 worksheets (858 tables) show that our approach is feasible and effectively identifies tables within partially structured spreadsheets.
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Big data management challenges in SUPERSEDE
http://hdl.handle.net/2117/113246
Big data management challenges in SUPERSEDE
Nadal Francesch, Sergi; Abelló Gamazo, Alberto; Romero Moral, Óscar; Varga, Jovan
The H2020 SUPERSEDE (www.supersede.eu) project aims to support decision-making in the evolution and adaptation of software services and applications by exploiting end-user feedback and runtime data, with the overall goal of improving the end-users quality of experience (QoE). Such QoE is defined as the overall performance of a system from the point of view of users, which must consider both feedback and runtime data gathered. End-user’s feedback is extracted from online forums, app stores, social networks and novel direct feedback channels, which connect software applications and service users to developers. Runtime data is primarily gathered by monitoring environmental sensors, infrastructures and usage logs. Hereafter, we discuss our solutions for the main data management challenges in SUPERSEDE.
2018-01-26T10:20:34Z
Nadal Francesch, Sergi
Abelló Gamazo, Alberto
Romero Moral, Óscar
Varga, Jovan
The H2020 SUPERSEDE (www.supersede.eu) project aims to support decision-making in the evolution and adaptation of software services and applications by exploiting end-user feedback and runtime data, with the overall goal of improving the end-users quality of experience (QoE). Such QoE is defined as the overall performance of a system from the point of view of users, which must consider both feedback and runtime data gathered. End-user’s feedback is extracted from online forums, app stores, social networks and novel direct feedback channels, which connect software applications and service users to developers. Runtime data is primarily gathered by monitoring environmental sensors, infrastructures and usage logs. Hereafter, we discuss our solutions for the main data management challenges in SUPERSEDE.
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SM4MQ: a semantic model for multidimensional queries
http://hdl.handle.net/2117/113242
SM4MQ: a semantic model for multidimensional queries
Varga, Jovan; Dobrokhotova, Ekaterina; Romero Moral, Óscar; Bach Pedersen, Torben; Thomsen, Christian
On-Line Analytical Processing (OLAP) is a data analysis approach to support decision-making. On top of that, Exploratory OLAP is a novel initiative for the convergence of OLAP and the Semantic Web (SW) that enables the use of OLAP techniques on SW data. Moreover, OLAP approaches exploit different metadata artifacts (e.g., queries) to assist users with the analysis. However, modeling and sharing of most of these artifacts are typically overlooked. Thus, in this paper we focus on the query metadata artifact in the Exploratory OLAP context and propose an RDF-based vocabulary for its representation, sharing, and reuse on the SW. As OLAP is based on the underlying multidimensional (MD) data model we denote such queries as MD queries and define SM4MQ: A Semantic Model for Multidimensional Queries. Furthermore, we propose a method to automate the exploitation of queries by means of SPARQL. We apply the method to a use case of transforming queries from SM4MQ to a vector representation. For the use case, we developed the prototype and performed an evaluation that shows how our approach can significantly ease and support user assistance such as query recommendation.
2018-01-26T09:49:56Z
Varga, Jovan
Dobrokhotova, Ekaterina
Romero Moral, Óscar
Bach Pedersen, Torben
Thomsen, Christian
On-Line Analytical Processing (OLAP) is a data analysis approach to support decision-making. On top of that, Exploratory OLAP is a novel initiative for the convergence of OLAP and the Semantic Web (SW) that enables the use of OLAP techniques on SW data. Moreover, OLAP approaches exploit different metadata artifacts (e.g., queries) to assist users with the analysis. However, modeling and sharing of most of these artifacts are typically overlooked. Thus, in this paper we focus on the query metadata artifact in the Exploratory OLAP context and propose an RDF-based vocabulary for its representation, sharing, and reuse on the SW. As OLAP is based on the underlying multidimensional (MD) data model we denote such queries as MD queries and define SM4MQ: A Semantic Model for Multidimensional Queries. Furthermore, we propose a method to automate the exploitation of queries by means of SPARQL. We apply the method to a use case of transforming queries from SM4MQ to a vector representation. For the use case, we developed the prototype and performed an evaluation that shows how our approach can significantly ease and support user assistance such as query recommendation.
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A visualization tool based on traffic simulation for the analysis and evaluation of smart city policies, innovative vehicles and mobility concepts
http://hdl.handle.net/2117/113082
A visualization tool based on traffic simulation for the analysis and evaluation of smart city policies, innovative vehicles and mobility concepts
Montero Mercadé, Lídia; Linares Herreros, María Paz; Serch, Oriol; Casanovas Garcia, Josep
The CitScale tool is a software platform for visualizing, analyzing and comparing the impacts of smart city policies based on innovative mobility concepts in urban areas. It places emphasis on new automotive vehicles aimed at reducing traffic or environmental impacts. This paper introduces this traffic simulation-based tool, and two case studies developed for different scenarios in Barcelona City are briefly presented to demonstrate the capabilities of the tool when it is combined with microscopic traffic simulation software. The first case presents an extensive evaluation of new innovative vehicles (electric vehicles, bikes and three-wheeled scooters) and mobility concepts (trip-sharing). In the second one, data provided by connected cars is analyzed in order to compare different developed navigation strategies and how they affect the city. Finally, some of the obtained results from both cases are concisely presented in order to show the potential of the proposed tool.
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
2018-01-22T21:00:37Z
Montero Mercadé, Lídia
Linares Herreros, María Paz
Serch, Oriol
Casanovas Garcia, Josep
The CitScale tool is a software platform for visualizing, analyzing and comparing the impacts of smart city policies based on innovative mobility concepts in urban areas. It places emphasis on new automotive vehicles aimed at reducing traffic or environmental impacts. This paper introduces this traffic simulation-based tool, and two case studies developed for different scenarios in Barcelona City are briefly presented to demonstrate the capabilities of the tool when it is combined with microscopic traffic simulation software. The first case presents an extensive evaluation of new innovative vehicles (electric vehicles, bikes and three-wheeled scooters) and mobility concepts (trip-sharing). In the second one, data provided by connected cars is analyzed in order to compare different developed navigation strategies and how they affect the city. Finally, some of the obtained results from both cases are concisely presented in order to show the potential of the proposed tool.
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Notes on using simulation-optimization techniques in traffic simulation
http://hdl.handle.net/2117/113081
Notes on using simulation-optimization techniques in traffic simulation
Ros Oton, Xavier; Montero Mercadé, Lídia; Barceló Bugeda, Jaime
Mathematical and simulation models of systems lay at the core of many decision support systems, and their role becomes more critical when the system is more complex. The decision process usually involves optimizing some utility function that evaluates the performance indicators measuring the impacts of the decisions. The complexity of the system directly increases the difficulty when the associated function to be optimized is a non-analytical, non-differentiable, non-linear function that can only be evaluated by simulation. Simulation-optimization techniques are especially suited to these cases, and its use is becoming increasingly used with traffic models, which represent an archetypal case of complex, dynamic systems that exhibit highly stochastic characteristics. In this approach, simulation is used to evaluate the objective function, and it is combined with a non-differentiable optimization technique for solving the associated optimization problem. Of these techniques, one of the most commonly used is Stochastic Perturbation Stochastic Approximation (SPSA).
This paper analyses, discusses and presents the computational results from applying this technique in the calibration of traffic simulation models. This study uses variants of the SPSA by replacing the usual gradient approach with a combination of projected gradient and trust region methods. A special approach has also been analyzed for parameter calibration cases in which each variable has a different magnitude.
© <year>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
2018-01-22T20:44:33Z
Ros Oton, Xavier
Montero Mercadé, Lídia
Barceló Bugeda, Jaime
Mathematical and simulation models of systems lay at the core of many decision support systems, and their role becomes more critical when the system is more complex. The decision process usually involves optimizing some utility function that evaluates the performance indicators measuring the impacts of the decisions. The complexity of the system directly increases the difficulty when the associated function to be optimized is a non-analytical, non-differentiable, non-linear function that can only be evaluated by simulation. Simulation-optimization techniques are especially suited to these cases, and its use is becoming increasingly used with traffic models, which represent an archetypal case of complex, dynamic systems that exhibit highly stochastic characteristics. In this approach, simulation is used to evaluate the objective function, and it is combined with a non-differentiable optimization technique for solving the associated optimization problem. Of these techniques, one of the most commonly used is Stochastic Perturbation Stochastic Approximation (SPSA).
This paper analyses, discusses and presents the computational results from applying this technique in the calibration of traffic simulation models. This study uses variants of the SPSA by replacing the usual gradient approach with a combination of projected gradient and trust region methods. A special approach has also been analyzed for parameter calibration cases in which each variable has a different magnitude.
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Practical update management in ontology-based data access
http://hdl.handle.net/2117/113038
Practical update management in ontology-based data access
Giacomo, Giuseppe De; Lembo, Domenico; Oriol Hilari, Xavier; Savo, Domenico Fabio; Teniente López, Ernest
Ontology-based Data Access (OBDA) is gaining importance both scientifically and practically. However, little attention has been paid so far to the problem of updating OBDA systems. This is an essential issue if we want to be able to cope with modifications of data both at the ontology and at the source level, while maintaining the independence of the data sources. In this paper, we propose mechanisms to properly handle updates in this context. We show that updating data both at the ontology and source level is first-order rewritable. We also provide a practical implementation of such updating mechanisms based on non-recursive Datalog.
2018-01-22T11:08:19Z
Giacomo, Giuseppe De
Lembo, Domenico
Oriol Hilari, Xavier
Savo, Domenico Fabio
Teniente López, Ernest
Ontology-based Data Access (OBDA) is gaining importance both scientifically and practically. However, little attention has been paid so far to the problem of updating OBDA systems. This is an essential issue if we want to be able to cope with modifications of data both at the ontology and at the source level, while maintaining the independence of the data sources. In this paper, we propose mechanisms to properly handle updates in this context. We show that updating data both at the ontology and source level is first-order rewritable. We also provide a practical implementation of such updating mechanisms based on non-recursive Datalog.
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A Data-driven approach to improve the process of data-intensive API creation and evolution
http://hdl.handle.net/2117/113036
A Data-driven approach to improve the process of data-intensive API creation and evolution
Abelló Gamazo, Alberto; Ayala Martínez, Claudia Patricia; Farré Tost, Carles; Gómez Seoane, Cristina; Oriol Hilari, Marc; Romero Moral, Óscar
The market of data-intensive Application Programming Interfaces (APIs) has recently experienced an exponential growth, but the creation and evolution of such APIs is still done ad-hoc, with little automated support and reported deficiencies. These drawbacks hinder the productivity of developers of those APIs and the services built on top of them. In this exploratory paper, we promote a data-driven approach to improve the automatization of data-intensive API creation and evolution. In a release cycle, data coming from API usage and developers will be gathered to compute several indicators whose analysis will guide the planning of the next release. This data will also help to generate complete documentation facilitating APIs adoption by third parties.
2018-01-22T10:41:45Z
Abelló Gamazo, Alberto
Ayala Martínez, Claudia Patricia
Farré Tost, Carles
Gómez Seoane, Cristina
Oriol Hilari, Marc
Romero Moral, Óscar
The market of data-intensive Application Programming Interfaces (APIs) has recently experienced an exponential growth, but the creation and evolution of such APIs is still done ad-hoc, with little automated support and reported deficiencies. These drawbacks hinder the productivity of developers of those APIs and the services built on top of them. In this exploratory paper, we promote a data-driven approach to improve the automatization of data-intensive API creation and evolution. In a release cycle, data coming from API usage and developers will be gathered to compute several indicators whose analysis will guide the planning of the next release. This data will also help to generate complete documentation facilitating APIs adoption by third parties.