An empirical study of power consumption of Web-based communications in mobile phones

Currently, mobile devices are the most popular pervasive computing device, and they are becoming the primer way for Web access. Energy is a critical resource in such pervasive computing devices, being network communication one of the primary energy consuming operations in mobile apps. Indeed, web-based communication is the most used, but also energy demanding. So, mobile web developers should be aware of how much energy consumes the different web-based communication alternatives. The goal of this paper is to measure and compare the energy consumption of three asynchronous Web-based methods in mobile devices. Our experiments consider three different Web applications models that allow a web server to push data to a browser: Polling, Long Polling and WebSockets. The obtained results are analyzed to get more accurate understanding of the impact in energy consumption of a mobile browser for each of these three methods. The utility of these experiments is to show developers what are the factors that influence the energy consumption when different web-based asynchronous communication is used. With this information mobile web developers could reduce the power consumption of web applications on mobile devices, by selecting the most appropriate method for asynchronous server communication.MUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Model Driven Evolution of an Agent-Based Home Energy Management System

Advanced smart home appliances and new models of energy tariffs imposed by energy providers pose new challenges in the automation of home energy management. Users need some assistant tool that helps them to make complex decisions with different goals, depending on the current situation. Multi-agent systems have proved to be a suitable technology to develop self-management systems, able to take the most adequate decision under different context-dependent situations, like the home energy management. The heterogeneity of home appliances and also the changes in the energy policies of providers introduce the necessity of explicitly modeling this variability. But, multi-agent systems lack of mechanisms to effectively deal with the different degrees of variability required by these kinds of systems. Software Product Line technologies, including variability models, has been successfully applied to different domains to explicitly model any kind of variability. We have defined a software product line development process that performs a model driven generation of agents embedded in heterogeneous smart objects with different degrees of self-management. However, once deployed, the home energy assistant system has to be able to evolve to self-adapt its decision making or devices to new requirements. So, in this paper we propose a model driven mechanism to automatically manage the evolution of multi-agent systems distributed among several devices.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Model Driven Development of Agents for Ambient Intelligence

En esta tesis se define un proceso dirigido por modelos para el desarrollo de sistemas de Inteligencia Ambiental (AmI) basados en agentes auto-gestionados que pueden ser ejecutados en los dispositivos más usuales de los entornos AmI, teléfonos inteligentes o sensores. Nuestra solución está centrada en una arquitectura de MAS totalmente distribuida y descentralizada, gracias a la integración de los agentes en los dispositivos heterogéneos que suelen formar parte de un sistema AmI

A neural network for semantic labelling of structured information

Intelligent systems rely on rich sources of information to make informed decisions. Using information from external sources requires establishing correspondences between the information and known information classes. This can be achieved with semantic labelling, which assigns known labels to structured information by classifying it according to computed features. The existing proposals have explored different sets of features, without focusing on what classification techniques are used. In this paper we present three contributions: first, insights on architectural issues that arise when using neural networks for semantic labelling; second, a novel implementation of semantic labelling that uses a state-of-the-art neural network classifier which achieves significantly better results than other four traditional classifiers; third, a comparison of the results obtained by the former network when using different subsets of features, comparing textual features to structural ones, and domain-dependent features to domain-independent ones. The experiments were carried away with datasets from three real world sources. Our results show that there is a need to develop more semantic labelling proposals with sophisticated classification techniques and large features catalogues.Ministerio de Economía y Competitividad TIN2016-75394-

TAPON: a two-phase machine learning approach for semantic labelling

Through semantic labelling we enrich structured information from sources such as HTML pages, tables, or JSON files, with labels to integrate it into a local ontology. This process involves measuring some features of the information and then nding the classes that best describe it. The problem with current techniques is that they do not model relationships between classes. Their features fall short when some classes have very similar structures or textual formats. In order to deal with this problem, we have devised TAPON: a new semantic labelling technique that computes novel features that take into account the relationships. TAPON computes these features by means of a two-phase approach. In the first phase, we compute simple features and obtain a preliminary set of labels (hints). In the second phase, we inject our novel features and obtain a refined set of labels. Our experimental results show that our technique, thanks to our rich feature catalogue and novel modelling, achieves higher accuracy than other state-of-the-art techniques.Ministerio de Economía y Competitividad TIN2016-75394-

AYNEC: All you need for evaluating completion techniques in knowledge graphs

The popularity of knowledge graphs has led to the development of techniques to refine them and increase their quality. One of the main refinement tasks is completion (also known as link prediction for knowledge graphs), which seeks to add missing triples to the graph, usually by classifying potential ones as true or false. While there is a wide variety of graph completion techniques, there is no standard evaluation setup, so each proposal is evaluated using different datasets and metrics. In this paper we present AYNEC, a suite for the evaluation of knowledge graph completion techniques that covers the entire evaluation workflow. It includes a customisable tool for the generation of datasets with multiple variation points related to the preprocessing of graphs, the splitting into training and testing examples, and the generation of negative examples. AYNEC also provides a visual summary of the graph and the optional exportation of the datasets in an open format for their visualisation. We use AYNEC to generate a library of datasets ready to use for evaluation purposes based on several popular knowledge graphs. Finally, it includes a tool that computes relevant metrics and uses significance tests to compare each pair of techniques. These open source tools, along with the datasets, are freely available to the research community and will be maintained.Ministerio de Economía y Competitividad TIN2016-75394-

Energy efficient adaptation engines for android applications

Context The energy consumption of mobile devices is increasing due to the improvement in their components (e.g., better processors, larger screens). Although the hardware consumes the energy, the software is responsible for managing hardware resources such as the camera software and its functionality, and therefore, affects the energy consumption. Energy consumption not only depends on the installed code, but also on the execution context (environment, devices status) and how the user interacts with the application. Objective In order to reduce the energy consumption based on user behavior, it is necessary to dynamically adapt the application. However, the adaptation mechanism also consumes a certain amount of energy in itself, which may lead to an important increase in the energy expenditure of the application in comparison with the benefits of the adaptation. Therefore, this footprint must be measured and compared with the benefit obtained. Method In this paper, we (1) determine the benefits, in terms of energy consumption, of dynamically adapting mobile applications, based on user behavior; and (2) advocate the most energy-efficient adaptation mechanism. We provide four different implementations of a proposed adaptation model and measure their energy consumption. Results The proposed adaptation engines do not increase the energy consumption when compared to the benefits of the adaptation, which can reduce the energy consumption by up to 20%. Conclusion The adaptation engines proposed in this paper can decrease the energy consumption of the mobile devices based on user behavior. The overhead introduced by the adaptation engines is negligible in comparison with the benefits obtained by the adaptation.Junta de Andalucía MAGIC P12-TIC1814Ministerio de Economía y Competitividad TIN2015-64841-RMinisterio de Ciencia, Innovación y Universidades TIN2017-90644-REDTMinisterio de Ciencia, Innovación y Universidades RTI2018-099213-B-I00Universidad de Málaga LEIA UMA18-FEDERJA-15

Analysis and optimisation of SPL products using goal models.

https://conf.researchr.org/details/RE-2023/RE-2023-Research-Papers/10/Analysis-and-optimisation-of-SPL-products-using-goal-modelsThe Internet of Things is one of the core drivers of variability modelling and requires explicit mechanisms to manage it. A key technology for addressing this variability is product line engineering. This approach uses a reference architecture to establish a well-designed set of assets that fit together, the Software Product Line (SPL). One of the limitations of variability models is they do not provide information about the quality of new products or how they achieve stakeholder requirements. Several approaches tackle this issue by integrating variability models with goal models. The main challenge is conciliating the different variability perspectives to make the joint use of both models possible without the loss of information or alterations to the models’ semantics. In this work, we present a framework for analysing and optimising SPL products considering stakeholders’ requirements that respects the semantics of both models. The framework is based on Integer Linear Programming (ILP), a field of mathematical programming. Variability and goal models are formalised as a set of linear constraints and are linked using mapping functions. As a proof of concept, we present a tool that takes both models and mapping functions to generate an ILP problem that can be solved using Matlab.This work is supported by the projects IRIS PID2021-12281 2OB-I00 (co-financed by FEDER funds) and by DISCO B1- 201212 funded by Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Multi-source dataset of e-commerce products with attributes for property matching

Schema/ontology matching consists in finding matches between types, properties and entities in heterogeneous sources of data in order to integrate them, which has become increasingly relevant with the development of web technologies and open data initiatives. One of the involved tasks is the matching of data properties, which attempts to try to find correspondences between the attributes of the entities. This is challenging due to the at times different names of equivalent properties. Furthermore, some properties may not be equivalent, but still match in 1..n relationships. These difficulties create the need for varied evaluation datasets for two reasons. First, they are needed to evaluate existing techniques in a variety of scenarios. Second, they enable the training of supervised techniques that may even become context-independent if trained with data from diverse enough contexts. To support the evaluation and training of data property matching techniques, we present a collection dataset consisting of product records from four different contexts. These datasets are the result of transforming two different existing datasets. In one of the datasets, some properties were filtered for being too noisy. The resulting processed dataset consists of json files with a listing of the product records and their properties, and a separate grouping of the properties that determines which ones match. It contains information about 2860 entities, with 4386 properties and 13350 pairwise matches.Ministerio de Ciencia, Innovación y Universidades PID2019–105471RB-I00Junta de Andalucía P18-RT-1060Junta de Andalucía US-138056

LEAPME: learning-based property matching with embeddings

Data integration tasks such as the creation and extension of knowledge graphs involve the fusion of heterogeneous entities from many sources. Matching and fusion of such entities require to also match and combine their properties (attributes). However, previous schema matching approaches mostly focus on two sources only and often rely on simple similarity measurements. They thus face problems in challenging use cases such as the integration of heterogeneous product entities from many sources. We therefore present a new machine learning-based property matching approach called LEAPME (LEArning-based Property Matching with Embeddings) that utilizes numerous features of both property names and instance values. The approach heavily makes use of word embeddings to better utilize the domain-specific semantics of both property names and instance values. The use of supervised machine learning helps exploit the predictive power of word embeddings. Our comparative evaluation against five baselines for several multi-source datasets with real-world data shows the high effectiveness of LEAPME. We also show that our approach is even effective when training data from another domain (transfer learning) is used.Ministerio de Economía y Competitividad TIN2016-75394-RMinisterio de Ciencia e Innovación PID2019-105471RB-I00Junta de Andalucía P18-RT-106