Pub Date : 2022-06-20DOI: 10.1109/ie54923.2022.9826775
H. H. Álvarez-Valera, Marc Dalmau, P. Roose, J. Larracoechea, Christina Herzog
Nowadays, many researchers work to identify microservices-based application deployments and scheduling solutions to save energy without decreasing functional QoS. In this work, we present PISCO: A simulator that allows facing this challenge in a simple and efficient way, enabling its users to focus uniquely on microservices deployment/scheduling algorithms and its hardware/software repercussions (load vs. energy consumption) without worrying about low-level network configurations or operating system issues. PISCO is able to deploy and schedule (move, duplicate, start/stop) microservices and their dependencies on various devices with software and hardware heterogeneity (CPU, bandwidth, RAM, Battery, etc.), taking into account various scheduling heuristics algorithms: centralized vs non-centralized. To do this, PISCO allows deploying custom network topologies based on client-server schemes or p2p distributions, where devices can (dis)appear, turn on/off obeying random circumstances or user strategies.Finally, the simulator performs relevant operations such as QoS definition, resource monitoring, calculation of energy saved and consumption tracking (at device and network level). We tested some ideas based on our previous work “Kaligreen” to demonstrate the effectiveness of PISCO.
{"title":"PISCO: A smart simulator to deploy energy saving methods in microservices based networks","authors":"H. H. Álvarez-Valera, Marc Dalmau, P. Roose, J. Larracoechea, Christina Herzog","doi":"10.1109/ie54923.2022.9826775","DOIUrl":"https://doi.org/10.1109/ie54923.2022.9826775","url":null,"abstract":"Nowadays, many researchers work to identify microservices-based application deployments and scheduling solutions to save energy without decreasing functional QoS. In this work, we present PISCO: A simulator that allows facing this challenge in a simple and efficient way, enabling its users to focus uniquely on microservices deployment/scheduling algorithms and its hardware/software repercussions (load vs. energy consumption) without worrying about low-level network configurations or operating system issues. PISCO is able to deploy and schedule (move, duplicate, start/stop) microservices and their dependencies on various devices with software and hardware heterogeneity (CPU, bandwidth, RAM, Battery, etc.), taking into account various scheduling heuristics algorithms: centralized vs non-centralized. To do this, PISCO allows deploying custom network topologies based on client-server schemes or p2p distributions, where devices can (dis)appear, turn on/off obeying random circumstances or user strategies.Finally, the simulator performs relevant operations such as QoS definition, resource monitoring, calculation of energy saved and consumption tracking (at device and network level). We tested some ideas based on our previous work “Kaligreen” to demonstrate the effectiveness of PISCO.","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130195966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-20DOI: 10.1109/ie54923.2022.9826777
{"title":"Organization, Sponsors and Supports","authors":"","doi":"10.1109/ie54923.2022.9826777","DOIUrl":"https://doi.org/10.1109/ie54923.2022.9826777","url":null,"abstract":"","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116171269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-20DOI: 10.1109/ie54923.2022.9826762
Timm Wächter, J. Rexilius, Martin Hoffmann, Matthias König
In this work, three different algorithms are examined for the evacuation of smart buildings, in particular a Static Evacuation Algorithm (SEA), a Dynamic Evacuation Algorithm (DEA) and a Fire Dynamic Algorithm (FDA). The Static Evacuation Algorithm represents the shortest path out of the building to a safe area. The Dynamic Algorithm calculates the optimal escape route based on the current position and position of a detected fire hazard. The Fire Dynamic escape route predicts how fast the fire will spread and includes this into the calculation of the escape route. The used simulation environment is based on the cross-platform game engine Unity3D and a building model was created using the building plan of the main building at the Campus Minden of University of Applied Sciences Bielefeld, Germany. We found that our proposed FDA performed 31.64% better than the SEA and 23.8% better than the DEA in terms of the hazard area over a minimally longer distance.
{"title":"Intelligent Building Evacuation under Consideration of Temporary Events and Dynamic Fire Propagation","authors":"Timm Wächter, J. Rexilius, Martin Hoffmann, Matthias König","doi":"10.1109/ie54923.2022.9826762","DOIUrl":"https://doi.org/10.1109/ie54923.2022.9826762","url":null,"abstract":"In this work, three different algorithms are examined for the evacuation of smart buildings, in particular a Static Evacuation Algorithm (SEA), a Dynamic Evacuation Algorithm (DEA) and a Fire Dynamic Algorithm (FDA). The Static Evacuation Algorithm represents the shortest path out of the building to a safe area. The Dynamic Algorithm calculates the optimal escape route based on the current position and position of a detected fire hazard. The Fire Dynamic escape route predicts how fast the fire will spread and includes this into the calculation of the escape route. The used simulation environment is based on the cross-platform game engine Unity3D and a building model was created using the building plan of the main building at the Campus Minden of University of Applied Sciences Bielefeld, Germany. We found that our proposed FDA performed 31.64% better than the SEA and 23.8% better than the DEA in terms of the hazard area over a minimally longer distance.","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130522688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-20DOI: 10.1109/ie54923.2022.9826770
Belén López, Francisco Arcas-Túnez, Magdalena Cantabella, Fernando Terroso-Sáenz, M. Curado, Andrés Muñoz
Due to the COVID-19 pandemic, most universities have adapted their learning infrastructure to an increasing demand for online training modalities. However, this type of learning, usually through Learning Management Systems (LMSs), suffer from a lack of direct feedback between students and the educational staff. For that reason, the present work introduces the EMO-learning project, whose key goal is to capture the emotions of students. This is done by means of a deep learning approach, able to timely analyse the face expressions of the students during online lectures. The module has been tested with different students during the academic year 2020-21, showing quite promising results.
{"title":"EMO-Learning: Towards an intelligent tutoring system to assess online students’ emotions","authors":"Belén López, Francisco Arcas-Túnez, Magdalena Cantabella, Fernando Terroso-Sáenz, M. Curado, Andrés Muñoz","doi":"10.1109/ie54923.2022.9826770","DOIUrl":"https://doi.org/10.1109/ie54923.2022.9826770","url":null,"abstract":"Due to the COVID-19 pandemic, most universities have adapted their learning infrastructure to an increasing demand for online training modalities. However, this type of learning, usually through Learning Management Systems (LMSs), suffer from a lack of direct feedback between students and the educational staff. For that reason, the present work introduces the EMO-learning project, whose key goal is to capture the emotions of students. This is done by means of a deep learning approach, able to timely analyse the face expressions of the students during online lectures. The module has been tested with different students during the academic year 2020-21, showing quite promising results.","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128687188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-20DOI: 10.1109/ie54923.2022.9826765
Santiago Ruiz, Juan Morales-García, C. Calafate, Juan-Carlos Cano, P. Manzoni, José M. Cecilia
Nowadays, human overpopulation is stressing our ecosystems in different ways, being agriculture a critical example as different predictions point towards food shortages in the near future. In such context, smart farming is becoming key to optimize natural resources so that different crops are grown efficiently, consuming as few resources as possible. In particular, greenhouses have shown to be an effective approach to producing a high volume of vegetables/fruits in a reduced space and within a short time span. Hence, optimizing greenhouse functioning results in less water and nutrient consumption, less energy use, faster growth, and better product quality. In this paper, we take a step in this direction by studying the best approach to forecast greenhouse temperature based on univariate time-series analysis. In particular, several widely used time-series libraries such as Prophet by Facebook, Greykite by LinkedIn and TPOT are studied to figure out which performs better for this particular scenario. Results show that the maximum prediction error ranges from 1.5 to 3 degrees Celsius, and, in general terms, Greykite is found to be the best performing library for this particular environment.
{"title":"Evaluation of time-series libraries for temperature prediction in smart greenhouses","authors":"Santiago Ruiz, Juan Morales-García, C. Calafate, Juan-Carlos Cano, P. Manzoni, José M. Cecilia","doi":"10.1109/ie54923.2022.9826765","DOIUrl":"https://doi.org/10.1109/ie54923.2022.9826765","url":null,"abstract":"Nowadays, human overpopulation is stressing our ecosystems in different ways, being agriculture a critical example as different predictions point towards food shortages in the near future. In such context, smart farming is becoming key to optimize natural resources so that different crops are grown efficiently, consuming as few resources as possible. In particular, greenhouses have shown to be an effective approach to producing a high volume of vegetables/fruits in a reduced space and within a short time span. Hence, optimizing greenhouse functioning results in less water and nutrient consumption, less energy use, faster growth, and better product quality. In this paper, we take a step in this direction by studying the best approach to forecast greenhouse temperature based on univariate time-series analysis. In particular, several widely used time-series libraries such as Prophet by Facebook, Greykite by LinkedIn and TPOT are studied to figure out which performs better for this particular scenario. Results show that the maximum prediction error ranges from 1.5 to 3 degrees Celsius, and, in general terms, Greykite is found to be the best performing library for this particular environment.","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127396732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-20DOI: 10.1109/ie54923.2022.9826779
Sofia Kleisarchaki, L. Gürgen, Y. Kassa, M. Krystek, Daniel González Vidal
A quarter of global greenhouse emissions come from transport, with modern cities producing more than 60% of these emissions. To reduce carbon footprint, several solutions on soft mobility (e.g., optimizing electric vehicles locations) have been proposed using IoT resources and AI techniques. However, these solutions either lack replicability since they ignore city’s needs per area and economic restrictions or lack algorithmic fairness since they account no social criteria (e.g., disabled, age, gender). In this work, we developed AI-based methods to automatically detect the different areas (e.g., rural, urban) and propose two heuristics which incorporate social, environmental and economic criteria of the area in their decision making in the form of sustainability policy templates. Our heuristics solve the p-median problem; they minimize the distance of stations to important points constrained by the cost of new stations. We show that our proposed solution is able to disperse the new stations within the city while covering local neighbourhoods. This work is replicated in two big European cities adapted to different open data and demonstrated by a dedicated visual dashboard.
{"title":"Optimization of Soft Mobility Localization with Sustainable Policies and Open Data","authors":"Sofia Kleisarchaki, L. Gürgen, Y. Kassa, M. Krystek, Daniel González Vidal","doi":"10.1109/ie54923.2022.9826779","DOIUrl":"https://doi.org/10.1109/ie54923.2022.9826779","url":null,"abstract":"A quarter of global greenhouse emissions come from transport, with modern cities producing more than 60% of these emissions. To reduce carbon footprint, several solutions on soft mobility (e.g., optimizing electric vehicles locations) have been proposed using IoT resources and AI techniques. However, these solutions either lack replicability since they ignore city’s needs per area and economic restrictions or lack algorithmic fairness since they account no social criteria (e.g., disabled, age, gender). In this work, we developed AI-based methods to automatically detect the different areas (e.g., rural, urban) and propose two heuristics which incorporate social, environmental and economic criteria of the area in their decision making in the form of sustainability policy templates. Our heuristics solve the p-median problem; they minimize the distance of stations to important points constrained by the cost of new stations. We show that our proposed solution is able to disperse the new stations within the city while covering local neighbourhoods. This work is replicated in two big European cities adapted to different open data and demonstrated by a dedicated visual dashboard.","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128563714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-20DOI: 10.1109/ie54923.2022.9826782
Yuhe Cui, Zezhi Guo, Yuxin Wang, Xiangzhou Peng, Joon Park, Carlos Aguiar
During the pandemic, many people have found themselves physically isolated from each other for long periods of time, so online chatting tools have become the main path of communication. However, texts and other chatting tools do not properly transmit the complex emotions hidden behind them. However, texts and other visual information have created an overload of information and made people ignore the complex emotions hidden behind them. Without the stimulation of sensations from face-to-face communications, people, especially lovers, lose their ability to observe their beloved ones’ emotions and feelings effectively.In this work, we propose a device to improve the efficiency of emotional communication - a multi-sensation interaction installation called Doki, which utilizes light, digital display, vibrations, and tactile interaction to transfer emotions. In addition, this device is comfortable to touch and enjoyable to play with. When used in conjunction with its texting applications, this product will help people express emotions over long distances and alleviate feelings of isolation.
{"title":"Doki: A Multi-sensation Interaction Device that Communicates Emotions","authors":"Yuhe Cui, Zezhi Guo, Yuxin Wang, Xiangzhou Peng, Joon Park, Carlos Aguiar","doi":"10.1109/ie54923.2022.9826782","DOIUrl":"https://doi.org/10.1109/ie54923.2022.9826782","url":null,"abstract":"During the pandemic, many people have found themselves physically isolated from each other for long periods of time, so online chatting tools have become the main path of communication. However, texts and other chatting tools do not properly transmit the complex emotions hidden behind them. However, texts and other visual information have created an overload of information and made people ignore the complex emotions hidden behind them. Without the stimulation of sensations from face-to-face communications, people, especially lovers, lose their ability to observe their beloved ones’ emotions and feelings effectively.In this work, we propose a device to improve the efficiency of emotional communication - a multi-sensation interaction installation called Doki, which utilizes light, digital display, vibrations, and tactile interaction to transfer emotions. In addition, this device is comfortable to touch and enjoyable to play with. When used in conjunction with its texting applications, this product will help people express emotions over long distances and alleviate feelings of isolation.","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133937295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-20DOI: 10.1109/ie54923.2022.9826776
Lucia Esposito, F. Leotta, Massimo Mecella, Silvestro V. Veneruso
Smart homes represent examples of cyber-physical environments realizing the paradigm known as ambient intelligence. An information system supporting ambient intelligence takes as input raw sensor measurements and analyzes them to eventually make decisions following final user preferences. Unfortunately, algorithms in this research area are mostly supervised, thus requiring a manual labeling of training instances usually involving final users in annoying and imprecise training sessions. In this paper, we propose a methodology allowing, given a sensor log, to automatically segment human habits by applying a bottom-up discretization strategy to the timestamp attribute of the sensor log. In particular, we show how classical quality measures, computed over Petri nets automatically mined from sensor logs filtered by timestamp, can be used as an heuristic to drive the discretization process, thus providing a likely subdivision of the day in human habits.
{"title":"Unsupervised Segmentation of Smart Home Logs for Human Habit Discovery","authors":"Lucia Esposito, F. Leotta, Massimo Mecella, Silvestro V. Veneruso","doi":"10.1109/ie54923.2022.9826776","DOIUrl":"https://doi.org/10.1109/ie54923.2022.9826776","url":null,"abstract":"Smart homes represent examples of cyber-physical environments realizing the paradigm known as ambient intelligence. An information system supporting ambient intelligence takes as input raw sensor measurements and analyzes them to eventually make decisions following final user preferences. Unfortunately, algorithms in this research area are mostly supervised, thus requiring a manual labeling of training instances usually involving final users in annoying and imprecise training sessions. In this paper, we propose a methodology allowing, given a sensor log, to automatically segment human habits by applying a bottom-up discretization strategy to the timestamp attribute of the sensor log. In particular, we show how classical quality measures, computed over Petri nets automatically mined from sensor logs filtered by timestamp, can be used as an heuristic to drive the discretization process, thus providing a likely subdivision of the day in human habits.","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127265818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-20DOI: 10.1109/ie54923.2022.9826767
Emna Ben Abdallah, Rima Grati, Khouloud Boukadi
Smart irrigation has many advantages in optimizing resource usage (e.g., saving water, reducing energy consumption) and improving crop productivity. In this paper, we contribute to this field by proposing a robust and accurate machine learning-based approach that combines the power of feature selection methods and stacking ensemble method to effectively determine the optimal quantity of water needed for a plant. Random Forest, Recursive Feature Elimination (RFE), and SelectKBest are used to assess the importance of the features. Then, based on the best subset of features, a stacking ensemble model is proposed that combines CART, Gradient Boost Regression (GBR), Random Forest (RF) and XGBoost regressors. The different models involved in this approach are trained and tested using a collected dataset about various crops such as tomatoes, grapes, and lemon and encompasses different features such as meteorological data, soil data, irrigation data, and crop data. The experiments demonstrated the performance of RF in analyzing the feature importance. The findings of feature selection highlight the importance level of the evapotranspiration, the depletion, and the deficit to maximize the model’s accuracy. The results also showed that the proposed stacking model (Stacking_GBR+CART+RF+XGB) with the 10 most essential features outperforms individual models and other stacking models by achieving low error rates (i.e., MSE=0.0026, MAE=0.0279, RMSE=0.0509) and high R2 score (i.e., 0.9927).
{"title":"A machine learning-based approach for smart agriculture via stacking-based ensemble learning and feature selection methods","authors":"Emna Ben Abdallah, Rima Grati, Khouloud Boukadi","doi":"10.1109/ie54923.2022.9826767","DOIUrl":"https://doi.org/10.1109/ie54923.2022.9826767","url":null,"abstract":"Smart irrigation has many advantages in optimizing resource usage (e.g., saving water, reducing energy consumption) and improving crop productivity. In this paper, we contribute to this field by proposing a robust and accurate machine learning-based approach that combines the power of feature selection methods and stacking ensemble method to effectively determine the optimal quantity of water needed for a plant. Random Forest, Recursive Feature Elimination (RFE), and SelectKBest are used to assess the importance of the features. Then, based on the best subset of features, a stacking ensemble model is proposed that combines CART, Gradient Boost Regression (GBR), Random Forest (RF) and XGBoost regressors. The different models involved in this approach are trained and tested using a collected dataset about various crops such as tomatoes, grapes, and lemon and encompasses different features such as meteorological data, soil data, irrigation data, and crop data. The experiments demonstrated the performance of RF in analyzing the feature importance. The findings of feature selection highlight the importance level of the evapotranspiration, the depletion, and the deficit to maximize the model’s accuracy. The results also showed that the proposed stacking model (Stacking_GBR+CART+RF+XGB) with the 10 most essential features outperforms individual models and other stacking models by achieving low error rates (i.e., MSE=0.0026, MAE=0.0279, RMSE=0.0509) and high R2 score (i.e., 0.9927).","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129164675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-20DOI: 10.1109/ie54923.2022.9826763
Nabila Guennouni, Sébastien Laborie, C. Sallaberry, R. Chbeir, Elio Mansour
Smart connected environments as well as digital contents are more and more present in our daily life. The former monitors various data produced by sensors, while the latter contains valuable additional information (e.g., technical data sheets, maintenance reports, employee register). When an event occurs, users generally want to figure out why this event happened. Unfortunately, most information systems in connected environments do not combine sensor network data with document corpora. Consequently, users have to look for an event explanation by querying both complementary sources with different systems, which is indeed very tedious, time consuming and requires a huge compilation effort. In this paper, we present ISEEapp1, a prototype for event explanation in smart connected environments. The functionalities of ISEEapp are illustrated and the results of a user interface evaluation are presented.
{"title":"ISEEapp: An Event Explanation Prototype bridging the gap between sensor network and document corpora data","authors":"Nabila Guennouni, Sébastien Laborie, C. Sallaberry, R. Chbeir, Elio Mansour","doi":"10.1109/ie54923.2022.9826763","DOIUrl":"https://doi.org/10.1109/ie54923.2022.9826763","url":null,"abstract":"Smart connected environments as well as digital contents are more and more present in our daily life. The former monitors various data produced by sensors, while the latter contains valuable additional information (e.g., technical data sheets, maintenance reports, employee register). When an event occurs, users generally want to figure out why this event happened. Unfortunately, most information systems in connected environments do not combine sensor network data with document corpora. Consequently, users have to look for an event explanation by querying both complementary sources with different systems, which is indeed very tedious, time consuming and requires a huge compilation effort. In this paper, we present ISEEapp1, a prototype for event explanation in smart connected environments. The functionalities of ISEEapp are illustrated and the results of a user interface evaluation are presented.","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121406221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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