{"id":{"repo_id":"brazil-ufpe","oai_identifier":"oai:repositorio.ufpe.br:123456789/35362"},"canonical_url":"https://search.dev.ndltd.org/etd/brazil-ufpe/oai:repositorio.ufpe.br:123456789/35362","repository":{"repo_id":"brazil-ufpe","name":"Brazil UFPE","base_url":"https://repositorio.ufpe.br/oai/request"},"display":{"title":"A Performance Analysis of a Reactive-based Complex Event Processing Library","abstract":"Reactive applications are an important class of software designed to respond to events or changes surrounding an area of interest in a timely manner. Many different approaches have been proposed to project those applications, such as Complex Event Processing (CEP) and Reactive Languages (RLs). Despite being developed by different communities, they offer complementary solutions that could benefit their development. Meanwhile, the Internet of Things (IoT) is among the recent areas where reactive application solutions have been applied. IoT has a tremendous potential of allowing the creation of innovative applications, so the acquisition of IoT devices aligned with a great production of data, often called Big Data, is posing many challenges. As an alternative to deal with challenges faced by IoT stream processing placed on the cloud, Edge Analitycs has been proposed, consisting of placing part of the processing in the edge of the network. Pushing the processing toward the edge may incur in other challenges as well, since the devices are often resource-constrained. Combining the support for stream processing in those constrained devices and the proper adjustment of performance, a constant requirement in reactive applications, will be very important to allow this new trend. Therefore, this study presents CEP.js, a library to code complex event processing reactively that we have been developing, and reports an empirical study where CEP.js’ underlying reactive libraries, Most.js and RxJS, are varied to find out which performance aspects are more affected by those libraries while running in an Edge Analytics scenario. The results have shown that Most.js produced the worst results under different load levels and the differences were statistically significant. Consequently, both considered aspects, memory consumption and CPU usage, are more affected by the reactive library, Most.js.","abstract_html":"Reactive applications are an important class of software designed to respond to events or changes surrounding an area of interest in a timely manner. Many different approaches have been proposed to project those applications, such as Complex Event Processing (CEP) and Reactive Languages (RLs). Despite being developed by different communities, they offer complementary solutions that could benefit their development. Meanwhile, the Internet of Things (IoT) is among the recent areas where reactive application solutions have been applied. IoT has a tremendous potential of allowing the creation of innovative applications, so the acquisition of IoT devices aligned with a great production of data, often called Big Data, is posing many challenges. As an alternative to deal with challenges faced by IoT stream processing placed on the cloud, Edge Analitycs has been proposed, consisting of placing part of the processing in the edge of the network. Pushing the processing toward the edge may incur in other challenges as well, since the devices are often resource-constrained. Combining the support for stream processing in those constrained devices and the proper adjustment of performance, a constant requirement in reactive applications, will be very important to allow this new trend. Therefore, this study presents CEP.js, a library to code complex event processing reactively that we have been developing, and reports an empirical study where CEP.js’ underlying reactive libraries, Most.js and RxJS, are varied to find out which performance aspects are more affected by those libraries while running in an Edge Analytics scenario. The results have shown that Most.js produced the worst results under different load levels and the differences were statistically significant. Consequently, both considered aspects, memory consumption and CPU usage, are more affected by the reactive library, Most.js.","abstract_has_math":false,"creators":["LIMA, Carlos Eduardo Zimmerle de"],"institution":"Universidade Federal de Pernambuco","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["GAMA, Kiev Santos da"],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-02","date_published":"2019-08-02","updated_at":"2026-07-24T01:18:40Z","subjects":["Engenharia de Software","Internet das Coisas","Analytics na Borda","Aplicações Reativas"],"languages":["eng"],"rights":["openAccess","Attribution-NonCommercial-NoDerivs 3.0 Brazil"],"rights_urls":["http://creativecommons.org/licenses/by-nc-nd/3.0/br/"],"identifier_entries":[]},"links":{"outbound_url":"https://repositorio.ufpe.br/handle/123456789/35362","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["GAMA, Kiev Santos da"]},{"key":"dc:creator","label":"Author","values":["LIMA, Carlos Eduardo Zimmerle de"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-11-28T22:32:48Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2019-11-28T22:32:48Z"]},{"key":"dc:date.issued","label":"Date","values":["2019-08-02"]},{"key":"dc:publisher","label":"Institution","values":["Universidade Federal de Pernambuco"]},{"key":"dc:type","label":"Dc Type","values":["masterThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engenharia de Software","Internet das Coisas","Analytics na Borda","Aplicações Reativas"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["openAccess","Attribution-NonCommercial-NoDerivs 3.0 Brazil"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by-nc-nd/3.0/br/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://repositorio.ufpe.br/handle/123456789/35362"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Reactive applications are an important class of software designed to respond to events or changes surrounding an area of interest in a timely manner. Many different approaches have been proposed to project those applications, such as Complex Event Processing (CEP) and Reactive Languages (RLs). Despite being developed by different communities, they offer complementary solutions that could benefit their development. Meanwhile, the Internet of Things (IoT) is among the recent areas where reactive application solutions have been applied. IoT has a tremendous potential of allowing the creation of innovative applications, so the acquisition of IoT devices aligned with a great production of data, often called Big Data, is posing many challenges. As an alternative to deal with challenges faced by IoT stream processing placed on the cloud, Edge Analitycs has been proposed, consisting of placing part of the processing in the edge of the network. Pushing the processing toward the edge may incur in other challenges as well, since the devices are often resource-constrained. Combining the support for stream processing in those constrained devices and the proper adjustment of performance, a constant requirement in reactive applications, will be very important to allow this new trend. Therefore, this study presents CEP.js, a library to code complex event processing reactively that we have been developing, and reports an empirical study where CEP.js’ underlying reactive libraries, Most.js and RxJS, are varied to find out which performance aspects are more affected by those libraries while running in an Edge Analytics scenario. The results have shown that Most.js produced the worst results under different load levels and the differences were statistically significant. Consequently, both considered aspects, memory consumption and CPU usage, are more affected by the reactive library, Most.js."]},{"key":"dc:title","label":"Title","values":["A Performance Analysis of a Reactive-based Complex Event Processing Library"]}]}],"canonical_facts":{"dc:contributor.advisor":["GAMA, Kiev Santos da"],"dc:creator":["LIMA, Carlos Eduardo Zimmerle de"],"dc:date.accessioned":["2019-11-28T22:32:48Z"],"dc:date.available":["2019-11-28T22:32:48Z"],"dc:date.issued":["2019-08-02"],"dc:description.abstract":["Reactive applications are an important class of software designed to respond to events or changes surrounding an area of interest in a timely manner. Many different approaches have been proposed to project those applications, such as Complex Event Processing (CEP) and Reactive Languages (RLs). Despite being developed by different communities, they offer complementary solutions that could benefit their development. Meanwhile, the Internet of Things (IoT) is among the recent areas where reactive application solutions have been applied. IoT has a tremendous potential of allowing the creation of innovative applications, so the acquisition of IoT devices aligned with a great production of data, often called Big Data, is posing many challenges. As an alternative to deal with challenges faced by IoT stream processing placed on the cloud, Edge Analitycs has been proposed, consisting of placing part of the processing in the edge of the network. Pushing the processing toward the edge may incur in other challenges as well, since the devices are often resource-constrained. Combining the support for stream processing in those constrained devices and the proper adjustment of performance, a constant requirement in reactive applications, will be very important to allow this new trend. Therefore, this study presents CEP.js, a library to code complex event processing reactively that we have been developing, and reports an empirical study where CEP.js’ underlying reactive libraries, Most.js and RxJS, are varied to find out which performance aspects are more affected by those libraries while running in an Edge Analytics scenario. The results have shown that Most.js produced the worst results under different load levels and the differences were statistically significant. Consequently, both considered aspects, memory consumption and CPU usage, are more affected by the reactive library, Most.js."],"dc:identifier.uri":["https://repositorio.ufpe.br/handle/123456789/35362"],"dc:language.iso":["eng"],"dc:publisher":["Universidade Federal de Pernambuco"],"dc:rights":["openAccess","Attribution-NonCommercial-NoDerivs 3.0 Brazil"],"dc:rights.uri":["http://creativecommons.org/licenses/by-nc-nd/3.0/br/"],"dc:subject":["Engenharia de Software","Internet das Coisas","Analytics na Borda","Aplicações Reativas"],"dc:title":["A Performance Analysis of a Reactive-based Complex Event Processing Library"],"dc:type":["masterThesis"]},"updated_at":"2026-07-24T01:18:40Z"}