Recently, there has been a growing interest in improving students' competitiveness in STEM education. Self-reporting and observation are the most used tools for the assessment of STEM education. Despite their effectiveness, such assessment tools face several challenges, such as being labor-intensive and time-consuming, prone to subjective awareness, depending on memory limitations, and being influenced due to social expectations. To address these challenges, in this research, we propose an approach called Image4Assess that---by benefiting from state-of-the-art machine learning like convolutional neural networks and transfer learning---automatically and uninterruptedly assesses students' learning processes during STEM activities using image processing. Our findings reveal that the Image4Assess approach can achieve accuracy, precision, and recall higher than 85% in the learning process recognition of students. This implies that it is feasible to accurately measure the learning process of students in STEM education using their imagery data. We also found that there is a significant correlation between the learning processes automatically identified by our proposed approach and students' post-test, confirming the effectiveness of the proposed approach in real-world classrooms.
{"title":"Image4Assess: Automatic learning processes recognition using image processing","authors":"Hsin-Yu Lee, Maral Hooshyar, Chia-Ju Lin, Wei-Sheng Wang, Yueh-Min Huang","doi":"10.1145/3555776.3577643","DOIUrl":"https://doi.org/10.1145/3555776.3577643","url":null,"abstract":"Recently, there has been a growing interest in improving students' competitiveness in STEM education. Self-reporting and observation are the most used tools for the assessment of STEM education. Despite their effectiveness, such assessment tools face several challenges, such as being labor-intensive and time-consuming, prone to subjective awareness, depending on memory limitations, and being influenced due to social expectations. To address these challenges, in this research, we propose an approach called Image4Assess that---by benefiting from state-of-the-art machine learning like convolutional neural networks and transfer learning---automatically and uninterruptedly assesses students' learning processes during STEM activities using image processing. Our findings reveal that the Image4Assess approach can achieve accuracy, precision, and recall higher than 85% in the learning process recognition of students. This implies that it is feasible to accurately measure the learning process of students in STEM education using their imagery data. We also found that there is a significant correlation between the learning processes automatically identified by our proposed approach and students' post-test, confirming the effectiveness of the proposed approach in real-world classrooms.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"27 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74765397","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}
HotStuff is a Byzantine fault-tolerant state machine replication protocol that incurs linear communication costs to achieve consensus. This linear scalability promoted the protocol to be adopted as the consensus mechanism in permissioned blockchains. This paper discusses the architecture and evaluation of our extensible framework to implement three HotStuff variants. This reimplementation demonstrates the extensibility of our framework to implement other HotStuff-like protocols. Leveraging our deployment tool, we evaluated our implementation on a wide variety of configurations.
{"title":"An Extensible Framework for Implementing Byzantine Fault-Tolerant Protocols","authors":"Hanish Gogada, J. Olsen, H. Meling, Leander Jehl","doi":"10.1145/3555776.3578614","DOIUrl":"https://doi.org/10.1145/3555776.3578614","url":null,"abstract":"HotStuff is a Byzantine fault-tolerant state machine replication protocol that incurs linear communication costs to achieve consensus. This linear scalability promoted the protocol to be adopted as the consensus mechanism in permissioned blockchains. This paper discusses the architecture and evaluation of our extensible framework to implement three HotStuff variants. This reimplementation demonstrates the extensibility of our framework to implement other HotStuff-like protocols. Leveraging our deployment tool, we evaluated our implementation on a wide variety of configurations.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"17 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83409586","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}
P. Pereira, Carlos Gonçalves, Lara Lopes Nunes, P. Cortez, A. Pilastri
Nowadays, the general interest in Machine Learning (ML) based solutions is increasing. However, to develop and deploy a ML solution often requires experience and it involves developing large code scripts. In this paper, we propose AI4CITY, an automated technological platform that aims to reduce the complexity of designing ML solutions, with a particular focus on Smart Cities applications. We compare our solution with popular Automated ML (AutoML) tools (e.g., H2O, AutoGluon) and the results achieved by AI4CITY were quite interesting and competitive.
{"title":"AI4CITY - An Automated Machine Learning Platform for Smart Cities","authors":"P. Pereira, Carlos Gonçalves, Lara Lopes Nunes, P. Cortez, A. Pilastri","doi":"10.1145/3555776.3578740","DOIUrl":"https://doi.org/10.1145/3555776.3578740","url":null,"abstract":"Nowadays, the general interest in Machine Learning (ML) based solutions is increasing. However, to develop and deploy a ML solution often requires experience and it involves developing large code scripts. In this paper, we propose AI4CITY, an automated technological platform that aims to reduce the complexity of designing ML solutions, with a particular focus on Smart Cities applications. We compare our solution with popular Automated ML (AutoML) tools (e.g., H2O, AutoGluon) and the results achieved by AI4CITY were quite interesting and competitive.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"69 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83469896","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}
F. Basso, Bruno Marcelo Soares Ferreira, Rafael Torres, R. Z. Frantz, D. Kreutz, Maicon Bernardino, Elder de Macedo Rodrigues
Open Services for Lifecycle Collaboration (OSLC) is an open standard for tool interoperability, which allows data federation throughout Software Engineering (SE) application lifecycles. The OSLC community has been active since 2008, and there is still an open question: "What is the state-of-the-art and practice of OSLC for tool integration in Application Lifecycle Management (ALM) for Software Engineering environments?". Objective: To answer this question, our main goal is to map the state-of-the-art and practice on the adoption of OSLC in SE lifecycles. Method: This paper presents a Systematic Mapping Study (SMS) by analyzing 59 primary studies and addressing integration issues such as building SE toolchains. Results: Our findings show that OSLC has been mostly implemented with the development of adapters and MDE. Conclusions: The main advantages of OSLC are related to linked data, involving not only tool adapters for point-to-point integrations, but also proposing solutions for tool replacement in the toolchain, as well as including modifications of OSLC domain specifications and solutions for automated activities for tool integration.
{"title":"Model-Driven Integration and the OSLC Standard: a Mapping of Applied Studies","authors":"F. Basso, Bruno Marcelo Soares Ferreira, Rafael Torres, R. Z. Frantz, D. Kreutz, Maicon Bernardino, Elder de Macedo Rodrigues","doi":"10.1145/3555776.3577761","DOIUrl":"https://doi.org/10.1145/3555776.3577761","url":null,"abstract":"Open Services for Lifecycle Collaboration (OSLC) is an open standard for tool interoperability, which allows data federation throughout Software Engineering (SE) application lifecycles. The OSLC community has been active since 2008, and there is still an open question: \"What is the state-of-the-art and practice of OSLC for tool integration in Application Lifecycle Management (ALM) for Software Engineering environments?\". Objective: To answer this question, our main goal is to map the state-of-the-art and practice on the adoption of OSLC in SE lifecycles. Method: This paper presents a Systematic Mapping Study (SMS) by analyzing 59 primary studies and addressing integration issues such as building SE toolchains. Results: Our findings show that OSLC has been mostly implemented with the development of adapters and MDE. Conclusions: The main advantages of OSLC are related to linked data, involving not only tool adapters for point-to-point integrations, but also proposing solutions for tool replacement in the toolchain, as well as including modifications of OSLC domain specifications and solutions for automated activities for tool integration.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"11 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89285469","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}
Xinwei Ji, Tianming Zhao, Wei Li, Albert Y. Zomaya
Automatic pain assessment systems can help patients get timely and effective pain relief treatment whenever needed. Such a system aims to provide the service with pain identification and pain intensity rating functions. Among the physiological signals, the electrodermal activity (EDA) signal emerges as a promising feature to support both functions in pain assessment. In this work, we propose a machine learning framework to implement pain identification and pain intensity rating using only EDA and its derived features. Our solution also explores the feasibility of using ultra-short EDA segmentation of about 5 seconds to meet real-time requirements. We evaluate our system on two datasets: Biovid, a publicly available dataset, and Apon, the one we build. Experimental results demonstrate that using just the ultra-short EDA signal as input, our algorithm outperforms state-of-the-art baselines and achieves a low regression error of 0.90.
{"title":"Automatic Pain Assessment with Ultra-short Electrodermal Activity Signal","authors":"Xinwei Ji, Tianming Zhao, Wei Li, Albert Y. Zomaya","doi":"10.1145/3555776.3577721","DOIUrl":"https://doi.org/10.1145/3555776.3577721","url":null,"abstract":"Automatic pain assessment systems can help patients get timely and effective pain relief treatment whenever needed. Such a system aims to provide the service with pain identification and pain intensity rating functions. Among the physiological signals, the electrodermal activity (EDA) signal emerges as a promising feature to support both functions in pain assessment. In this work, we propose a machine learning framework to implement pain identification and pain intensity rating using only EDA and its derived features. Our solution also explores the feasibility of using ultra-short EDA segmentation of about 5 seconds to meet real-time requirements. We evaluate our system on two datasets: Biovid, a publicly available dataset, and Apon, the one we build. Experimental results demonstrate that using just the ultra-short EDA signal as input, our algorithm outperforms state-of-the-art baselines and achieves a low regression error of 0.90.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"103 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91301899","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}
The verification of computations performed by an untrusted server is a cornerstone for delegated computations, especially in multi-clients setting where inputs are provided by different parties. Assuming a common secret between clients, a garbled circuit offers the attractive property to ensure the correctness of a result computed by the untrusted server while keeping the input and the function private. Yet, this verification can be guaranteed only once. Based on the notion of multi-key homomorphic encryption (MKHE), we propose RMC-PVC a multi-client verifiable computation protocol, able to verify the correctness of computations performed by an untrusted server for inputs (encoded for a garbled circuit) provided by multiple clients. Thanks to MKHE, the garbled circuit is reusable an arbitrary number of times. In addition, each client can verify the computation by its own. Compared to a single-key FHE scheme, the MKHE usage in RMC-PVC allows to reduce the workload of the server and thus the response delay for the client. It also enforce the privacy of inputs, which are provided by different clients.
{"title":"RMC-PVC: A Multi-Client Reusable Verifiable Computation Protocol","authors":"Gael Marcadet, P. Lafourcade, Léo Robert","doi":"10.1145/3555776.3577680","DOIUrl":"https://doi.org/10.1145/3555776.3577680","url":null,"abstract":"The verification of computations performed by an untrusted server is a cornerstone for delegated computations, especially in multi-clients setting where inputs are provided by different parties. Assuming a common secret between clients, a garbled circuit offers the attractive property to ensure the correctness of a result computed by the untrusted server while keeping the input and the function private. Yet, this verification can be guaranteed only once. Based on the notion of multi-key homomorphic encryption (MKHE), we propose RMC-PVC a multi-client verifiable computation protocol, able to verify the correctness of computations performed by an untrusted server for inputs (encoded for a garbled circuit) provided by multiple clients. Thanks to MKHE, the garbled circuit is reusable an arbitrary number of times. In addition, each client can verify the computation by its own. Compared to a single-key FHE scheme, the MKHE usage in RMC-PVC allows to reduce the workload of the server and thus the response delay for the client. It also enforce the privacy of inputs, which are provided by different clients.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"8 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87532830","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}
Prabir Mondal, Daipayan Chakder, Subham Raj, S. Saha, N. Onoe
The Recommendation System (RS) development and recommending customers' preferred products to the customer are highly desirable motives in today's digital market. Most of the RSs are mainly based on textual information of the engaged entities in the platform and the ratings provided by the users to the products. This paper develops a movie recommendation system where the cold-start problem relating to rating information dependency has been dealt with and the multi-modality approach is introduced. The proposed method differs from existing approaches in three main aspects: (a) implementation of knowledge graph for text embedding, (b) besides textual information, other modalities of movies like video, and audio are employed rather than rating information for generating movie/user representation and this approach deals with the cold-start problem effectively, (c) utilization of graph convolutional network (GCN) for generating some further hidden features and also for developing regression system.
{"title":"Graph Convolutional Neural Network for Multimodal Movie Recommendation","authors":"Prabir Mondal, Daipayan Chakder, Subham Raj, S. Saha, N. Onoe","doi":"10.1145/3555776.3577853","DOIUrl":"https://doi.org/10.1145/3555776.3577853","url":null,"abstract":"The Recommendation System (RS) development and recommending customers' preferred products to the customer are highly desirable motives in today's digital market. Most of the RSs are mainly based on textual information of the engaged entities in the platform and the ratings provided by the users to the products. This paper develops a movie recommendation system where the cold-start problem relating to rating information dependency has been dealt with and the multi-modality approach is introduced. The proposed method differs from existing approaches in three main aspects: (a) implementation of knowledge graph for text embedding, (b) besides textual information, other modalities of movies like video, and audio are employed rather than rating information for generating movie/user representation and this approach deals with the cold-start problem effectively, (c) utilization of graph convolutional network (GCN) for generating some further hidden features and also for developing regression system.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"96 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76865314","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}
The key challenges that recommendation systems must overcome are data isolation and privacy protection issues. Federated learning can efficiently train global models using decentralized data while preserving privacy. In real-world applications, however, it is difficult to achieve high prediction accuracy due to the heterogeneity of devices, the lack of data, and the limited generalization capacity of models. In this research, we introduce a personalized federated knowledge distillation model for a recommendation system based on a multi-head attention mechanism for recommendation systems. Specifically, we first employ federated distillation to improve the performance of student models and introduce a multi-head attention mechanism to enhance user encoding information. Next, we incorporate Wasserstein distance into the objective function of combined distillation to reduce the distribution gap between teacher and student networks and also use an adaptive learning rate technique to enhance convergence. We show that the proposed approach achieves better effectiveness and robustness through benchmarks.
{"title":"MAFD: A Federated Distillation Approach with Multi-head Attention for Recommendation Tasks","authors":"Aming Wu, Young-Woo Kwon","doi":"10.1145/3555776.3577849","DOIUrl":"https://doi.org/10.1145/3555776.3577849","url":null,"abstract":"The key challenges that recommendation systems must overcome are data isolation and privacy protection issues. Federated learning can efficiently train global models using decentralized data while preserving privacy. In real-world applications, however, it is difficult to achieve high prediction accuracy due to the heterogeneity of devices, the lack of data, and the limited generalization capacity of models. In this research, we introduce a personalized federated knowledge distillation model for a recommendation system based on a multi-head attention mechanism for recommendation systems. Specifically, we first employ federated distillation to improve the performance of student models and introduce a multi-head attention mechanism to enhance user encoding information. Next, we incorporate Wasserstein distance into the objective function of combined distillation to reduce the distribution gap between teacher and student networks and also use an adaptive learning rate technique to enhance convergence. We show that the proposed approach achieves better effectiveness and robustness through benchmarks.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"13 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76952591","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}
Embedded Systems (ES) are present in several domains like automotive, smart homes, smart cities, industry, and healthcare, to name but a few. ES brings new challenges to designing embedded software that requires a high level of abstraction and being aware of resource consumption, mainly on resource-constrained devices. Modern programming languages like JavaScript (JS) can help solve these issues. However, JS is an interpreted language that demands attention to develop applications considering the balance between performance and resource consumption. In this scenario, this paper introduces an architecture design that proposes to model software for embedded systems as event-driven applications. Our design combines traditional architectures traits of Time-triggered (TT) and Event-triggered (ET) into a framework named JSEVAsync, promoting a hybrid system that explores JavaScript's non-blocking concept as a development interface to structure the algorithms into asynchronous units. As a result, we aid the development of applications with high abstraction levels and better resource consumption. To validate it, we compare C- and JavaScript-based applications, analyze the source code (static code analysis) to extract software quality metrics, and explore the results from the energy consumption perspective. We found that writing code through JSEVAsync can be up to 21% more energy efficient than the traditional method and can improve design-time metrics.
{"title":"Student Research Abstract: A Hybrid Approach to Design Embedded Software Using JavaScript's Non-blocking Principle","authors":"Fernando L. Oliveira","doi":"10.1145/3555776.3577210","DOIUrl":"https://doi.org/10.1145/3555776.3577210","url":null,"abstract":"Embedded Systems (ES) are present in several domains like automotive, smart homes, smart cities, industry, and healthcare, to name but a few. ES brings new challenges to designing embedded software that requires a high level of abstraction and being aware of resource consumption, mainly on resource-constrained devices. Modern programming languages like JavaScript (JS) can help solve these issues. However, JS is an interpreted language that demands attention to develop applications considering the balance between performance and resource consumption. In this scenario, this paper introduces an architecture design that proposes to model software for embedded systems as event-driven applications. Our design combines traditional architectures traits of Time-triggered (TT) and Event-triggered (ET) into a framework named JSEVAsync, promoting a hybrid system that explores JavaScript's non-blocking concept as a development interface to structure the algorithms into asynchronous units. As a result, we aid the development of applications with high abstraction levels and better resource consumption. To validate it, we compare C- and JavaScript-based applications, analyze the source code (static code analysis) to extract software quality metrics, and explore the results from the energy consumption perspective. We found that writing code through JSEVAsync can be up to 21% more energy efficient than the traditional method and can improve design-time metrics.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"44 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79933869","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}
Shakthi Weerasinghe, A. Zaslavsky, S. Loke, A. Abken, A. Hassani, A. Medvedev
We contend that performance metrics-driven adaptive context caching has a profound impact on performance efficiency in distributed context management systems (CMS). This paper proposes an adaptive context caching approach based on (i) a model of economics-inspired expected returns of caching particular items, and (ii) learning from historical context caching performance, i.e., our approach adaptively (with respect to statistics on historical performance) caches "context" with the objective of minimizing the cost incurred by a CMS in responding to context queries. Our novel algorithm enables context queries and sub-queries to reuse and repurpose cached context in an efficient manner, different from traditional data caching. The paper also proposes heuristics and adaptive policies such as eviction and context cache memory scaling. The method is evaluated using a synthetically generated load of sub-queries inspired by a real-world scenario. We further investigate optimal adaptive caching configurations under different settings. This paper presents and discusses our findings that the proposed statistical selective caching method reaches short-term cost optimality fast under massively volatile queries. The proposed method outperforms related algorithms by up to 47.9% in cost efficiency.
{"title":"Adaptive Context Caching for Efficient Distributed Context Management Systems","authors":"Shakthi Weerasinghe, A. Zaslavsky, S. Loke, A. Abken, A. Hassani, A. Medvedev","doi":"10.1145/3555776.3577602","DOIUrl":"https://doi.org/10.1145/3555776.3577602","url":null,"abstract":"We contend that performance metrics-driven adaptive context caching has a profound impact on performance efficiency in distributed context management systems (CMS). This paper proposes an adaptive context caching approach based on (i) a model of economics-inspired expected returns of caching particular items, and (ii) learning from historical context caching performance, i.e., our approach adaptively (with respect to statistics on historical performance) caches \"context\" with the objective of minimizing the cost incurred by a CMS in responding to context queries. Our novel algorithm enables context queries and sub-queries to reuse and repurpose cached context in an efficient manner, different from traditional data caching. The paper also proposes heuristics and adaptive policies such as eviction and context cache memory scaling. The method is evaluated using a synthetically generated load of sub-queries inspired by a real-world scenario. We further investigate optimal adaptive caching configurations under different settings. This paper presents and discusses our findings that the proposed statistical selective caching method reaches short-term cost optimality fast under massively volatile queries. The proposed method outperforms related algorithms by up to 47.9% in cost efficiency.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"22 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90098562","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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