A framework for ERP post-implementation amendments : A literature analysis
- Oseni, Taiwo, Foster, Susan, Rahim, Mahbubur, Smith, Stephen Patrick
- Authors: Oseni, Taiwo , Foster, Susan , Rahim, Mahbubur , Smith, Stephen Patrick
- Date: 2017
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 21, no. (2017), p.
- Full Text:
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- Description: Post-implementation amendments to ERP systems (ERP-PIA) are of importance for advancing ERP research, but more importantly essential if ERP systems are to be used as a strategic and competitive business tool. For ease of clarity, we have adopted the term “amendments” to encompass the main forms of post implementation activities: maintenance, enhancements and upgrades. The term “amendments” is used to counteract one of the major findings from this research - the inconsistency of terms used by many authors to explain post implementation activities. This paper presents a review of the ERP post-implementation amendment literature in order to provide answers to two specific questions: first, what is the current state of research in the field of ERP-PIA; and second, what are the future research directions that need to be explored in the field of ERP-PIA. From the review, we develop a framework to identify: (a) major themes concerning ERP post-implementation amendments, (b) inherent gaps in the post-implementation amendments literature, and (c) specific areas that require further research attention influencing the uptake of amendments. Suggestions on empirical evaluation of research directions and their relevance in the extension of existing literature is presented.
- Authors: Oseni, Taiwo , Foster, Susan , Rahim, Mahbubur , Smith, Stephen Patrick
- Date: 2017
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 21, no. (2017), p.
- Full Text:
- Reviewed:
- Description: Post-implementation amendments to ERP systems (ERP-PIA) are of importance for advancing ERP research, but more importantly essential if ERP systems are to be used as a strategic and competitive business tool. For ease of clarity, we have adopted the term “amendments” to encompass the main forms of post implementation activities: maintenance, enhancements and upgrades. The term “amendments” is used to counteract one of the major findings from this research - the inconsistency of terms used by many authors to explain post implementation activities. This paper presents a review of the ERP post-implementation amendment literature in order to provide answers to two specific questions: first, what is the current state of research in the field of ERP-PIA; and second, what are the future research directions that need to be explored in the field of ERP-PIA. From the review, we develop a framework to identify: (a) major themes concerning ERP post-implementation amendments, (b) inherent gaps in the post-implementation amendments literature, and (c) specific areas that require further research attention influencing the uptake of amendments. Suggestions on empirical evaluation of research directions and their relevance in the extension of existing literature is presented.
A fuzzy-neural approach for interpretation and fusion of colour and texture features for CBIR systems
- Verma, Brijesh, Kulkarni, Siddhivinayak
- Authors: Verma, Brijesh , Kulkarni, Siddhivinayak
- Date: 2004
- Type: Text , Journal article
- Relation: Journal of Applied Soft Computing Vol. 5, no. 1 (2004), p. 119-130
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- Description: This paper presents a fuzzy-neural approach for interpretation and fusion of colour and texture features for CBIR systems. The presented approach uses fuzzy logic to interpret queries expressed in natural language such as mostly red, many green, few red for colour feature. Tamura feature is used to represent the texture of an image in the database. A term set on each Tamura feature is generated using a fuzzy clustering algorithm to pose a query in terms of natural language. The query can be expressed as a logic combination of natural language terms and Tamura feature values. A fusion of multiple queries is incorporated into the proposed approach. The performance of the technique was evaluated on Brodatz texture benchmark database and it was noticed that there was a prominent increase in the confidence factor for the images. Fusion experiments were conducted using neurofuzzy, fuzzy AND and binary AND techniques. A comparative analysis showed that fuzzy-neural approach has significantly improved the performance of CBIR system.
- Description: C1
- Description: 2003002798
- Authors: Verma, Brijesh , Kulkarni, Siddhivinayak
- Date: 2004
- Type: Text , Journal article
- Relation: Journal of Applied Soft Computing Vol. 5, no. 1 (2004), p. 119-130
- Full Text:
- Reviewed:
- Description: This paper presents a fuzzy-neural approach for interpretation and fusion of colour and texture features for CBIR systems. The presented approach uses fuzzy logic to interpret queries expressed in natural language such as mostly red, many green, few red for colour feature. Tamura feature is used to represent the texture of an image in the database. A term set on each Tamura feature is generated using a fuzzy clustering algorithm to pose a query in terms of natural language. The query can be expressed as a logic combination of natural language terms and Tamura feature values. A fusion of multiple queries is incorporated into the proposed approach. The performance of the technique was evaluated on Brodatz texture benchmark database and it was noticed that there was a prominent increase in the confidence factor for the images. Fusion experiments were conducted using neurofuzzy, fuzzy AND and binary AND techniques. A comparative analysis showed that fuzzy-neural approach has significantly improved the performance of CBIR system.
- Description: C1
- Description: 2003002798
A global optimisation approach to classification in medical diagnosis and prognosis
- Bagirov, Adil, Rubinov, Alex, Yearwood, John, Stranieri, Andrew
- Authors: Bagirov, Adil , Rubinov, Alex , Yearwood, John , Stranieri, Andrew
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at 34th Hawaii International Conference on System Sciences, HICSS-34, Maui, Hawaii, USA : 3rd-6th January 2001
- Full Text:
- Description: In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
- Description: 2003003950
- Authors: Bagirov, Adil , Rubinov, Alex , Yearwood, John , Stranieri, Andrew
- Date: 2001
- Type: Text , Conference paper
- Relation: Paper presented at 34th Hawaii International Conference on System Sciences, HICSS-34, Maui, Hawaii, USA : 3rd-6th January 2001
- Full Text:
- Description: In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
- Description: 2003003950
A mediating effect on erp km model for the performance of oil and gas sector in klang valley: A preliminary study
- Ma’arif, Muhamad, Satar, N. S. M., Singh, D. S. V., Motahar, S. M.
- Authors: Ma’arif, Muhamad , Satar, N. S. M. , Singh, D. S. V. , Motahar, S. M.
- Date: 2019
- Type: Text , Journal article
- Relation: International Journal of Advanced Trends in Computer Science and Engineering Vol. 8, no. 1.4 S1 (2019), p. 463-468
- Full Text:
- Reviewed:
- Description: The development of information technology and the internet has created a borderless business environment and increased market competition. Driving globalization trends, information technology facilitates the organization in the aspect of the decision-making process, increasing productivity with cost-effective and fast delivery to meet customer needs. This article presents a conceptual study of ERP KM model and proposes a direction for further investigation. In this study, a literature review on Incentive as mediating effects in ERP KM model against operational and financial performance was analyzed. In order to achieve this target, to maintain the competitive advantage, oil and gas industry players implement Knowledge Management (KM) on Enterprise Resource Planning (ERP) systems. However, most studies focus only on the implementation and improvement of the ERP process flows as compared to KM concepts. This paper covers literary studies related to KM and ERP as well as merging these two concepts to form the appropriate ERP KM model for the oil and gas sector in Klang Valley, Malaysia. The new model of ERP KM Rizam 2019 introduced in this study will be tested for its effectiveness in the oil and gas sector especially in the Klang Valley. It was found that the mediating effect ‘Incentives’ in addition to KM is expected to have a positive relationship on operational and financial performance compared to the direct influences of ERP usage on performance. © 2019, World Academy of Research in Science and Engineering. All rights reserved.
- Authors: Ma’arif, Muhamad , Satar, N. S. M. , Singh, D. S. V. , Motahar, S. M.
- Date: 2019
- Type: Text , Journal article
- Relation: International Journal of Advanced Trends in Computer Science and Engineering Vol. 8, no. 1.4 S1 (2019), p. 463-468
- Full Text:
- Reviewed:
- Description: The development of information technology and the internet has created a borderless business environment and increased market competition. Driving globalization trends, information technology facilitates the organization in the aspect of the decision-making process, increasing productivity with cost-effective and fast delivery to meet customer needs. This article presents a conceptual study of ERP KM model and proposes a direction for further investigation. In this study, a literature review on Incentive as mediating effects in ERP KM model against operational and financial performance was analyzed. In order to achieve this target, to maintain the competitive advantage, oil and gas industry players implement Knowledge Management (KM) on Enterprise Resource Planning (ERP) systems. However, most studies focus only on the implementation and improvement of the ERP process flows as compared to KM concepts. This paper covers literary studies related to KM and ERP as well as merging these two concepts to form the appropriate ERP KM model for the oil and gas sector in Klang Valley, Malaysia. The new model of ERP KM Rizam 2019 introduced in this study will be tested for its effectiveness in the oil and gas sector especially in the Klang Valley. It was found that the mediating effect ‘Incentives’ in addition to KM is expected to have a positive relationship on operational and financial performance compared to the direct influences of ERP usage on performance. © 2019, World Academy of Research in Science and Engineering. All rights reserved.
A simulated annealing-based maximum-margin clustering algorithm
- Seifollahi, Sattar, Bagirov, Adil, Borzeshi, Ehsan, Piccardi, Massimo
- Authors: Seifollahi, Sattar , Bagirov, Adil , Borzeshi, Ehsan , Piccardi, Massimo
- Date: 2019
- Type: Text , Journal article
- Relation: Computational Intelligence Vol. 35, no. 1 (2019), p. 23-41
- Full Text:
- Reviewed:
- Description: Maximum-margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing-based algorithm that is able to mitigate the issue of local minima in the maximum-margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k-means++ and SVM at each step of the annealing process. More precisely, k-means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms.
- Authors: Seifollahi, Sattar , Bagirov, Adil , Borzeshi, Ehsan , Piccardi, Massimo
- Date: 2019
- Type: Text , Journal article
- Relation: Computational Intelligence Vol. 35, no. 1 (2019), p. 23-41
- Full Text:
- Reviewed:
- Description: Maximum-margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing-based algorithm that is able to mitigate the issue of local minima in the maximum-margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k-means++ and SVM at each step of the annealing process. More precisely, k-means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms.
A survey on context awareness in big data analytics for business applications
- Dinh, Loan, Karmakar, Gour, Kamruzzaman, Joarder
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2020
- Type: Text , Journal article
- Relation: Knowledge and Information Systems Vol. 62, no. 9 (2020), p. 3387-3415
- Full Text:
- Reviewed:
- Description: The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2020
- Type: Text , Journal article
- Relation: Knowledge and Information Systems Vol. 62, no. 9 (2020), p. 3387-3415
- Full Text:
- Reviewed:
- Description: The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
Addressing the complexities of big data analytics in healthcare : The diabetes screening case
- De Silva, Daswin, Burstein, Frada, Jelinek, Herbert, Stranieri, Andrew
- Authors: De Silva, Daswin , Burstein, Frada , Jelinek, Herbert , Stranieri, Andrew
- Date: 2015
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 19, no. (2015), p. S99-S115
- Full Text:
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- Description: The healthcare industry generates a high throughput of medical, clinical and omics data of varying complexity and features. Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards better management of this data for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges to effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. Big Data analytics (BDA) presents the potential to advance this industry with reforms in clinical decision-support and translational research. However, adoption of big data analytics has been slow due to complexities posed by the nature of healthcare data. The success of these systems is hard to predict, so further research is needed to provide a robust framework to ensure investment in BDA is justified. In this paper we investigate these complexities from the perspective of updated Information Systems (IS) participation theory. We present a case study on a large diabetes screening project to integrate, converge and derive expedient insights from such an accumulation of data and make recommendations for a successful BDA implementation grounded in a participatory framework and the specificities of big data in healthcare context. © 2015 De Silva, Burstein, Jelinek, Stranieri.
- Authors: De Silva, Daswin , Burstein, Frada , Jelinek, Herbert , Stranieri, Andrew
- Date: 2015
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 19, no. (2015), p. S99-S115
- Full Text:
- Reviewed:
- Description: The healthcare industry generates a high throughput of medical, clinical and omics data of varying complexity and features. Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards better management of this data for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges to effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. Big Data analytics (BDA) presents the potential to advance this industry with reforms in clinical decision-support and translational research. However, adoption of big data analytics has been slow due to complexities posed by the nature of healthcare data. The success of these systems is hard to predict, so further research is needed to provide a robust framework to ensure investment in BDA is justified. In this paper we investigate these complexities from the perspective of updated Information Systems (IS) participation theory. We present a case study on a large diabetes screening project to integrate, converge and derive expedient insights from such an accumulation of data and make recommendations for a successful BDA implementation grounded in a participatory framework and the specificities of big data in healthcare context. © 2015 De Silva, Burstein, Jelinek, Stranieri.
An empirical evaluation of the potential of public e-procurement to reduce corruption
- Neupane, Arjun, Soar, Jeffrey, Vaidya, Kishor
- Authors: Neupane, Arjun , Soar, Jeffrey , Vaidya, Kishor
- Date: 2014
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 18, no. 2 (2014), p. 21-44
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- Description: One of the significant potential benefits of e-procurement technology is reducing opportunities for corruption in public procurement processes. The authors identified anticorruption capabilities of e-procurement through an extensive literature review and a theoretical model representing the impact of three latent variables: monopoly of power, information asymmetry, and transparency and accountability upon the dependent variable, the intent-to-adopt e-procurement. This research was guided by the Principal- Agent theory and collected the perceptions of 46 government officers of the potential of public e-procurement to reduce corruption in public procurement processes. Results were analysed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The findings suggest that the intent-to-adopt e-procurement has a positive and significant relationship with the independent variables that might inform developing countries in strategies to combat corruption in public procurement.
- Authors: Neupane, Arjun , Soar, Jeffrey , Vaidya, Kishor
- Date: 2014
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 18, no. 2 (2014), p. 21-44
- Full Text:
- Reviewed:
- Description: One of the significant potential benefits of e-procurement technology is reducing opportunities for corruption in public procurement processes. The authors identified anticorruption capabilities of e-procurement through an extensive literature review and a theoretical model representing the impact of three latent variables: monopoly of power, information asymmetry, and transparency and accountability upon the dependent variable, the intent-to-adopt e-procurement. This research was guided by the Principal- Agent theory and collected the perceptions of 46 government officers of the potential of public e-procurement to reduce corruption in public procurement processes. Results were analysed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The findings suggest that the intent-to-adopt e-procurement has a positive and significant relationship with the independent variables that might inform developing countries in strategies to combat corruption in public procurement.
An evaluation methodology for interactive reinforcement learning with simulated users
- Bignold, Adam, Cruz, Francisco, Dazeley, Richard, Vamplew, Peter, Foale, Cameron
- Authors: Bignold, Adam , Cruz, Francisco , Dazeley, Richard , Vamplew, Peter , Foale, Cameron
- Date: 2021
- Type: Text , Journal article
- Relation: Biomimetics Vol. 6, no. 1 (2021), p. 1-15
- Full Text:
- Reviewed:
- Description: Interactive reinforcement learning methods utilise an external information source to evaluate decisions and accelerate learning. Previous work has shown that human advice could significantly improve learning agents’ performance. When evaluating reinforcement learning algorithms, it is common to repeat experiments as parameters are altered or to gain a sufficient sample size. In this regard, to require human interaction every time an experiment is restarted is undesirable, particularly when the expense in doing so can be considerable. Additionally, reusing the same people for the experiment introduces bias, as they will learn the behaviour of the agent and the dynamics of the environment. This paper presents a methodology for evaluating interactive reinforcement learning agents by employing simulated users. Simulated users allow human knowledge, bias, and interaction to be simulated. The use of simulated users allows the development and testing of reinforcement learning agents, and can provide indicative results of agent performance under defined human constraints. While simulated users are no replacement for actual humans, they do offer an affordable and fast alternative for evaluative assisted agents. We introduce a method for performing a preliminary evaluation utilising simulated users to show how performance changes depending on the type of user assisting the agent. Moreover, we describe how human interaction may be simulated, and present an experiment illustrating the applicability of simulating users in evaluating agent performance when assisted by different types of trainers. Experimental results show that the use of this methodology allows for greater insight into the performance of interactive reinforcement learning agents when advised by different users. The use of simulated users with varying characteristics allows for evaluation of the impact of those characteristics on the behaviour of the learning agent. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Bignold, Adam , Cruz, Francisco , Dazeley, Richard , Vamplew, Peter , Foale, Cameron
- Date: 2021
- Type: Text , Journal article
- Relation: Biomimetics Vol. 6, no. 1 (2021), p. 1-15
- Full Text:
- Reviewed:
- Description: Interactive reinforcement learning methods utilise an external information source to evaluate decisions and accelerate learning. Previous work has shown that human advice could significantly improve learning agents’ performance. When evaluating reinforcement learning algorithms, it is common to repeat experiments as parameters are altered or to gain a sufficient sample size. In this regard, to require human interaction every time an experiment is restarted is undesirable, particularly when the expense in doing so can be considerable. Additionally, reusing the same people for the experiment introduces bias, as they will learn the behaviour of the agent and the dynamics of the environment. This paper presents a methodology for evaluating interactive reinforcement learning agents by employing simulated users. Simulated users allow human knowledge, bias, and interaction to be simulated. The use of simulated users allows the development and testing of reinforcement learning agents, and can provide indicative results of agent performance under defined human constraints. While simulated users are no replacement for actual humans, they do offer an affordable and fast alternative for evaluative assisted agents. We introduce a method for performing a preliminary evaluation utilising simulated users to show how performance changes depending on the type of user assisting the agent. Moreover, we describe how human interaction may be simulated, and present an experiment illustrating the applicability of simulating users in evaluating agent performance when assisted by different types of trainers. Experimental results show that the use of this methodology allows for greater insight into the performance of interactive reinforcement learning agents when advised by different users. The use of simulated users with varying characteristics allows for evaluation of the impact of those characteristics on the behaviour of the learning agent. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
An intelligent learning environment for traditional Chinese medicine practitioners and students
- Jia, Long, Stranieri, Andrew, Shen, J
- Authors: Jia, Long , Stranieri, Andrew , Shen, J
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at HIC 2008 Australia's Health Informatics Conference; The Person in the Centre, Brunswick East, Victoria : 31st August - 2nd September 2008
- Full Text:
- Description: Objectives: This study aims to support the training of Traditional Chinese Medicine practitioners by embedding an expert diagnostic model for arthritis into an Intelligent Interactive Learning Environment (IILE). Background: The increasing prevalence of Traditional Chinese Medicine (TCM) outside China is characterised by the emergence of university level practitioner training and stringent regulatory requirements. TCM differential diagnosis is a difficult task that was traditionally taught by exposure to large numbers of patients in a master-apprentice context. In university degree programs, students and novice diagnosticians cannot have the exposure to cases possible in the traditional context. An online system that engages students in the interactive construction of a virtual case and provides immediate feedback on the appropriateness of student actions and the accuracy of diagnostic conclusions can enhance student learning. The system, an Intelligent Interactive Learning Environment (IILE) is based on an approach that has been shown to improve learning outcomes in intensive care nurse training. Methods: An expert model of diagnostic reasoning elicited from TCM expert practitioners lies at the core of the IILE. The knowledge acquisition is performed using an argumentation tree representation that has been shown to be effective in structuring complex knowledge and facilitating engineer - expert interactions. Problems associated with keeping knowledge bases up to date are mitigated with the use of a knowledge model known as ripple down rules permits dynamic updating of knowledge so that knowledge bases evolve over time. A simple narrative model builds up the virtual case study as user interaction proceeds. Results and discussion: This article reports preliminary results in the study that includes an overview of TCM differential diagnosis, the argument tree, the ripple down rule representation and the narrative based IILE. Segments of the knowledge model based solely on TCM literature are illustrated.
- Description: 2003006755
- Authors: Jia, Long , Stranieri, Andrew , Shen, J
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at HIC 2008 Australia's Health Informatics Conference; The Person in the Centre, Brunswick East, Victoria : 31st August - 2nd September 2008
- Full Text:
- Description: Objectives: This study aims to support the training of Traditional Chinese Medicine practitioners by embedding an expert diagnostic model for arthritis into an Intelligent Interactive Learning Environment (IILE). Background: The increasing prevalence of Traditional Chinese Medicine (TCM) outside China is characterised by the emergence of university level practitioner training and stringent regulatory requirements. TCM differential diagnosis is a difficult task that was traditionally taught by exposure to large numbers of patients in a master-apprentice context. In university degree programs, students and novice diagnosticians cannot have the exposure to cases possible in the traditional context. An online system that engages students in the interactive construction of a virtual case and provides immediate feedback on the appropriateness of student actions and the accuracy of diagnostic conclusions can enhance student learning. The system, an Intelligent Interactive Learning Environment (IILE) is based on an approach that has been shown to improve learning outcomes in intensive care nurse training. Methods: An expert model of diagnostic reasoning elicited from TCM expert practitioners lies at the core of the IILE. The knowledge acquisition is performed using an argumentation tree representation that has been shown to be effective in structuring complex knowledge and facilitating engineer - expert interactions. Problems associated with keeping knowledge bases up to date are mitigated with the use of a knowledge model known as ripple down rules permits dynamic updating of knowledge so that knowledge bases evolve over time. A simple narrative model builds up the virtual case study as user interaction proceeds. Results and discussion: This article reports preliminary results in the study that includes an overview of TCM differential diagnosis, the argument tree, the ripple down rule representation and the narrative based IILE. Segments of the knowledge model based solely on TCM literature are illustrated.
- Description: 2003006755
Applying anatomical therapeutic chemical (ATC) and critical term ontologies to Australian drug safety data for association rules and adverse event signalling
- Saunders, Gary, Ivkovic, Sasha, Ghosh, Ranadhir, Yearwood, John
- Authors: Saunders, Gary , Ivkovic, Sasha , Ghosh, Ranadhir , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Conferences in Research and Practice in Information Technology, Advances in Ontologies 2005: Proceedings of the Australasian Ontology Workshop AOW 2005 Vol. 58, no. (2005), p. 93-98
- Full Text:
- Reviewed:
- Description: C1
- Description: 2003001450
- Authors: Saunders, Gary , Ivkovic, Sasha , Ghosh, Ranadhir , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Conferences in Research and Practice in Information Technology, Advances in Ontologies 2005: Proceedings of the Australasian Ontology Workshop AOW 2005 Vol. 58, no. (2005), p. 93-98
- Full Text:
- Reviewed:
- Description: C1
- Description: 2003001450
Applying Turner's three-process theory of power to the study of power relations in a troubled information systems implementation
- Ye, Michelle, de Salas, Kristy, Ollington, Nadia, McKay, Judy
- Authors: Ye, Michelle , de Salas, Kristy , Ollington, Nadia , McKay, Judy
- Date: 2017
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 21, no. (2017), p. 1-25
- Full Text:
- Reviewed:
- Description: This paper explores the nature and exercise of power in an interpretive case study of a troubled information systems (IS) implementation in a university in the Asia Pacific region using Turner's Three-Process Theory of Power based on Social Identity Theory and Self-Categorisation Theory. The findings demonstrate the value of Turner's theoretical lens as well as its insufficiency for explaining all power related activities. This research has led to the development of an extended Three-Process Theory of Power by adding the alternative components that emerged from the data in the case study in relation to the nature and exercises of power. Based on the findings, we further recommend specific guidelines for IS theoreticians and practitioners including advice to project managers on a range of key issues. Thus, this paper contributes theorising the sources of power and tactical applications of power in given situations, particularly in IS implementation projects. © 2017 Ye, de Salas, Ollington & McKay.
- Authors: Ye, Michelle , de Salas, Kristy , Ollington, Nadia , McKay, Judy
- Date: 2017
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 21, no. (2017), p. 1-25
- Full Text:
- Reviewed:
- Description: This paper explores the nature and exercise of power in an interpretive case study of a troubled information systems (IS) implementation in a university in the Asia Pacific region using Turner's Three-Process Theory of Power based on Social Identity Theory and Self-Categorisation Theory. The findings demonstrate the value of Turner's theoretical lens as well as its insufficiency for explaining all power related activities. This research has led to the development of an extended Three-Process Theory of Power by adding the alternative components that emerged from the data in the case study in relation to the nature and exercises of power. Based on the findings, we further recommend specific guidelines for IS theoreticians and practitioners including advice to project managers on a range of key issues. Thus, this paper contributes theorising the sources of power and tactical applications of power in given situations, particularly in IS implementation projects. © 2017 Ye, de Salas, Ollington & McKay.
Being smart and being green : Entrepreneurial innovation in challenging times
- Braun, Patrice, Lowe, Julian
- Authors: Braun, Patrice , Lowe, Julian
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 32nd Institute for Small Business & Entrepreneurship Conference, ISBE 2009, Liverpool, UK : 3rd-6th November 2009
- Full Text:
- Description: In difficult times business operators are looking for clever and affordable ways to grow their enterprises. This paper seeks to make a contribution to a better understanding of proactive environmental and innovation strategies for SMEs and the interaction between demand and supply towards sustainable and innovative business practices. The paper discusses the combined outcomes of the exit survey of a greening small business 2008 pilot program and the entry survey for the 2009 online training and networking version of the program, which fuses environmental, business and ICT- enabled skilling to enhance both SME entrepreneurship and innovation. The study suggests that SME business sustainability cannot be reduced to an oversimplified business case and that pro-environmental strategy adoption and behaviour, and particularly behavioural change, is highly complex. The outcomes of this research are expected to contribute to good practice in environmental and innovation skilling for SMEs, especially skilling that differentiates between supply and demand side skilling and brings together the two sides in a proactive resource acquisition, knowledge transfer and networking environment.
- Description: 2003007572
- Authors: Braun, Patrice , Lowe, Julian
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 32nd Institute for Small Business & Entrepreneurship Conference, ISBE 2009, Liverpool, UK : 3rd-6th November 2009
- Full Text:
- Description: In difficult times business operators are looking for clever and affordable ways to grow their enterprises. This paper seeks to make a contribution to a better understanding of proactive environmental and innovation strategies for SMEs and the interaction between demand and supply towards sustainable and innovative business practices. The paper discusses the combined outcomes of the exit survey of a greening small business 2008 pilot program and the entry survey for the 2009 online training and networking version of the program, which fuses environmental, business and ICT- enabled skilling to enhance both SME entrepreneurship and innovation. The study suggests that SME business sustainability cannot be reduced to an oversimplified business case and that pro-environmental strategy adoption and behaviour, and particularly behavioural change, is highly complex. The outcomes of this research are expected to contribute to good practice in environmental and innovation skilling for SMEs, especially skilling that differentiates between supply and demand side skilling and brings together the two sides in a proactive resource acquisition, knowledge transfer and networking environment.
- Description: 2003007572
Business analytics-based enterprise information systems
- Sun, Zhaohao, Strang, Kenneth, Firmin, Sally
- Authors: Sun, Zhaohao , Strang, Kenneth , Firmin, Sally
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Computer Information Systems Vol. 57, no. 2 (2017), p. 169-178
- Full Text:
- Reviewed:
- Description: Big data analytics and business analytics are a disruptive technology and innovative solution for enterprise development. However, what is the relationship between business analytics, big data analytics, and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? How can analytics be incorporated into EIS? These are still big issues. This article addresses these three issues by proposing ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA is viable for developing EIS. This article then examines incorporation of business analytics into EIS through proposing a model for business analytics service-based EIS, or ASEIS for short. The proposed approach in this article might facilitate the research and development of EIS, business analytics, big data analytics, and business intelligence.
- Authors: Sun, Zhaohao , Strang, Kenneth , Firmin, Sally
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Computer Information Systems Vol. 57, no. 2 (2017), p. 169-178
- Full Text:
- Reviewed:
- Description: Big data analytics and business analytics are a disruptive technology and innovative solution for enterprise development. However, what is the relationship between business analytics, big data analytics, and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? How can analytics be incorporated into EIS? These are still big issues. This article addresses these three issues by proposing ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA is viable for developing EIS. This article then examines incorporation of business analytics into EIS through proposing a model for business analytics service-based EIS, or ASEIS for short. The proposed approach in this article might facilitate the research and development of EIS, business analytics, big data analytics, and business intelligence.
C6 : A holistic model for decision making in web services
- Sun, Zhaohao, Meredith, Grant, Jia, Long
- Authors: Sun, Zhaohao , Meredith, Grant , Jia, Long
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 20th Australasian Conference on Information Systems, Monash University, Melbourne, Victoria : 2nd-4th Dec 2009 p. 904-914
- Full Text:
- Description: Web services are playing a pivotal role in e-business, service intelligence, service science and information systems. This article will examine how the main players make decisions for activities in web service lifecycle (WSLC) and propose a holistic model for decision making in web services. More specifically, this article first examines main players in web services. It also reviews the existing web service lifecycles and proposes a demand-driven web service lifecycle for web service requesters. It will then examine six driving factors for web services, look at their interrelationships and propose a holistic model for decision making in web services, C6, which consists of six Cs: communication, competition, coordination, collaboration, cooperation and control, taking into account the main players in web services and web service lifecycle (WSLC). The proposed approach will facilitate research and development of web services, e-services, service intelligence, service science and service computing.
- Description: 2003007874
- Authors: Sun, Zhaohao , Meredith, Grant , Jia, Long
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 20th Australasian Conference on Information Systems, Monash University, Melbourne, Victoria : 2nd-4th Dec 2009 p. 904-914
- Full Text:
- Description: Web services are playing a pivotal role in e-business, service intelligence, service science and information systems. This article will examine how the main players make decisions for activities in web service lifecycle (WSLC) and propose a holistic model for decision making in web services. More specifically, this article first examines main players in web services. It also reviews the existing web service lifecycles and proposes a demand-driven web service lifecycle for web service requesters. It will then examine six driving factors for web services, look at their interrelationships and propose a holistic model for decision making in web services, C6, which consists of six Cs: communication, competition, coordination, collaboration, cooperation and control, taking into account the main players in web services and web service lifecycle (WSLC). The proposed approach will facilitate research and development of web services, e-services, service intelligence, service science and service computing.
- Description: 2003007874
Community-diversified influence maximization in social networks
- Li, Jianxin, Cai, Taotao, Deng, Ke, Wang, Xinjue, Sellis, Timos, Xia, Feng
- Authors: Li, Jianxin , Cai, Taotao , Deng, Ke , Wang, Xinjue , Sellis, Timos , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: Information Systems Vol. 92, no. (2020), p.
- Full Text:
- Reviewed:
- Description: To meet the requirement of social influence analytics in various applications, the problem of influence maximization has been studied in recent years. The aim is to find a limited number of nodes (i.e., users) which can activate (i.e. influence) the maximum number of nodes in social networks. However, the community diversity of influenced users is largely ignored even though it has unique value in practice. For example, the higher community diversity reduces the risk of marketing campaigns as you should not put all your eggs in one basket; the diversity can also prolong the effect of a marketing campaign in the future promotion. Motivated by this observation, this paper investigates Community-diversified Influence Maximization (CDIM) problem to efficiently find k nodes such that, if a message is initiated and spread by the k nodes, the number as well as the community diversity of the activated nodes will be maximized at the end of propagation process. This work proposes a metric to measure the community-diversified influence and addresses a series of computational challenges. Two algorithms and an innovative CPSP-Tree index have been developed. This study also investigates the situation that community definition is not specified. The effectiveness and efficiency of the proposed solutions have been verified through extensive experimental studies on five real-world social network datasets. © 2020 Elsevier Ltd
- Authors: Li, Jianxin , Cai, Taotao , Deng, Ke , Wang, Xinjue , Sellis, Timos , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: Information Systems Vol. 92, no. (2020), p.
- Full Text:
- Reviewed:
- Description: To meet the requirement of social influence analytics in various applications, the problem of influence maximization has been studied in recent years. The aim is to find a limited number of nodes (i.e., users) which can activate (i.e. influence) the maximum number of nodes in social networks. However, the community diversity of influenced users is largely ignored even though it has unique value in practice. For example, the higher community diversity reduces the risk of marketing campaigns as you should not put all your eggs in one basket; the diversity can also prolong the effect of a marketing campaign in the future promotion. Motivated by this observation, this paper investigates Community-diversified Influence Maximization (CDIM) problem to efficiently find k nodes such that, if a message is initiated and spread by the k nodes, the number as well as the community diversity of the activated nodes will be maximized at the end of propagation process. This work proposes a metric to measure the community-diversified influence and addresses a series of computational challenges. Two algorithms and an innovative CPSP-Tree index have been developed. This study also investigates the situation that community definition is not specified. The effectiveness and efficiency of the proposed solutions have been verified through extensive experimental studies on five real-world social network datasets. © 2020 Elsevier Ltd
Current programs and future needs in health literacy for older people : a literature review
- Lê, Quynh, Terry, Daniel, Woodroffe, Jess
- Authors: Lê, Quynh , Terry, Daniel , Woodroffe, Jess
- Date: 2013
- Type: Text , Journal article , Review
- Relation: Journal of Consumer Health on the Internet Vol. 17, no. 4 (2013), p. 369-388
- Full Text:
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- Description: Inadequate health literacy occurs more amongst older adults and can result in difficulties synthesising information and communicating with health professionals, increased emergency visits and hospitalizations, poor uptake of preventative interventions, increased mortality, and ultimately greater health care costs. A literature review was conducted that identified 12 articles that discussed and examined health literacy interventions among older adults. It revealed few papers exist which highlight programs that examine health literacy outcomes for older adults. The review identified evidence-based best-practice models of health literacy interventions need to be further developed to meet the health literacy needs of aging population. © 2013 Copyright Quynh Le, Daniel R. Terry, and Jess Woodroffe.
- Authors: Lê, Quynh , Terry, Daniel , Woodroffe, Jess
- Date: 2013
- Type: Text , Journal article , Review
- Relation: Journal of Consumer Health on the Internet Vol. 17, no. 4 (2013), p. 369-388
- Full Text:
- Reviewed:
- Description: Inadequate health literacy occurs more amongst older adults and can result in difficulties synthesising information and communicating with health professionals, increased emergency visits and hospitalizations, poor uptake of preventative interventions, increased mortality, and ultimately greater health care costs. A literature review was conducted that identified 12 articles that discussed and examined health literacy interventions among older adults. It revealed few papers exist which highlight programs that examine health literacy outcomes for older adults. The review identified evidence-based best-practice models of health literacy interventions need to be further developed to meet the health literacy needs of aging population. © 2013 Copyright Quynh Le, Daniel R. Terry, and Jess Woodroffe.
Data mining with combined use of optimization techniques and self-organizing maps for improving risk grouping rules : Application to prostate cancer patients
- Churilov, Leonid, Bagirov, Adil, Schwartz, Daniel, Smith, Kate, Dally, Michael
- Authors: Churilov, Leonid , Bagirov, Adil , Schwartz, Daniel , Smith, Kate , Dally, Michael
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Management Information Systems Vol. 21, no. 4 (2005), p. 85-100
- Full Text:
- Reviewed:
- Description: Data mining techniques provide a popular and powerful tool set to generate various data-driven classification systems. In this paper, we investigate the combined use of self-organizing maps (SOM) and nonsmooth nonconvex optimization techniques in order to produce a working case of a data-driven risk classification system. The optimization approach strengthens the validity of SOM results, and the improved classification system increases both the quality of prediction and the homogeneity within the risk groups. Accurate classification of prostate cancer patients into risk groups is important to assist in the identification of appropriate treatment paths. We start with the existing rules and aim to improve classification accuracy by identifying inconsistencies utilizing self-organizing maps as a data visualization tool. Then, we progress to the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. Using the case of prostate cancer patients grouping, we demonstrate strong potential of data-driven risk classification schemes for addressing the risk grouping issues in more general organizational settings. © 2005 M.E. Sharpe, Inc.
- Description: C1
- Description: 2003001265
- Authors: Churilov, Leonid , Bagirov, Adil , Schwartz, Daniel , Smith, Kate , Dally, Michael
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Management Information Systems Vol. 21, no. 4 (2005), p. 85-100
- Full Text:
- Reviewed:
- Description: Data mining techniques provide a popular and powerful tool set to generate various data-driven classification systems. In this paper, we investigate the combined use of self-organizing maps (SOM) and nonsmooth nonconvex optimization techniques in order to produce a working case of a data-driven risk classification system. The optimization approach strengthens the validity of SOM results, and the improved classification system increases both the quality of prediction and the homogeneity within the risk groups. Accurate classification of prostate cancer patients into risk groups is important to assist in the identification of appropriate treatment paths. We start with the existing rules and aim to improve classification accuracy by identifying inconsistencies utilizing self-organizing maps as a data visualization tool. Then, we progress to the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. Using the case of prostate cancer patients grouping, we demonstrate strong potential of data-driven risk classification schemes for addressing the risk grouping issues in more general organizational settings. © 2005 M.E. Sharpe, Inc.
- Description: C1
- Description: 2003001265
Data-driven computational social science : A survey
- Zhang, Jun, Wang, Wei, Xia, Feng, Lin, Yu-Ru, Tong, Hanghang
- Authors: Zhang, Jun , Wang, Wei , Xia, Feng , Lin, Yu-Ru , Tong, Hanghang
- Date: 2020
- Type: Text , Journal article
- Relation: Big Data Research Vol. 21, no. (2020), p. 1-22
- Full Text:
- Reviewed:
- Description: Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on datadriven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.
- Authors: Zhang, Jun , Wang, Wei , Xia, Feng , Lin, Yu-Ru , Tong, Hanghang
- Date: 2020
- Type: Text , Journal article
- Relation: Big Data Research Vol. 21, no. (2020), p. 1-22
- Full Text:
- Reviewed:
- Description: Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on datadriven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.
Deep Reinforcement Learning for Vehicular Edge Computing: An Intelligent Offloading System
- Ning, Zhaolong, Dong, Peiran, Wang, Xiaojie, Rodrigues, Joel, Xia, Feng
- Authors: Ning, Zhaolong , Dong, Peiran , Wang, Xiaojie , Rodrigues, Joel , Xia, Feng
- Date: 2019
- Type: Text , Journal article
- Relation: ACM Transactions on Intelligent Systems and Technology Vol. 10, no. 6 (Dec 2019), p. 24
- Full Text:
- Reviewed:
- Description: The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.
- Authors: Ning, Zhaolong , Dong, Peiran , Wang, Xiaojie , Rodrigues, Joel , Xia, Feng
- Date: 2019
- Type: Text , Journal article
- Relation: ACM Transactions on Intelligent Systems and Technology Vol. 10, no. 6 (Dec 2019), p. 24
- Full Text:
- Reviewed:
- Description: The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.