In pervasive computing environments, wireless sensor networks play an important infrastructure role, collecting reliable and accurate context information so that applications are able to provide services to users on demand. In such environments, sensors should be self-adaptive by taking correct decisions based on sensed data in real-time in a decentralised manner; however, sensed data is often faulty. We thus design a decentralised scheme for fault detection and classification in sensor data in which each sensor node does localised fault detection. A combination of neighbourhood voting and time series data analysis techniques are used to detect faults. We also study the comparative accuracy of both the union and the intersection of the two techniques. Then, detected faults are classified into known fault categories. An initial evaluation with SensorScope, an outdoor temperature dataset, confirms that our solution is able to detect and classify faulty readings into four fault types, namely, 1) random, 2) mal-function, 3) bias, and 4) drift with accuracy up to 95%. The results also show that, with the experimental dataset, the time series data analysis technique performs comparable well in most of the cases, whilst in some other cases the support from neighbourhood voting technique and histogram analysis helps our hybrid solution to successfully detects the faults of all types.
{"title":"Applying time series analysis and neighbourhood voting in a decentralised approach for fault detection and classification in WSNs","authors":"T. Nguyen, Doina Bucur, Marco Aiello, K. Tei","doi":"10.1145/2542050.2542080","DOIUrl":"https://doi.org/10.1145/2542050.2542080","url":null,"abstract":"In pervasive computing environments, wireless sensor networks play an important infrastructure role, collecting reliable and accurate context information so that applications are able to provide services to users on demand. In such environments, sensors should be self-adaptive by taking correct decisions based on sensed data in real-time in a decentralised manner; however, sensed data is often faulty. We thus design a decentralised scheme for fault detection and classification in sensor data in which each sensor node does localised fault detection. A combination of neighbourhood voting and time series data analysis techniques are used to detect faults. We also study the comparative accuracy of both the union and the intersection of the two techniques. Then, detected faults are classified into known fault categories. An initial evaluation with SensorScope, an outdoor temperature dataset, confirms that our solution is able to detect and classify faulty readings into four fault types, namely, 1) random, 2) mal-function, 3) bias, and 4) drift with accuracy up to 95%. The results also show that, with the experimental dataset, the time series data analysis technique performs comparable well in most of the cases, whilst in some other cases the support from neighbourhood voting technique and histogram analysis helps our hybrid solution to successfully detects the faults of all types.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127445323","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}
Recently, with the overwhelming growth of Cloud Computing (CC) and mobile devices, Mobile Cloud Computing (MCC) has emerged and become an attractive area of research. Unlike CC, MCC is formed from the properties of mobile devices such as hardware limitation, mobility, etc. Through MCC, the cloud resources are utilized to support mobile devices in heavy computing services and leverage mobile cloud service efficiently. In this talk, we will present an overview of MCC with our on-going deployed architecture, which involves tremendous potential mobile cloud services in future. In addition, mobile devices -based virtual desktop infrastructure (mVDI) emerges an efficient and convenient method in MCC. Relying on the current remote desktop technology to access to VMs on cloud, mobile devices can easily communicate, and utilize the cloud resources. All computational complexity is executed on the cloud servers rather than on mobile devices. Since offloading computing tasks to the cloud, we can run any services/applications regardless of capability of mobile devices. Consolidating many users into mVDI environment can significantly lower IT management expenses and enables new features such as "available-anywhere" desktops. However, there are many barriers to broad adoption including the slow performance of virtualized I/O, CPU scheduling interference problems. In this talk, we will discuss trends and issues, also the corresponding solutions and challenges in future mobile service through Cloud Computing including mobile cloud services, mobile virtualization, mobile DaaS, N-Screened mobile services and mVDI. From this talk, effective business model is also covered by connecting between mobile contents, platform, wearable devices and cloud infrastructure. Also, some implemented test results will be shared to help audiences for future mobile cloud service design.
{"title":"Data mobile cloud technology: mVDI","authors":"E. Huh","doi":"10.1145/2542050.2542052","DOIUrl":"https://doi.org/10.1145/2542050.2542052","url":null,"abstract":"Recently, with the overwhelming growth of Cloud Computing (CC) and mobile devices, Mobile Cloud Computing (MCC) has emerged and become an attractive area of research. Unlike CC, MCC is formed from the properties of mobile devices such as hardware limitation, mobility, etc. Through MCC, the cloud resources are utilized to support mobile devices in heavy computing services and leverage mobile cloud service efficiently. In this talk, we will present an overview of MCC with our on-going deployed architecture, which involves tremendous potential mobile cloud services in future. In addition, mobile devices -based virtual desktop infrastructure (mVDI) emerges an efficient and convenient method in MCC. Relying on the current remote desktop technology to access to VMs on cloud, mobile devices can easily communicate, and utilize the cloud resources. All computational complexity is executed on the cloud servers rather than on mobile devices. Since offloading computing tasks to the cloud, we can run any services/applications regardless of capability of mobile devices. Consolidating many users into mVDI environment can significantly lower IT management expenses and enables new features such as \"available-anywhere\" desktops. However, there are many barriers to broad adoption including the slow performance of virtualized I/O, CPU scheduling interference problems. In this talk, we will discuss trends and issues, also the corresponding solutions and challenges in future mobile service through Cloud Computing including mobile cloud services, mobile virtualization, mobile DaaS, N-Screened mobile services and mVDI. From this talk, effective business model is also covered by connecting between mobile contents, platform, wearable devices and cloud infrastructure. Also, some implemented test results will be shared to help audiences for future mobile cloud service design.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116887430","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}
B. Quang, N. Quang, E. Medernach, V. Breton, Pham Quoc Long
Virtual screening has proven very effective on grid infrastructures where large scale deployments have led to the identification of active inhibitors for biological targets of interest against malaria, SARS or diabetes. Operating a dedicated virtual screening platform on grid resources requires optimizing the scheduling policy. The scheduling can be done at 2 levels; at site level and at platform level. Site scheduling is done at each site independently; each site is autonomous in its choice of job scheduling. Each site allocates time slots for different groups of users. Platform scheduling is done at group level: inside a time slot jobs from many users are allocated. Pilot agents are sent to sites and act as a container of actual users jobs. They pick up users jobs from a central queue where the second stage scheduling is done. In this paper, we focus on pilot-agent platform shared by many virtual screening users. They need a suitable scheduling algorithm to ensure a certain fairness between users. We have studied the scheduling of users jobs inside central queue and examined the relevance and impact of different scheduling policies (FIFO, SPT, LPT and Round Robin) on the user experience. Optimal criterion used in our research is the stretch, a measure for user experience on the platform. In a first step, we simulated the operation of virtual screening applications on the pilot-agent platform in order to compare the scheduling policies. According to simulation, SPT algorithm was shown to significantly improve scheduling performances. In a second step, the Shortest Processing Time (SPT) and Longest Processing Time (LPT) scheduling policies were implemented on a DIRAC pilot-agent platform at IFI in Hanoi and tested on EGI Biomed Virtual Organization. Experimental results are in good agreement with simulation and confirm that SPT algorithm significantly improves user experience. The relevance of our conclusions also extends to cloud computing. Indeed, cloud infrastructures are also characterized by limited machine availability.
{"title":"Stretch optimization for virtual screening on multi-user pilot-agent platforms on grid/cloud","authors":"B. Quang, N. Quang, E. Medernach, V. Breton, Pham Quoc Long","doi":"10.1145/2542050.2542063","DOIUrl":"https://doi.org/10.1145/2542050.2542063","url":null,"abstract":"Virtual screening has proven very effective on grid infrastructures where large scale deployments have led to the identification of active inhibitors for biological targets of interest against malaria, SARS or diabetes. Operating a dedicated virtual screening platform on grid resources requires optimizing the scheduling policy. The scheduling can be done at 2 levels; at site level and at platform level. Site scheduling is done at each site independently; each site is autonomous in its choice of job scheduling. Each site allocates time slots for different groups of users. Platform scheduling is done at group level: inside a time slot jobs from many users are allocated. Pilot agents are sent to sites and act as a container of actual users jobs. They pick up users jobs from a central queue where the second stage scheduling is done. In this paper, we focus on pilot-agent platform shared by many virtual screening users. They need a suitable scheduling algorithm to ensure a certain fairness between users. We have studied the scheduling of users jobs inside central queue and examined the relevance and impact of different scheduling policies (FIFO, SPT, LPT and Round Robin) on the user experience. Optimal criterion used in our research is the stretch, a measure for user experience on the platform. In a first step, we simulated the operation of virtual screening applications on the pilot-agent platform in order to compare the scheduling policies. According to simulation, SPT algorithm was shown to significantly improve scheduling performances. In a second step, the Shortest Processing Time (SPT) and Longest Processing Time (LPT) scheduling policies were implemented on a DIRAC pilot-agent platform at IFI in Hanoi and tested on EGI Biomed Virtual Organization. Experimental results are in good agreement with simulation and confirm that SPT algorithm significantly improves user experience. The relevance of our conclusions also extends to cloud computing. Indeed, cloud infrastructures are also characterized by limited machine availability.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132101077","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}
Document clustering has become an increasingly important technique for unsupervised document organization, automatic topic extraction, and fast information retrieval or filtering. This paper proposes a Dirichlet process mixture (DPM) model approach to clustering directional data based on the von Mises-Fisher (vMF) distribution, which arises naturally for data distributed on the unit hypersphere. We have developed a mean-field variational inference algorithm for the DPM model of vMFs that is applied to clustering text documents. Using this model, the number of clusters is determined automatically after the clustering process rather than pre-estimated. We conducted extensive experiments to evaluate the proposed approach on a large number of high dimensional text datasets. Empirical experimental results over NMI (Normalized Mutual Information) and Purity evaluation measures demonstrate that our approach outperforms the four state-of-the-art clustering algorithms.
{"title":"Document clustering using dirichlet process mixture model of von Mises-Fisher distributions","authors":"N. K. Anh, Tam The Nguyen, Ngo Van Linh","doi":"10.1145/2542050.2542079","DOIUrl":"https://doi.org/10.1145/2542050.2542079","url":null,"abstract":"Document clustering has become an increasingly important technique for unsupervised document organization, automatic topic extraction, and fast information retrieval or filtering. This paper proposes a Dirichlet process mixture (DPM) model approach to clustering directional data based on the von Mises-Fisher (vMF) distribution, which arises naturally for data distributed on the unit hypersphere. We have developed a mean-field variational inference algorithm for the DPM model of vMFs that is applied to clustering text documents. Using this model, the number of clusters is determined automatically after the clustering process rather than pre-estimated. We conducted extensive experiments to evaluate the proposed approach on a large number of high dimensional text datasets. Empirical experimental results over NMI (Normalized Mutual Information) and Purity evaluation measures demonstrate that our approach outperforms the four state-of-the-art clustering algorithms.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116796555","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}
Natural Language Processing (NLP) for Vietnamese has been researched for more than a decade but still lacks of an open-source NLP pipeline. As the result, researchers have to spend a lot of time on various fundamental tasks before working on the task of interest. Besides, the circumstance holds back text processing technology in Vietnam because an application costs much more money and time to reach a deliverable state. This work is an attempt to solve this issue. By incorporating available open-source software packages and implementing new ones, we have created an open-source, production-ready solution for Vietnamese text processing. Via three experiments, we demonstrated its effectiveness and efficiency. The software has helped us to develop our solution for Vietnamese sentiment analysis and online reputation management and we hope that it will also facilitate research in Vietnamese NLP.
{"title":"VNLP: an open source framework for Vietnamese natural language processing","authors":"N. Le, Bich Ngoc Do, Vien Nguyen, Thi Dam Nguyen","doi":"10.1145/2542050.2542062","DOIUrl":"https://doi.org/10.1145/2542050.2542062","url":null,"abstract":"Natural Language Processing (NLP) for Vietnamese has been researched for more than a decade but still lacks of an open-source NLP pipeline. As the result, researchers have to spend a lot of time on various fundamental tasks before working on the task of interest. Besides, the circumstance holds back text processing technology in Vietnam because an application costs much more money and time to reach a deliverable state. This work is an attempt to solve this issue. By incorporating available open-source software packages and implementing new ones, we have created an open-source, production-ready solution for Vietnamese text processing. Via three experiments, we demonstrated its effectiveness and efficiency. The software has helped us to develop our solution for Vietnamese sentiment analysis and online reputation management and we hope that it will also facilitate research in Vietnamese NLP.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126023175","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}
M. Adriani, Y. W. Choong, Ba-Hung Ngo, Laurent d'Orazio, D. Laurent, N. Spyratos
Cloud computing provides access to "infinite" storage and computing resources, offering promising perspectives for many applications, particularly e-learning. However, this new paradigm requires rethinking of database management principles in order to allow deployment on scalable, easy to access infrastructures, applying a pay-as-you-go model in which failures are not exceptions but rather the norm. The GOD project aims to provide an optimized data management system for e-learning in the cloud by rethinking traditional database management techniques, extending them to consider the specificities of this paradigm.
{"title":"Optimized data management for e-learning in the clouds towards Cloodle","authors":"M. Adriani, Y. W. Choong, Ba-Hung Ngo, Laurent d'Orazio, D. Laurent, N. Spyratos","doi":"10.1145/2542050.2542089","DOIUrl":"https://doi.org/10.1145/2542050.2542089","url":null,"abstract":"Cloud computing provides access to \"infinite\" storage and computing resources, offering promising perspectives for many applications, particularly e-learning. However, this new paradigm requires rethinking of database management principles in order to allow deployment on scalable, easy to access infrastructures, applying a pay-as-you-go model in which failures are not exceptions but rather the norm. The GOD project aims to provide an optimized data management system for e-learning in the cloud by rethinking traditional database management techniques, extending them to consider the specificities of this paradigm.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126586584","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}
A novel approach based on structure information extraction in frequency domain is proposed for image representation problem. Regarding this problem, a new subspace method based on Two-dimensional Fractional Principle Component Analysis (2D-FPCA) in frequency domain is applied to images, thus extracting the texture information. In order to extract the structure information, the system utilizes this new subspace as the bilateral consideration of 2D-FPCA technique called B2D-FPCA. For this purpose: (1) we first introduce the theory of 2D-FPCA based on the definition of fractional variance and fractional covariance matrix; (2) then show its improvement called Bilateral 2D-FPCA and (3) the robustness of 2D-DCT is also described as the preprocessing step. This approach is applied to facial expression representation problem to prove the stability and robustness of the proposed framework. For demonstration, facial expressions datasets (JAFFE, Pain expression subset and Cohn-Kanade) are used in order to compare the proposed framework with some other approaches.
{"title":"On approaching 2D-FPCA technique to improve image representation in frequency domain","authors":"T. Le, Hung Phuoc Truong, H. T. Do, Duc Minh Vo","doi":"10.1145/2542050.2542061","DOIUrl":"https://doi.org/10.1145/2542050.2542061","url":null,"abstract":"A novel approach based on structure information extraction in frequency domain is proposed for image representation problem. Regarding this problem, a new subspace method based on Two-dimensional Fractional Principle Component Analysis (2D-FPCA) in frequency domain is applied to images, thus extracting the texture information. In order to extract the structure information, the system utilizes this new subspace as the bilateral consideration of 2D-FPCA technique called B2D-FPCA. For this purpose: (1) we first introduce the theory of 2D-FPCA based on the definition of fractional variance and fractional covariance matrix; (2) then show its improvement called Bilateral 2D-FPCA and (3) the robustness of 2D-DCT is also described as the preprocessing step. This approach is applied to facial expression representation problem to prove the stability and robustness of the proposed framework. For demonstration, facial expressions datasets (JAFFE, Pain expression subset and Cohn-Kanade) are used in order to compare the proposed framework with some other approaches.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124275072","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}
Although both high maturity and agility appeared as different ways to address and overcome issues related to software development (including maximizing resources and minimizing risks), there has been a mixed understanding about the possibility for their co-existence within an organization. Outside of the dogmatic debate regarding their co-existence, however, voices have been raised recently that recognize that both approaches have their merits. This paper presents the results of a case study on the practices that a purely agile organization has put in place in order to profit from the opportunities that higher maturity can offer in respect to value creation for clients. Our conclusion is that both high maturity and agility contribute to customer satisfaction, high quality and waste reduction; and that complying with standards does not necessarily impose restriction on 'being agile'. Implication for practice is that companies and their clients can benefit from a development approach that embraces both maturity and agility. To achieve this goal, guidelines are needed that direct organizations towards adopting practices that are linked to higher maturity, as well as to agility.
{"title":"Combining maturity with agility: lessons learnt from a case study","authors":"N. Tuan, H. Thang","doi":"10.1145/2542050.2542072","DOIUrl":"https://doi.org/10.1145/2542050.2542072","url":null,"abstract":"Although both high maturity and agility appeared as different ways to address and overcome issues related to software development (including maximizing resources and minimizing risks), there has been a mixed understanding about the possibility for their co-existence within an organization. Outside of the dogmatic debate regarding their co-existence, however, voices have been raised recently that recognize that both approaches have their merits. This paper presents the results of a case study on the practices that a purely agile organization has put in place in order to profit from the opportunities that higher maturity can offer in respect to value creation for clients. Our conclusion is that both high maturity and agility contribute to customer satisfaction, high quality and waste reduction; and that complying with standards does not necessarily impose restriction on 'being agile'. Implication for practice is that companies and their clients can benefit from a development approach that embraces both maturity and agility. To achieve this goal, guidelines are needed that direct organizations towards adopting practices that are linked to higher maturity, as well as to agility.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116526873","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}
N. Thang, Nguyen Huynh Minh Tam, Tran Le Giang, Vo Nhut Tuan, Lan Anh Trinh, Hoang-Hai Tran, V. Toi
Near infrared spectroscopy (NIRS) is currently becoming an effective technique for noninvasive functional brain imaging. Therefore, the methods to improve the quality of measured NIRS signals play an important role to make NIRS broadly accepted in practical applications. Previously, there have been approaches using state-space modeling to recover the NIRS signals from basic component signals to eliminate the artifacts presented in the NIRS measurements. However, the proposed approach requires us an onset vector to determine the starting position of stimulus that is not always available in practical situation. In this work, we provide a new way to find the basic components for efficient implementations of the state-space modeling. We apply principal component analysis to estimate eigenvector-based basis that presents the compact information of the whole signals. We utilize the oxygenated-deoxygenated correlation to find another set of basic components to enhance the quality of NIRS signals. The state-space modeling based on Kalman filter is used to reconstruct the NIRS signals from these basic components. We tested the proposed algorithm with actual data and showed significant improvements of the contrast-to-noise (CNR) of the NIRS signals after filtered by our proposed approach.
{"title":"State-space modeling based on principal component analysis and oxygenated-deoxygenated correlation to improve near-infrared spectroscopy signals","authors":"N. Thang, Nguyen Huynh Minh Tam, Tran Le Giang, Vo Nhut Tuan, Lan Anh Trinh, Hoang-Hai Tran, V. Toi","doi":"10.1145/2542050.2542094","DOIUrl":"https://doi.org/10.1145/2542050.2542094","url":null,"abstract":"Near infrared spectroscopy (NIRS) is currently becoming an effective technique for noninvasive functional brain imaging. Therefore, the methods to improve the quality of measured NIRS signals play an important role to make NIRS broadly accepted in practical applications. Previously, there have been approaches using state-space modeling to recover the NIRS signals from basic component signals to eliminate the artifacts presented in the NIRS measurements. However, the proposed approach requires us an onset vector to determine the starting position of stimulus that is not always available in practical situation. In this work, we provide a new way to find the basic components for efficient implementations of the state-space modeling. We apply principal component analysis to estimate eigenvector-based basis that presents the compact information of the whole signals. We utilize the oxygenated-deoxygenated correlation to find another set of basic components to enhance the quality of NIRS signals. The state-space modeling based on Kalman filter is used to reconstruct the NIRS signals from these basic components. We tested the proposed algorithm with actual data and showed significant improvements of the contrast-to-noise (CNR) of the NIRS signals after filtered by our proposed approach.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121075489","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}
Graph has emerged as a powerful data structure to describe various data. Query evaluation on distributed graphs takes much cost due to the complexity of links among sites. Dan Suciu has proposed algorithms for query evaluation on semistructured data that is a rooted, edge-labeled graph, and algorithms are proved to be efficient in terms of communication steps and data transferring during the evaluation. However, one disadvantage is that communication data are collected to one single site, which leads to a bottleneck in the evaluation for real-life data. In this paper, we propose two algorithms to improve Dan Suciu's algorithms: one-pass algorithm is to significantly reduce a large amount of redundant data in the evaluation, and iter_acc algorithm is to resolve the bottleneck. Then, we design an efficient implementation with only one MapReduce job for our algorithms in Hadoop environment by utilizing features of Hadoop file system. Experiments on cloud system show that one-pass algorithm can detect and remove 50% of data being redundant in the evaluation process on YouTube and DBLP datasets, and iter_acc algorithm is running without the bottleneck even when we double the size of input data.
{"title":"Efficient query evaluation on distributed graphs with Hadoop environment","authors":"Le-Duc Tung, Quyet Nguyen-Van, Zhenjiang Hu","doi":"10.1145/2542050.2542086","DOIUrl":"https://doi.org/10.1145/2542050.2542086","url":null,"abstract":"Graph has emerged as a powerful data structure to describe various data. Query evaluation on distributed graphs takes much cost due to the complexity of links among sites. Dan Suciu has proposed algorithms for query evaluation on semistructured data that is a rooted, edge-labeled graph, and algorithms are proved to be efficient in terms of communication steps and data transferring during the evaluation. However, one disadvantage is that communication data are collected to one single site, which leads to a bottleneck in the evaluation for real-life data. In this paper, we propose two algorithms to improve Dan Suciu's algorithms: one-pass algorithm is to significantly reduce a large amount of redundant data in the evaluation, and iter_acc algorithm is to resolve the bottleneck. Then, we design an efficient implementation with only one MapReduce job for our algorithms in Hadoop environment by utilizing features of Hadoop file system. Experiments on cloud system show that one-pass algorithm can detect and remove 50% of data being redundant in the evaluation process on YouTube and DBLP datasets, and iter_acc algorithm is running without the bottleneck even when we double the size of input data.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127089811","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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