Pub Date : 2020-11-04DOI: 10.1109/ICAIT51105.2020.9261810
Thura Zaw, Aung Nway Oo, Swe Swe Kyaw
Rainfall is one of the most vital factors for rice cultivation. The predicting how much rainfall will be in the future by historical data is the process of rainfall forecasting. Depending on the forecasted rainfall by ARMA model, specific rice types are suggested to cultivate. And then the price of those rice types are being forecasted by BPNN model. This paper introduces combination of effective ARMA and scalable BPNN model for rice types and rice prices, specifying different aspects of view on dataset to achieve the efficiency of the proposed combined model. The proposed combined model is exploited as case study in the application area of Rainfall and Rice Price Data of Pyapon Region in Ayeyarwaddy Division, Republic of the Union of Myanmar. The input neurons to visible layers of the BPNN model are hereby four main factors influenced on rice price and rice production. The proposed combined model proves that the accuracy is more efficient and effective.
{"title":"Combination of ARMA and BPNN Model to predict Rice Type and Rice Price","authors":"Thura Zaw, Aung Nway Oo, Swe Swe Kyaw","doi":"10.1109/ICAIT51105.2020.9261810","DOIUrl":"https://doi.org/10.1109/ICAIT51105.2020.9261810","url":null,"abstract":"Rainfall is one of the most vital factors for rice cultivation. The predicting how much rainfall will be in the future by historical data is the process of rainfall forecasting. Depending on the forecasted rainfall by ARMA model, specific rice types are suggested to cultivate. And then the price of those rice types are being forecasted by BPNN model. This paper introduces combination of effective ARMA and scalable BPNN model for rice types and rice prices, specifying different aspects of view on dataset to achieve the efficiency of the proposed combined model. The proposed combined model is exploited as case study in the application area of Rainfall and Rice Price Data of Pyapon Region in Ayeyarwaddy Division, Republic of the Union of Myanmar. The input neurons to visible layers of the BPNN model are hereby four main factors influenced on rice price and rice production. The proposed combined model proves that the accuracy is more efficient and effective.","PeriodicalId":173291,"journal":{"name":"2020 International Conference on Advanced Information Technologies (ICAIT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117266158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-04DOI: 10.1109/ICAIT51105.2020.9261783
Ei Ei Moel, Tin Maung Wynn, M. Oo, Nay Min Htaik
Traditional traffic signal controls at intersections are ineffective that causes traffic congestion, time wasting, and environmental problems. This paper proposes an intersection traffic light management system that incorporates machine learning technique with the traditional traffic light management system to solve the above challenges. To tackle traffic congestion and waiting time problems, Q learning algorithm is used as the reinforcement learning to choose new action. Action states include various traffic signal phases that are important in generating in realistic control mechanism. In this work, SUMO open source traffic simulator is used to construct realistic traffic intersection settings and the intersection Discretized Representation method is applied to get environment state and to calculate reward. Then, experience replay technique is used to enable reinforcement learning agent to memorize and reuse past experience. The results show that the proposed traffic system outperforms traditional traffic system in Mandalay city. The waiting time reduced 3 times compared with the traditional traffic control.
{"title":"Analysis of Intersection Traffic Light Management System in Mandalay City","authors":"Ei Ei Moel, Tin Maung Wynn, M. Oo, Nay Min Htaik","doi":"10.1109/ICAIT51105.2020.9261783","DOIUrl":"https://doi.org/10.1109/ICAIT51105.2020.9261783","url":null,"abstract":"Traditional traffic signal controls at intersections are ineffective that causes traffic congestion, time wasting, and environmental problems. This paper proposes an intersection traffic light management system that incorporates machine learning technique with the traditional traffic light management system to solve the above challenges. To tackle traffic congestion and waiting time problems, Q learning algorithm is used as the reinforcement learning to choose new action. Action states include various traffic signal phases that are important in generating in realistic control mechanism. In this work, SUMO open source traffic simulator is used to construct realistic traffic intersection settings and the intersection Discretized Representation method is applied to get environment state and to calculate reward. Then, experience replay technique is used to enable reinforcement learning agent to memorize and reuse past experience. The results show that the proposed traffic system outperforms traditional traffic system in Mandalay city. The waiting time reduced 3 times compared with the traditional traffic control.","PeriodicalId":173291,"journal":{"name":"2020 International Conference on Advanced Information Technologies (ICAIT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127186314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-04DOI: 10.1109/ICAIT51105.2020.9261796
Wine Myat Chal, Khine Thin Zar
Today video surveillance applications are not only enough for object monitoring and recording but also necessary for object detection and decision making to prevent and alert unauthorized cases. The main objective of the paper is to detect a person who is passing through into a restricted area. In this paper, there are two main stages, the first stages is human detection and classification. The second stage is the detection of the passing across in a restricted area. In this proposed technique, human motion detection and tracking is implemented using Gaussian Mixture Model. Arbitrary restricted region can be drawn as polygon shape on input surveillance video clip. By comparing the foreground motion object region and the restricted region area, the pass cross event of the motion object can be detected. The accuracy and performance of the proposed technique is proved with various experimental results.
{"title":"An Effective Trespasser Detection System using Video Surveillance Data","authors":"Wine Myat Chal, Khine Thin Zar","doi":"10.1109/ICAIT51105.2020.9261796","DOIUrl":"https://doi.org/10.1109/ICAIT51105.2020.9261796","url":null,"abstract":"Today video surveillance applications are not only enough for object monitoring and recording but also necessary for object detection and decision making to prevent and alert unauthorized cases. The main objective of the paper is to detect a person who is passing through into a restricted area. In this paper, there are two main stages, the first stages is human detection and classification. The second stage is the detection of the passing across in a restricted area. In this proposed technique, human motion detection and tracking is implemented using Gaussian Mixture Model. Arbitrary restricted region can be drawn as polygon shape on input surveillance video clip. By comparing the foreground motion object region and the restricted region area, the pass cross event of the motion object can be detected. The accuracy and performance of the proposed technique is proved with various experimental results.","PeriodicalId":173291,"journal":{"name":"2020 International Conference on Advanced Information Technologies (ICAIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128519729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-04DOI: 10.1109/ICAIT51105.2020.9261814
Ohmmar Min Mon, M. Mon
The Software Defined Network (SDN) offers centralized traffic flow control from a global view of the underlying network, covering the route for SDN's various traffic engineering research. The continuous development of virtual machine migration, data backup, or MapReduce operations, the scale of the current network become more and more enormous. Equal-Cost Multi-Path (ECMP) is a method of multipath routing to distribute the flows using static hashing method. These flows do not reveal degrading due to static hashing for the resulting collisions. Traffic overloads due to long delay and low throughput will frequently cause connection congestion and result in less flow transmission performance. The paper focuses on the Flow based Traffic Scheduling FTS scheme that provides rerouting solution by scheduling the flow to the maximum available bandwidth path between the several paths. The experimental evaluations are used to show the efficiency of FTS in relation to ECMP and Hedera with reference to average throughput and flow completion time (FCT).
{"title":"Flow based Traffic Scheduling in Software Defined Network","authors":"Ohmmar Min Mon, M. Mon","doi":"10.1109/ICAIT51105.2020.9261814","DOIUrl":"https://doi.org/10.1109/ICAIT51105.2020.9261814","url":null,"abstract":"The Software Defined Network (SDN) offers centralized traffic flow control from a global view of the underlying network, covering the route for SDN's various traffic engineering research. The continuous development of virtual machine migration, data backup, or MapReduce operations, the scale of the current network become more and more enormous. Equal-Cost Multi-Path (ECMP) is a method of multipath routing to distribute the flows using static hashing method. These flows do not reveal degrading due to static hashing for the resulting collisions. Traffic overloads due to long delay and low throughput will frequently cause connection congestion and result in less flow transmission performance. The paper focuses on the Flow based Traffic Scheduling FTS scheme that provides rerouting solution by scheduling the flow to the maximum available bandwidth path between the several paths. The experimental evaluations are used to show the efficiency of FTS in relation to ECMP and Hedera with reference to average throughput and flow completion time (FCT).","PeriodicalId":173291,"journal":{"name":"2020 International Conference on Advanced Information Technologies (ICAIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128703815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-04DOI: 10.1109/ICAIT51105.2020.9261809
Phyo Wah Wah Myint, Swe Zin Hlaing, Ei Chaw Htoon
Since a lot of information is outsourcing into cloud servers, data confidentiality becomes a higher risk to service providers. To assure data security, Ciphertext Policy Attributes-Based Encryption (CP-ABE) is observed for the cloud environment. Because ciphertexts and secret keys are relying on attributes, the revocation issue becomes a challenge for CP-ABE. This paper proposes an encryption access control (EAC) scheme to fulfill policy revocation which covers both attribute and user revocation. When one of the attributes in an access policy is changed by the data owner, the authorized users should be updated immediately because the revoked users who have gained previous access policy can observe the ciphertext. Especially for data owners, four types of updating policy levels are predefined. By classifying those levels, each secret token key is distinctly generated for each level. Consequently, a new secret key is produced by hashing the secret token key. This paper analyzes the execution times of key generation, encryption, and decryption times between non-revocation and policy revocation cases. Performance analysis for policy revocation is also presented in this paper.
{"title":"EAC: Encryption Access Control Scheme for Policy Revocation in Cloud Data","authors":"Phyo Wah Wah Myint, Swe Zin Hlaing, Ei Chaw Htoon","doi":"10.1109/ICAIT51105.2020.9261809","DOIUrl":"https://doi.org/10.1109/ICAIT51105.2020.9261809","url":null,"abstract":"Since a lot of information is outsourcing into cloud servers, data confidentiality becomes a higher risk to service providers. To assure data security, Ciphertext Policy Attributes-Based Encryption (CP-ABE) is observed for the cloud environment. Because ciphertexts and secret keys are relying on attributes, the revocation issue becomes a challenge for CP-ABE. This paper proposes an encryption access control (EAC) scheme to fulfill policy revocation which covers both attribute and user revocation. When one of the attributes in an access policy is changed by the data owner, the authorized users should be updated immediately because the revoked users who have gained previous access policy can observe the ciphertext. Especially for data owners, four types of updating policy levels are predefined. By classifying those levels, each secret token key is distinctly generated for each level. Consequently, a new secret key is produced by hashing the secret token key. This paper analyzes the execution times of key generation, encryption, and decryption times between non-revocation and policy revocation cases. Performance analysis for policy revocation is also presented in this paper.","PeriodicalId":173291,"journal":{"name":"2020 International Conference on Advanced Information Technologies (ICAIT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124765689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-04DOI: 10.1109/ICAIT51105.2020.9261788
Yan Naing Htun, Bawin Aye, Aung Aung
Digital processing of remotely sensed image data has been great importance in recent times. This research work discusses task distribution method in parallel image processing and load balancing under the circumstance of multi-tasks and multi-processors in remote sensing. Task distribution method can speed up computation and improve efficiency and perform larger computations which are not possible on single processor system. The tasks are distributed based on the segmentation of color and the Support Vector Machine (SVM) is used to classify the indices of the input image and intends to design and improve the color segmentation based task distribution method for index classification using machine learning. In the system, the RGB satellite image is used as an input image and the output is the four indices of forest, building, road and land The results of the system are more accurate and less time consumption than non-distributed computing methods. It is implemented in MATLAB platform with parallel computation toolbox because the system can solve computationally and data-intensive tasks using multicore processors and clusters of computer.
{"title":"Task Distribution Method for Index Classification based on Color Segmentation in Remote Sensing","authors":"Yan Naing Htun, Bawin Aye, Aung Aung","doi":"10.1109/ICAIT51105.2020.9261788","DOIUrl":"https://doi.org/10.1109/ICAIT51105.2020.9261788","url":null,"abstract":"Digital processing of remotely sensed image data has been great importance in recent times. This research work discusses task distribution method in parallel image processing and load balancing under the circumstance of multi-tasks and multi-processors in remote sensing. Task distribution method can speed up computation and improve efficiency and perform larger computations which are not possible on single processor system. The tasks are distributed based on the segmentation of color and the Support Vector Machine (SVM) is used to classify the indices of the input image and intends to design and improve the color segmentation based task distribution method for index classification using machine learning. In the system, the RGB satellite image is used as an input image and the output is the four indices of forest, building, road and land The results of the system are more accurate and less time consumption than non-distributed computing methods. It is implemented in MATLAB platform with parallel computation toolbox because the system can solve computationally and data-intensive tasks using multicore processors and clusters of computer.","PeriodicalId":173291,"journal":{"name":"2020 International Conference on Advanced Information Technologies (ICAIT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124048988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-04DOI: 10.1109/ICAIT51105.2020.9261806
K. Han, Bawin Aye, Hlaing Moe Than, C. Linn
The proposed Vehicle Information Inspection System (VIIS) includes inspection of the vehicle color, type, and license plate. The aim of this paper is to recognize the moving vehicle's color, type, and license plate characters. The vehicle color is recognized using K-means clustering method and K-Nearest Neighbors (K-NN) method. The vehicle type is recognized using Convolutional Neural Network (CNN). The proposed vehicle license plate recognition system consists of four processes: license plate localization, license plate skew correction, character segmentation, and character recognition. CNN is also mainly used to recognize license plate characters. The extracted vehicle information is stored in the database. This information is matched and inspected with the blacklist vehicle information in the database. The proposed method can help in monitoring and inspection of the blacklist vehicles on the road without any human effort.
{"title":"Vision Based Vehicle Information Inspection System Using Deep Learning","authors":"K. Han, Bawin Aye, Hlaing Moe Than, C. Linn","doi":"10.1109/ICAIT51105.2020.9261806","DOIUrl":"https://doi.org/10.1109/ICAIT51105.2020.9261806","url":null,"abstract":"The proposed Vehicle Information Inspection System (VIIS) includes inspection of the vehicle color, type, and license plate. The aim of this paper is to recognize the moving vehicle's color, type, and license plate characters. The vehicle color is recognized using K-means clustering method and K-Nearest Neighbors (K-NN) method. The vehicle type is recognized using Convolutional Neural Network (CNN). The proposed vehicle license plate recognition system consists of four processes: license plate localization, license plate skew correction, character segmentation, and character recognition. CNN is also mainly used to recognize license plate characters. The extracted vehicle information is stored in the database. This information is matched and inspected with the blacklist vehicle information in the database. The proposed method can help in monitoring and inspection of the blacklist vehicles on the road without any human effort.","PeriodicalId":173291,"journal":{"name":"2020 International Conference on Advanced Information Technologies (ICAIT)","volume":"30 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128132821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-04DOI: 10.1109/ICAIT51105.2020.9261812
Thandar Aung, Hla Yin Min, A. Maw
In big data era, the reliability of real-time messages is the most important point for many business needs. Real-time messaging system establishes to handle real-time requirements and reliability requirements in business applications. Apache Kafka is a highly scalable publish-subscribe messaging system. The effect of reliability in Apache Kafka impact on the different use cases. Fault tolerance becomes the main factor to improve the reliability of Apache Kafka. Apache Kafka needs to handle the challenges of the message loss rate in server failure. To improve the message recovery, Kafka requires to manage the message delivery process. This paper introduces CIMLA (Checkpoint Interval Message Logging Algorithm) to enhance the message delivery in Apache Kafka. The system evaluates the message loss rate, throughput and latency in the server failure case. The system achieves the message recovery process by using CIMLA which effectively controls the lost messages with identification. The experimental results emphasize the improvement of message recovery and verify the reliability of message delivery in Apache Kafka.
{"title":"CIMLA: Checkpoint Interval Message Logging Algorithm in Kafka Pipeline Architecture","authors":"Thandar Aung, Hla Yin Min, A. Maw","doi":"10.1109/ICAIT51105.2020.9261812","DOIUrl":"https://doi.org/10.1109/ICAIT51105.2020.9261812","url":null,"abstract":"In big data era, the reliability of real-time messages is the most important point for many business needs. Real-time messaging system establishes to handle real-time requirements and reliability requirements in business applications. Apache Kafka is a highly scalable publish-subscribe messaging system. The effect of reliability in Apache Kafka impact on the different use cases. Fault tolerance becomes the main factor to improve the reliability of Apache Kafka. Apache Kafka needs to handle the challenges of the message loss rate in server failure. To improve the message recovery, Kafka requires to manage the message delivery process. This paper introduces CIMLA (Checkpoint Interval Message Logging Algorithm) to enhance the message delivery in Apache Kafka. The system evaluates the message loss rate, throughput and latency in the server failure case. The system achieves the message recovery process by using CIMLA which effectively controls the lost messages with identification. The experimental results emphasize the improvement of message recovery and verify the reliability of message delivery in Apache Kafka.","PeriodicalId":173291,"journal":{"name":"2020 International Conference on Advanced Information Technologies (ICAIT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125672476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-04DOI: 10.1109/ICAIT51105.2020.9261787
Khin Nandar Tun, Aye Myat Myat Paing
Fog computing is the new favorable technology that can support real-time cloud application services near to the physical IoT device at the network edge rather than the cloud. It is the decentralized internet-based computing and a layer between the cloud and IoT devices. For real time analytics and critical services, it must process the data as fast as possible. Fog computing can process and manipulate the data at the network edge devices in near real time. However, there is a resource limitation because the services are performed by the fog devices at network edge. So, it needs to utilize the resources of the fog devices. As a result, the placement of IoT (edge) devices on what fog devices is considered to get the efficient resource utilization of the fog devices in this paper. In order to get efficient resource utilization of the fog devices and reduce application delay, network usage and cost of execution in cloud, Fog Node Placement algorithm that finds the fog devices which is minimum distance and enough resource for the IoT (edge) devices is proposed. In addition, the effectiveness of the algorithm will be showed by comparing with traditional cloud placement and fog cloud placement with nearest distance.
{"title":"Resource Aware Placement of IoT Devices in Fog Computing","authors":"Khin Nandar Tun, Aye Myat Myat Paing","doi":"10.1109/ICAIT51105.2020.9261787","DOIUrl":"https://doi.org/10.1109/ICAIT51105.2020.9261787","url":null,"abstract":"Fog computing is the new favorable technology that can support real-time cloud application services near to the physical IoT device at the network edge rather than the cloud. It is the decentralized internet-based computing and a layer between the cloud and IoT devices. For real time analytics and critical services, it must process the data as fast as possible. Fog computing can process and manipulate the data at the network edge devices in near real time. However, there is a resource limitation because the services are performed by the fog devices at network edge. So, it needs to utilize the resources of the fog devices. As a result, the placement of IoT (edge) devices on what fog devices is considered to get the efficient resource utilization of the fog devices in this paper. In order to get efficient resource utilization of the fog devices and reduce application delay, network usage and cost of execution in cloud, Fog Node Placement algorithm that finds the fog devices which is minimum distance and enough resource for the IoT (edge) devices is proposed. In addition, the effectiveness of the algorithm will be showed by comparing with traditional cloud placement and fog cloud placement with nearest distance.","PeriodicalId":173291,"journal":{"name":"2020 International Conference on Advanced Information Technologies (ICAIT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115046675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-04DOI: 10.1109/ICAIT51105.2020.9261805
Khin Myat Kyu, Aung Nway Oo
During couples of decades, the popularity of Semantic Web is increased in both academic and industry. RDF is a data model standardized by W3C for the Semantic Web and a query language used for retrieving the RDF data is SPARQL query. Most of the real-world applications has widely used RDF as a standardized data format, so that efficient data storage and query processing techniques for the RDF data need to be considered. In this paper, an indexing structure and query processing technique are developed to enhance the execution time of three different types of SPARQL queries such as conjunctive, optional, and alternative queries. As the RDF data is formed in graph structure, the indices are constructed by extracting the subgraphs around each vertex based on the edges nature of the vertex. Although the size of query is large especially in conjunctive query, our proposed graph-based index and query processing method can help to save the query execution time. Experimental results show that our proposed method works well for all three different types of SPARQL queries and outpaces the competition at processing the conjunctive queries.
{"title":"Enhancement of Query Execution Time in SPARQL Query Processing","authors":"Khin Myat Kyu, Aung Nway Oo","doi":"10.1109/ICAIT51105.2020.9261805","DOIUrl":"https://doi.org/10.1109/ICAIT51105.2020.9261805","url":null,"abstract":"During couples of decades, the popularity of Semantic Web is increased in both academic and industry. RDF is a data model standardized by W3C for the Semantic Web and a query language used for retrieving the RDF data is SPARQL query. Most of the real-world applications has widely used RDF as a standardized data format, so that efficient data storage and query processing techniques for the RDF data need to be considered. In this paper, an indexing structure and query processing technique are developed to enhance the execution time of three different types of SPARQL queries such as conjunctive, optional, and alternative queries. As the RDF data is formed in graph structure, the indices are constructed by extracting the subgraphs around each vertex based on the edges nature of the vertex. Although the size of query is large especially in conjunctive query, our proposed graph-based index and query processing method can help to save the query execution time. Experimental results show that our proposed method works well for all three different types of SPARQL queries and outpaces the competition at processing the conjunctive queries.","PeriodicalId":173291,"journal":{"name":"2020 International Conference on Advanced Information Technologies (ICAIT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130715084","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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