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Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
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Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 7 pp. 207-212 |
|
Abstract |
Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier. |
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Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
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URL |
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Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 7 pp. 207-212 |
|
Abstract |
Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 7 pp. 207-212 |
|
Abstract |
Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
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|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 7 pp. 207-212 |
|
Abstract |
Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 7 pp. 207-212 |
|
Abstract |
Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 7 pp. 207-212 |
|
Abstract |
Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 7 pp. 207-212 |
|
Abstract |
Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 7 pp. 207-212 |
|
Abstract |
Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 7 pp. 207-212 |
|
Abstract |
Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 7 pp. 207-212 |
|
Abstract |
Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
|
|
Title |
Labeling Big Spatial Data: A Case Study of New York Taxi Limousine Dataset |
|
Author |
Fawaz AlBatati and Louai Alarabi |
| Citation |
Vol. 21 No. 7 pp. 207-212 |
|
Abstract |
Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier. |
|
Keywords |
Unsupervised Learning, K-means Clustering Algorithm, Unlabeled data, Spatial-data, Trajectory. |
|
URL |
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Title |
An Automation Instructor System using Finite State Machine within Web services |
|
Author |
Khalid Aldriwish |
| Citation |
Vol. 21 No. 7 pp. 233-240 |
|
Abstract |
The majority of the Web's success can be related to its productivity and flexibility. Web Services (WSs) have the means to create new patterns for the delivery of software capabilities. The WS easily provides the use of existing components available via the Internet. WSs are a new trend that shares ubiquitous systems with others, so the popularity of the Web is increased day by day with their associated systems. This paper will explore and adopt the possibility of developing a technique that will automate instructors' scheduling of timetables within a Web services environment. This technique has an advantage that facilitates users to reduce the time cost and effort by reducing errors and costs for institutes. Providing dependable tables to avoid mistakes related to instituting schedules is ensured by an automated repetitive manual procedure. Automated systems are increasingly developed based on organizations and their customers. Still, the setting's difficulty of automation systems increases to rise as the system architecture and applications must accomplish various requirements and specifications of ever-demanding project scenarios. The automation system is composed of an operating system, platforms, devices, machines, control system, and information technology. This architecture provides more productivity and optimized services. The main purpose of this paper is to apply an automation system to enhance both quality and productivity. This paper also covers an agile method of proving an automation system by Finite State Machine (FSM) and Attributed Graph Grammar (AGG) tool. |
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Keywords |
Web services, automation; control systems, UML, MAS, FSM, AGG. |
|
URL |
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Title |
An Automation Instructor System using Finite State Machine within Web services |
|
Author |
Khalid Aldriwish |
| Citation |
Vol. 21 No. 7 pp. 233-240 |
|
Abstract |
The majority of the Web's success can be related to its productivity and flexibility. Web Services (WSs) have the means to create new patterns for the delivery of software capabilities. The WS easily provides the use of existing components available via the Internet. WSs are a new trend that shares ubiquitous systems with others, so the popularity of the Web is increased day by day with their associated systems. This paper will explore and adopt the possibility of developing a technique that will automate instructors' scheduling of timetables within a Web services environment. This technique has an advantage that facilitates users to reduce the time cost and effort by reducing errors and costs for institutes. Providing dependable tables to avoid mistakes related to instituting schedules is ensured by an automated repetitive manual procedure. Automated systems are increasingly developed based on organizations and their customers. Still, the setting's difficulty of automation systems increases to rise as the system architecture and applications must accomplish various requirements and specifications of ever-demanding project scenarios. The automation system is composed of an operating system, platforms, devices, machines, control system, and information technology. This architecture provides more productivity and optimized services. The main purpose of this paper is to apply an automation system to enhance both quality and productivity. This paper also covers an agile method of proving an automation system by Finite State Machine (FSM) and Attributed Graph Grammar (AGG) tool. |
|
Keywords |
Web services, automation; control systems, UML, MAS, FSM, AGG. |
|
URL |
|
|
Title |
An Automation Instructor System using Finite State Machine within Web services |
|
Author |
Khalid Aldriwish |
| Citation |
Vol. 21 No. 7 pp. 233-240 |
|
Abstract |
The majority of the Web's success can be related to its productivity and flexibility. Web Services (WSs) have the means to create new patterns for the delivery of software capabilities. The WS easily provides the use of existing components available via the Internet. WSs are a new trend that shares ubiquitous systems with others, so the popularity of the Web is increased day by day with their associated systems. This paper will explore and adopt the possibility of developing a technique that will automate instructors' scheduling of timetables within a Web services environment. This technique has an advantage that facilitates users to reduce the time cost and effort by reducing errors and costs for institutes. Providing dependable tables to avoid mistakes related to instituting schedules is ensured by an automated repetitive manual procedure. Automated systems are increasingly developed based on organizations and their customers. Still, the setting's difficulty of automation systems increases to rise as the system architecture and applications must accomplish various requirements and specifications of ever-demanding project scenarios. The automation system is composed of an operating system, platforms, devices, machines, control system, and information technology. This architecture provides more productivity and optimized services. The main purpose of this paper is to apply an automation system to enhance both quality and productivity. This paper also covers an agile method of proving an automation system by Finite State Machine (FSM) and Attributed Graph Grammar (AGG) tool. |
|
Keywords |
Web services, automation; control systems, UML, MAS, FSM, AGG. |
|
URL |
|
|
Title |
An Automation Instructor System using Finite State Machine within Web services |
|
Author |
Khalid Aldriwish |
| Citation |
Vol. 21 No. 7 pp. 233-240 |
|
Abstract |
The majority of the Web's success can be related to its productivity and flexibility. Web Services (WSs) have the means to create new patterns for the delivery of software capabilities. The WS easily provides the use of existing components available via the Internet. WSs are a new trend that shares ubiquitous systems with others, so the popularity of the Web is increased day by day with their associated systems. This paper will explore and adopt the possibility of developing a technique that will automate instructors' scheduling of timetables within a Web services environment. This technique has an advantage that facilitates users to reduce the time cost and effort by reducing errors and costs for institutes. Providing dependable tables to avoid mistakes related to instituting schedules is ensured by an automated repetitive manual procedure. Automated systems are increasingly developed based on organizations and their customers. Still, the setting's difficulty of automation systems increases to rise as the system architecture and applications must accomplish various requirements and specifications of ever-demanding project scenarios. The automation system is composed of an operating system, platforms, devices, machines, control system, and information technology. This architecture provides more productivity and optimized services. The main purpose of this paper is to apply an automation system to enhance both quality and productivity. This paper also covers an agile method of proving an automation system by Finite State Machine (FSM) and Attributed Graph Grammar (AGG) tool. |
|
Keywords |
Web services, automation; control systems, UML, MAS, FSM, AGG. |
|
URL |
