The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
Latifur Khan - One of the best experts on this subject based on the ideXlab platform.
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DASFAA (2) - Tutorial: data stream mining and its applications
Database Systems for Advanced Applications, 2012Co-Authors: Latifur Khan, Wei FanAbstract:Data streams are continuous flows of data. Examples of data streams include network Traffic, Sensor data, call center records and so on. Their sheer volume and speed pose a great challenge for the data mining community to mine them. Data streams demonstrate several unique properties: infinite length, concept-drift, concept-evolution, feature-evolution and limited labeled data. Concept-drift occurs in data streams when the underlying concept of data changes over time. Concept-evolution occurs when new classes evolve in streams. Feature-evolution occurs when feature set varies with time in data streams. Data streams also suffer from scarcity of labeled data since it is not possible to manually label all the data points in the stream. Each of these properties adds a challenge to data stream mining. Multi-step methodologies and techniques, and multi-scan algorithms, suitable for knowledge discovery and data mining, cannot be readily applied to data streams. This is due to well-known limitations such as bounded memory, high speed data arrival, online/timely data processing, and need for one-pass techniques (i.e., forgotten raw data) issues etc. In spite of the success and extensive studies of stream mining techniques, there is no single tutorial dedicated to a unified study of the new challenges introduced by evolving stream data like change detection, novelty detection, and feature evolution. This tutorial presents an organized picture on how to handle various data mining techniques in data streams: in particular, how to handle classification and clustering in evolving data streams by addressing these challenges. The importance and significance of research in data stream mining has been manifested in most recent launch of large scale stream processing prototype in many important application areas. In the same time, commercialization of streams (e.g., IBM InfoSphere streams, etc.) brings new challenge and research opportunities to the Data Mining (DM) community. In this tutorial a number of applications of stream mining will be presented such as adaptive malicious code detection, on-line malicious URL detection, evolving insider threat detection and textual stream classification.
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ICTAI (2) - Data Stream Mining: Challenges and Techniques
2010 22nd IEEE International Conference on Tools with Artificial Intelligence, 2010Co-Authors: Latifur KhanAbstract:Data streams are continuous flows of data. Examples of data streams include network Traffic, Sensor data, call center records and so on. Their sheer volume and speed pose a great challenge for the data mining community to mine them. Data streams demonstrate several unique properties: infinite length, concept-drift, concept-evolution, and feature-evolution. Concept-drift occurs in data streams when the underlying concept of data changes over time. Concept-evolution occurs when new classes evolve in streams. Feature-evolution occurs when feature set varies with time in data streams. Each of these properties adds a challenge to data stream mining. This invited talk will present an organized picture on how to handle various data mining techniques in data streams: in particular, how to handle classification in evolving data streams by addressing these challenges.
Jianwu Wang - One of the best experts on this subject based on the ideXlab platform.
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A Hybrid Processing System for Large-Scale Traffic Sensor Data
IEEE Access, 2015Co-Authors: Zhuofeng Zhao, Weilong Ding, Jianwu Wang, Yanbo HanAbstract:In recent years, with the further adoption of the Internet of Things and Sensor technology, all kinds of intelligent transportation system (ITS) applications based on a wide range of Traffic Sensor data have had rapid development. Traffic Sensor data gathered by large amounts of Sensors show some new features, such as massiveness, continuity, streaming, and spatio-temporality. ITS applications utilizing Traffic Sensor data can be divided into three main types: 1) offline processing of historical data; 2) online processing of streaming data; and 3) hybrid processing of both. Current research tends to solve these problems in separate solutions, such as stream computing and batch processing. In this paper, we propose a hybrid processing approach and present corresponding system implementation for both streaming and historical Traffic Sensor data, which combines spatio-temporal data partitioning, pipelined parallel processing, and stream computing techniques to support hybrid processing of Traffic Sensor data in real-time. Three types of real-world applications are explained in detail to show the usability and generality of our approach and system. Our experiments show that the system can achieve better performance than a popular open-source streaming system called Storm.
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SERVICES - An Integrated Processing Platform for Traffic Sensor Data and Its Applications in Intelligent Transportation Systems
2014 IEEE World Congress on Services, 2014Co-Authors: Zhuofeng Zhao, Weilong Ding, Jun Fang, Jianwu WangAbstract:With the continuous expansion of the scope of Traffic Sensor networks, Traffic Sensor data becomes widely available and large in amount. Traffic Sensor data gathered by large amounts of Sensors shows the massive, continuous, streaming and spatio-temporal characteristics compared to traditional Traffic data. In order to satisfy the requirements of different applications in Intelligent Transportation Systems (ITS), we need to have the capability of real-time processing over both streaming and historical Traffic Sensor data. In this paper, we present DeCloud4SD, an integrated processing platform for Traffic Sensor data, which is designed to provide services for receiving, storing, acquiring and computing Traffic Sensor data in a scalable architecture with real-time guarantee. Three types of applications using DeCloud4SD in a real ITS project are also described in detail. Through the analysis of these applications, we can see that DeCloud4SD can ensure: 1) scalable and customizable Traffic Sensor data gathering and computing, 2) rapid application development and deployment using a MapReduce-like model, 3) seamless integration with existing relational data sources and applications.
Zhuofeng Zhao - One of the best experts on this subject based on the ideXlab platform.
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A Hybrid Processing System for Large-Scale Traffic Sensor Data
IEEE Access, 2015Co-Authors: Zhuofeng Zhao, Weilong Ding, Jianwu Wang, Yanbo HanAbstract:In recent years, with the further adoption of the Internet of Things and Sensor technology, all kinds of intelligent transportation system (ITS) applications based on a wide range of Traffic Sensor data have had rapid development. Traffic Sensor data gathered by large amounts of Sensors show some new features, such as massiveness, continuity, streaming, and spatio-temporality. ITS applications utilizing Traffic Sensor data can be divided into three main types: 1) offline processing of historical data; 2) online processing of streaming data; and 3) hybrid processing of both. Current research tends to solve these problems in separate solutions, such as stream computing and batch processing. In this paper, we propose a hybrid processing approach and present corresponding system implementation for both streaming and historical Traffic Sensor data, which combines spatio-temporal data partitioning, pipelined parallel processing, and stream computing techniques to support hybrid processing of Traffic Sensor data in real-time. Three types of real-world applications are explained in detail to show the usability and generality of our approach and system. Our experiments show that the system can achieve better performance than a popular open-source streaming system called Storm.
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SERVICES - An Integrated Processing Platform for Traffic Sensor Data and Its Applications in Intelligent Transportation Systems
2014 IEEE World Congress on Services, 2014Co-Authors: Zhuofeng Zhao, Weilong Ding, Jun Fang, Jianwu WangAbstract:With the continuous expansion of the scope of Traffic Sensor networks, Traffic Sensor data becomes widely available and large in amount. Traffic Sensor data gathered by large amounts of Sensors shows the massive, continuous, streaming and spatio-temporal characteristics compared to traditional Traffic data. In order to satisfy the requirements of different applications in Intelligent Transportation Systems (ITS), we need to have the capability of real-time processing over both streaming and historical Traffic Sensor data. In this paper, we present DeCloud4SD, an integrated processing platform for Traffic Sensor data, which is designed to provide services for receiving, storing, acquiring and computing Traffic Sensor data in a scalable architecture with real-time guarantee. Three types of applications using DeCloud4SD in a real ITS project are also described in detail. Through the analysis of these applications, we can see that DeCloud4SD can ensure: 1) scalable and customizable Traffic Sensor data gathering and computing, 2) rapid application development and deployment using a MapReduce-like model, 3) seamless integration with existing relational data sources and applications.
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ICCVE - A Real-Time Processing System for Massive Traffic Sensor Data
2012 International Conference on Connected Vehicles and Expo (ICCVE), 2012Co-Authors: Zhuofeng ZhaoAbstract:With the continuous expansion of the scope of the transportation Sensor networks, a new kind of data, namely Traffic Sensor data, becomes widely available. Traffic Sensor data gathered by large amounts of transportation Sensors shows the massive, continuous, streaming and probabilistic characteristics compared to traditional data. In order to satisfy the requirements of different Traffic Sensor data applications, the capability of real-time processing for massive Traffic Sensor data is emergently needed. In this paper, a Real-Time Processing System (shorted as RTPS), which adopts the decentralized distributed architecture to support the parallel processing of Traffic Sensor data, is presented with a case study of a real world application about vehicle license plate recognition data. And the parallel computing model behind RTPS and corresponding programing interface are proposed. The experiment based on application of vehicle license plate recognition data shows that our system has good scalability and the processing performance increases in linear progression as the number of processing nodes increases.
Yanbo Han - One of the best experts on this subject based on the ideXlab platform.
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A Service-Based Approach to Traffic Sensor Data Integration and Analysis to Support Community-Wide Green Commute in China
IEEE Transactions on Intelligent Transportation Systems, 2016Co-Authors: Yanbo Han, Guiling Wang, Chen Liu, Zhongmei Zhang, Meiling ZhuAbstract:With the increasing abundance of Traffic data from Sensors and devices, the integration and analysis of such streaming data are gaining importance in many application scenarios. This paper proposes a service-based approach for integrating and analyzing the Traffic Sensor data to support green commute in China by automatically discovering carpooling companions within a community. Our focuses are on modeling and design of the carpooling discovery services, algorithms for implementing the services, and performance enhancement when the data volume scales up. The proposed approach is verified with experiments using real-world data.
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A Hybrid Processing System for Large-Scale Traffic Sensor Data
IEEE Access, 2015Co-Authors: Zhuofeng Zhao, Weilong Ding, Jianwu Wang, Yanbo HanAbstract:In recent years, with the further adoption of the Internet of Things and Sensor technology, all kinds of intelligent transportation system (ITS) applications based on a wide range of Traffic Sensor data have had rapid development. Traffic Sensor data gathered by large amounts of Sensors show some new features, such as massiveness, continuity, streaming, and spatio-temporality. ITS applications utilizing Traffic Sensor data can be divided into three main types: 1) offline processing of historical data; 2) online processing of streaming data; and 3) hybrid processing of both. Current research tends to solve these problems in separate solutions, such as stream computing and batch processing. In this paper, we propose a hybrid processing approach and present corresponding system implementation for both streaming and historical Traffic Sensor data, which combines spatio-temporal data partitioning, pipelined parallel processing, and stream computing techniques to support hybrid processing of Traffic Sensor data in real-time. Three types of real-world applications are explained in detail to show the usability and generality of our approach and system. Our experiments show that the system can achieve better performance than a popular open-source streaming system called Storm.
Wei Fan - One of the best experts on this subject based on the ideXlab platform.
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DASFAA (2) - Tutorial: data stream mining and its applications
Database Systems for Advanced Applications, 2012Co-Authors: Latifur Khan, Wei FanAbstract:Data streams are continuous flows of data. Examples of data streams include network Traffic, Sensor data, call center records and so on. Their sheer volume and speed pose a great challenge for the data mining community to mine them. Data streams demonstrate several unique properties: infinite length, concept-drift, concept-evolution, feature-evolution and limited labeled data. Concept-drift occurs in data streams when the underlying concept of data changes over time. Concept-evolution occurs when new classes evolve in streams. Feature-evolution occurs when feature set varies with time in data streams. Data streams also suffer from scarcity of labeled data since it is not possible to manually label all the data points in the stream. Each of these properties adds a challenge to data stream mining. Multi-step methodologies and techniques, and multi-scan algorithms, suitable for knowledge discovery and data mining, cannot be readily applied to data streams. This is due to well-known limitations such as bounded memory, high speed data arrival, online/timely data processing, and need for one-pass techniques (i.e., forgotten raw data) issues etc. In spite of the success and extensive studies of stream mining techniques, there is no single tutorial dedicated to a unified study of the new challenges introduced by evolving stream data like change detection, novelty detection, and feature evolution. This tutorial presents an organized picture on how to handle various data mining techniques in data streams: in particular, how to handle classification and clustering in evolving data streams by addressing these challenges. The importance and significance of research in data stream mining has been manifested in most recent launch of large scale stream processing prototype in many important application areas. In the same time, commercialization of streams (e.g., IBM InfoSphere streams, etc.) brings new challenge and research opportunities to the Data Mining (DM) community. In this tutorial a number of applications of stream mining will be presented such as adaptive malicious code detection, on-line malicious URL detection, evolving insider threat detection and textual stream classification.
