Complex event processing (CEP) can extract meaningful events for real-time locating system (RTLS) applications. To identify complex event accurately in RTLS, we propose a new RFID complex event processing method GEEP,...Complex event processing (CEP) can extract meaningful events for real-time locating system (RTLS) applications. To identify complex event accurately in RTLS, we propose a new RFID complex event processing method GEEP, which is based on the timed automata (TA) theory. By devising RFID locating application into complex events, we model the timing diagram of RFID data streams based on the TA. We optimize the constraint of the event streams and propose a novel method to derive the constraint between objects, as well as the constraint between object and location. Experiments prove the proposed method reduces the cost of RFID complex event processing, and improves the efficiency of the RTLS.展开更多
随着RFID和传感器等数据采集设备的广泛使用及物联网的发展,产生了大量的事件类型的数据,原始的事件数据必须经过复杂事件处理(Complex Event Processing,CEP),才能变成具有丰富语意并对用户有价值的信息,复杂事件处理作为物联网智能处...随着RFID和传感器等数据采集设备的广泛使用及物联网的发展,产生了大量的事件类型的数据,原始的事件数据必须经过复杂事件处理(Complex Event Processing,CEP),才能变成具有丰富语意并对用户有价值的信息,复杂事件处理作为物联网智能处理层的重要组成部分,越来越受到重视.在实际应用中,许多事件流具有长过程的特点,要求相应的复杂事件处理需设置大时间窗口,相对于有限的内存,复杂事件处理面临新的挑战.现有的复杂事件处理均局限于内存进行,均未涉及外存的事件存储和检测.因此,现有的模型和系统均不能用于长过程复杂事件处理.为此,本文提出基于时间片划分的HTF(Hash structure by object ID in memory and Timeslice File in disk)事件实例存储策略和基于实例映射表的大时间窗口复杂事件检测方法,形成了面向长过程的复杂事件处理模型LPCEP(Complex Event Processing for Long Process).相关实验验证了模型用于长过程复杂事件处理的有效性和高效性.展开更多
One research crucial to wider adoption of Radio Frequency Identification (RFID) technology is how to efficiently transform sequences of RFID readings into meaningful business events. Contrary to traditional events, ...One research crucial to wider adoption of Radio Frequency Identification (RFID) technology is how to efficiently transform sequences of RFID readings into meaningful business events. Contrary to traditional events, RFID readings are usually of high volume and velocity, and have the attributes representing their reading objects, occurrence times and spots. Based on these characteristics and the Non-deterministic Finite Automata (NFA) implementation framework, this paper studies the performance issues of RFID complex event processing and proposes corresponding optimization techniques. Our techniques include: (1) taking advantage of negation events or exclusiveness between events to prune intermediate results, thus reduces memory consumption; (2) with different selectivities of complex events, purposefully reordering the join operations between events to improve overall efficiency, achieve higher stream throughput; (3) utilizing the slot-based or B+-tree-based approach to optimizing the processing performance with the time window constraint. We present the analytical results of these techniques and validate their effectiveness through experiments.展开更多
Radio frequency identification(RFID) enabled retail store management needs workflow optimization to facilitate real-time decision making. In this paper, complex event processing(CEP) based RFID-enabled retail store ma...Radio frequency identification(RFID) enabled retail store management needs workflow optimization to facilitate real-time decision making. In this paper, complex event processing(CEP) based RFID-enabled retail store management is studied, particularly focusing on automated shelf replenishment decisions. We define different types of event queries to describe retailer store workflow action over the RFID data streams on multiple tagging levels(e.g., item level and container level). Non-deterministic finite automata(NFA)based evaluation models are used to detect event patterns. To manage pattern match results in the process of event detection, optimization algorithm is applied in the event model to share event detection results. A simulated RFID-enabled retail store is used to verify the effectiveness of the method, experiment results show that the algorithm is effective and could optimize retail store management workflow.展开更多
Pesticides have become more necessary in modern agricultural production.However,these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the ecosystem.Due to a shortage of basic pes...Pesticides have become more necessary in modern agricultural production.However,these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the ecosystem.Due to a shortage of basic pesticide exposure awareness,farmers typically utilize pesticides extremely close to harvesting.Pesticide residues within foods,particularly fruits as well as veggies,are a significant issue among farmers,merchants,and particularly consumers.The residual concentrations were far lower than these maximal allowable limits,with only a few surpassing the restrictions for such pesticides in food.There is an obligation to provide a warning about this amount of pesticide use in farming.Previous technologies failed to forecast the large number of pesticides that were dangerous to people,necessitating the development of improved detection and early warning systems.A novel methodology for verifying the status and evaluating the level of pesticides in regularly consumed veggies as well as fruits has been identified,named as the Hybrid Chronic Multi-Residual Framework(HCMF),in which the harmful level of used pesticide residues has been predicted for contamination in agro products using Q-Learning based Recurrent Neural Network and the predicted contamination levels have been analyzed using Complex Event Processing(CEP)by processing given spatial and sequential data.The analysis results are used to minimize and effectively use pesticides in the agricultural field and also ensure the safety of farmers and consumers.Overall,the technique is carried out in a Python environment,with the results showing that the proposed model has a 98.57%accuracy and a training loss of 0.30.展开更多
文摘Complex event processing (CEP) can extract meaningful events for real-time locating system (RTLS) applications. To identify complex event accurately in RTLS, we propose a new RFID complex event processing method GEEP, which is based on the timed automata (TA) theory. By devising RFID locating application into complex events, we model the timing diagram of RFID data streams based on the TA. We optimize the constraint of the event streams and propose a novel method to derive the constraint between objects, as well as the constraint between object and location. Experiments prove the proposed method reduces the cost of RFID complex event processing, and improves the efficiency of the RTLS.
文摘随着RFID和传感器等数据采集设备的广泛使用及物联网的发展,产生了大量的事件类型的数据,原始的事件数据必须经过复杂事件处理(Complex Event Processing,CEP),才能变成具有丰富语意并对用户有价值的信息,复杂事件处理作为物联网智能处理层的重要组成部分,越来越受到重视.在实际应用中,许多事件流具有长过程的特点,要求相应的复杂事件处理需设置大时间窗口,相对于有限的内存,复杂事件处理面临新的挑战.现有的复杂事件处理均局限于内存进行,均未涉及外存的事件存储和检测.因此,现有的模型和系统均不能用于长过程复杂事件处理.为此,本文提出基于时间片划分的HTF(Hash structure by object ID in memory and Timeslice File in disk)事件实例存储策略和基于实例映射表的大时间窗口复杂事件检测方法,形成了面向长过程的复杂事件处理模型LPCEP(Complex Event Processing for Long Process).相关实验验证了模型用于长过程复杂事件处理的有效性和高效性.
基金supported by the National Natural Science Foundation of China under Grant No.60720106001
文摘One research crucial to wider adoption of Radio Frequency Identification (RFID) technology is how to efficiently transform sequences of RFID readings into meaningful business events. Contrary to traditional events, RFID readings are usually of high volume and velocity, and have the attributes representing their reading objects, occurrence times and spots. Based on these characteristics and the Non-deterministic Finite Automata (NFA) implementation framework, this paper studies the performance issues of RFID complex event processing and proposes corresponding optimization techniques. Our techniques include: (1) taking advantage of negation events or exclusiveness between events to prune intermediate results, thus reduces memory consumption; (2) with different selectivities of complex events, purposefully reordering the join operations between events to improve overall efficiency, achieve higher stream throughput; (3) utilizing the slot-based or B+-tree-based approach to optimizing the processing performance with the time window constraint. We present the analytical results of these techniques and validate their effectiveness through experiments.
基金supported by National Social Science Fund (No. 16CTQ013)the Application Fundamental Research Foundation of Sichuan Province, China (No. 2017JY0011)the Key Project of Sichuan Provincial Department of Education, China (No. 2017GZ0333)
文摘Radio frequency identification(RFID) enabled retail store management needs workflow optimization to facilitate real-time decision making. In this paper, complex event processing(CEP) based RFID-enabled retail store management is studied, particularly focusing on automated shelf replenishment decisions. We define different types of event queries to describe retailer store workflow action over the RFID data streams on multiple tagging levels(e.g., item level and container level). Non-deterministic finite automata(NFA)based evaluation models are used to detect event patterns. To manage pattern match results in the process of event detection, optimization algorithm is applied in the event model to share event detection results. A simulated RFID-enabled retail store is used to verify the effectiveness of the method, experiment results show that the algorithm is effective and could optimize retail store management workflow.
文摘Pesticides have become more necessary in modern agricultural production.However,these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the ecosystem.Due to a shortage of basic pesticide exposure awareness,farmers typically utilize pesticides extremely close to harvesting.Pesticide residues within foods,particularly fruits as well as veggies,are a significant issue among farmers,merchants,and particularly consumers.The residual concentrations were far lower than these maximal allowable limits,with only a few surpassing the restrictions for such pesticides in food.There is an obligation to provide a warning about this amount of pesticide use in farming.Previous technologies failed to forecast the large number of pesticides that were dangerous to people,necessitating the development of improved detection and early warning systems.A novel methodology for verifying the status and evaluating the level of pesticides in regularly consumed veggies as well as fruits has been identified,named as the Hybrid Chronic Multi-Residual Framework(HCMF),in which the harmful level of used pesticide residues has been predicted for contamination in agro products using Q-Learning based Recurrent Neural Network and the predicted contamination levels have been analyzed using Complex Event Processing(CEP)by processing given spatial and sequential data.The analysis results are used to minimize and effectively use pesticides in the agricultural field and also ensure the safety of farmers and consumers.Overall,the technique is carried out in a Python environment,with the results showing that the proposed model has a 98.57%accuracy and a training loss of 0.30.