Quickly matching the related primitive events for multiple complex events from the massive event streams usually faces with a great challenge due to the single-pattern characteristics of the existing complex event mat...Quickly matching the related primitive events for multiple complex events from the massive event streams usually faces with a great challenge due to the single-pattern characteristics of the existing complex event matching models. Aiming to solve the problem, amultiple-pattern complex event matching model based on merge sharing is proposed inthis paper. The achievement of the paper lies in the fact that a multiple-pattern complexevent matching model based on merge sharing is presented to successfully realize thequick matching of related primitive events for multiple complex events from the massiveevent streams. Specifically, in our scheme, we successfully use merge sharing technologyto merge all the same prefixes, suffixes or subpatterns existing in single-pattern matchingmodels into shared ones and to construct a multiple-pattern complex event matchingmodel. As a result, our proposed matching model in this paper can effectively solve theabove problem. The experimental results show that our proposed matching model in thispaper outperforms the existing single-pattern matching models in model constructionand related events matching for massive event streams.展开更多
Unlike traditional video cameras,event cameras capture asynchronous event streams in which each event encodes pixel location,triggers’timestamps,and the polarity of brightness changes.In this paper,we introduce a nov...Unlike traditional video cameras,event cameras capture asynchronous event streams in which each event encodes pixel location,triggers’timestamps,and the polarity of brightness changes.In this paper,we introduce a novel hypergraph-based framework for moving object classification.Specifically,we capture moving objects with an event camera,to perceive and collect asynchronous event streams in a high temporal resolution.Unlike stacked event frames,we encode asynchronous event data into a hypergraph,fully mining the high-order correlation of event data,and designing a mixed convolutional hypergraph neural network for training to achieve a more efficient and accurate motion target recognition.The experimental results show that our method has a good performance in moving object classification(e.g.,gait identification).展开更多
With the rapid development in business transactions,especially in recent years,it has become necessary to develop different mechanisms to trace business user records in web server log in an efficient way.Online busine...With the rapid development in business transactions,especially in recent years,it has become necessary to develop different mechanisms to trace business user records in web server log in an efficient way.Online business transactions have increased,especially when the user or customer cannot obtain the required service.For example,with the spread of the epidemic Coronavirus(COVID-19)throughout the world,there is a dire need to rely more on online business processes.In order to improve the efficiency and performance of E-business structure,a web server log must be well utilized to have the ability to trace and record infinite user transactions.This paper proposes an event stream mechanism based on formula patterns to enhance business processes and record all user activities in a structured log file.Each user activity is recorded with a set of tracing parameters that can predict the behavior of the user in business operations.The experimental results are conducted by applying clustering-based classification algorithms on two different datasets;namely,Online Shoppers Purchasing Intention and Instacart Market Basket Analysis.The clustering process is used to group related objects into the same cluster,then the classification process measures the predicted classes of clustered objects.The experimental results record provable accuracy in predicting user preferences on both datasets.展开更多
The analytical and monitoring capabilities of central event re-positories, such as log servers and intrusion detection sys-tems, are limited by the amount of structured information ex-tracted from the events they rece...The analytical and monitoring capabilities of central event re-positories, such as log servers and intrusion detection sys-tems, are limited by the amount of structured information ex-tracted from the events they receive. Diverse networks and ap-plications log their events in many different formats, and this makes it difficult to identify the type of logs being received by the central repository. The way events are logged by IT systems is problematic for developers of host-based intrusion-detection systems （specifically, host-based systems）, develop-ers of security-information systems, and developers of event-management systems. These problems preclude the develop-ment of more accurate, intrusive security solutions that obtain results from data included in the logs being processed. We propose a new method for dynamically normalizing events into a unified super-event that is loosely based on the Common Event Expression standard developed by Mitre Corporation. We explain how our solution can normalize seemingly unrelat-ed events into a single, unified format.展开更多
Script event stream prediction is a task that predicts events based on a given context or script.Most existing methods predict one subsequent event,limiting the ability to make a longer inference about the future.More...Script event stream prediction is a task that predicts events based on a given context or script.Most existing methods predict one subsequent event,limiting the ability to make a longer inference about the future.Moreover,external knowledge has been proven to be beneficial for event prediction and used in many methods in the form of relations between events.However,these methods focus mainly on the continuity of actions while ignoring the other components of events.To tackle these issues,we propose a Multi-step Script Event Prediction(MuSEP)method that can make a longer inference according to the given events.We adopt reinforcement learning to implement the multi-step prediction by treating the process as a Markov chain and setting the reward considering both chain-level and event-level thus ensuring the overall quality of prediction results.Additionally,we learn the representations of events with external knowledge which could better understand events and their components.Experimental results on four datasets demonstrate that our method not only outperforms state-of-the-art methods on one-step prediction but is also capable of making multi-step prediction.展开更多
With the aim of solving the detection problems for current complex event detection models in detecting a related event for a complex event from the high proportion disordered RFID event stream due to its big uncertain...With the aim of solving the detection problems for current complex event detection models in detecting a related event for a complex event from the high proportion disordered RFID event stream due to its big uncertainty arrival,an efficient complex event detection model based on Extended Nondeterministic Finite Automaton(ENFA)is proposed in this paper.The achievement of the paper rests on the fact that an efficient complex event detection model based on ENFA is presented to successfully realize the detection of a related event for a complex event from the high proportion disordered RFID event stream.Specially,in our model,we successfully use a new ENFA-based complex event detection model instead of an NFA-based complex event detection model to realize the detection of the related events for a complex event from the high proportion disordered RFID event stream by expanding the traditional NFA-based detection model,which can effectively address the problems above.The experimental results show that the proposed model in this paper outperforms some general models in saving detection time,memory consumption,detection latency and improving detection throughput for detecting a related event of a complex event from the high proportion out-of-order RFID event stream.展开更多
基金The work was supported by the National Natural Science Foundation of China(Nos.61602187 and 6180405)the National Key Research and Development Plan(No.2016YFD0200700)+1 种基金the Guangdong Science and Technology Projects(No.2019B020219002)Guangdong Laboratory of Lingnan Modern Agriculture.
文摘Quickly matching the related primitive events for multiple complex events from the massive event streams usually faces with a great challenge due to the single-pattern characteristics of the existing complex event matching models. Aiming to solve the problem, amultiple-pattern complex event matching model based on merge sharing is proposed inthis paper. The achievement of the paper lies in the fact that a multiple-pattern complexevent matching model based on merge sharing is presented to successfully realize thequick matching of related primitive events for multiple complex events from the massiveevent streams. Specifically, in our scheme, we successfully use merge sharing technologyto merge all the same prefixes, suffixes or subpatterns existing in single-pattern matchingmodels into shared ones and to construct a multiple-pattern complex event matchingmodel. As a result, our proposed matching model in this paper can effectively solve theabove problem. The experimental results show that our proposed matching model in thispaper outperforms the existing single-pattern matching models in model constructionand related events matching for massive event streams.
基金the National Key Research and Development Program of China(No.2021ZD0112400)。
文摘Unlike traditional video cameras,event cameras capture asynchronous event streams in which each event encodes pixel location,triggers’timestamps,and the polarity of brightness changes.In this paper,we introduce a novel hypergraph-based framework for moving object classification.Specifically,we capture moving objects with an event camera,to perceive and collect asynchronous event streams in a high temporal resolution.Unlike stacked event frames,we encode asynchronous event data into a hypergraph,fully mining the high-order correlation of event data,and designing a mixed convolutional hypergraph neural network for training to achieve a more efficient and accurate motion target recognition.The experimental results show that our method has a good performance in moving object classification(e.g.,gait identification).
文摘With the rapid development in business transactions,especially in recent years,it has become necessary to develop different mechanisms to trace business user records in web server log in an efficient way.Online business transactions have increased,especially when the user or customer cannot obtain the required service.For example,with the spread of the epidemic Coronavirus(COVID-19)throughout the world,there is a dire need to rely more on online business processes.In order to improve the efficiency and performance of E-business structure,a web server log must be well utilized to have the ability to trace and record infinite user transactions.This paper proposes an event stream mechanism based on formula patterns to enhance business processes and record all user activities in a structured log file.Each user activity is recorded with a set of tracing parameters that can predict the behavior of the user in business operations.The experimental results are conducted by applying clustering-based classification algorithms on two different datasets;namely,Online Shoppers Purchasing Intention and Instacart Market Basket Analysis.The clustering process is used to group related objects into the same cluster,then the classification process measures the predicted classes of clustered objects.The experimental results record provable accuracy in predicting user preferences on both datasets.
文摘The analytical and monitoring capabilities of central event re-positories, such as log servers and intrusion detection sys-tems, are limited by the amount of structured information ex-tracted from the events they receive. Diverse networks and ap-plications log their events in many different formats, and this makes it difficult to identify the type of logs being received by the central repository. The way events are logged by IT systems is problematic for developers of host-based intrusion-detection systems （specifically, host-based systems）, develop-ers of security-information systems, and developers of event-management systems. These problems preclude the develop-ment of more accurate, intrusive security solutions that obtain results from data included in the logs being processed. We propose a new method for dynamically normalizing events into a unified super-event that is loosely based on the Common Event Expression standard developed by Mitre Corporation. We explain how our solution can normalize seemingly unrelat-ed events into a single, unified format.
基金supported in part by the Project of the National Natural Science Foundation of China(Nos.62206166 and 61991410)Shanghai Sailing Program(No.23YF1413000)Shanghai Pujiang Program(No.22PJ1403800).
文摘Script event stream prediction is a task that predicts events based on a given context or script.Most existing methods predict one subsequent event,limiting the ability to make a longer inference about the future.Moreover,external knowledge has been proven to be beneficial for event prediction and used in many methods in the form of relations between events.However,these methods focus mainly on the continuity of actions while ignoring the other components of events.To tackle these issues,we propose a Multi-step Script Event Prediction(MuSEP)method that can make a longer inference according to the given events.We adopt reinforcement learning to implement the multi-step prediction by treating the process as a Markov chain and setting the reward considering both chain-level and event-level thus ensuring the overall quality of prediction results.Additionally,we learn the representations of events with external knowledge which could better understand events and their components.Experimental results on four datasets demonstrate that our method not only outperforms state-of-the-art methods on one-step prediction but is also capable of making multi-step prediction.
基金the National Natural Science Foundation of China(No.61502110)and(No.61602187)and(No.61601189)the Guangdong Science and Technology Projects(No.2016A020209007)and(No.2016A020210088)the Guangzhou Science and Technology Projects(N0.201707010482)。
文摘With the aim of solving the detection problems for current complex event detection models in detecting a related event for a complex event from the high proportion disordered RFID event stream due to its big uncertainty arrival,an efficient complex event detection model based on Extended Nondeterministic Finite Automaton(ENFA)is proposed in this paper.The achievement of the paper rests on the fact that an efficient complex event detection model based on ENFA is presented to successfully realize the detection of a related event for a complex event from the high proportion disordered RFID event stream.Specially,in our model,we successfully use a new ENFA-based complex event detection model instead of an NFA-based complex event detection model to realize the detection of the related events for a complex event from the high proportion disordered RFID event stream by expanding the traditional NFA-based detection model,which can effectively address the problems above.The experimental results show that the proposed model in this paper outperforms some general models in saving detection time,memory consumption,detection latency and improving detection throughput for detecting a related event of a complex event from the high proportion out-of-order RFID event stream.
