Estimating the cycle time of each job over event streams in intelligent manufacturing is critical. These streams include many long-lasting events which have certain durations. The temporal relationships among those in...Estimating the cycle time of each job over event streams in intelligent manufacturing is critical. These streams include many long-lasting events which have certain durations. The temporal relationships among those interval-based events are often complex. Meanwhile, network latencies and machine failures in intelligent manufacturing may cause events to be out-of-order. This topic has rarely been discussed because most existing methods do not consider both interval-based and out-of-order events. In this work, we analyze the preliminaries of event temporal semantics. A tree-plan model of interval-based out-of-order events is proposed. A hybrid solution is correspondingly introduced. Extensive experimental studies demonstrate the efficiency of our approach.展开更多
A heavy rainfall event that occurred over the middle and lower reaches of the Yangtze River Basin （YRB） during July 11-13 2000 is explored in this study. The potential/stream function is used to analyze the upstream...A heavy rainfall event that occurred over the middle and lower reaches of the Yangtze River Basin （YRB） during July 11-13 2000 is explored in this study. The potential/stream function is used to analyze the upstream ＂strong signals＂ of the water vapor transport in the Tibetan Plateau （TP）. The studied time period covers from 2000 LST 5 July to 2000 LST 15 July （temporal resolution： 6 hours）. By analyzing the three-dimensional structure of the water vapor flux, vorticity and divergence prior to and during the heavy rainfall event, the upstream ＂strong signals＂ related to this heavy rainfall event are revealed. A strong correlation exists between the heavy rainfall event in the YRB and the convective clouds over the TE The ＂convergence zone＂ of the water vapor transport is also identified, based on correlation analysis of the water vapor flux two days and one day prior to, and on the day of, the heavy rainfall. And this ＂convergence zone＂ coincides with the migration of the maximum rainfall over the YRB. This specific coupled structure actually plays a key role in generating heavy rainfall over the YRB. The eastward movement of the coupled system with a divergence]convergence center of the potential function at the upper/lower level resembles the spatiotemporal evolution of the heavy rainfall event over the YRB. These upstream ＂strong signals＂ are clearly traced in this study through analyzing the three-dimensional structure of the potential/stream function of upstream water vapor transport.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
In recent years, there has been a growing need for complex event processing （CEP）, ranging from supply chain management to security monitoring. In many scenarios events are generated in different sources but arrive ...In recent years, there has been a growing need for complex event processing （CEP）, ranging from supply chain management to security monitoring. In many scenarios events are generated in different sources but arrive at the central server out of order, due to the differences of network latencies. Most state-of-the-art techniques process out-of-order events by buffering the events until the total event order within a specified range can be guaranteed. Their main problems are leading to increasing response time and reducing system throughput. This paper aims to build a high performance out-of- order event processing mechanism, which can match events as soon as they arrive instead of buffering them till all arrive. A suffix-automaton-based event matching algorithm is proposed to speed up query processing, and a confidence-based accuracy evaluation is proposed to control the query result quality. The performance of our approach is evaluated through detailed accuracy and response time analysis. As experimental results show, our approach can obviously speed up the query matching time and produce reasonable query results.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
文摘Estimating the cycle time of each job over event streams in intelligent manufacturing is critical. These streams include many long-lasting events which have certain durations. The temporal relationships among those interval-based events are often complex. Meanwhile, network latencies and machine failures in intelligent manufacturing may cause events to be out-of-order. This topic has rarely been discussed because most existing methods do not consider both interval-based and out-of-order events. In this work, we analyze the preliminaries of event temporal semantics. A tree-plan model of interval-based out-of-order events is proposed. A hybrid solution is correspondingly introduced. Extensive experimental studies demonstrate the efficiency of our approach.
基金supported by the National Natural Science Foundation of China [grant number 41991281]the National Key R&D Program of China [grant number 2018YFA0606403]the National Natural Science Foundation of China [grant number 41790472]。
文摘A heavy rainfall event that occurred over the middle and lower reaches of the Yangtze River Basin （YRB） during July 11-13 2000 is explored in this study. The potential/stream function is used to analyze the upstream ＂strong signals＂ of the water vapor transport in the Tibetan Plateau （TP）. The studied time period covers from 2000 LST 5 July to 2000 LST 15 July （temporal resolution： 6 hours）. By analyzing the three-dimensional structure of the water vapor flux, vorticity and divergence prior to and during the heavy rainfall event, the upstream ＂strong signals＂ related to this heavy rainfall event are revealed. A strong correlation exists between the heavy rainfall event in the YRB and the convective clouds over the TE The ＂convergence zone＂ of the water vapor transport is also identified, based on correlation analysis of the water vapor flux two days and one day prior to, and on the day of, the heavy rainfall. And this ＂convergence zone＂ coincides with the migration of the maximum rainfall over the YRB. This specific coupled structure actually plays a key role in generating heavy rainfall over the YRB. The eastward movement of the coupled system with a divergence]convergence center of the potential function at the upper/lower level resembles the spatiotemporal evolution of the heavy rainfall event over the YRB. These upstream ＂strong signals＂ are clearly traced in this study through analyzing the three-dimensional structure of the potential/stream function of upstream water vapor transport.
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.
基金supported by the National Natural Science Foundation of China under Grant Nos.61003058,60933001the Fundamental Research Funds for the Central Universities under Grant No.N090104001
文摘In recent years, there has been a growing need for complex event processing （CEP）, ranging from supply chain management to security monitoring. In many scenarios events are generated in different sources but arrive at the central server out of order, due to the differences of network latencies. Most state-of-the-art techniques process out-of-order events by buffering the events until the total event order within a specified range can be guaranteed. Their main problems are leading to increasing response time and reducing system throughput. This paper aims to build a high performance out-of- order event processing mechanism, which can match events as soon as they arrive instead of buffering them till all arrive. A suffix-automaton-based event matching algorithm is proposed to speed up query processing, and a confidence-based accuracy evaluation is proposed to control the query result quality. The performance of our approach is evaluated through detailed accuracy and response time analysis. As experimental results show, our approach can obviously speed up the query matching time and produce reasonable query results.
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.
