Alarm systems are essential to the process safety and efficiency of complex industrial facilities. However, with the increasing size of plants and the growing complexity of industrial processes, alarm flooding is beco...
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Alarm systems are essential to the process safety and efficiency of complex industrial facilities. However, with the increasing size of plants and the growing complexity of industrial processes, alarm flooding is becoming a serious problem and posing challenges to alarm systems. Extracting alarm patterns from an alarm flood database can assist with an alarm root cause analysis, decision support, and the configuration of an alarm suppression model. However, due to the large size of the alarm database and the problem of sequence ambiguity in the alarm sequence, existing algorithms suffer from excessive computational overhead, incomplete alarm patterns, and redundant outputs. In order to solve these problems, we propose an alarm pattern extraction method based on the improved prefixspan algorithm. Firstly, a priority-based pre-matching strategy is proposed to cluster similar sequences in advance. Secondly, we improved prefixspan by considering timestamps to tolerate short-term order ambiguity in alarm flood sequences. Thirdly, an alarm pattern compression method is proposed for the further distillation of pattern information in order to output representative alarm patterns. Finally, we evaluated the effectiveness and applicability of the proposed method by using an alarm flood database from a real diesel hydrogenation unit.
Proper monitoring of performance of an alarm system throughout its life cycle is an important factor in safety and reliability of industrial plants. Complexity and extent of modern industrial plants and poor design an...
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Proper monitoring of performance of an alarm system throughout its life cycle is an important factor in safety and reliability of industrial plants. Complexity and extent of modern industrial plants and poor design and management of alarm systems, have increased the importance of monitoring of alarm systems. Alarm floods, defined as a large number of alarms triggered in a short interval, is one of the problems that modern complexes are facing regularly. Many researchers have been focusing on this issue both in academia and industry. One approach to deal with alarm flood is analyzing alarms triggered in different floods and finding similar patterns. The identified patterns could help in locating the root cause of an alarm flood. In this paper a modified prefixspan sequential pattern recognition algorithm is used to find alarm patterns in different floods. The effectiveness of the algorithm is demonstrated with real alarm floods from a natural gas processing plant. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.
Aiming at the problem of constructing huge amounts of projected databases in prefixspan algorithm, this paper proposes an Improved prefixspan algorithm for Mining Sequential Patterns, called BLSPM algorithm (based on ...
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ISBN:
(纸本)9781479932795
Aiming at the problem of constructing huge amounts of projected databases in prefixspan algorithm, this paper proposes an Improved prefixspan algorithm for Mining Sequential Patterns, called BLSPM algorithm (based on bi-level Sequential Patterns Mining). The algorithm use duplicated projection and certain specific sequential patterns pruning, reduce the scale of projected databases and the runtime of scanning projected databases, thus, the efficiency of algorithm could be raised up greatly, and all needed sequential patterns are obtained. Experiment results shows that BLSPM algorithm is more efficient than prefixspan algorithm in large databases.
The rapid increase in information and technology has led to the increased amount of web pages, which raises the complexity in sticking to relevant web pages, and the visitor suffers due to wastage of time resulting in...
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The rapid increase in information and technology has led to the increased amount of web pages, which raises the complexity in sticking to relevant web pages, and the visitor suffers due to wastage of time resulting in lack of satisfaction. This paper proposes a web page prediction method using a weighed support and Bhattacharya distance-based (WS-BD) two-level match. The major aim of the proposed method is to attain customer satisfaction. Initially, interesting sequential patterns are obtained using the weighed support that filters the sequential patterns obtained using a prefixspan algorithm based on the frequency, duration and recurrence of the web pages. Interesting sequential patterns are clustered using the proposed dice similarity-based Bayesian fuzzy clustering, and the web page is predicted using the two-level match based on Bhattacharya distance. The experimentation is performed using the CTI and MSNBC data which proves the effectiveness of the proposed method. The proposed method shows 9.59, 21.22 and 10.17% improvement than the existing FCM-KNN in terms of precision, recall and F measure for the CTI dataset. Also, the proposed method shows 2.58, 22.17 and 7.83% improvement than the existing FCM-KNN in terms of precision, recall and F measure for the MSNBC dataset.
Traffic pattern analysis is an active and essential part of transportation research. When traffic condition is adverse and unprecedented, traffic sequences are useful in the analysis of traffic behaviour. The sequence...
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Traffic pattern analysis is an active and essential part of transportation research. When traffic condition is adverse and unprecedented, traffic sequences are useful in the analysis of traffic behaviour. The sequence through which traffic congestion has arisen can be predicted using sequence rules from the generated traffic sequence. This work aims at mining traffic sequence pattern and prediction of traffic volume based on traffic sequence rules. To mine peak hour traffic sequences in order to make better travel decision, travel time based prefixspan (TT-prefixspan) algorithm is proposed to analyse traffic flow on highways. As a result, the prediction of traffic volume is effected by the generated traffic sequences. Such analysis would pave the way for devising data driven computational methods in reducing traffic congestion. Real-time traffic volume data for 53 weeks is collected at a centralised toll system comprising toll collections centres at three different sites. To show the significance of this problem-solving approach, TT-prefixspan is experimented on three different sites. The extraction of a frequent traffic sequence pattern is reported with experimental analysis. The evaluation of different traffic condition present at each site has shown promising results. Towards the end, a summary of results is presented with directions for future research.
With the rapid development of wireless networks, the issue of information security in wireless communications has gradually emerged, and it has become one of the biggest obstacles to the popularization of this technol...
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With the rapid development of wireless networks, the issue of information security in wireless communications has gradually emerged, and it has become one of the biggest obstacles to the popularization of this technology. To meet the increasing security and reliability requirements of new network environments, wireless network security standards and protocols are constantly being updated and enhanced. Based on the current development of wireless networks in Beijing, this paper discusses the optimization of encryption protocols in network security protocols, introduces the prefixspan algorithm and improves its algorithms, and then performs data mining based on Prefix, and analyzes the intrusion lines and their correlations. Finally, the feasibility of using the improved prefixspan algorithm to optimize the encryption protocol is verified through experiments.
Aiming at the prefixspan algorithm produce huge amount of project databases in mining sequence patterns, this paper proposes an Improved prefixspan algorithm for Mining Sequential Patterns(IPMSP) algorithm. By avoid p...
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ISBN:
(纸本)9781509009848
Aiming at the prefixspan algorithm produce huge amount of project databases in mining sequence patterns, this paper proposes an Improved prefixspan algorithm for Mining Sequential Patterns(IPMSP) algorithm. By avoid produce duplicated project databases with the same prefix pattern through checking the prefix with regard to prefix of the sequence database and abnegating the non-frequent items and project databases which sequential number is lower than minimum support in the recursive mining process, the performance of prefixspan is well improved. Experiment results shows that the time and space performance of IPMSP algorithm are better than that of prefixspan.
For mining sequential patterns on massive data set,the distributed sequential pattern mining algorithm based on MapReduce programming model and prefixspan is *** tasks are decomposed to many small tasks,the Map functi...
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For mining sequential patterns on massive data set,the distributed sequential pattern mining algorithm based on MapReduce programming model and prefixspan is *** tasks are decomposed to many small tasks,the Map function is used to mine each Prefix-Projected sequential pattern,and the projected databases were constructed *** simplifies the search space and acquires a higher mining *** the intermediate values are passed to a Reduce function which merges together all these values to produce a possibly smaller set of *** theoretical analyses and experimental results show MR-prefixspan reduces the time of scanning *** solves the problem of mining massive data effectively,has considerable speedup and scaleup performances with an increasing number of processors on the Hadoop platform.
The prefixspan algorithm, which is broadly applied to data mining field, is one of the most high-efficiency classical algorithms. Taking account of insufficiency of prefixspan algorithm, the thesis trys to optimize th...
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ISBN:
(纸本)9781424467129
The prefixspan algorithm, which is broadly applied to data mining field, is one of the most high-efficiency classical algorithms. Taking account of insufficiency of prefixspan algorithm, the thesis trys to optimize the algorithm by reducing frequency of exchanging between the memory and the external memory in the Prefix part, and reducing the size of the projection database by discarding the non-frequent items which created in the process of sequence patterns mining. The result of test demonstrates that the operating efficiency is enhanced more than 30%. The conclusion of the experimental analysis shows that the improved algorithm is applicable to the invasion detection.
To avoid huge amount of projected databases produced by Prefix-Span algorithm and reduce unnecessary storage space and scanning time, an improved projection position-based sequential pattern mining algorithm is propos...
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To avoid huge amount of projected databases produced by Prefix-Span algorithm and reduce unnecessary storage space and scanning time, an improved projection position-based sequential pattern mining algorithm is proposed. The ideas of the improved algorithm are as follows: (1) utilize Apriori property to delete the non-frequent items and divide search space, reducing unnecessary storage space and scanning time;(2) record projected position to locate projected sequence position for mining local frequency items and mine each recursively, so as to avoid physically constructing corresponding projected databases. The algorithm's analysis and experimental results show that proposed algorithm has better feasibility and scalability compared with other algorithms.
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