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TCMS: A multi-Sequence Log Parsing Method Based on Token Conversion

IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 3028-3045页

作者： Wei, Mingkuan Wen, Jigang He, Shiming Xie, Kun Liang, Wei Xie, Gaogang Li, Kenli Zhu, Ziyu Hunan University Ministry of Education Key Laboratory of "Fusion Computing of Supercomputing and Artificial Intelligence" College of Computer Science and Electronics Engineering Changsha410012 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Changsha University of Science and Technology School of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha410114 China Beijing100045 China Columbia University Electrical Engineering New YorkNY10027 United States

Detailed system operations are recorded in logs. To ensure system reliability, developers can detect system anomalies through log anomaly detection. Log parsing, which converts semi-structured log messages into structured data, is a crucial step in log anomaly detection and advanced program analysis and verification. Despite the availability of various log parsing tools, they generally suffer from low parsing accuracy and slow efficiency due to the ignorance of variable characteristics and the use of costly pairwise comparison methods. In this paper, we propose a TCMS framework to parse logs, consisting of two main technologies. First, by studying 16 public log datasets, we find that most log variable tokens are structured variable tokens. Based on this discovery, we propose a token conversion algorithm to improve parsing accuracy. This algorithm converts the changed parts in structured variable tokens into wildcards (‘’), preventing these tokens from being directly identified as constant tokens. Second, to improve efficiency, we propose the LogMLCS algorithm, which intelligently constructs a graph to facilitate the extraction of common parts from multiple log messages at once, instead of using pairwise comparisons. Comprehensive experiments conducted on 16 log datasets reveal that our TCMS outperforms seven other parsing methods, achieving the highest parsing accuracy at the fastest speed. Furthermore, experimental results from running a log anomaly detection algorithm in conjunction with different log parsing methods demonstrate that TCMS significantly boosts detection accuracy. For instance, on the OpenStack dataset, our TCMS-facilitated log anomaly detection algorithm achieves a perfect F1-score, precision, and recall of 100% each, surpassing the best peer method by 32.2, 0.8, and 19.5 percentage points, respectively. © 2004-2012 IEEE.

关键词： Accuracy Anomaly Detection Clustering Algorithms Peer To Peer Computing Electronic Mail Source Coding Particle Separators Codes Tokenization Itemsets Log Parsing Log Analysis Token Conversion Log MLCS Log Parsing Pairwise Comparisons Low Accuracy Public Datasets Anomaly Detection Common Part multiple Messages Long Short Term Memory Point Source Target Domain Logarithm Of The Number Captive Animals multi objective optimization problem Regular Expressions Biological Sequences Rule Based Methods Point In The Graph Kinds Of Datasets Template Generation Number Of Templates Similar Template Parse Tree Syntax Errors Harmonic Average Of Precision Log Length Earliest Methods Common Subsequence Precision And Recall Leaf Node Part Of The Message

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multi-objective generalized predictive control for switching systems under unknown switching sequences

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ISA TRANSACTIONS 2022年 126卷 144-159页

作者： Thamallah, Ali Sakly, Anis M'Sahli, Faouzi Univ Monastir Natl Engn Sch Monastir ENIM Res Lab Automat Elect Syst & Environm LAS2E Ibn El Jazzar Monastir 5019 Skanes Tunisia

This paper describes a new generalized predictive control to track and stabilize a class of discrete time switching system. The focus is specifically centered on classes of switching systems characterized by unstable modes, undetermined switching signal, a non-minimum phase, and variable dead-times. To overcome this type of issue, an effective predictive control law is established by solving a dynamic multi-objective optimization function. Control problems are formulated in order to stabilize and regulate the system response around the targeted reference. The theoretical background of the proposed method is inspired by the standard generalized predictive control (GPC), in such a way that all desirable features are retained as possible. As a result, the obtained controller is more efficient in terms of stability and tracking. In fact within the framework of this research, the control problem has been formulated by taking into account behaviors of subsystems as well as the switching phase. The optimization of the problem is established in such a way that the obtained control law will be adapted to system dynamics, regardless of the mode. A number of simulation tests are established to evaluate the performance of the developed method. Four benchmark examples were considered for the simulation tests. Simulation results have shown the potential of the developed strategy to control and stabilize switching systems under unknown switching sequences. For further evaluation, the closedloop performance of the developed strategy has been compared to that obtained with the multi-Criteria Predictive Control (MOMPC) method. Comparison results have highlighted the effectiveness of the proposed method in terms of stability and tracking than MOMPC method. (c) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Switching system Generalized predictive control multi objective optimization problem Unknown switching sequence

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optimization of a flexible floating structure for wave energy production and protection effectiveness

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ENGINEERING STRUCTURES 2015年 85卷 249-263页

作者： Michailides, Constantine Angelides, Demos C. Aristotle Univ Thessaloniki Dept Civil Engn Thessaloniki 54124 Greece

In the present paper, a multi objective optimization process of a novel Flexible Floating Structure (FFS) subjected to regular and irregular incident waves is developed and presented. The FFS introduced here is considered as an effective structure capable for both wave energy production, with the utilization of a linear hydraulic Power Take-Off (PTO) mechanism, and protection effectiveness. FFS consists of a grid of floating modules connected flexibly in two directions by PTOs with known linear damping characteristics and by connectors with known properties. The optimization process includes two distinct optimization stages;the first one is related with the identification of the optimal set of non dominated solutions and the second one with the selection of the optimum design configuration of the FFS. The performance criteria considered for the selection of the optimum design of the FFS are the produced power, the protection effectiveness and the structural integrity of structural parts that compose the FFS and more specifically of the connectors of the floating modules of the FFS. The performance criteria are thoroughly depended upon design variables, continuous and discrete, associated with the excitation and the characteristics of the structure. The optimization process, based on genetic algorithms and global criterion method, is developed and applied for specific predefined wave field characteristics in order to properly address the characteristics of the structure toward a most preferable (optimum) design. The results that are obtained demonstrate the suitability of the proposed optimization process and the capability of the novel introduced FFS to operate up to a desired level for both wave energy production and protection effectiveness. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Flexible floating structures Wave energy production Floating breakwaters Protection effectiveness multi objective optimization problem Genetic algorithms

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Modeling Power System Operation with Intermittent Resources

Modeling Power System Operation with Intermittent Resources

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Conference of the IEEE PES on Innovative Smart Grid Technologies (ISGT)

作者： Marinovici, M. C. Kirkham, H. Glass, K. A. Carlsen, L. C. Pacific NW Natl Lab Richland WA 99352 USA

ISBN: (纸本)9781467348966;9781467348942

Electricity generating companies and power system operators face the need to minimize total fuel cost and maximize total profit over a given time period. These issues become optimization problems subject to a large number of constraints that must be satisfied simultaneously. The grid updates due to smart-grid technologies, plus the penetration of intermittent re-sources in electrical grid, introduce additional complexity to the optimization problem. The Renewable Integration Model (RIM) is a computer model of an interconnected power system. It is intended to provide insight and advice on complex power systems management, as well as answers to questions relating to the integration of renewable energy. This paper describes the RIM basic design concept, solution method, and the initial suite of modules that it supports.

关键词： economic dispatch load following multi objective optimization problem renewable energies unit commitment

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MOPSO for Dynamic Feature Selection problem Based Big Data Fusion

MOPSO for Dynamic Feature Selection Problem Based Big Data F...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Aboud, Ahlem Fdhila, Raja Alimi, Adel M. Univ S Fax REGIM REs Grp Intelligent Machines Natl Sch Engn ENIS BP 1173 Sfax 3038 Tunisia

ISBN: (纸本)9781509018970

optimization process occurs in many aspects and areas of everyday life. However, the big use of the internet in recent years caused a complex management of large quantities of data that are stored in many different data sources and optimization attend the domain of big data to optimize multi and dynamic data that stored in a complex dataset including all types of transactions in the data sources. So, the diversity of data stored in different data sources caused a complexity to access the information and user find a problem to present the same real world object from different sources in a clear and complementary one representation. Therefore, the high complexity of the representation of a target concept " object" that provided from different data sources, the dynamic feature selection problem based big data fusion present as a solution and a novel approach that will be applicable to solve a dynamic multi-objective optimization feature selection problem (MOOP) based on multi-objective Particle Swarm optimization (MOPSO). This paper carried out on the state-ofthe- art of the research done to present an overview of static and dynamic optimization in literature approach, then to define an overview of big data and to present an idea about the future work that will be able to solve the dynamic feature selection based on big data fusion with MOPSO.

关键词： dynamic optimization multi objective optimization problem dynamic multi objective optimization dynamic feature selection big data fusion

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Interactive Decoration Design Support System by Fitness Evaluation Based on Design Knowledge and Subjective Evaluation 18

Interactive Decoration Design Support System by Fitness Eval...

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18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

作者： Unehara, Muneyuki Ekihiro, Yoshiki Matsumoto, Eriko Yamada, Koichi Suzuki, Izumi Nagaoka Univ Technol Dept Management & Informat Syst Sci Nagaoka Niigata Japan

ISBN: (纸本)9781509055043

This paper proposes interactive decoration design support system introducing interactive evolutionary computation. Evaluation problem existing human can be evaluated synthesizing multi objectives. The proposed system made it importance dividing the human evaluation into two part, which is including regular level of design quality and user's subjective evaluation by affective or Kansei image, and assumed to acquire good results by executing in serial order in a certain evaluation phase. From the experimental results, effectiveness of proposed methodology involving evolution by using fitness evaluation of decoration designs for quality and evolution by user evaluation are confirmed.

关键词： Interactive Genetic Algorithm Decoration Design Affective Engineering multi objective optimization problem

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HGS-Assisted Detection Algorithm for 4G and Beyond Wireless Mobile Communication Systems

HGS-Assisted Detection Algorithm for 4G and Beyond Wireless ...

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17th Asia-Pacific Conference on Communications (APCC)

作者： Ismail, Mahamod Sayadi, Fares Nordin, Rosdiadee Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Ukm Bangi Selangor Darul 43600 Malaysia

ISBN: (纸本)9781457703881;9781457703898

multiple-Input multiple-Output (MIMO) antennas and multi-Carrier Code Division multiple Access (MC-CDMA) are very promising candidates for simultaneous utilize in 4G systems, since they attain robustness, high spectral efficiency, and high data rates in rich scattering environments. This paper proposes an efficient hybrid detection algorithm for a downlink MC-CDMA system based on Orthogonal Frequency Division multiplexing (OFDM). The proposed algorithm combines Genetic Algorithm-assisted multiobjective optimization problem Solutions (GAMOPS) with Simplified Local Search (SLS) technique, which accelerate the search process to locate the exact global optimum throughout the iterative Linear Minimum Mean Square Error (LMMSE) approach, so called Hybrid GAMOPS-SLS (HGS) algorithm. The HGS algorithm contributes steadfastly to an accurate system design and evaluation in the framework of a practical MC-CDMA system. Beneath high order modulation, the simulation results are presented to exhibit an improved Bit Error Rate (BER) performance up to 8.2 dB and reducing of computational complexity order in terms of floating point operations (flops) by at least one order of magnitude at joint channel estimation and multiuser detection as well as intense trade-off between them.

关键词： multi objective optimization problem multiuser detection genetic algorithm MIMO MC-CDMA systems simplified local search

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Web Semantic-Based MOOP Algorithm for Facilitating Allocation problems in the Supply Chain Domain

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INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS 2023年第1期19卷 1页

作者： Lin, Chun-Yuan Rahaman, Mosiur Moslehpour, Massoud Chattopadhyay, Sourasis Arya, Varsha Asia Univ Dept Comp Sci & Informat Engn Taichung Taiwan Asia Univ Dept Business Adm Taichung Taiwan Lebanese Amer Univ Beirut Lebanon Univ Petr & Energy Studies UPES Ctr Interdisciplinary Res Dehra Dun Uttarakhand India

The facility allocation of the supply chain is critical since it directly influences cost efficiency, customer service, supply chain responsiveness, risk reduction, network optimization, and overall competitiveness. When enterprises deploy their facilities wisely, they may achieve operational excellence, exceed customer expectations, and obtain a competitive advantage in today's volatile business climate. Due to this reason, a multi-objective facility allocation problem is introduced in this research with cooperative-based multi-level backup coverage considering distance-based facility attractiveness. The facility of the coverage is further described as two different layers of the coverage process, where demand can be covered as full, partial, and no coverage by their respective facilities. The main objectives of this facility allocation problem are to maximize the coverage of the facility to maximize overall facility coverage in the supply chain network and simultaneously minimize the overall cost.

关键词： Cluster Network Facility Allocation Heuristic optimization multi objective optimization problem Semantic Web Supply Chain

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Breeding Particle Swarm optimization for Railways Rolling Stock Preventive Maintenance Scheduling

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American Journal of Operations Research 2021年第5期11卷 242-251页

作者： Tarek Aboueldah Hanan Farag Research and Development: Ministry of Transport Cairo Egypt Faculty of Graduate Studies for statistical Research Department: Management & Operations Research Cairo University Cairo Egypt

The goal of railway rolling stock maintenance and replacement approaches is to reduce overall cost while increasing reliability which is multi objective optimization problem and a proper predictive maintenance scheduling table should be adequately designed. We propose Breeding Particle Swarm Optimization (BPSO) model based on the concepts of Breeding Swarm and Genetic Algorithm (GA) operators to design this table. The practical experiment shows that our model reduces cost while increasing reliability compared to other models previously utilized.

关键词： Railways Rolling Stock Predictive Maintenance Scheduling Table multi objective optimization problem Breeding Particle Swarm optimization

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MOPSO for Dynamic Feature Selection problem Based Big Data Fusion

MOPSO for Dynamic Feature Selection Problem Based Big Data F...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Ahlem Aboud Raja Fdhila Adel M. Alimi REsearch Group on Intelligent Machines University of Sfax National School of Engineers (ENIS)BP 1173 Sfax 3038 Tunisia

ISBN: (纸本)9781509018987

optimization process occurs in many aspects and areas of everyday life. However, the big use of the internet in recent years caused a complex management of large quantities of data that are stored in many different data sources and optimization attend the domain of big data to optimize multi and dynamic data that stored in a complex dataset including all types of transactions in the data sources. So, the diversity of data stored in different data sources caused a complexity to access the information and user find a problem to present the same real world object from different sources in a clear and complementary one representation. Therefore, the high complexity of the representation of a target concept "object" that provided from different data sources, the dynamic feature selection problem based big data fusion present as a solution and a novel approach that will be applicable to solve a dynamic multi-objective optimization feature selection problem (MOOP) based on multi-objective Particle Swarm optimization (MOPSO). This paper carried out on the state-of-the-art of the research done to present an overview of static and dynamic optimization in literature approach, then to define an overview of big data and to present an idea about the future work that will be able to solve the dynamic feature selection based on big data fusion with MOPSO.

关键词： big data fusion dynamic optimization multi objective optimization problem dynamic multi objective optimization dynamic feature selection

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