An Update Method for Shortest Path Caching with Burst Paths Based on Sliding Windows

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

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One dynamic shortest path algorithm in a traffic network based on a genetic algorithm

Advances in Civil, Transportation and Environmental Engineering ◽

10.2495/ctee120281 ◽

2013 ◽

Author(s):

Shuijian Zhang

Keyword(s):

Genetic Algorithm ◽

Shortest Path ◽

Traffic Network ◽

Shortest Path Algorithm

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Polarization analysis and separation of P- and SV-wavefields with adaptive sliding windows

Advances in Electrical and Electronics Engineering ◽

10.2495/iceee140161 ◽

2014 ◽

Author(s):

Baotong Liu

Keyword(s):

Polarization Analysis ◽

Sliding Windows

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Finding Shortest Path Basing on Dijkstra's Algorithm For Baghdad Universities by using Geographic Information System Applications

10.25212/icoit17.030 ◽

2017 ◽

Author(s):

Roaa Ebada Saeed ◽

Amer Saleem Elameer ◽

Rafah Shahab Alhamdani

Keyword(s):

Information System ◽

Geographic Information System ◽

Shortest Path ◽

Geographic Information ◽

Dijkstra’S Algorithm ◽

Dijkstra's Algorithm

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Anomaly Detection via Mining Numerical Workflow Relations from Logs

10.36227/techrxiv.12570926.v1 ◽

2020 ◽

Author(s):

Bo Zhang ◽

Hongyu Zhang ◽

Pablo Moscato

Keyword(s):

Distributed Systems ◽

Anomaly Detection ◽

Text Messages ◽

Distributed File System ◽

Log Data ◽

Sliding Windows ◽

Novel Approach ◽

Hadoop Distributed File System ◽

Blue Gene ◽

Online Anomaly Detection

<div>Complex software intensive systems, especially distributed systems, generate logs for troubleshooting. The logs are text messages recording system events, which can help engineers determine the system's runtime status. This paper proposes a novel approach named ADR (stands for Anomaly Detection by workflow Relations) that employs matrix nullspace to mine numerical relations from log data. The mined relations can be used for both offline and online anomaly detection and facilitate fault diagnosis. We have evaluated ADR on log data collected from two distributed systems, HDFS (Hadoop Distributed File System) and BGL (IBM Blue Gene/L supercomputers system). ADR successfully mined 87 and 669 numerical relations from the logs and used them to detect anomalies with high precision and recall. For online anomaly detection, ADR employs PSO (Particle Swarm Optimization) to find the optimal sliding windows' size and achieves fast anomaly detection.</div><div>The experimental results confirm that ADR is effective for both offline and online anomaly detection. </div>

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