scholarly journals An Algorithm for Message Type Discovery in Unstructured Log Data

2019 ◽  
Author(s):  
Daniel Tovarňák
Keyword(s):  
Log Data ◽  
Message Type

ANALISA POTENSI MINYAK DAN GAS BUMI DENGAN ATRIBUT SEISMIK PADA BATUAN KARBONAT LAPANGAN *ZEFARA* CEKUNGAN SUMATRA SELATAN

KURVATEK ◽  
2017 ◽  
Vol 1 (2) ◽  
pp. 21-31
Author(s):  
Fatimah Miharno
Keyword(s):  
Instantaneous Frequency ◽  
Carbonate Reservoir ◽  
Control Analysis ◽  
High Porosity ◽  
Hydrocarbon Accumulation ◽  
Seismic Attribute ◽  
Data Set ◽  
Log Data ◽  
Low Amplitude ◽  
Rms Amplitude

ABSTRACT*Zefara* Field formation Baturaja on South Sumatra Basin is a reservoir carbonate and prospective gas. Data used in this research were 3D seismik data, well logs, and geological information. According to geological report known that hidrocarbon traps in research area were limestone lithological layer as stratigraphical trap and faulted anticline as structural trap. The study restricted in effort to make a hydrocarbon accumulation and a potential carbonate reservoir area maps with seismic attribute. All of the data used in this study are 3D seismic data set, well-log data and check-shot data. The result of the analysis are compared to the result derived from log data calculation as a control analysis. Hydrocarbon prospect area generated from seismic attribute and are divided into three compartments. The seismic attribute analysis using RMS amplitude method and instantaneous frequency is very effective to determine hydrocarbon accumulation in *Zefara* field, because low amplitude from Baturaja reservoir. Low amplitude hints low AI, determined high porosity and high hydrocarbon contact (HC).  Keyword: Baturaja Formation, RMS amplitude seismic attribute, instantaneous frequency seismic attribute

A Web Usage Mining Approach to User Navigation Pattern and Prediction in Web Log Data

2012 ◽  
Vol 3 (4) ◽  
pp. 92-94
Author(s):  
SUJATHA PADMAKUMAR ◽  
◽  
Dr.PUNITHAVALLI Dr.PUNITHAVALLI ◽  
Dr.RANJITH Dr.RANJITH
Keyword(s):  
Web Usage Mining ◽  
Log Data ◽  
Web Log ◽  
Web Usage ◽  
User Navigation

Anomaly Detection via Mining Numerical Workflow Relations from Logs

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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