Prediction of Web User Behavior by Discovering Temporal Relational Rules from Web Log Data

Yu, Xiuming; Li, Meijing; Paik, Incheon; Ryu, Keun Ho

doi:10.1007/978-3-642-32597-7_3

Cited by 11 publications

(14 citation statements)

References 3 publications

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“…Logs are critical runtime information recorded by developers, which are widely analyzed for all sorts of tasks. 1) Log analysis is conducted for various targets, such as code testing [27], problem identification [28], [29], user behavior analysis [30], [31], security monitoring [32], etc. Most of these tasks use data mining models to extract critical features or patterns from a large volume of software logs.…”

Section: Related Workmentioning

confidence: 99%

Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression

Liu

Zhu

et al. 2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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System logs record detailed runtime information of software systems and are used as the main data source for many tasks around software engineering. As modern software systems are evolving into large scale and complex structures, logs have become one type of fast-growing big data in industry. In particular, such logs often need to be stored for a long time in practice (e.g., a year), in order to analyze recurrent problems or track security issues. However, archiving logs consumes a large amount of storage space and computing resources, which in turn incurs high operational cost. Data compression is essential to reduce the cost of log storage. Traditional compression tools (e.g., gzip) work well for general texts, but are not tailed for system logs. In this paper, we propose a novel and effective log compression method, namely logzip. Logzip is capable of extracting hidden structures from raw logs via fast iterative clustering and further generating coherent intermediate representations that allow for more effective compression. We evaluate logzip on five large log datasets of different system types, with a total of 63.6 GB in size. The results show that logzip can save about half of the storage space on average over traditional compression tools. Meanwhile, the design of logzip is highly parallel and only incurs negligible overhead. In addition, we share our industrial experience of applying logzip to Huawei's real products.

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Section: Related Workmentioning

confidence: 99%

Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression

Liu

Zhu

et al. 2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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“…Temporal rule mining focuses in time-series datasets that utilize the timestamps in the data. It has been used by researchers to discover knowledge from such datasets that can include temporal association rules [ 11 – 24 ], similar time sequences [ 25 – 27 ], as well as sequential patterns [ 28 – 32 ].…”

Section: Related Workmentioning

confidence: 99%

“…Nonetheless, the literature has shown very few works such as in Yu et al [ 11 ] who captures temporal relationships from web log data. Lack of analysis on temporal relationships may result in loss of important time-series characteristics of the data, which will subsequently affect the quality of rules mined regarding the user navigation behaviours.…”

Section: Introductionmentioning

confidence: 99%

Effect of Temporal Relationships in Associative Rule Mining for Web Log Data

Khairudin

Ahmad

2014

The Scientific World Journal

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The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analysed the effect of various temporal parameters. The rules generated from temporal relational rule mining are then compared against the rules generated from the classical rule mining approach such as the Apriori and FP-Growth algorithms. The results showed that by incorporating the temporal attribute via time, the number of rules generated is subsequently smaller but is comparable in terms of quality.

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“…Overall, a log entry is produced by a log printing statement in a system program's source code. Techniques have been developed to relieve this manual work and to take advantage of the rich information present in logs in an automated manner, such as process mining [8], [12], [25], anomaly detection [6], [10], [11], [28], [27], fault localisation [26], [32], invariant inference [7], performance diagnosis [15], [20], [22], [31], online trace checking [5], and behaviour analysis [21], [29].…”

Section: Introductionmentioning

confidence: 99%

Tracing Back Log Data to its Log Statement: From Research to Practice

Schipper¹,

Aniche²,

Deursen³

2019

2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)

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Logs are widely used as a source of information to understand the activity of computer systems and to monitor their health and stability. However, most log analysis techniques require the link between the log messages in the raw log file and the log statements in the source code that produce them. Several solutions have been proposed to solve this non-trivial challenge, of which the approach based on static analysis reaches the highest accuracy. We, at Adyen, implemented the state-ofthe-art research on log parsing in our logging environment and evaluated their accuracy and performance. Our results show that, with some adaptation, the current static analysis techniques are highly efficient and performant. In other words, ready for use.

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Prediction of Web User Behavior by Discovering Temporal Relational Rules from Web Log Data

Cited by 11 publications

References 3 publications

Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression

Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression

Effect of Temporal Relationships in Associative Rule Mining for Web Log Data

Tracing Back Log Data to its Log Statement: From Research to Practice

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