Raja Chiky

Logs record valuable system information at runtime. They are widely used by data-driven approaches for development and monitoring purposes. Parsing log messages to structure their format is a classic preliminary step for log-mining tasks. As they appear upstream, parsing operations can become a processing time bottleneck for downstream applications. The quality of parsing also has a direct influence on their efficiency. Here, we propose USTEP, an online log parsing method based on an evolving tree structure. Evaluation results on a wide panel of datasets coming from different real-world systems demonstrate USTEP superiority in terms of both effectiveness and robustness when compared to other online methods.

Logs are a fundamental component of modern computer systems. They enable the analysis and monitoring teams to understand any abnormal or malicious behavior that may have occurred. The continuous increase in the volume of logs generated by these systems made it unsuitable for manual inspection and represents a real challenge with regard to process automation. In order to process these data, several log-structuring solutions have been developed. In this article, we analyze the capabilities of two solutions in order to meet the challenges of Cloud Computing in terms of efficiency and effectiveness. Our work focuses on the impact of parameterization and preprocessing on the performance of these methods -- two important steps as they require human intervention, which is incompatible with with the automation of the log-structuring process.

Detecting anomalies in streaming data is an important issue for many application domains, such as cybersecurity, natural disasters, or bank frauds. Different approaches have been designed in order to detect anomalies: statistics-based, isolation-based, clustering-based, etc. In this paper, we present a structured survey of the existing anomaly detection methods for data streams with a deep view on Isolation Forest (iForest). We first provide an implementation of Isolation Forest Anomalies detection in Stream Data (IForestASD), a variant of iForest for data streams. This implementation is built on top of scikit-multiflow (River), which is an open source machine learning framework for data streams containing a single anomaly detection algorithm in data streams, called Streaming half-space trees. We performed experiments on different real and well known data sets in order to compare the performance of our implementation of IForestASD and half-space trees. Moreover, we extended the IForestASD algorithm to handle drifting data by proposing three algorithms that involve two main well known drift detection methods: ADWIN and KSWIN. ADWIN is an adaptive sliding window algorithm for detecting change in a data stream. KSWIN is a more recent method and it refers to the Kolmogorov–Smirnov Windowing method for concept drift detection. More precisely, we extended KSWIN to be able to deal with n-dimensional data streams. We validated and compared all of the proposed methods on both real and synthetic data sets. In particular, we evaluated the F1-score, the execution time, and the memory consumption. The experiments show that our extensions have lower resource consumption than the original version of IForestASD with a similar or better detection efficiency.

Detecting anomalies in streaming data is an important issue in a variety of real-word applications as it provides some critical information, e.g., Cyber security attacks, Fraud detection or others real-time applications. Different approaches have been designed in order to detect anomalies: statistics-based, isolation-based, clustering-based. In this paper, we present a quick survey of the existing anomaly detection methods for data streams. We focus on Isolation Forest (iForest), a state-of-the-art method for anomaly detection. We provide the implementation of IForestASD, a variant of iForest for data streams. This implementation is built on top of scikit-multiflow, an open source machine learning framework for data streams. In fact, few anomalies detection methods are provided in the well-known data streams mining frameworks such as MOA or StreamDM. Hence, we extend scikit-multiflow providing an additional tool. We performed experiments on 3 real-world data sets to evaluate predictive performance and resource consumption (memory and time) of IForestASD and compare it with a well known and state-of-the-art anomaly detection algorithm for data streams called Half-Space Trees.

In this paper, we use movies and series subtitles and applied text mining and Natural Language Processing methods to evaluate emotions in videos. Three different word lexicons were used and one of the outcomes of this research is the generation of a secondary dataset with more than 3658 records which can be used for other data analysis and data mining research. We used our secondary dataset to find and display correlations between different emotions on the videos and the correlation between emotions on the movies and users’ scores on IMDb using the Pearson correlation method and found some statistically significant correlations.

Sustainable agriculture is crucial to society since it aims at supporting the world’s current food needs without compromising future generations. Recent developments in Smart Agriculture and Internet of Things have made possible the collection of unprecedented amounts of agricultural data with the goal of making agricultural processes better and more efficient, and thus supporting sustainable agriculture. These data coming from different types of IoT devices can also be combined with relevant information published in online social networks and on the Web in the form of textual documents. Our objective is to integrate such heterogeneous data into knowledge bases that can support farmers in their activities, and to present global, real-time and comprehensive information to researchers. Semantic technologies and linked data provide a possibility for data integration and for automatic information extraction. This paper aims to give a brief review on the current semantic web technology applications for agricultural corpus, then to discuss the limits and potentials in construction and maintenance of existing ontologies in agricultural domain.

This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the new users to interact with the system by presenting them with a questionnaire that aims to understand their preferences to the related items. In this paper, we propose an active learning technique that exploits past users’ interests and past users’ predictions in order to identify the best questions to ask. Our technique achieves a better performance in terms of precision (RMSE), which leads to learn the users’ preferences in less questions. The experimentations were carried out in a small and public dataset to prove the applicability for handling cold start issues.