Detecting complex patterns in large volumes of event logs has diverse applications in various domains, such as business processes and fraud detection. Existing systems like ELK are commonly used to tackle this challenge, but their performance deteriorates for large patterns, while they suffer from limitations in terms of expressiveness and explanatory capabilities for their responses. In this work, we propose a solution that integrates a Complex Event Processing (CEP) engine into a broader query processor on top of a decoupled storage infrastructure containing inverted in- dices of log events. The results demonstrate that our system excels in scalability and robustness, particularly in handling complex queries. Notably, our proposed system delivers responses for large complex patterns within seconds, while ELK experiences timeouts after 10 min. It also significantly outperforms solutions relying on FlinkCEP and executing MATCH_RECOGNIZE SQL queries.

Abstract Detecting pattern matches underpins key operations across fields, such as complex event processing (CEP), sequential pattern mining (SPM), string pattern matching, pattern mining from a large sequence, and business process mining. These fields employ various notations and definitions for the detected patterns, posing challenges in recognizing their shared underlying concepts. This work aims to bridge these gaps by proposing a unified notation and terminology and then cataloging various pattern queries and constraints identified in different fields into a comprehensive framework. Our analysis reveals substantial similarities among the various pattern types, suggesting a promising avenue for the transfer of techniques between disciplines. This approach paves the way to leverage existing knowledge efficiently and circumvent the redundancy of “reinventing the wheel”.

This study introduces a scalable approach for link prediction in Wikipedia pages, specifically designed to handle the challenges arising from the large volume of data. The proposed solution combines partial reconstruction of the original graph using node descriptions, the generation of node pair vectors based on graph metrics, and the application of a threshold similarity using the TF-IDF method. Our proposed solution achieve a high F1 score of 0.948.

Sequential pattern analysis has become a mature topic with a lot of techniques for a variety of sequential pattern mining-related problems. Moreover, tailored solutions for specific domains, such as business process mining, have been developed. However, there is a gap in the literature for advanced techniques for efficient detection of arbitrary sequences in large collections of activity logs. In this work, we introduce the SIESTA (Scalableinfrastructureofsequential patternanalysis) solution making a threefold contribution: (i) we employ a novel architecture that relies on inverted indices during preprocessing and we introduce an advanced query processor that can detect and explore arbitrary patterns efficiently; (ii) we discuss and evaluate different configurations to optimize both the preprocessing and the querying phase; and (iii) we present evaluation results competing against representatives of the state-of-the-art with a focus on Big Data. The experimental results are particularly encouraging, e.g., when all methods are deployed in a cluster and the volume of the data is increased,SIESTA creates the indices in almost half the time compared to the state-of-the-art Elasticsearch-based solution, while also yielding faster query responses than all its competitors by up to 1 order of magnitude.