20 papers · ranked by Valyu relevance
Anton Yeshchenko, Jan Mendling
Event sequence data is increasingly available. Many business operations are supported by information systems that record transactions, events, state changes, message exchanges, and so forth. This observation is equally valid for various industries, including production, logistics, healthcare, financial services…
Aleena Siji, Joscha Cüppers, Osman Ali Mian, Jilles Vreeken
Summarizing event sequences is a key aspect of data mining. Most existing methods neglect conditional dependencies and focus on discovering sequential patterns only. In this paper, we study the problem of discovering both conditional and unconditional dependencies from event sequence data. We do so by discovering rules…
Somin Wadhwa, Oktie Hassanzadeh, Debarun Bhattacharjya, Ken Barker + 1 more
'Jian Ni'] > Abstract. Event sequence models have been found to be highly effective in the analysis and prediction of events. Building such models requires availability of abundant high-quality event sequence data. In certain applications, however, clean structured event sequences are not available, and automated…
Jian Zhu, Xiaoye Chen, Wensheng Gan, Zefeng Chen + 1 more
—The era characterized by an exponential increase in data has led to the widespread adoption of data intelligence as a crucial task. Within the field of data mining, frequent episode mining has emerged as an effective tool for extracting valuable and essential information from event sequences. Various algorithms have…
László Bántay, János Abonyi, Praveen Kumar Donta
Frequent sequence pattern mining is an excellent tool to discover patterns in event chains. In complex systems, events from parallel processes are present, often without proper labelling. To identify the groups of events related to the subprocess, frequent sequential pattern mining can be applied. Since most algorithms…
Kazi Tasnim Zinat, Jinhua Yang, Arjun Gandhi, Nistha Mitra + 1 more
Early event sequence visualization tools focused on displaying each individual records, [Kar94; PMR96; PMS03; HOB94; WPQ08; FKSS06]. and they can only handle a small number of sequences. Later tools aggregate events across sequences to generate tree structures or directed-acyclic graph (DAG) structures [WGP11; WG12…
Van Long Ho, Nguyen Ho, Torben Bach Pedersen, Panagiotis Papapetrou
—Big time series are increasingly available from an ever wider range of IoT-enabled sensors deployed in various environments. Significant insights can be gained by mining temporal patterns from these time series. Temporal pattern mining (TPM) extends traditional pattern mining by adding event time intervals into…
Isidoro J. Casanova, Manuel Campos, Jose M. Juarez, Antonio Gomariz + 3 more
'Bernardo Canovas-Segura' 'Marta Lorente-Ros' 'Jose A. Lorente'] Background Pattern mining techniques are helpful tools when extracting new knowledge in real practice, but the overwhelming number of patterns is still a limiting factor in the health-care domain. Current efforts concerning the definition of measures of…
Christian Lovis, Dian Hu, Mikko Nuutinen, Azizollah Arbabisarjou + 6 more
Background It is important to exploit all available data on patients in settings such as intensive care burn units (ICBUs), where several variables are recorded over time. It is possible to take advantage of the multivariate patterns that model the evolution of patients to predict their survival. However, pattern…
Van Ho Long, Nguyen Ho, Trinh Le Cong, Anh-Vu Dinh-Duc + 1 more
'Tu Nguyen Thi Ngoc'] Abstract. Time series data from various domains is continuously growing, and extracting and analyzing temporal patterns within these series can provide valuable insights. Temporal pattern mining (TPM) extends traditional pattern mining by incorporating event time intervals into patterns, making…
Shiting Ding, Zhiheng Li, Kai Zhang, Feng Mao + 1 more
Sequential pattern mining (SPM) is a major class of data mining topics with a wide range of applications. The continuity and uncertain nature of trajectory data make it distinctively different from typical transactional data, which requires additional data transformation to prepare for SPM. However, little research…
Thomas Pircher, Bianca Pircher, Andreas Feigenspan
Spontaneous synaptic activity is a hallmark of neural networks. A thorough description of these synaptic signals is essential for understanding neurotransmitter release and the generation of a postsynaptic response. However, the complexity of synaptic current trajectories has either precluded an in-depth analysis or it…
Sohom Ghosh, Shefali Yadav, Xin Wang, Bibhash Chakrabarty + 1 more
'Serdar Kadıoğlu'] Sequential pattern mining remains a challenging task due to the large number of redundant candidate patterns and the exponential search space. In addition, further analysis is still required to map extracted patterns to different outcomes. In this paper, we introduce a pattern mining framework that…
Min Shi, Yongshun Gong, Tiantian Xu, Long Zhao + 1 more
High utility sequential pattern (HUSP) mining aims to mine actionable patterns with high utilities, widely applied in real-world learning scenarios such as market basket analysis, scenic route planning and click-stream analysis. The existing HUSP mining algorithms mainly attempt to improve computation efficiency while…
Nikitha Karkera, Nikshita Karkera, Mahanash Kumar, Samik Ghosh + 1 more
The pathway curation task involves analyzing scientific literature to identify and represent cellular processes as pathways. This process, often time-consuming and labor-intensive, requires significant curation efforts amidst the rapidly growing biomedical literature. Natural Language Processing (NLP) offers a…
Herui Zhang, Forouzan Farahani, Eden Tefera, Zayn Ahmed + 10 more
Patients with epilepsy (PWE), especially temporal lobe epilepsy (TLE), experience impaired memory for personally experienced events. However, current assessments of episodic memory are limited in their ecological validity with a potential to miss detection of subtle cognitive decline. We conducted an exploratory study…
William Daniels, Meng Jia, Dorit Hammerling
We propose a generic, modular framework for emission event detection, localization, and quantification on oil and gas production sites that uses concentration data collected by pointin-space continuous monitoring systems (CMS). The framework uses a gradient-based spike detection algorithm to estimate emission start and…
Zakieh Tayyebi, Allison R. Pine, Christina S. Leslie
Standard scATAC-seq analysis pipelines represent cells as sparse numeric vectors relative to an atlas of peaks or genomic tiles and consequently ignore genomic sequence information at accessible loci. We present CellSpace, an efficient and scalable sequence-informed embedding algorithm for scATAC-seq that learns a…
Peter C. Bermant, Leandra Brickson, Alexander J. Titus
While deep learning has revolutionized ecological data analysis, existing strategies often rely on supervised learning, which is subject to limitations on real-world applicability. In this paper, we apply self-supervised deep learning methods to bioacoustic data to enable unsupervised detection of bioacoustic event…
William Daniels, Meng Jia, Dorit Hammerling
We propose a method for estimating the duration of methane emissions on oil and gas sites, referred to as the Probabilistic Duration Model (PDM), that uses concentration data from continuous monitoring systems (CMS). The PDM probabilistically addresses a key limitation of CMS: non-detect times, or the times when wind…