20 papers · ranked by Valyu relevance
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…
Andreas Mathisen, Kaj Grønbæk
Extracting and visualizing informative insights from temporal event sequences becomes increasingly difficult when data volume and variety increase. Besides dealing with high event type cardinality and many distinct sequences, it can be difficult to tell whether it is appropriate to combine multiple events into one or…
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…
Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria + 2 more
'Jack Gibson' 'Richard Hubbard'] Abstract— The wealth of computerised medical information becoming readily available presents the opportunity to examine patterns of illnesses, therapies and responses. These patterns may be able to predict illnesses that a patient is likely to develop, allowing the implementation of…
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…
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…
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…
Pavlina Kröckel, Freimut Bodendorf
The paper explores process mining and its usefulness for analyzing football event data. We work with professional event data provided by OPTA Sports from the European Championship in 2016. We analyze one game of a favorite team (England) against an underdog team (Iceland). The success of the underdog teams in the Euro…
Van Long Ho, Nguyen Ho, Torben Bach Pedersen
Very large time series are increasingly available from an ever wider range of IoT-enabled sensors deployed in different environments. Significant insights can be gained by mining temporal patterns from these time series. Unlike traditional pattern mining, temporal pattern mining (TPM) adds event time intervals into…
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…
Nazli Mohd Khairudin, Aida Mustapha, Mohd Hanif Ahmad
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…
Abdur Rahman M.A. Basher, Alexander S. Purdy, Inanç Birol
The breadth and scope of the biomedical literature hinders a timely and thorough comprehension of its content. PubMed, the leading repository for biomedical literature, currently holds over 26 million records, and is growing at a rate of over 1.2 million records per year, with about 300 records added daily that mention…
Sandeep Kaur, Timothy J. Peters, Pengyi Yang, Laurence Don Wai Luu + 3 more
Temporal changes in omics events can now be routinely measured, however current analysis methods are often inadequate, especially for multiomics experiments. We report a novel analysis method that can infer event ordering at better temporal resolution than the experiment, and integrates omic events into two concise…
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…
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…
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…
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…
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…
Authors not listed
Background: Chemical reactions form intricate, highly connected networks whose exploration is essential for discovering more efficient and sustainable synthetic routes. As reaction data from literature, patents, and high‑throughput experimentation continue to surge, so does the need for tools that can collectively…