Christian S. Jensen
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View article: Occurrence of impaired swallowing ability and change over a year in older adults living in nursing homes
Occurrence of impaired swallowing ability and change over a year in older adults living in nursing homes Open
The aim of the present study was to investigate the prevalence of dysphagia in three nursing homes and to investigate whether swallowing function changes over a year. Seventy-three individuals participated (median age 89 years) and were te…
View article: Comprehending Spatio-temporal Data via Cinematic Storytelling using Large Language Models
Comprehending Spatio-temporal Data via Cinematic Storytelling using Large Language Models Open
Spatio-temporal data captures complex dynamics across both space and time, yet traditional visualizations are complex, require domain expertise and often fail to resonate with broader audiences. Here, we propose MapMuse, a storytelling-bas…
View article: MH-GIN: Multi-scale Heterogeneous Graph-based Imputation Network for AIS Data (Extended Version)
MH-GIN: Multi-scale Heterogeneous Graph-based Imputation Network for AIS Data (Extended Version) Open
Location-tracking data from the Automatic Identification System, much of which is publicly available, plays a key role in a range of maritime safety and monitoring applications. However, the data suffers from missing values that hamper dow…
View article: Prioritizing Alignment Paradigms over Task-Specific Model Customization in Time-Series LLMs
Prioritizing Alignment Paradigms over Task-Specific Model Customization in Time-Series LLMs Open
Recent advances in Large Language Models (LLMs) have enabled unprecedented capabilities for time-series reasoning in diverse real-world applications, including medical, financial, and spatio-temporal domains. However, existing approaches t…
View article: Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review
Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review Open
Spatio-temporal deep learning models aims to utilize useful patterns in such data to support tasks like prediction. However, previous deep learning models designed for specific tasks typically require separate training for each use case, l…
View article: A Multi-Modal Knowledge-Enhanced Framework for Vessel Trajectory Prediction
A Multi-Modal Knowledge-Enhanced Framework for Vessel Trajectory Prediction Open
Accurate vessel trajectory prediction facilitates improved navigational safety, routing, and environmental protection. However, existing prediction methods are challenged by the irregular sampling time intervals of the vessel tracking data…
View article: ACE: A Cardinality Estimator for Set-Valued Queries
ACE: A Cardinality Estimator for Set-Valued Queries Open
Cardinality estimation is a fundamental functionality in database systems. Most existing cardinality estimators focus on handling predicates over numeric or categorical data. They have largely omitted an important data type, set-valued dat…
View article: K Nearest Neighbor-Guided Trajectory Similarity Learning
K Nearest Neighbor-Guided Trajectory Similarity Learning Open
Trajectory similarity is fundamental to many spatio-temporal data mining applications. Recent studies propose deep learning models to approximate conventional trajectory similarity measures, exploiting their fast inference time once traine…
View article: OneDB:A Distributed Multi-Metric Data Similarity Search System
OneDB:A Distributed Multi-Metric Data Similarity Search System Open
Increasingly massive volumes of multi-modal data are being accumulated in many {real world} settings, including in health care and e-commerce. This development calls for effective general-purpose data management solutions for multi-modal d…
View article: EasyTime: Time Series Forecasting Made Easy
EasyTime: Time Series Forecasting Made Easy Open
Time series forecasting has important applications across diverse domains. EasyTime, the system we demonstrate, facilitates easy use of time-series forecasting methods by researchers and practitioners alike. First, EasyTime enables one-cli…
View article: SWOOP: top-k similarity joins over set streams
SWOOP: top-k similarity joins over set streams Open
We provide efficient support for applications that aim to continuously find pairs of similar sets in rapid streams, such as Twitter streams that emit tweets as sets of words. Using a sliding window model, the top- k result changes as new s…
View article: HIGGS: HIerarchy-Guided Graph Stream Summarization
HIGGS: HIerarchy-Guided Graph Stream Summarization Open
Graph stream summarization refers to the process of processing a continuous stream of edges that form a rapidly evolving graph. The primary challenges in handling graph streams include the impracticality of fully storing the ever-growing d…
View article: iRangeGraph: Improvising Range-dedicated Graphs for Range-filtering Nearest Neighbor Search
iRangeGraph: Improvising Range-dedicated Graphs for Range-filtering Nearest Neighbor Search Open
Range-filtering approximate nearest neighbor (RFANN) search is attracting increasing attention in academia and industry. Given a set of data objects, each being a pair of a high-dimensional vector and a numeric value, an RFANN query with a…
View article: RED: Effective Trajectory Representation Learning with Comprehensive Information
RED: Effective Trajectory Representation Learning with Comprehensive Information Open
Trajectory representation learning (TRL) maps trajectories to vectors that can then be used for various downstream tasks, including trajectory similarity computation, trajectory classification, and travel-time estimation. However, existing…
View article: Grid and Road Expressions Are Complementary for Trajectory Representation Learning
Grid and Road Expressions Are Complementary for Trajectory Representation Learning Open
Trajectory representation learning (TRL) maps trajectories to vectors that can be used for many downstream tasks. Existing TRL methods use either grid trajectories, capturing movement in free space, or road trajectories, capturing movement…
View article: Fully Automated Correlated Time Series Forecasting in Minutes
Fully Automated Correlated Time Series Forecasting in Minutes Open
Societal and industrial infrastructures and systems increasingly leverage sensors that emit correlated time series. Forecasting of future values of such time series based on recorded historical values has important benefits. Automatically …
View article: Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching--Extended Version
Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching--Extended Version Open
The expanding instrumentation of processes throughout society with sensors yields a proliferation of time series data that may in turn enable important applications, e.g., related to transportation infrastructures or power grids. Machine-l…
View article: Modeling and Monitoring of Indoor Populations using Sparse Positioning Data (Extension)
Modeling and Monitoring of Indoor Populations using Sparse Positioning Data (Extension) Open
In large venues like shopping malls and airports, knowledge on the indoor populations fuels applications such as business analytics, venue management, and safety control. In this work, we provide means of modeling populations in partitions…
View article: Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis
Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis Open
Multivariate Time Series (MTS) analysis is crucial to understanding and managing complex systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting have been proposed recently. However, we often observe inc…
View article: TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting
TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting Open
Time Series Forecasting (TSF) is key functionality in numerous fields, such as financial investment, weather services, and energy management. Although increasingly capable TSF methods occur, many of them require domain-specific data collec…
View article: iRangeGraph: Improvising Range-dedicated Graphs for Range-filtering Nearest Neighbor Search
iRangeGraph: Improvising Range-dedicated Graphs for Range-filtering Nearest Neighbor Search Open
Range-filtering approximate nearest neighbor (RFANN) search is attracting increasing attention in academia and industry. Given a set of data objects, each being a pair of a high-dimensional vector and a numeric value, an RFANN query with a…
View article: Digital Twin-Empowered Autonomous Driving for E-mobility: Concept, framework, and modeling
Digital Twin-Empowered Autonomous Driving for E-mobility: Concept, framework, and modeling Open
As a disruptive technology in the power and transport sectors, electric mobility (e-mobility) is receiving increasing attention. E-mobility encompasses the electrification of transportation by means of diversified electric vehicles (EVs) i…
View article: ReCTSi: Resource-efficient Correlated Time Series Imputation via Decoupled Pattern Learning and Completeness-aware Attentions
ReCTSi: Resource-efficient Correlated Time Series Imputation via Decoupled Pattern Learning and Completeness-aware Attentions Open
Imputation of Correlated Time Series (CTS) is essential in data preprocessing for many tasks, particularly when sensor data is often incomplete. Deep learning has enabled sophisticated models that improve CTS imputation by capturing tempor…
View article: UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddings
UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddings Open
Spatiotemporal trajectories are sequences of timestamped locations, which enable a variety of analyses that in turn enable important real-world applications. It is common to map trajectories to vectors, called embeddings, before subsequent…
View article: Efficient Stochastic Routing in Path-Centric Uncertain Road Networks -- Extended Version
Efficient Stochastic Routing in Path-Centric Uncertain Road Networks -- Extended Version Open
The availability of massive vehicle trajectory data enables the modeling of road-network constrained movement as travel-cost distributions rather than just single-valued costs, thereby capturing the inherent uncertainty of movement and ena…