SAND: streaming subsequence anomaly detection
With the increasing demand for real-time analytics and decision making, anomaly detection
methods need to operate over streams of values and handle drifts in data distribution …
methods need to operate over streams of values and handle drifts in data distribution …
Deep Learning Approaches for Similarity Computation: A Survey
The requirement for appropriate ways to measure the similarity between data objects is a
common but vital task in various domains, such as data mining, machine learning and so on …
common but vital task in various domains, such as data mining, machine learning and so on …
Learned data-aware image representations of line charts for similarity search
Finding line-chart images similar to a given line-chart image query is a common task in data
exploration and image query systems, eg finding similar trends in stock markets or medical …
exploration and image query systems, eg finding similar trends in stock markets or medical …
Elpis: Graph-based similarity search for scalable data science
I Azizi, K Echihabi, T Palpanas - Proceedings of the VLDB Endowment, 2023 - dl.acm.org
The recent popularity of learned embeddings has fueled the growth of massive collections of
high-dimensional (high-d) vectors that model complex data. Finding similar vectors in these …
high-dimensional (high-d) vectors that model complex data. Finding similar vectors in these …
Hercules against data series similarity search
We propose Hercules, a parallel tree-based technique for exact similarity search on massive
disk-based data series collections. We present novel index construction and query …
disk-based data series collections. We present novel index construction and query …
dcam: Dimension-wise class activation map for explaining multivariate data series classification
Data series classification is an important and challenging problem in data science.
Explaining the classification decisions by finding the discriminant parts of the input that led …
Explaining the classification decisions by finding the discriminant parts of the input that led …
New trends in high-d vector similarity search: al-driven, progressive, and distributed
Similarity search is a core operation of many critical applications, involving massive
collections of high-dimensional (high-d) objects. Objects can be data series, text …
collections of high-dimensional (high-d) objects. Objects can be data series, text …
Fast data series indexing for in-memory data
Data series similarity search is a core operation for several data series analysis applications
across many different domains. However, the state-of-the-art techniques fail to deliver the …
across many different domains. However, the state-of-the-art techniques fail to deliver the …
Odyssey: A journey in the land of distributed data series similarity search
M Chatzakis, P Fatourou, E Kosmas… - arXiv preprint arXiv …, 2023 - arxiv.org
This paper presents Odyssey, a novel distributed data-series processing framework that
efficiently addresses the critical challenges of exhibiting good speedup and ensuring high …
efficiently addresses the critical challenges of exhibiting good speedup and ensuring high …
IEDeaL: a deep learning framework for detecting highly imbalanced interictal epileptiform discharges
Q Wang, S Whitmarsh, V Navarro… - Proceedings of the VLDB …, 2022 - dl.acm.org
Epilepsy is a chronic neurological disease, ranked as the second most burdensome
neurological disorder worldwide. Detecting Interictal Epileptiform Discharges (IEDs) is …
neurological disorder worldwide. Detecting Interictal Epileptiform Discharges (IEDs) is …