The knobs of modern database management systems have significant impact ...
Learned cardinality estimation methods have achieved high precision comp...
With the emergence of multimodal electronic health records, the evidence...
Recent studies have shown great promise in unsupervised representation
l...
Innovative learning based structures have recently been proposed to tack...
Multivariate time series classification (MTSC) is an important data mini...
Machine learning has emerged as a powerful tool for time series analysis...
Neural predictors currently show great potential in the performance
eval...
Hierarchical data storage is crucial for cloud-edge-end time-series data...
This paper studies how to develop accurate and interpretable time series...
In a complex disease such as tuberculosis, the evidence for the disease ...
Learning rate adaptation is a popular topic in machine learning. Gradien...
Nowadays, graph becomes an increasingly popular model in many real
appli...
To accelerate learning process with few samples, meta-learning resorts t...
Meta-learning is used to efficiently enable the automatic selection of
m...
Automatic Time Series Forecasting (TSF) model design which aims to help ...
Dynamic relation repair aims to efficiently validate and repair the inst...
Model compression methods can reduce model complexity on the premise of
...
Trajectory Prediction (TP) is an important research topic in computer vi...
Global warming leads to the increase in frequency and intensity of clima...
Recent years, the database committee has attempted to develop automatic
...
Current GNN-oriented NAS methods focus on the search for different layer...
Recently, some Neural Architecture Search (NAS) techniques are proposed ...
The Group-By query is an important kind of query, which is common and wi...
To effectively manage increasing knowledge graphs in various domains, a ...
Big data management aims to establish data hubs that support data in mul...
Knowledge graph is an important cornerstone of artificial intelligence. ...
Efficient Latin hypercube designs (LHDs), including maximin distance LHD...
In recent years, many spatial-temporal graph convolutional network (STGC...
Knowledge graph completion aims to predict the new links in given entiti...
In the use of database systems, the design of the storage engine and dat...
Since knowledge graphs (KGs) describe and model the relationships betwee...
The great amount of datasets generated by various data sources have pose...
The search space of neural architecture search (NAS) for convolutional n...
We propose a new approach of NoSQL database index selection. For differe...
Computing Depth-First Search (DFS) results, i.e. depth-first order or
DF...
The Pancreatic beta cell is an important target in diabetes research. Fo...
In this demo, we present ConsciousControlFlow(CCF), a prototype system t...
Querying on big data is a challenging task due to the rapid growth of da...
Machine learning algorithms have made remarkable achievements in the fie...
The problem of hyperparameter optimization exists widely in the real lif...
In many fields, a mass of algorithms with completely different
hyperpara...
Data repairing is a key problem in data cleaning which aims to uncover a...
In industrial data analytics, one of the fundamental problems is to util...
Time series prediction with missing values is an important problem of ti...
In this paper, a method of prediction on continuous time series variable...
Central venous catheters (CVCs) are commonly used in critical care setti...
In many applications, it is necessary to retrieve pairs of vertices with...
The growing data has brought tremendous pressure for query processing an...
Knowledge base (KB) is an important aspect in artificial intelligence. O...