12 citations · 68 across the 49 of their papers we have counts for
49 papers
Dynamic Relation Repairing for Knowledge Enhancement
Rui Kang, Hongzhi Wang
Dynamic relation repair aims to efficiently validate and repair the instances for knowledge graph enhancement (KGE), where KGE captures missing relations from unstructured data and…
Experience-Enhanced Learning: One Size Still does not Fit All in Automatic Database
Yu Yan, Hongzhi Wang, Jian Ma +2
Recent years, the database committee has attempted to develop automatic database management systems. Although some researches show that the applying AI to data management is a sign…
Search For Deep Graph Neural Networks
Guosheng Feng, Chunnan Wang, Hongzhi Wang
Current GNN-oriented NAS methods focus on the search for different layer aggregate components with shallow and simple architectures, which are limited by the 'over-smooth' problem.…
TENSILE: A Tensor granularity dynamic GPU memory scheduling method toward multiple dynamic workloads system
Kaixin Zhang, Hongzhi Wang, Han Hu +4
Recently, deep learning has been an area of intense research. However, as a kind of computing-intensive task, deep learning highly relies on the scale of GPU memory, which is usual…
FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search
Chunnan Wang, Bozhou Chen, Geng Li +1
Recently, some Neural Architecture Search (NAS) techniques are proposed for the automatic design of Graph Convolutional Network (GCN) architectures. They bring great convenience to…
Approximate Query Processing for Group-By Queries based on Conditional Generative Models
Meifan Zhang, Hongzhi Wang
The Group-By query is an important kind of query, which is common and widely used in data warehouses, data analytics, and data visualization. Approximate query processing is an eff…
Misplaced Subsequences Repairing with Application to Multivariate Industrial Time Series Data
Xiaoou Ding, Hongzhi Wang, Jiaxuan Su +1
Both the volume and the collection velocity of time series generated by monitoring sensors are increasing in the Internet of Things (IoT). Data management and analysis requires hig…
Exploring Data and Knowledge combined Anomaly Explanation of Multivariate Industrial Data
Xiaoou Ding, Hongzhi Wang, Chen Wang +2
The demand for high-performance anomaly detection techniques of IoT data becomes urgent, especially in industry field. The anomaly identification and explanation in time series dat…
A Dual-Store Structure for Knowledge Graphs
Zhixin Qi, Hongzhi Wang, Haoran Zhang
To effectively manage increasing knowledge graphs in various domains, a hot research topic, knowledge graph storage management, has emerged. Existing methods are classified to rela…
Multi-SQL: An extensible multi-model data query language
Yu Yan, Nan Jiang, Hongzhi Wang +3
Big data management aims to establish data hubs that support data in multiple models and types in an all-around way. Thus, the multi-model database system is a promising architectu…
Efficient RDF Graph Storage based on Reinforcement Learning
Lei Zheng, Ziming Shen, Hongzhi Wang
Knowledge graph is an important cornerstone of artificial intelligence. The construction and release of large-scale knowledge graphs in various fields pose new challenges to knowle…
Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search Based on Reinforcement Learning and Existing Research Results
Chunnan Wang, Kaixin Zhang, Hongzhi Wang +1
In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem. These STGCN model…
EM-RBR: a reinforced framework for knowledge graph completion from reasoning perspective
Zhaochong An, Bozhou Chen, Houde Quan +2
Knowledge graph completion aims to predict the new links in given entities among the knowledge graph (KG). Most mainstream embedding methods focus on fact triplets contained in the…
Automatic Storage Structure Selection for hybrid Workload
Hongzhi Wang, Yan Wei, Hao Yan
In the use of database systems, the design of the storage engine and data model directly affects the performance of the database when performing queries. Therefore, the users of th…
Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation
Chunnan Wang, Hongzhi Wang, Guosheng Feng +1
The search space of neural architecture search (NAS) for convolutional neural network (CNN) is huge. To reduce searching cost, most NAS algorithms use fixed outer network level str…
Reachability Queries with Label and Substructure Constraints on Knowledge Graphs
Xiaolong Wan, Hongzhi Wang
Since knowledge graphs (KGs) describe and model the relationships between entities and concepts in the real world, reasoning on KGs often correspond to the reachability queries wit…
Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-Network
Tianyu Mu, Hongzhi Wang, Chunnan Wang +1
The great amount of datasets generated by various data sources have posed the challenge to machine learning algorithm selection and hyperparameter configuration. For a specific mac…
Index Selection for NoSQL Database with Deep Reinforcement Learning
Shun Yao, Hongzhi Wang, Yu Yan
We propose a new approach of NoSQL database index selection. For different workloads, we select different indexes and their different parameters to optimize the database performanc…
Efficient Semi-External Depth-First Search
Xiaolong Wan, Hongzhi Wang
As the sizes of graphs grow rapidly, currently many real-world graphs can hardly be loaded in the main memory. It becomes a hot topic to compute depth-first search (DFS) results, i…
ConsciousControlFlow(CCF): A Demonstration for conscious Artificial Intelligence
Hongzhi Wang, Bozhou Chen, Yueyang Xu +2
In this demo, we present ConsciousControlFlow(CCF), a prototype system to demonstrate conscious Artificial Intelligence (AI). The system is based on the computational model for con…
LAQP: Learning-based Approximate Query Processing
Meifan Zhang, Hongzhi Wang
Querying on big data is a challenging task due to the rapid growth of data amount. Approximate query processing (AQP) is a way to meet the requirement of fast response. In this pap…
Automatic Hyper-Parameter Optimization Based on Mapping Discovery from Data to Hyper-Parameters
Bozhou Chen, Kaixin Zhang, Longshen Ou +3
Machine learning algorithms have made remarkable achievements in the field of artificial intelligence. However, most machine learning algorithms are sensitive to the hyper-paramete…
ExperienceThinking: Constrained Hyperparameter Optimization based on Knowledge and Pruning
Chunnan Wang, Hongzhi Wang, Chang Zhou +1
Machine learning algorithms are very sensitive to the hyperparameters, and their evaluations are generally expensive. Users desperately need intelligent methods to quickly optimize…
Auto-Model: Utilizing Research Papers and HPO Techniques to Deal with the CASH problem
Chunnan Wang, Hongzhi Wang, Tianyu Mu +2
In many fields, a mass of algorithms with completely different hyperparameters have been developed to address the same type of problems. Choosing the algorithm and hyperparameter s…