arXiv subjects

Hongzhi Wang

Publications and source records attributed to Hongzhi Wang.

At least 91 records · Page 5Linked to original sources

arXiv2021

Multi-SQL: An extensible multi-model data query language

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 architecture for building such a multi-model data store. For an integrated data hub, a unified and flexible query language is incredibly necessary. In this paper, an extensible and practical query language--Multi-SQL is proposed to realize the unified management of multi-model data considering the co-processing of multi-model data. To the best of our knowledge, Multi-SQL is the first query language based on various data models. Multi-SQL can also be expanded to suit more complicated scenarios as it is flexible to support other data models. Moreover, we provide a formal semantic definition of the core features of Multi-SQL, including the multi-model definition, multi-model filters, multi-model joins, etc. Furthermore, we propose a two-level query implementation method to totally exploit the existing query optimization capabilities of the underlying engines which could largely improve the query excution efficiency.

cs.DB↗

arXiv2021

Impacts of Dirty Data: and Experimental Evaluation

Data quality issues have attracted widespread attention due to the negative impacts of dirty data on data mining and machine learning results. The relationship between data quality and the accuracy of results could be applied on the selection of the appropriate algorithm with the consideration of data quality and the determination of the data share to clean. However, rare research has focused on exploring such relationship. Motivated by this, this paper conducts an experimental comparison for the effects of missing, inconsistent and conflicting data on classification and clustering algorithms. Based on the experimental findings, we provide guidelines for algorithm selection and data cleaning.

cs.DB↗

arXiv2021

FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search

Recently, some Neural Architecture Search (NAS) techniques are proposed for the automatic design of Graph Convolutional Network (GCN) architectures. They bring great convenience to the use of GCN, but could hardly apply to the Federated Learning (FL) scenarios with distributed and private datasets, which limit their applications. Moreover, they need to train many candidate GCN models from scratch, which is inefficient for FL. To address these challenges, we propose FL-AGCNS, an efficient GCN NAS algorithm suitable for FL scenarios. FL-AGCNS designs a federated evolutionary optimization strategy to enable distributed agents to cooperatively design powerful GCN models while keeping personal information on local devices. Besides, it applies the GCN SuperNet and a weight sharing strategy to speed up the evaluation of GCN models. Experimental results show that FL-AGCNS can find better GCN models in short time under the FL framework, surpassing the state-of-the-arts NAS methods and GCN models.

cs.LG↗

arXiv2021

ConsciousControlFlow(CCF): A Demonstration for conscious Artificial Intelligence

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 consciousness and the hierarchy of needs. CCF supports typical scenarios to show the behaviors and the mental activities of conscious AI. We demonstrate that CCF provides a useful tool for effective machine consciousness demonstration and human behavior study assistance.

cs.AI↗

arXiv2021

Approximate Query Processing for Group-By Queries based on Conditional Generative Models

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 effective way to increase the querying efficiency on big data. The answer to a group-by query involves multiple values, which makes it difficult to provide sufficiently accurate estimations for all the groups. Stratified sampling improves the accuracy compared with the uniform sampling, but the samples chosen for some special queries cannot work for other queries. Online sampling chooses samples for the given query at query time, but it requires a long latency. Thus, it is a challenge to achieve both accuracy and efficiency at the same time. Facing such challenge, in this work, we propose a sample generation framework based on a conditional generative model. The sample generation framework can generate any number of samples for the given query without accessing the data. The proposed framework based on the lightweight model can be combined with stratified sampling and online aggregation to improve the estimation accuracy for group-by queries. The experimental results show that our proposed methods are both efficient and accurate.

cs.DB↗

arXiv2021

Misplaced Subsequences Repairing with Application to Multivariate Industrial Time Series Data

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 high quality and applicability of the IoT data. However, errors are prevalent in original time series data. Inconsistency in time series is a serious data quality problem existing widely in IoT. Such problem could be hardly solved by existing techniques. Motivated by this, we define an inconsistent subsequences problem in multivariate time series, and propose an integrity data repair approach to solve inconsistent problems. Our proposed repairing method consists of two parts: (1) we design effective anomaly detection method to discover latent inconsistent subsequences in the IoT time series; and (2) we develop repair algorithms to precisely locate the start and finish time of inconsistent intervals, and provide reliable repairing strategies. A thorough experiment on two real-life datasets verifies the superiority of our method compared to other practical approaches. Experimental results also show that our method captures and repairs inconsistency problems effectively in industrial time series in complex IIoT scenarios.

cs.DB↗

arXiv2021

Exploring Data and Knowledge combined Anomaly Explanation of Multivariate Industrial Data

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 data is one essential task in IoT data mining. Since that the existing anomaly detection techniques focus on the identification of anomalies, the explanation of anomalies is not well-solved. We address the anomaly explanation problem for multivariate IoT data and propose a 3-step self-contained method in this paper. We formalize and utilize the domain knowledge in our method, and identify the anomalies by the violation of constraints. We propose set-cover-based anomaly explanation algorithms to discover the anomaly events reflected by violation features, and further develop knowledge update algorithms to improve the original knowledge set. Experimental results on real datasets from large-scale IoT systems verify that our method computes high-quality explanation solutions of anomalies. Our work provides a guide to navigate the explicable anomaly detection in both IoT fault diagnosis and temporal data cleaning.

cs.DB↗

arXiv2020

A Hybrid Solution to improve Iteration Efficiency in the Distributed Learning

Currently, many machine learning algorithms contain lots of iterations. When it comes to existing large-scale distributed systems, some slave nodes may break down or have lower efficiency. Therefore traditional machine learning algorithm may fail because of the instability of distributed system.We presents a hybrid approach which not only own a high fault-tolerant but also achieve a balance of performance and efficiency.For each iteration, the result of slow machines will be abandoned. Then, we discuss the relationship between accuracy and abandon rate. Next we debate the convergence speed of this process. Finally, our experiments demonstrate our idea can dramatically reduce calculation time and be used in many platforms.

cs.DC↗

arXiv2020

A Dual-Store Structure for Knowledge Graphs

To effectively manage increasing knowledge graphs in various domains, a hot research topic, knowledge graph storage management, has emerged. Existing methods are classified to relational stores and native graph stores. Relational stores are able to store large-scale knowledge graphs and convenient in updating knowledge, but the query performance weakens obviously when the selectivity of a knowledge graph query is large. Native graph stores are efficient in processing complex knowledge graph queries due to its index-free adjacent property, but they are inapplicable to manage a large-scale knowledge graph due to limited storage budgets or inflexible updating process. Motivated by this, we propose a dual-store structure which leverages a graph store to accelerate the complex query process in the relational store. However, it is challenging to determine what data to transfer from relational store to graph store at what time. To address this problem, we formulate it as a Markov Decision Process and derive a physical design tuner DOTIL based on reinforcement learning. With DOTIL, the dual-store structure is adaptive to dynamic changing workloads. Experimental results on real knowledge graphs demonstrate that our proposed dual-store structure improves query performance up to average 43.72% compared with the most commonly used relational stores.

cs.DB↗

arXiv2020

Efficient RDF Graph Storage based on Reinforcement Learning

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 knowledge graph data management. Due to the maturity and stability, relational database is also suitable for RDF data storage. However, the complex structure of RDF graph brings challenges to storage structure design for RDF graph in the relational database. To address the difficult problem, this paper adopts reinforcement learning (RL) to optimize the storage partition method of RDF graph based on the relational database. We transform the graph storage into a Markov decision process, and develop the reinforcement learning algorithm for graph storage design. For effective RL-based storage design, we propose the data feature extraction method of RDF tables and the query rewriting priority policy during model training. The extensive experimental results demonstrate that our approach outperforms existing RDF storage design methods.

cs.DB↗

arXiv2020

Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search Based on Reinforcement Learning and Existing Research Results

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 models have their own advantages, i.e., each of them puts forward many effective operations and achieves good prediction results in the real applications. If users can effectively utilize and combine these excellent operations integrating the advantages of existing models, then they may obtain more effective STGCN models thus create greater value using existing work. However, they fail to do so due to the lack of domain knowledge, and there is lack of automated system to help users to achieve this goal. In this paper, we fill this gap and propose Auto-STGCN algorithm, which makes use of existing models to automatically explore high-performance STGCN model for specific scenarios. Specifically, we design Unified-STGCN framework, which summarizes the operations of existing architectures, and use parameters to control the usage and characteristic attributes of each operation, so as to realize the parameterized representation of the STGCN architecture and the reorganization and fusion of advantages. Then, we present Auto-STGCN, an optimization method based on reinforcement learning, to quickly search the parameter search space provided by Unified-STGCN, and generate optimal STGCN models automatically. Extensive experiments on real-world benchmark datasets show that our Auto-STGCN can find STGCN models superior to existing STGCN models with heuristic parameters, which demonstrates the effectiveness of our proposed method.

cs.LG↗

arXiv2020

EM-RBR: a reinforced framework for knowledge graph completion from reasoning perspective

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 given KG, however, ignoring the rich background information provided by logic rules driven from knowledge base implicitly. To solve this problem, in this paper, we propose a general framework, named EM-RBR(embedding and rule-based reasoning), capable of combining the advantages of reasoning based on rules and the state-of-the-art models of embedding. EM-RBR aims to utilize relational background knowledge contained in rules to conduct multi-relation reasoning link prediction rather than superficial vector triangle linkage in embedding models. By this way, we can explore relation between two entities in deeper context to achieve higher accuracy. In experiments, we demonstrate that EM-RBR achieves better performance compared with previous models on FB15k, WN18 and our new dataset FB15k-R, especially the new dataset where our model perform futher better than those state-of-the-arts. We make the implementation of EM-RBR available at https://github.com/1173710224/link-prediction-with-rule-based-reasoning.

cs.AI↗

arXiv2020

Automatic Storage Structure Selection for hybrid Workload

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 the database need to select the storage engine and design data model according to the workload encountered. However, in a hybrid workload, the query set of the database is dynamically changing, and the design of its optimal storage structure is also changing. Motivated by this, we propose an automatic storage structure selection system based on learning cost, which is used to dynamically select the optimal storage structure of the database under hybrid workloads. In the system, we introduce a machine learning method to build a cost model for the storage engine, and a column-oriented data layout generation algorithm. Experimental results show that the proposed system can choose the optimal combination of storage engine and data model according to the current workload, which greatly improves the performance of the default storage structure. And the system is designed to be compatible with different storage engines for easy use in practical applications.

cs.DB↗

arXiv2020

Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation

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 structure, and search the repeatable cell structure only. Such kind of fixed architecture performs well when enough cells and channels are used. However, when the architecture becomes more lightweight, the performance decreases significantly. To obtain better lightweight architectures, more flexible and diversified neural architectures are in demand, and more efficient methods should be designed for larger search space. Motivated by this, we propose MoARR algorithm, which utilizes the existing research results and historical information to quickly find architectures that are both lightweight and accurate. We use the discovered high-performance cells to construct network architectures. This method increases the network architecture diversity while also reduces the search space of cell structure design. In addition, we designs a novel multi-objective method to effectively analyze the historical evaluation information, so as to efficiently search for the Pareto optimal architectures with high accuracy and small parameter number. Experimental results show that our MoARR can achieve a powerful and lightweight model (with 1.9% error rate and 2.3M parameters) on CIFAR-10 in 6 GPU hours, which is better than the state-of-the-arts. The explored architecture is transferable to ImageNet and achieves 76.0% top-1 accuracy with 4.9M parameters.

cs.CV↗

arXiv2020

Reachability Queries with Label and Substructure Constraints on Knowledge Graphs

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 with label and substructure constraints (LSCR). Specially, for a search path p, LSCR queries not only require that the labels of the edges passed by p are in a certain label set, but also claim that a vertex in p could satisfy a certain substructure constraint. LSCR queries is much more complex than the label-constraint reachability (LCR) queries, and there is no efficient solution for LSCR queries on KGs, to the best of our knowledge. Motivated by this, we introduce two solutions for such queries on KGs, UIS and INS. The former can also be utilized for general edge-labeled graphs, and is relatively handy for practical implementation. The latter is an efficient local-index-based informed search strategy. An extensive experimental evaluation, on both synthetic and real KGs, illustrates that our solutions can efficiently process LSCR queries on KGs.

cs.DB↗

arXiv2020

Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-Network

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 machine learning task, it usually takes domain experts plenty of time to select an appropriate algorithm and configure its hyperparameters. If the problem of algorithm selection and hyperparameter optimization can be solved automatically, the task will be executed more efficiently with performance guarantee. Such problem is also known as the CASH problem. Early work either requires a large amount of human labor, or suffers from high time or space complexity. In our work, we present Auto-CASH, a pre-trained model based on meta-learning, to solve the CASH problem more efficiently. Auto-CASH is the first approach that utilizes Deep Q-Network to automatically select the meta-features for each dataset, thus reducing the time cost tremendously without introducing too much human labor. To demonstrate the effectiveness of our model, we conduct extensive experiments on 120 real-world classification datasets. Compared with classical and the state-of-art CASH approaches, experimental results show that Auto-CASH achieves better performance within shorter time.

cs.LG↗

arXiv2020

Index Selection for NoSQL Database with Deep Reinforcement Learning

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 performance. The approach builds a deep reinforcement learning model to select an optimal index for a given fixed workload and adapts to a changing workload. Experimental results show that, Deep Reinforcement Learning Index Selection Approach (DRLISA) has improved performance to varying degrees according to traditional single index structures.

cs.DB↗

arXiv2020

Auto-Model: Utilizing Research Papers and HPO Techniques to Deal with the CASH problem

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 setting correctly can promote the overall performance greatly, but users often fail to do so due to the absence of knowledge. How to help users to effectively and quickly select the suitable algorithm and hyperparameter settings for the given task instance is an important research topic nowadays, which is known as the CASH problem. In this paper, we design the Auto-Model approach, which makes full use of known information in the related research paper and introduces hyperparameter optimization techniques, to solve the CASH problem effectively. Auto-Model tremendously reduces the cost of algorithm implementations and hyperparameter configuration space, and thus capable of dealing with the CASH problem efficiently and easily. To demonstrate the benefit of Auto-Model, we compare it with classical Auto-Weka approach. The experimental results show that our proposed approach can provide superior results and achieves better performance in a short time.

cs.AI↗