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Advances in Artificial Intelligence (AI)-Driven Data Mining

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 10853

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Special Issue Editor

Dr. Luoyi Fu

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Guest Editor

Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: social networking; data mining & engineering; fundamental limits
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data mining is the process of finding anomalies, patterns and correlations within large data sets. In the era of data explosion, AI-driven data mining has attracted great attention, as these machines can inspect, discover and visualize data patterns automatically. IoT systems, as well as network information systems such as social networks and search engines, not only provide richer sources for data mining but also pose higher challenges to algorithms handling huge numbers of data with complex correlations and highly dynamic and noisy annotations. AI-driven data mining explores algorithms and techniques that can handle numerous data and extract useful pattern information with little human intervention.

This Special Issue seeks new ideas, methods and achievements for the intersection between artificial intelligence and data mining. Topics of interest include, but are not limited to, the following: data preprocessing, data mining, machine learning and deep learning.

Dr. Luoyi Fu
Guest Editor

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Keywords

artificial intelligence
machine learning
deep learning
big data
data mining
data preprocessing

Published Papers (6 papers)

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Research

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13 pages, 464 KiB

Open AccessArticle

Exploring Prior Knowledge from Human Mobility Patterns for POI Recommendation

by Jingbo Song, Qiuhua Yi, Haoran Gao, Buyu Wang and Xiangjie Kong

Appl. Sci. 2023, 13(11), 6495; https://0-doi-org.brum.beds.ac.uk/10.3390/app13116495 - 26 May 2023

Cited by 1 | Viewed by 1093

Abstract

Point of interest (POI) recommendation is an important task in location-based social networks. It plays a critical role in smart tourism and makes it more likely for tourists to have personalized travel experiences. However, most current recommendation methods are based on learning the users’ check-in history and the users’ relationship network in the social network to make recommendations.Therefore, urban crowds’ regular travel patterns cannot be effectively utilized. In this paper, we propose a POI recommendation algorithm (HMRec) based on prior knowledge of human mobility patterns to solve this problem. Specifically, we propose the Human Mobility Pattern Extraction (HMPE) framework, which utilizes graph neural networks as extractors for human mobility patterns. The framework incorporates attention mechanisms to capture spatio-temporal information in urban traffic patterns. HMPE employs downstream tasks and design upsampling modules to reconstruct representation vectors for task objectives, enabling end-to-end training of the framework and obtaining pre-trained parameters for the human mobility pattern extractor. Furthermore, we introduce the Human Mobility Recommendation (HMRec) algorithm, which improves feature cross-interactions in the breadth model and incorporates prior knowledge of human patterns. This ensures that the recommendation results align more closely with human travel patterns in urban environments. Comparative experiments conducted on the Foursquare dataset demonstrate that HMRec outperforms baseline models with an average performance improvement of approximately 3%. Finally, we discuss existing challenges and future research directions, including approaches to address the issue of data sparsity. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI)-Driven Data Mining)

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19 pages, 1176 KiB

Open AccessArticle

Knowledge-Aware Enhanced Network Combining Neighborhood Information for Recommendations

by Xiaole Wang, Jiwei Qin, Shangju Deng and Wei Zeng

Appl. Sci. 2023, 13(7), 4577; https://0-doi-org.brum.beds.ac.uk/10.3390/app13074577 - 04 Apr 2023

Cited by 1 | Viewed by 1287

Abstract

In recent years, the application of knowledge graphs to alleviate cold start and data sparsity problems of users and items in recommendation systems, has aroused great interest. In this paper, in order to address the insufficient representation of user and item embeddings in existing knowledge graph-based recommendation methods, a knowledge-aware enhanced network, combining neighborhood information recommendation (KCNR), is proposed. Specifically, KCNR first encodes prior information about the user–item interaction, and obtains the user’s different knowledge neighbors by propagating them in the knowledge graph, and uses a knowledge-aware attention network to distinguish and aggregate the contributions of the different neighbors in the knowledge graph, as a way to enrich the user’s description. Similarly, KCNR samples multiple-hop neighbors of item entities in the knowledge graph, and has a bias to aggregate the neighborhood information, to enhance the item embedding representation. With the above processing, KCNR can automatically discover structural and associative semantic information in the knowledge graph, and capture users’ latent distant personalized preferences, by propagating them across the knowledge graph. In addition, considering the relevance of items to entities in the knowledge graph, KCNR has designed an information complementarity module, which automatically shares potential interaction characteristics of items and entities, and enables items and entities to complement the available information. We have verified that KCNR has excellent recommendation performance through extensive experiments in three real-life scenes: movies, books, and music. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI)-Driven Data Mining)

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15 pages, 2426 KiB

Open AccessArticle

Real-Time Semantic Segmentation of Point Clouds Based on an Attention Mechanism and a Sparse Tensor

by Fei Wang, Yujie Yang, Zhao Wu, Jingchun Zhou and Weishi Zhang

Appl. Sci. 2023, 13(5), 3256; https://0-doi-org.brum.beds.ac.uk/10.3390/app13053256 - 03 Mar 2023

Cited by 3 | Viewed by 2022

Abstract

A 3D point cloud is one of the main data sources for robot environmental cognition and understanding. Due to the limited computation and memory capacities of the robotic platform, existing semantic segmentation models of 3D point clouds cannot meet the requirements of real-time applications. To solve this problem, a lightweight, fully convolutional network based on an attention mechanism and a sparse tensor is proposed to better balance the accuracy and real-time performance of point cloud semantic segmentation. On the basis of the 3D-Unet structure, a global feature-learning module and a multi-scale feature fusion module are designed. The former improves the ability of features to describe important areas by learning the importance of spatial neighborhoods. The latter realizes the fusion of multi-scale semantic information and suppresses useless information through the task correlation learning of multi-scale features. Additionally, to efficiently process the large-scale point clouds acquired in real time, a sparse tensor-based implementation method is introduced. It is able to reduce unnecessary computation according to the sparsity of the 3D point cloud. As demonstrated by the results of experiments conducted with the SemanticKITTI and NuScenes datasets, our model improves the mIoU metric by 6.4% and 5%, respectively, over existing models that can be applied in real time. Our model is a lightweight model that can meet the requirements of real-time applications. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI)-Driven Data Mining)

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23 pages, 3756 KiB

Open AccessArticle

High-Performance Actionable Knowledge Miner for Boosting Business Revenue

by Katarzyna A. Tarnowska, Arunkumar Bagavathi and Zbigniew W. Ras

Appl. Sci. 2022, 12(23), 12393; https://0-doi-org.brum.beds.ac.uk/10.3390/app122312393 - 03 Dec 2022

Cited by 3 | Viewed by 1110

Abstract

This research proposes a novel strategy for constructing a knowledge-based recommender system (RS) based on both structured data and unstructured text data. We present its application to improve the services of heavy equipment repair companies to better adjust to their customers’ needs. The ultimate outcome of this work is a visualized web-based interactive recommendation dashboard that shows options that are predicted to improve the customer loyalty metric, known as Net Promoter Score (NPS). We also present a number of techniques aiming to improve the performance of action rule mining by allowing to have convenient periodic updates of the system’s knowledge base. We describe the preprocessing-based and distributed-processing-based method and present the results of testing them for performance within the RS framework. The proposed modifications for the actionable knowledge miner were implemented and compared with the original method in terms of the mining results/times and generated recommendations. Preprocessing-based methods decreased mining by 10–20×, while distributed mining implementation decreased mining timesby 300–400×, with negligible knowledge loss. The article concludes with the future directions for the scalability of the NPS recommender system and remaining challenges in its big data processing. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI)-Driven Data Mining)

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Review

Jump to: Research

21 pages, 411 KiB

Open AccessReview

Automatic Parsing and Utilization of System Log Features in Log Analysis: A Survey

by Junchen Ma, Yang Liu, Hongjie Wan and Guozi Sun

Appl. Sci. 2023, 13(8), 4930; https://0-doi-org.brum.beds.ac.uk/10.3390/app13084930 - 14 Apr 2023

Cited by 3 | Viewed by 2133

Abstract

System logs are almost the only data that records system operation information, so they play an important role in anomaly analysis, intrusion detection, and situational awareness. However, it is still a challenge to obtain effective data from massive system logs. On the one hand, system logs are unstructured data, and, on the other hand, system log records cannot be directly analyzed and calculated by computers. In order to deal with these problems, current researchers digitize system logs through two key steps of log parsing and feature extraction. This paper classifies, analyzes, and summarizes the current log analysis research in terms of log parsing and feature extraction by investigating articles in recent years (including ICSE, TKDD, ICDE, IJCAI, ISSRE, ICDM, ICWS, ICSME, etc.). Finally, in combination with the existing research, the research prospects in the field are elaborated and predicted. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI)-Driven Data Mining)

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43 pages, 915 KiB

Open AccessReview

Process-Oriented Stream Classification Pipeline: A Literature Review

by Lena Clever, Janina Susanne Pohl, Jakob Bossek, Pascal Kerschke and Heike Trautmann

Appl. Sci. 2022, 12(18), 9094; https://0-doi-org.brum.beds.ac.uk/10.3390/app12189094 - 09 Sep 2022

Cited by 3 | Viewed by 1858

Abstract

Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse—ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI)-Driven Data Mining)

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