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International Conference on data engineering

作者： Xiaoou Ding Yida Liu Hongzhi Wang Chen Wang Yichen Song Donghua Yang Jianmin Wang School of Computer Science and Technology Harbin Institute of Technology National Engineering Research Center for Big Data Software EIRI Tsinghua University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data governance. However, with the diverse range of data sources and the exponential growth in data volume, exact FDs can sometimes be impractical for real-world applications. In contrast, relaxed functional dependencies (RFDs), which allows for some flexibility in attribute value comparisons, demonstrates greater adaptability and flexibility for big data scenarios. To address the efficient discovery of RFDs, this paper proposes a novel mining method to supplement the current research gaps. By establishing a difference table for tuples, we transform the problem into a specialized minimal set covering problem. Additionally, we introduce two optimization strategies: reducing the time complexity of enumerating the left-hand side of the base RFDs to 0 (1) and decreasing the search complexity for feasible LHS attributes and threshold candidates from O(2 m-l ) to O(1.5 m-1 ). We rigorously proof that our mining approach guarantees the identification of validity and minimal RFDs. Experiments on nine real-world datasets reveal that our method significantly improves efficiency compared to existing techniques. Furthermore, it uncovers more concise and higher-quality RFDs. Importantly, the RFDs extracted through our methodology exhibit better performance in downstream cleaning tasks.

关键词： data integrity Soft sensors Transforms data engineering Cleaning data governance data mining

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TSDDISCOVER: Discovering data Dependency for Time Series data

TSDDISCOVER: Discovering Data Dependency for Time Series Dat...

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International Conference on data engineering

作者： Xiaoou Ding Yingze Li Hongzhi Wang Chen Wang Yida Liu Jianmin Wang School of Computer Science and Technology Harbin Institute of Technology National Engineering Research Center for Big Data Software EIRI Tsinghua University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains inadequate in accurately representing the data quality of time series datasets. In recognition of the obvious characteristics inherent in time series data, we introduce a novel data dependency, termed TSDD. It effectively captures the contextual relationships embedded within multivariate time series, thereby enhancing the semantic richness of data quality representations. We analyze the complexity of both implication and consistency problems for TSDD reasoning, and develop TSDD discovery algorithm TSDDISCOVER, which consists of functional structure discovery, allowable error bound determination, and validation of TSDD patterns. Experimental results on real-life datasets verify TSDDISCOVER efficiently discovers high-quality TSDD patterns. In comparing the performance of TSDD-based error detection with several leading data quality constraints, our findings reveal that the former achieves an average improvement of 12% in accuracy and 30% in the F1 score over other dependency-based detection methods.

关键词： data integrity Time series analysis Semantics Maintenance engineering data engineering Cognition Cleaning

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Modeling and Study of Defense Outsourcing Against Advanced Persistent Threat Through Impulsive Differential Game Approach

SSRN

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SSRN 2024年

作者： Qin, Yang Yang, Xiaofan Yang, Lu-Xing Huang, Kaifan School of Big Data & Software Engineering Chongqing University Chongqing400044 China School of Information Technology Deakin University MelbourneVIC3125 Australia

Advanced persistent threat (APT) poses serious threat to organizations with rich digital assets. APT detection programs designed for quickly finding possibly hijacked hosts are now commercially available. This greatly reduces the workload of APT defense. In practice, the identification and repair of APT-hijacked hosts are out of a system administrator’s capability and have to be outsourced to an established cybersecurity firm. Owing to the limited security budget, the APT defense can be outsourced only in a small number of maintenance periods. We refer to the sequence of outsourcing costs paid in these maintenance periods as an impulsive defense (ID) strategy. On the other hand, APT is time-continuous. We refer to the growth rate function of the attack cost over time as a continuous attack (CA) strategy. In the context that the APT actor is strategic and pursues a cost-effective CA strategy, the organization faces the problem of finding a cost-effective ID strategy (the single-impulsive defense (SID) problem). This paper addresses the SID problem through game-theoretic modeling. Based on an impulsive state evolutionary model, the SID problem is boiled down to a single-impulsive differential game model (the SID model). By applying single-impulsive differential game theory, an iterative algorithm of solving the SID problem is presented. The ID strategy obtained by running the algorithm is corroborated to be cost-effective under the Nash equilibrium solution concept. Therefore, we recommend the ID strategy. This work takes the first step toward the theoretic study of APT defense outsourcing in the presence of strategic attacker. © 2024, The Authors. All rights reserved.

关键词： Outsourcing

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Fuzzy Deduplication Scheme Supporting Pre-verification of Label Consistency 17th

Fuzzy Deduplication Scheme Supporting Pre-verification of L...

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17th International Conference on Provable and Practical Security, ProvSec 2023

作者： Tang, Zehui Zeng, Shengke Li, Tao Cheng, Shuai Zheng, Haoyu School of Computer and Software Engineering Xihua University Chengdu610039 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China School of Computer Science Qufu Normal University Rizhao276825 China

ISBN: (纸本)9783031455124

Efficiently and securely removing encrypted redundant data with cross-user in the cloud is challenging. Convergent Encryption (CE) is difficult to resist dictionary attacks for its deterministic tag. Server-aided mechanism is against such attacks while it may exist collusion. Focus on multimedia data, this paper proposes an efficient and secure fuzzy deduplication system without any additional servers. We also propose a notion of pre-verification of label consistency to compensate for the irreparable post-verification loss. Compared with other fuzzy deduplication schemes, our work has apparent advantages in deduplication efficiency and security based on a natural data set. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Cryptography

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Log Parsing: How Far Can ChatGPT Go?

Log Parsing: How Far Can ChatGPT Go?

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IEEE International Conference on Automated software engineering (ASE)

作者： Van-Hoang Le Hongyu Zhang School of Information and Physical Sciences University of Newcastle Australia School of Big Data and Software Engineering Chongqing University China

software logs play an essential role in ensuring the reliability and maintainability of large-scale software systems, as they are often the sole source of runtime information. Log parsing, which converts raw log messages into structured data, is an important initial step towards downstream log analytics. In recent studies, ChatGPT, the current cutting-edge large language model (LLM), has been widely applied to a wide range of software engineering tasks. However, its performance in automated log parsing remains unclear. In this paper, we evaluate ChatGPT's ability to undertake log parsing by addressing two research questions. (1) Can ChatGPT effectively parse logs? (2) How does ChatGPT perform with different prompting methods? Our results show that ChatGPT can achieve promising results for log parsing with appropriate prompts, especially with few-shot prompting. Based on our findings, we outline several challenges and opportunities for ChatGPT-based log parsing.

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Optimal Control of Malware Propagation in IoT Networks

arXiv

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arXiv 2024年

作者： Jafar, Mousa Tayseer Yang, Lu-Xing Li, Gang Yang, Xiaofan The School of Information Technology Deakin University MelbourneVIC3125 Australia The School of Big Data and Software Engineering Chongqing University Chongqing400044 China

The rapid proliferation of Internet of Things (IoT) devices in recent years has resulted in a significant surge in the number of cyber-attacks targeting these devices. Recent data indicates that the number of such attacks has increased by over 100 percent, highlighting the urgent need for robust cybersecurity measures to mitigate these threats. In addition, a cyber-attack will begin to spread malware across the network once it has successfully compromised an IoT network. However, to mitigate this attack, a new patch must be applied immediately. In reality, the time required to prepare and apply the new patch can vary significantly depending on the nature of the cyber-attack. In this paper, we address the issue of how to mitigate cyber-attacks before the new patch is applied by formulating an optimal control strategy that reduces the impact of malware propagation and minimise the number of infected devices across IoT networks in the smart home. A novel node-based epidemiological model susceptible, infected high, infected low, recover first, and recover complete (SIHILRFRC) is established with immediate response state for the restricted environment. After that, the impact of malware on IoT devices using both high and low infected rates will be analyzed. Finally, to illustrate the main results, several numerical analyses are carried out in addition to simulate the real-world scenario of IoT networks in the smart home, we built a dataset to be used in the experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Internet of things

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TaskVis: Task-oriented Visualization Recommendation

TaskVis: Task-oriented Visualization Recommendation

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23rd Eurographics Conference on Visualization, EuroVis 2021

作者： Shen, Leixian Shen, Enya Tai, Zhiwei Song, Yiran Wang, Jianmin School of Software Tsinghua University China National Engineering Laboratory for Big Data Software China

ISBN: (纸本)9783038681434

General visualization recommendation systems typically make design decisions of the dataset automatically. However, these systems are only able to prune meaningless visualizations but fail to recommend targeted results. In this paper, we contributed TaskVis, a task-oriented visualization recommendation approach with detailed modeling of the user's analysis task. We first summarized a task base with 18 analysis tasks by a survey both in academia and industry. On this basis, we further maintained a rule base, which extends empirical wisdom with our targeted modeling of analysis tasks. Inspired by Draco, we enumerated candidate visualizations through answer set programming. After visualization generation, TaskVis supports four ranking schemes according to the complexity of charts, coverage of the user's interested columns and tasks. In two user studies, we found that TaskVis can well reflect the user's preferences and strike a great balance between automation and the user's intent. © 2021 Eurographics Association. All rights reserved.

关键词： Visualization

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Log Parsing with Prompt-based Few-shot Learning

Log Parsing with Prompt-based Few-shot Learning

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International Conference on software engineering (ICSE)

作者： Van-Hoang Le Hongyu Zhang School of Information and Physical Sciences The University of Newcastle Australia School of Big Data and Software Engineering Chongqing University China

Logs generated by large-scale software systems provide crucial information for engineers to understand the system status and diagnose problems of the systems. Log parsing, which converts raw log messages into structured data, is the first step to enabling automated log analytics. Existing log parsers extract the common part as log templates using statistical features. However, these log parsers often fail to identify the correct templates and parameters because: 1) they often overlook the semantic meaning of log messages, and 2) they require domain-specific knowledge for different log datasets. To address the limitations of existing methods, in this paper, we propose LogPPT to capture the patterns of templates using prompt-based few-shot learning. LogPPT utilises a novel prompt tuning method to recognise keywords and parameters based on a few labelled log data. In addition, an adaptive random sampling algorithm is designed to select a small yet diverse training set. We have conducted extensive experiments on 16 public log datasets. The experimental results show that LogPPT is effective and efficient for log parsing.

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Research on Integrated Circuit Packaging Testing Technology Based on Large Language Model

Research on Integrated Circuit Packaging Testing Technology ...

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Semiconductor and Electronic Technology (ISSET), International Symposium on

作者： Qiang Li Xinqiang Ma Meng Yan School of Big Data and Software Engineering Chongqing University Chongqing China School of Mathematics and Artificial Intelligence Chongqing University of Arts and Sciences Chongqing China

ISBN: (数字)9798350390285

ISBN: (纸本)9798350390292

The integrated circuit packaging testing technology is highly demanding, and the task is arduous. The existing sorting efficiency of integrated circuit packaging testing is low, and it is difficult to detect unqualified products for rapid centralized processing. There is an urgent need for upgrading and improvement. This paper proposes to fine-tune the large language model and establish a large language model monitoring system for integrated circuit packaging and inspection, embedding it in a CNC system, thereby providing support for the intelligent transformation and upgrading of integrated circuit production and processing.

关键词： Semiconductor device modeling Production systems Large language models Knowledge graphs Packaging Integrated circuit packaging Integrated circuit modeling Monitoring Testing Sorting

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Edge Learning-Based Efficient data Imputation of Water Quality 7th

Edge Learning-Based Efficient Data Imputation of Water Quali...

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7th International Conference on Computing, Control and Industrial engineering, CCIE 2023

作者： Wang, Yongsheng Chen, Zhen Liu, Limin Gao, Jing Liu, Guangwen Xu, Zhiwei School of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering Technology Research Center of Big Data Based Software Service Hohhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100000 China

ISBN: (纸本)9789819927296

Water quality monitoring is critical for developing effective surface water management strategies, whereas data preprocessing is the foundation for monitoring water quality. Water quality monitoring usually suffers from abnormal and missing values in the collected data of surface water quality, and thus an efficient data imputation method for water quality data is highly desired. To handle these challenges, we propose an edge learning-based data imputation method. By using VAE as the generator of WGAN-LP, the performance of value imputation of water quality is generalized and improved. In addition, we demonstrate the performance of the proposed method on two real-world datasets, collected from Wuliangsu Lake and Taihu Lake in Inner Mongolia. Compared with state of the art, the enhanced imputation accuarcy of water quality is achieved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Water quality

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