2023 IEEE 39th International Conference on Data Engineering (ICDE)最新文献_第2页

SAMI: A Shape-Aware Cycling Map Inference Framework for Designated Driving Service SAMI:用于指定驾驶服务的形状感知自行车地图推理框架

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00251

Wenyi Shen, Wen-Jie Wu, Jiali Mao, Jie Chen, Shaosheng Cao, Lisheng Zhao, Aoying Zhou, Lin Zhou

{"title":"SAMI: A Shape-Aware Cycling Map Inference Framework for Designated Driving Service","authors":"Wenyi Shen, Wen-Jie Wu, Jiali Mao, Jie Chen, Shaosheng Cao, Lisheng Zhao, Aoying Zhou, Lin Zhou","doi":"10.1109/ICDE55515.2023.00251","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00251","url":null,"abstract":"Along with the increase in strict regulation of drunk driving behavior in China, the demands for designated driving services have risen in popularity. In the absence of specialized cycling map for the designated drivers who use foldable electric bicycles, they tend to take a detour or are lost on the way to the car owners’ appointed parking places. With gradual popularization of chauffeur services, cycling trajectories generated by designated drivers almost spread all over the city. It provides a chance for inferring the cycling map dedicated to the designated drivers. However, to infer an accurate map using trajectories faces severe challenges stemming from random cycling behaviors of designated drivers, including (i) trajectories contain a lot of noises and incomplete segments, (ii) turning trajectories at minor intersections are very sparse and (iii) trajectories on the roads of distinct shapes are obviously different. To address the above challenges, we propose a three-phase map inference framework, called SAMI, consisting of trajectory refinement, intersection pinpointing, and road curve interlinking. Specifically, cycling behavioral differences from neighbor regions are incorporated into intersection identification process to ensure obtaining high detection precision even when trajectory data is sparse. Further, shape-aware based centerline fitting strategy is put forward to guarantee that inferred road curves are consistent with real road shape as possible. Finally, extensive comparative experiments on two real data sets demonstrate that SAMI significantly outperforms state-of-the-art methods by 13.31% in F1-score of map inference and by 44.88% in recall rate of minor intersection detection.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122011106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Competition-Aware Approach to Accurate TV Show Recommendation 电视节目精准推荐的竞争意识方法

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00216

Hong-Kyun Bae, Yeon-Chang Lee, Kyungsik Han, Sang-Wook Kim

{"title":"A Competition-Aware Approach to Accurate TV Show Recommendation","authors":"Hong-Kyun Bae, Yeon-Chang Lee, Kyungsik Han, Sang-Wook Kim","doi":"10.1109/ICDE55515.2023.00216","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00216","url":null,"abstract":"As the number of TV shows increases, designing recommendation systems to provide users with their favorable TV shows becomes more important. In a TV show domain, watching a TV show (i.e., giving implicit feedback to the show) among the TV shows broadcast at the same time frame implies that the currently watching show is the winner in the competition with others (i.e., losers). However, in previous studies, such a notion of limited competitions has not been considered in estimating the user’s preferences for TV shows. In this paper, we propose a new recommendation framework to take this new notion into account based on pair-wise models. Our framework is composed of the following ideas: (i) identify winners and losers by determining pairs of competing TV shows; (ii) learn the pairs of competing TV shows based on the confidence for the pair-wise preference between the winner and the loser; (iii) recommend the most favorable TV shows by considering the time factors with respect to users and TV shows. Using a real-world TV show dataset, our experimental results show that our proposed framework consistently improves the accuracy of recommendation by up to 38%, compared with the best state-of-the-art method. The code and datasets of our framework are available in an external link (https://github.com/hongkyun-bae/tvshow_rs).","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122113870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical Crowdsourcing for Data Labeling with Heterogeneous Crowd 基于分层众包的异构人群数据标注

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00099

Haodi Zhang, Wenxi Huang, Zhenhan Su, Junyang Chen, Di Jiang, Lixin Fan, Chen Zhang, Defu Lian, Kaishun Wu

{"title":"Hierarchical Crowdsourcing for Data Labeling with Heterogeneous Crowd","authors":"Haodi Zhang, Wenxi Huang, Zhenhan Su, Junyang Chen, Di Jiang, Lixin Fan, Chen Zhang, Defu Lian, Kaishun Wu","doi":"10.1109/ICDE55515.2023.00099","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00099","url":null,"abstract":"With the rapid and continuous development of data-driven technologies such as supervised learning, high-quality labeled data sets are commonly required by many applications. Due to the easiness of crowdsourcing small tasks with low cost, a straightforward solution for label quality improvement is to collect multiple labels from a crowd, and then aggregate the answers. The aggregation strategies include majority voting and its many variants, EM-based approaches, Graph Neural Nets and so on. However, due to the uncertainty information loss and commonly existing task correlations, the aggregated labels usually contain errors and may damnify the downstream model training.To address the above problem, we propose a hierarchical crowdsourcing framework1 for data labeling with noisy answers about correlated data. We make use of the heterogeneity of the labeling crowd and form an initialization-checking-update loop to improve the quality of labeled data. We formalize and successfully solve the core optimization problem, namely, selecting a proper set of checking tasks for each round. We prove that maximizing the expected quality improvement is equivalent to minimizing the conditional entropy of the observations given the crowdsourced answer families for the selected task set, which is NP-hard to solve. Therefore, we design an efficient approximation algorithm and conduct a series of experiments on real data. The experimental results show that the proposed method effectively improves the quality of the labeled data sets as well as the SOTA performance, yet without extra human labor costs.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116994660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Cross Dynamic Task Assignment in Spatial Crowdsourcing 空间众包中的高效交叉动态任务分配

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00113

Tianyue Ren, Xu Zhou, Kenli Li, Yunjun Gao, Ji Zhang, Kuan-Ching Li

{"title":"Efficient Cross Dynamic Task Assignment in Spatial Crowdsourcing","authors":"Tianyue Ren, Xu Zhou, Kenli Li, Yunjun Gao, Ji Zhang, Kuan-Ching Li","doi":"10.1109/ICDE55515.2023.00113","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00113","url":null,"abstract":"As a novel intelligent sensing paradigm, spatial crowdsourcing has received extensive attention. Task assignment is a key issue in spatial crowdsourcing. In practice, tasks are unevenly distributed in time and space. Accordingly, the problem of cross task assignment attracts growing attention in both industry and academia. Although there has been a research on this problem, it focuses only on maximizing total revenues for inner platforms. Therefore, it can also be improved to bring a multi-win situation for outer workers and task requesters as well as the inner platform. Inspired by this, we first formulate a new cross dynamic task assignment (CDTA) problem by introducing the reputation scores of workers, and prove it to be NP-hard. For the CDTA problem, a hybrid batch-based framework is presented on the basis of a new cross-platform incentive mechanism and a hybrid batch processing strategy, which are efficient in solving the problem of uneven spatial and time distribution of tasks, respectively. After that, a KM-based algorithm and a density-aware greedy algorithm are proposed to gain an accurate assignment result of tasks in each batch and good performance, respectively. Furthermore, the CDTA problem is modeled as a potential game that is proven to have at least a pure Nash Equilibrium theoretically. Last but not least, a game-theoretic approach is developed to maximize the revenues of the inner platform and outer workers at the same time. Extensive experiments on both real and synthetic datasets are conducted to demonstrate the effectiveness and efficiency of the proposed algorithms.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128357735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Understanding the Instability of Network Embedding (Extended Abstract) 对网络嵌入不稳定性的认识(扩展摘要)

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00339

Chenxu Wang, Wei Rao, Wenna Guo, P. Wang, J. Liu, Xiaohong Guan

引用次数: 0

Anomaly Detection in Quasi-Periodic Time Series based on Automatic Data Segmentation and Attentional LSTM-CNN (Extended Abstract) 基于自动数据分割和注意力LSTM-CNN的准周期时间序列异常检测(扩展摘要)

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00315

Fan Liu, Xingshe Zhou, Jinli Cao, Zhu Wang, Tianben Wang, Hua Wang, Yanchun Zhang

{"title":"Anomaly Detection in Quasi-Periodic Time Series based on Automatic Data Segmentation and Attentional LSTM-CNN (Extended Abstract)","authors":"Fan Liu, Xingshe Zhou, Jinli Cao, Zhu Wang, Tianben Wang, Hua Wang, Yanchun Zhang","doi":"10.1109/ICDE55515.2023.00315","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00315","url":null,"abstract":"Quasi-periodic time series (QTS) exists widely in the real world, and it is important to detect the anomalies of QTS. In this paper, we propose an automatic QTS anomaly detection framework (AQADF) consisting of a two-level clustering-based QTS segmentation algorithm (TCQSA) and a hybrid attentional LSTM-CNN model (HALCM). TCQSA first automatically splits the QTS into quasi-periods which are then classified by HALCM into normal periods or anomalies. Notably, TCQSA integrates a hierarchical clustering and the k-means technique, making itself highly universal and noise-resistant. HALCM hybridizes LSTM and CNN to simultaneously extract the overall variation trends and local features of QTS for modeling its fluctuation pattern. Furthermore, we embed a trend attention gate (TAG) into the LSTM, a feature attention mechanism (FAM) and a location attention mechanism (LAM) into the CNN to finely tune the extracted variation trends and local features according to their true importance to yield a better representation of the fluctuation pattern of the QTS. On four public datasets, HALCM exceeds four state-of-the-art baselines and obtains at least 97.3% accuracy, TCQSA exceeds two cutting-edge QTS segmentation algorithms and can be applied to different types of QTSs.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128000533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PrivNUD: Effective Range Query Processing under Local Differential Privacy PrivNUD:本地差分隐私下的有效范围查询处理

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00204

Ning Wang, Yaohua Wang, Zhigang Wang, Jie Nie, Zhiqiang Wei, Peng Tang, Yu Gu, Ge Yu

{"title":"PrivNUD: Effective Range Query Processing under Local Differential Privacy","authors":"Ning Wang, Yaohua Wang, Zhigang Wang, Jie Nie, Zhiqiang Wei, Peng Tang, Yu Gu, Ge Yu","doi":"10.1109/ICDE55515.2023.00204","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00204","url":null,"abstract":"Local differential privacy (LDP) has been established as a strong privacy standard for collecting sensitive information from users. Although it has attracted much research attention in recent years, the majority of existing works focus on applying LDP to frequency distribution estimation for each individual value in a discrete domain. This paper concerns the important range queries involving multiple discrete values. Till now, only a few works target this problem. They all rely on the B-ary tree to construct a uniform and hierarchical decomposition, so as to decrease the error when answering large range queries. However, the uniform splitting manner ignores the properties of decomposed sub-domains and processes them equally without preferences, which leads to significant performance penalty.In this paper, we tackle the problem head on: our proposal, privNUD, is a novel domain hierarchical decomposition mechanism. It dynamically decomposes each domain with a tailored granularity into some sub-domains, which sensitively considers the potential chances to answer one range query. The issue of granularity is carefully analyzed for better performance. It also can smartly prune the sub-domains with small frequencies. Besides, an adaptive user allocation technique is designed to dynamically decide the scale of users that are involved in each sub-domain’s frequency estimation. Extensive experiments using real and synthetic datasets demonstrate that privNUD achieves significantly higher result accuracy compared to the up-to-date solutions.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"298 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127881754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Database Deadlock Diagnosis for Large-Scale ORM-Based Web Applications 基于orm的大型Web应用的数据库死锁诊断

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00219

Zhiyuan Dong, Zhaoguo Wang, Chuanwei Yi, Xian Xu, Jinyuan Zhang, Jinyang Li, Haibo Chen

{"title":"Database Deadlock Diagnosis for Large-Scale ORM-Based Web Applications","authors":"Zhiyuan Dong, Zhaoguo Wang, Chuanwei Yi, Xian Xu, Jinyuan Zhang, Jinyang Li, Haibo Chen","doi":"10.1109/ICDE55515.2023.00219","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00219","url":null,"abstract":"Today, most database-backed web applications depend on the database to handle deadlocks. At runtime, the database monitors the progress of transaction execution to detect deadlocks and abort affected transactions. However, this common detect-and-recover strategy is costly to performance as aborted transactions waste CPU resources.To avoid deadlock-induced performance degradation, developers aim to reorganize the application code to remove deadlocks. Unfortunately, doing so is difficult for web applications. Not only do their implementations include hundreds of thousands of LoCs, but they also use third-party object-relational mapping (ORM) frameworks which hide database access details. Consequently, it is hard for developers to accurately diagnose deadlocks.We propose WeSEER, a deadlock diagnosis tool for web applications. To overcome the opacity of ORMs, WeSEER performs concolic execution on unit tests to extract a web application’s transactions as a sequence of template statements with symbolic inputs as well as path conditions that enable the sequence. WeSEER then analyzes the extracted transactions based on fine-grained lock modeling to identify potential deadlocks and report the code locations that cause them. We implement WeSEER for Java-based (OpenJDK) web applications, and use it to analyze two popular open-source e-commerce applications, Broadleaf and Shopizer. WeSEER has successfully identified 18 potential deadlocks in Broadleaf and Shopizer. Eliminating these identified deadlocks can result in up to 39.5× and 4.5× throughput improvement for Broadleaf and Shopizer, respectively.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115794013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sequential Recommendation with User Causal Behavior Discovery 用户因果行为发现的顺序推荐

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00010

Zhenlei Wang, Xu Chen, Rui Zhou, Quanyu Dai, Zhenhua Dong, Jirong Wen

{"title":"Sequential Recommendation with User Causal Behavior Discovery","authors":"Zhenlei Wang, Xu Chen, Rui Zhou, Quanyu Dai, Zhenhua Dong, Jirong Wen","doi":"10.1109/ICDE55515.2023.00010","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00010","url":null,"abstract":"The key of sequential recommendation lies in the accurate item correlation modeling. Previous models infer such information based on item co-occurrences, which may fail to capture the real causal relations, and impact the recommendation performance and explainability. In this paper, we equip sequential recommendation with a novel causal discovery module to capture causalities among user behaviors. Our general idea is firstly assuming a causal graph underlying item correlations, and then we learn the causal graph jointly with the sequential recommender model by fitting the real user behavior data. More specifically, in order to satisfy the causality requirement, the causal graph is regularized by a differentiable directed acyclic constraint. Considering that the number of items in recommender systems can be very large, we represent different items with a unified set of latent clusters, and the causal graph is defined on the cluster level, which enhances the model scalability and robustness. In addition, we provide theoretical analysis on the identifiability of the learned causal graph. To the best of our knowledge, this paper makes a first step towards combining sequential recommendation with causal discovery. For evaluating the recommendation performance, we implement our framework with different neural sequential architectures, and compare them with many state-of-the-art methods based on real-world datasets. Empirical studies manifest that our model can on average improve the performance by about 6.1% and 11.3% on F1 and NDCG, respectively. To evaluate the model explainability, we build a new dataset with human labeled explanations for both quantitative and qualitative analysis.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131961303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Attributed Transition-Based Domain Control Knowledge for Domain-Independent Planning (Extended Abstract) 基于属性转换的领域独立规划控制知识(扩展摘要)

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00366

L. Chrpa, R. Barták, J. Vodrázka, M. Vomlelová

引用次数: 0