2023 IEEE 39th International Conference on Data Engineering (ICDE)最新文献

{"title":"Federated IoT Interaction Vulnerability Analysis","authors":"Guangjing Wang, Hanqing Guo, Anran Li, Xiaorui Liu, Qiben Yan","doi":"10.1109/ICDE55515.2023.00120","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00120","url":null,"abstract":"IoT devices provide users with great convenience in smart homes. However, the interdependent behaviors across devices may yield unexpected interactions. To analyze the potential IoT interaction vulnerabilities, in this paper, we propose a federated and explicable IoT interaction data management system FexIoT. To address the lack of information in the closed-source platforms, FexIoT captures causality information by fusing multi-domain data, including the descriptions of apps and real-time event logs, into interaction graphs. The interaction graph representation is encoded by graph neural networks (GNNs). To collaboratively train the GNN model without sharing the raw data, we design a layer-wise clustering-based federated GNN framework for learning intrinsic clustering relationships among GNN model weights, which copes with the statistical heterogeneity and the concept drift problem of graph data. In addition, we propose the Monte Carlo beam search with the SHAP method to search and measure the risk of subgraphs, in order to explain the potential vulnerability causes. We evaluate our prototype on datasets collected from five IoT automation platforms. The results show that FexIoT achieves more than 90% average accuracy for interaction vulnerability detection, outperforming the existing methods. Moreover, FexIoT offers an explainable result for the detected vulnerabilities.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"106 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":"130411615","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}

{"title":"EulerFD: An Efficient Double-Cycle Approximation of Functional Dependencies","authors":"Qiongqiong Lin, Yunfan Gu, Jing Sai, Jinfei Liu, Kui Ren, Li Xiong, Tianzhen Wang, Yanbei Pang, Sheng Wang, Feifei Li","doi":"10.1109/ICDE55515.2023.00220","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00220","url":null,"abstract":"Functional dependencies (FDs) have been extensively employed in discovering inferential relationships in databases, which provide feasible approaches for many data mining tasks, such as data obfuscation, query optimization, and schema normalization. Since the explosive growth of data leads to a rapid increase of FDs on large datasets, existing algorithms that pay more attention to the exact FD discovery cannot extract FDs efficiently. To bridge this gap, we propose an Efficient double-cycle approximation of Functional Dependency (EulerFD) discovery algorithm, which ensures both efficiency and accuracy of FD discovery. EulerFD induces FDs from invalid ones as invalidating an FD only requires comparing and verifying some pairs of tuples (that violate the dependency) while validating an FD requires examining and verifying all tuples. Considering the abundant tuple pairs in large datasets, a novel sampling strategy is employed in EulerFD to quickly extract invalid FDs by revising the sampling range according to previous sampling results. Furthermore, EulerFD evaluates the stopping criteria in a double-cycle structure as feedback for further sampling. The sampling strategy and the double-cycle structure complement each other to achieve a more efficient sampling effect. Experimental results on real-world and synthetic datasets, especially the massive datasets from DMS of Alibaba Cloud, justify the design and verify the efficiency and effectiveness of the proposed EulerFD.","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":"131164981","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}

{"title":"Dynamic Activation of Clients and Parameters for Federated Learning over Heterogeneous Graphs","authors":"Zishan Gu, Ke Zhang, Liang Chen, Sun","doi":"10.1109/ICDE55515.2023.00126","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00126","url":null,"abstract":"The data generated in many real-world applications can be modeled as heterogeneous graphs of multi-typed entities (nodes) and relations (links). Nowadays, such data are commonly generated and stored by distributed clients, making direct centralized model training unpractical. While the data in each client are prone to biased local distributions, generalizable global models are still in frequent need for large-scale applications. However, the large number of clients enforce significant computational overhead due to the communication and synchronization among the clients, whereas the biased local data distributions indicate that not all clients and parameters should be computed and updated at all times. Motivated by specifically designed preliminary studies on training a state-of-the-art heterogeneous graph neural network (HGN) with the vanilla FedAvg framework, in this work, we propose to leverage the characteristics of heterogeneous graphs by designing dynamic activation strategies for the clients and parameters during the federated training of HGN, named FedDA. Moreover, we design a novel disentangled model D-HGN to enable type-oriented activation of model parameters for FedDA. The effectiveness and efficiency of our proposed techniques are backed by both theoretical and empirical analysis– We theoretically analyze the validity and convergence of FedDA and mathematically illustrate its efficiency gain; meanwhile, we demonstrate the significant performance gains of FedDA and corroborate its efficiency gains with extensive experiments over multiple realistic FL settings synthesized based on real-world heterogeneous graphs.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"34 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":"131661936","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}

{"title":"Efficient Sink-Reachability Analysis via Graph Reduction (Extended Abstract)","authors":"Jens Dietrich, Lijun Chang, Long Qian, Lyndon M. Henry, Catherine McCartin, Bernhard Scholz","doi":"10.1109/ICDE55515.2023.00375","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00375","url":null,"abstract":"We study a variation of the elementary graph reachability problem, called the sink-reachability problem, which can be found in many applications such as static program analysis, social network analysis, large scale web graph analysis, XML document link path analysis, and the study of gene regulation relationships. To scale sink-reachablity analysis to large graphs, we develop a highly scalable sink-reachability preserving graph reduction strategy for input sink graphs, by using a composition framework. That is, individual sink-reachability preserving condensation operators, each running in linear time, are pipelined together to produce graph reduction algorithms that result in close to maximum reduction, while keeping the computation efficient. Experiments on large real-world sink graphs demonstrate that our compositional approach achieves a reduction rate of up to 99.74% for vertices and a rate of up to 99.46% for edges.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"11 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":"134073695","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}

{"title":"Data Ambiguity Profiling for the Generation of Training Examples","authors":"Enzo Veltri","doi":"10.1109/ICDE55515.2023.00041","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00041","url":null,"abstract":"Several applications, such as text-to-SQL and computational fact checking, exploit the relationship between relational data and natural language text. However, state of the art solutions simply fail in managing \"data-ambiguity\", i.e., the case when there are multiple interpretations of the relationship between text and data. Given the ambiguity in language, text can be mapped to different subsets of data, but existing training corpora only have examples in which every sentence/question is annotated precisely w.r.t. the relation. This unrealistic assumption leaves the target applications unable to handle ambiguous cases. To tackle this problem, we present an end-to-end solution that, given a table D, generates examples that consist of text, annotated with its data evidence, with factual ambiguities w.r.t. D. We formulate the problem of profiling relational tables to identify row and attribute data ambiguity. For the latter, we propose a deep learning method that identifies every pair of data ambiguous attributes and a label that describes both columns. Such metadata is then used to generate examples with data ambiguities for any input table. To enable scalability, we finally introduce a SQL approach that can generate millions of examples in seconds. We show the high accuracy of our solution in profiling relational tables and report on how our automatically generated examples lead to drastic quality improvements in two fact-checking applications, including a website with thousands of users, and in a text-to-SQL system.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"127 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":"131614828","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}

{"title":"Intention-aware Sequential Recommendation with Structured Intent Transition : (Extended Abstract)","authors":"Haoyang Li, Xin Wang, Ziwei Zhang, Jianxin Ma, Peng Cui, Wenwu Zhu","doi":"10.1109/ICDE55515.2023.00306","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00306","url":null,"abstract":"Human behaviors in recommendation systems are driven by many high-level, complex, and evolving intentions behind their decision making processes. In order to achieve better performance, it is important for recommendation systems to be aware of user intentions besides considering the historical interaction behaviors. However, user intentions are seldom fully or easily observed in practice, so that the existing works are incapable of fully tracking and modeling user intentions, not to mention using them effectively into recommendation. In this paper, we present the Intention-Aware Sequential Recommendation (ISRec) method, for capturing the underlying intentions of each user that may lead to her next consumption behavior and improving recommendation performance. Specifically, we first extract the intentions of the target user from sequential contexts, then take complex intent transition into account through the message-passing mechanism on an intention graph, and finally obtain the future intentions of this target user from inference on the intention graph. The sequential recommendation for a user will be made based on the predicted user intentions, offering more transparent and explainable intermediate results for each recommendation. Extensive experiments on various real-world datasets demonstrate the superiority of our method against several state-of-the-art baselines in sequential recommendation in terms of different metrics.","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":"122856751","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}

{"title":"An Optimal Online Semi-connected PLA Algorithm with Maximum Error Bound (Extended Abstract)","authors":"Huanyu Zhao, Chaoyi Pang, K. Ramamohanarao, Christopher Kuo Pang, Ke Deng, Jian Yang, Tongliang Li","doi":"10.1109/ICDE55515.2023.00321","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00321","url":null,"abstract":"Piecewise Linear Approximation (PLA) is one of the most widely used approaches for representing a time series with a set of approximated line segments. With this compressed form of representation, many large complicated time series can be efficiently stored, transmitted and analyzed. In this article, with the introduced concept of \"semi-connection\" that allowing two representation lines to be connected at a point between two consecutive time stamps, we propose a new optimal linear-time PLA algorithm SemiOptConnAlg for generating the least number of semi-connected line segments with guaranteed maximum error bound. With extended experimental tests, we demonstrate that the proposed algorithm is very efficient in execution and achieves better performances than the state-of-art solutions.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"22 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":"128603638","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}

{"title":"Time-Aware Location Prediction by Convolutional Area-of-Interest Modeling and Memory-Augmented Attentive LSTM (Extended abstract)","authors":"Chi Harold Liu, Yu Wang, Chengzhe Piao, Zipeng Dai, Ye Yuan, Guoren Wang, Dapeng Oliver Wu","doi":"10.1109/ICDE55515.2023.00357","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00357","url":null,"abstract":"Personalized location prediction is key to many mobile applications and services. In this paper, motivated by both statistical and visualized preliminary analysis on three real datasets, we observe a strong spatiotemporal correlation for user trajectories among the visited area-of-interests (AoIs) and different time periods on both weekly and daily basis, which directly motivates our time-aware location prediction model design called \"t-LocPred\". It models the spatial correlations among AoIs by coarse-grained convolutional processing of the user trajectories in AoIs of different time periods (\"ConvAoI\"); and predicts his/her fine-grained next visited PoI using a novel memory-augmented attentive LSTM model (\"mem-attLSTM\") to capture long-term behavior patterns. Experimental results show that t-LocPred outperforms 8 baselines. We also show the impact of hyperparameters and the benefits ConvAoI can bring to these baselines.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"28 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":"128538170","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}

{"title":"TSC-AutoML: Meta-learning for Automatic Time Series Classification Algorithm Selection","authors":"Tianyu Mu, Hongzhi Wang, Shenghe Zheng, Zhiyu Liang, Chunnan Wang, Xinyue Shao, Zheng Liang","doi":"10.1109/ICDE55515.2023.00084","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00084","url":null,"abstract":"With years of development, a significant number of Time Series Classification (TSC) algorithms have been proposed and applied to various fields such as scientific research and industry scenarios, including traditional statistical methods, machine learning methods, and recently deep learning models. However, choosing a suitable model along with good parameter values that perform well on a given task, which is also known as Combined Algorithm Selection and Hyperparameter optimization problem (CASH), is still challenging. How to automatically select the appropriate algorithm according to the task during analyzing is a topic worthy of further research. Nevertheless, for TSC, a field that has been developed for decades, there is no effective and efficient approach for automatic algorithm selection. To the best of our knowledge, the current approach is based on genetic search, which is very computationally intensive and time-consuming. Therefore, in this paper, we propose TSC-AutoML, a zero-configuration and meta-learning-based approach for the automatic Time Series Classification algorithm CASH (also known as TSC-CASH). TSC-AutoML extracts knowledge from historical tasks and performs automatic feature selection and knowledge filtering with a reinforcement learning policy. The experience extracted is filtered and transformed into metadata. The meta-learner trained on the metadata together with our proposed warm start strategy will select an optimal algorithm for tasks uploaded by users, and then our proposed Hyperparameter Optimization method based on the Fast Warm Start strategy searches for hyperparameter combinations of the selected algorithm and adjusts parameter configuration to achieve top performance. The entire process is pre-trained, automated for the new task, and parameter-free for the user to decide, making it easy for users with the little domain experience to get started easily. Experimental results illustrate that TSC-AutoML outperforms existing methods in terms of both time and accuracy of optimum algorithm selection.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"53 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":"129369971","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}

{"title":"Probabilistic Majority Rule-Based Group Recommendation","authors":"Karim Benouaret, K. Tan","doi":"10.1109/ICDE55515.2023.00118","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00118","url":null,"abstract":"Group recommendation has received increased attention over the past decade. The fundamental challenge in group recommendation is how to aggregate the preferences of group members to select a set of items maximizing the overall satisfaction of the group. Different aggregation methods with different semantics have been proposed. In this paper, we explore a novel semantics of group recommendation, that is, probabilistic majority rule, allowing group members to make a \"democratic\" decision on which items are appropriate. Specifically, we propose a probabilistic model that captures the probability that a given item satisfies the majority of the group. We show that the naive strategy for computing such a probability is exponential time complexity, and propose an efficient dynamic programming approach to avoid this shortcoming. Furthermore, we design and develop an efficient algorithm, which leverages effective pruning techniques, for recommending the k items with the highest majority satisfaction probabilities. Finally, we demonstrate both the retrieval effectiveness and the efficiency of our approach through extensive experimental evaluation on real datasets.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"1 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":"129386748","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}