2023 IEEE 39th International Conference on Data Engineering (ICDE)最新文献_第6页

Collective Decision for Open Set Recognition (Extended Abstract) 开放集识别的集体决策(扩展摘要)

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00313

Chuanxing Geng, Songcan Chen

{"title":"Collective Decision for Open Set Recognition (Extended Abstract)","authors":"Chuanxing Geng, Songcan Chen","doi":"10.1109/ICDE55515.2023.00313","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00313","url":null,"abstract":"In open set recognition (OSR), almost all existing methods are designed specially for recognizing individual instances, even these instances are collectively coming in batch. Recognizers in decision either reject or categorize them to some known class using empirically-set threshold. Thus the decision threshold plays a key role. However, the selection for it usually depends on the knowledge of known classes, inevitably incurring risks due to lacking available information from unknown classes. On the other hand, a more realistic OSR system should NOT just rest on a reject decision but should go further, especially for discovering the hidden unknown classes among the reject instances, whereas existing OSR methods do not pay special attention. In this paper, we introduce a novel collective/batch decision strategy with an aim to extend existing OSR for new class discovery while considering correlations among the testing instances. Specifically, a collective decision-based OSR framework (CD-OSR) is proposed by slightly modifying the Hierarchical Dirichlet process (HDP). Thanks to HDP, our CD-OSR does not need to define the decision threshold and can implement the open set recognition and new class discovery simultaneously. Finally, extensive experiments on benchmark datasets indicate the validity of CD-OSR.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"2022 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":"121789143","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

Discovering Temporal Patterns for Event Sequence Clustering via Policy Mixture Model (Extended Abstract) 通过策略混合模型发现事件序列聚类的时间模式(扩展摘要)

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00308

Weichang Wu, Junchi Yan, Xiaokang Yang, H. Zha

引用次数: 0

Automatic Synonym Extraction and Context-based Query Reformulation for Points-of-Interest Search 兴趣点搜索的自动同义词提取和基于上下文的查询重构

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00235

Pengfei Li, Gaurav

{"title":"Automatic Synonym Extraction and Context-based Query Reformulation for Points-of-Interest Search","authors":"Pengfei Li, Gaurav","doi":"10.1109/ICDE55515.2023.00235","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00235","url":null,"abstract":"In modern search engines, synonyms are widely used for query reformulation to improve search recall and relevance. The search query is reformulated based on the synonymous terms that are semantically related to the original query. The reformulated queries are used for improving or augmenting the original query to retrieve more relevant results. However, there are four main challenges in production environments: (1) How to obtain high-quality synonyms and validate their effectiveness, especially for low-resource languagesƒ (2) How to prevent search intent drift caused by over-reformulating the correct queryƒ (3) How to efficiently keep the synonyms and models up-to-date for large-scale production systemsƒ (4) How to ensure the latency introduced by query reformulation does not affect user’s search experienceƒ In this paper, we address these challenges by introducing a context-based query reformulation system for Points-of-Interest (POI) search based on the synonyms automatically extracted from search logs and language models. The synonyms are automatically validated using historical query samples. We also propose a lightweight term identification model to prevent over-reformulation by considering query context during reformulation. The proposed methods are unsupervised/self-supervised that can be easily applied to large-scale production systems. We deploy our system to eight Southeast Asia countries that have both English and low-resource languages. Both offline evaluations and online A/B tests show that our system enhances search recall and relevance significantly.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"61 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":"116527766","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

Rotary: A Resource Arbitration Framework for Progressive Iterative Analytics 扶轮:渐进式迭代分析的资源仲裁框架

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00166

Rui Liu, Aaron J. Elmore, M. Franklin, S. Krishnan

{"title":"Rotary: A Resource Arbitration Framework for Progressive Iterative Analytics","authors":"Rui Liu, Aaron J. Elmore, M. Franklin, S. Krishnan","doi":"10.1109/ICDE55515.2023.00166","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00166","url":null,"abstract":"Increasingly modern computing applications employ progressive iterative analytics, as best exemplified by two prevalent cases, approximate query processing (AQP) and deep learning training (DLT). In comparison to classic computing applications that only return the results after processing all the input data, progressive iterative analytics keep providing approximate or partial results to users by performing computations on a subset of the entire dataset until either the users are satisfied with the results, or the predefined completion criteria are achieved. Typically, progressive iterative analytic jobs have various completion criteria, produce diminishing returns, and process data at different rates, which necessitates a novel resource arbitration that can continuously prioritize the progressive iterative analytic jobs and determine if/when to reallocate and preempt the resources. We propose and design a resource arbitration framework, Rotary, and implement two resource arbitration systems, Rotary-AQP and Rotary-DLT, for approximate query processing and deep learning training. We build a TPC-H based AQP workload and a survey-based DLT workload to evaluate the two systems, respectively. The evaluation results demonstrate that Rotary-AQP and Rotary-DLT outperform the state-of-the-art systems and confirm the generality and practicality of the proposed resource arbitration framework.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"15 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":"133521258","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

Efficiently Sampling and Estimating Hypergraphs By Hybrid Random Walk 基于混合随机漫步的超图高效采样与估计

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00102

Lingling Zhang, Zhiwei Zhang, Guoren Wang, Ye Yuan

{"title":"Efficiently Sampling and Estimating Hypergraphs By Hybrid Random Walk","authors":"Lingling Zhang, Zhiwei Zhang, Guoren Wang, Ye Yuan","doi":"10.1109/ICDE55515.2023.00102","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00102","url":null,"abstract":"Hypergraphs provide a powerful tool for representing group interactions in complicated networks. Analyzing statical properties of hypergraphs by sampling is an increasing fundamental research problem in the field of data processing. However, the state-of-the-art sampling methods either focus on pairwise graphs or are insensitive to the structures formed by vertices and hyperedges, resulting in estimations with low accuracy and efficiency. To efficiently characterize the properties of both vertices and hyperedges, this paper first proposes a hybrid random walk based Markov Chain Monte Carlo (MCMC) model theoretically by carefully designing its mixture states and the transition matrix. For simplifying the implementation of this model, we develop an algorithm formed by vertex and hyperedge transitions saving costs for constructing mixture states in practice along with an estimating method for accurate estimations. Furthermore, we employ a non-backtracking strategy in the vertex transitions to accelerate the convergence of the hybrid random walk and propose to skip the sampled vertices in the hyperedge transitions to avoid being trapped in the local subgraph for improving accuracy and reducing query cost. Extensive experimental results on the real-world datasets confirm the higher accuracy and efficiency of our proposed methods than the sophisticated sampling methods.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"26 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":"134621897","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 Parallel Mining of High-utility Itemsets on Multicore Processors 多核处理器上高实用项集的高效并行挖掘

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00388

Genki Kimura, Yuto Hayamizu, R. U. Kiran, Masaru Kitsuregawa, K. Goda

{"title":"Efficient Parallel Mining of High-utility Itemsets on Multicore Processors","authors":"Genki Kimura, Yuto Hayamizu, R. U. Kiran, Masaru Kitsuregawa, K. Goda","doi":"10.1109/ICDE55515.2023.00388","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00388","url":null,"abstract":"High-utility itemset mining is a generalized problem of well-known frequent itemset mining, which considers not only the frequency of occurrence but also quantitative criteria such as unit profit. Because it can be applied to a wider spectrum of knowledge discovery work, various algorithmic improvements have been studied over the past two decades. On the other hand, limited efforts have been made to take advantage of hardware performance despite significant changes in hardware trends. This paper presents a novel parallelization method called DPHIM (Dynamic Parallelization for High-utility Itemset Mining). DPHIM dynamically decomposes the execution of high-utility itemset mining into subtasks in order to leverage logical data parallelism, and carefully assigns the subtasks and their related data to physical resources such as processing cores and nearby memory in the NUMA-aware manner. Our intensive and extensive experiments have confirmed that DPHIM performs up to 65.23 times faster than the fully-tuned serial execution, up to 23.54 times faster than static partitioning, and up to 2.51 times faster than the best case of alternative dynamic parallel executions for a variety of datasets and configurations on DRAM. As well, we have demonstrated that DPHIM effectively worked on persistent memory; it offered similar thread scalability trends and was 1.07 to 2.43 times slower on persistent memory.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"41 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":"133879185","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

Enhanced Discrete Multi-modal Hashing: More Constraints yet Less Time to Learn (Extended Abstract) 增强离散多模态哈希:更多的约束，更少的学习时间(扩展摘要)

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00355

Yong Chen, Hui Zhang, Zhibao Tian, Jun Wang, Dell Zhang, Xuelong Li

引用次数: 0

Modeling Intra- and Inter-community Information for Route and Time Prediction in Last-mile Delivery 基于最后一英里配送路线和时间预测的社区内部和社区间信息建模

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00238

Yuting Qiang, Haomin Wen, Lixia Wu, Xiaowei Mao, Fan Wu, Huaiyu Wan, Haoyuan Hu

{"title":"Modeling Intra- and Inter-community Information for Route and Time Prediction in Last-mile Delivery","authors":"Yuting Qiang, Haomin Wen, Lixia Wu, Xiaowei Mao, Fan Wu, Huaiyu Wan, Haoyuan Hu","doi":"10.1109/ICDE55515.2023.00238","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00238","url":null,"abstract":"Last-mile delivery, which refers to delivering packages from the depot to customers, is a crucial step for logistics service. The Route and Time Prediction (RTP) in last-mile package delivery is beneficial to improve customers’ experience and supervise couriers’ behavior. However, the limited raw information brings great challenges to accurately predict the route and delivery time. In this paper, we propose a deep model named I2RTP, which explores the heterogeneous representation of the package’s community to help predict the delivery route and estimate the arrival time of each package. Specifically, for the entire delivery route prediction, we model the inter- and intra-community information to learn the route features from global and local perspectives. Besides, by integrating the community representation with package features, our model could make more accurate predictions of the next-delivery package and its time duration. Experiments on the offline dataset and the online deployment on Cainiao’s Delivery System demonstrate the effectiveness of our proposed method, as well as validate the rationality of the global and local prediction pipeline.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"231 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":"123350025","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: 1

BrePartition: Optimized High-Dimensional kNN Search with Bregman Distances (Extended Abstract) BrePartition:基于Bregman距离的优化高维kNN搜索(扩展摘要)

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00368

Yang Song, Yu Gu, Rui Zhang, Ge Yu

{"title":"BrePartition: Optimized High-Dimensional kNN Search with Bregman Distances (Extended Abstract)","authors":"Yang Song, Yu Gu, Rui Zhang, Ge Yu","doi":"10.1109/ICDE55515.2023.00368","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00368","url":null,"abstract":"Bregman distances (also known as Bregman divergences) are widely used in machine learning, speech recognition and signal processing, and kNN searches with Bregman distances have become increasingly important with the rapid advances of multimedia applications. Data in multimedia applications such as images and videos are commonly transformed into space of hundreds of dimensions. Such high-dimensional space has posed significant challenges for existing kNN search algorithms with Bregman distances, which could only handle data of medium dimensionality (typically less than 100). This paper addresses the urgent problem of high-dimensional kNN search with Bregman distances. We propose a novel partition-filter-refinement framework. Specifically, we propose an optimized dimensionality partitioning scheme to solve several non-trivial issues. First, an effective bound from each partitioned subspace to obtain exact kNN results is derived. Second, we conduct an in-depth analysis of the optimized number of partitions and devise an effective strategy for partitioning. Third, we design an efficient integrated index structure for all the subspaces together to accelerate the search processing. Moreover, we extend our exact solution to an approximate version by a trade-off between the accuracy and efficiency. Experimental results on four real-world datasets and two synthetic datasets show the clear advantage of our method in comparison to state-of-the-art algorithms.","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":"124940277","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

iORDER: Mining Implicit Domain Orders iORDER:挖掘隐式领域顺序

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00283

Alexander Bianchi, Reza Karegar, P. Godfrey, Lukasz Golab, M. Kargar, D. Srivastava, Jaroslaw Szlichta

引用次数: 0