Proc. VLDB Endow.最新文献_第10页

A Deep Generative Model for Trajectory Modeling and Utilization 一种用于轨迹建模和应用的深度生成模型

Proc. VLDB Endow. Pub Date : 2022-12-01 DOI: 10.14778/3574245.3574277

Yong Wang, Guoliang Li, Kaiyu Li, Haitao Yuan

{"title":"A Deep Generative Model for Trajectory Modeling and Utilization","authors":"Yong Wang, Guoliang Li, Kaiyu Li, Haitao Yuan","doi":"10.14778/3574245.3574277","DOIUrl":"https://doi.org/10.14778/3574245.3574277","url":null,"abstract":"\u0000 Modern location-based systems have stimulated explosive growth of urban trajectory data and promoted many real-world applications,\u0000 e.g.\u0000 , trajectory prediction. However, heavy big data processing overhead and privacy concerns hinder trajectory acquisition and utilization. Inspired by regular trajectory distribution on transportation road networks, we propose to model trajectory data privately with a deep generative model and leverage the model to generate representative trajectories for downstream tasks or directly support these tasks (\u0000 e.g.\u0000 , popularity ranking), rather than acquiring and processing the original big trajectory data. Nevertheless, it is rather challenging to model high-dimensional trajectories with time-varying yet skewed distribution. To address this problem, we model and generate trajectory sequence with judiciously encoded spatio-temporal features over skewed distribution by leveraging an important factor neglected by the literature - the underlying road properties (\u0000 e.g.\u0000 , road types and directions), which are closely related to trajectory distribution. Specifically, we decompose trajectory into map-matched road sequence with temporal information and embed them to encode spatio-temporal features. Then, we enhance trajectory representation by encoding inherent route planning patterns from the underlying road properties. Later, we encode spatial correlations among edges and daily and weekly temporal periodicity information. Next, we employ a meta-learning module to generate trajectory sequence step by step by learning generalized trajectory distribution patterns from skewed trajectory data based on the well-encoded trajectory prefix. Last but not least, we preserve trajectory privacy by learning the model differential privately with clipping gradients. Experiments on real-world datasets show that our method significantly outperforms existing methods.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"16 1","pages":"973-985"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78312745","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

The LDBC Social Network Benchmark: Business Intelligence Workload LDBC社会网络基准:商业智能工作负载

Proc. VLDB Endow. Pub Date : 2022-12-01 DOI: 10.14778/3574245.3574270

Gábor Szárnyas, Jack Waudby, Benjamin A. Steer, Dávid Szakállas, Altan Birler, Mingxi Wu, Yuchen Zhang, P. Boncz

{"title":"The LDBC Social Network Benchmark: Business Intelligence Workload","authors":"Gábor Szárnyas, Jack Waudby, Benjamin A. Steer, Dávid Szakállas, Altan Birler, Mingxi Wu, Yuchen Zhang, P. Boncz","doi":"10.14778/3574245.3574270","DOIUrl":"https://doi.org/10.14778/3574245.3574270","url":null,"abstract":"The Social Network Benchmark's Business Intelligence workload (SNB BI) is a comprehensive graph OLAP benchmark targeting analytical data systems capable of supporting graph workloads. This paper marks the finalization of almost a decade of research in academia and industry via the Linked Data Benchmark Council (LDBC). SNB BI advances the state-of-the art in synthetic and scalable analytical database benchmarks in many aspects. Its base is a sophisticated data generator, implemented on a scalable distributed infrastructure, that produces a social graph with small-world phenomena, whose value properties follow skewed and correlated distributions and where values correlate with structure. This is a temporal graph where all nodes and edges follow lifespan-based rules with temporal skew enabling realistic and consistent temporal inserts and (recursive) deletes. The query workload exploiting this skew and correlation is based on LDBC's \"choke point\"-driven design methodology and will entice technical and scientific improvements in future (graph) database systems. SNB BI includes the first adoption of \"parameter curation\" in an analytical benchmark, a technique that ensures stable runtimes of query variants across different parameter values. Two performance metrics characterize peak single-query performance (power) and sustained concurrent query throughput. To demonstrate the portability of the benchmark, we present experimental results on a relational and a graph DBMS. Note that these do not constitute an official LDBC Benchmark Result - only audited results can use this trademarked term.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"32 1","pages":"877-890"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82370211","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: 40

Cache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration 缓存我，如果你可以:准确性感知推理引擎的差异私有数据探索

Proc. VLDB Endow. Pub Date : 2022-11-28 DOI: 10.48550/arXiv.2211.15732

Miti Mazmudar, Thomas Humphries, Jiaxiang Liu, Matthew Rafuse, Xi He

引用次数: 3

Efficient Triangle-Connected Truss Community Search In Dynamic Graphs 动态图中三角连接桁架社区的高效搜索

Proc. VLDB Endow. Pub Date : 2022-11-01 DOI: 10.14778/3570690.3570701

Tianyang Xu, Z. Lu, Yuanyuan Zhu

{"title":"Efficient Triangle-Connected Truss Community Search In Dynamic Graphs","authors":"Tianyang Xu, Z. Lu, Yuanyuan Zhu","doi":"10.14778/3570690.3570701","DOIUrl":"https://doi.org/10.14778/3570690.3570701","url":null,"abstract":"\u0000 Community search studies the retrieval of certain community structures containing query vertices, which has received lots of attention recently.\u0000 k\u0000 -truss is a fundamental community structure where each edge is contained in at least\u0000 k\u0000 - 2 triangles. Triangle-connected\u0000 k\u0000 -truss community (\u0000 k\u0000 -TTC) is a widely-used variant of\u0000 k\u0000 -truss, which is a maximal\u0000 k\u0000 -truss where edges can reach each other via a series of edge-adjacent triangles. Although existing works have provided indexes and query algorithms for\u0000 k\u0000 -TTC search, the cohesiveness of a\u0000 k\u0000 -TTC (diameter upper bound) has not been theoretically analyzed and the triangle connectivity has not been efficiently captured. Thus, we revisit the\u0000 k\u0000 -TTC search problem in dynamic graphs, aiming to achieve a deeper understanding of\u0000 k\u0000 -TTC. First, we prove that the diameter of a\u0000 k\u0000 -TTC with\u0000 n\u0000 vertices is bounded by [EQUATION]. Then, we encapsulate triangle connectivity with two novel concepts, partial class and truss-precedence, based on which we build our compact index, EquiTree, to support the efficient\u0000 k\u0000 -TTC search. We also provide efficient index construction and maintenance algorithms for the dynamic change of graphs. Compared with the state-of-the-art methods, our extensive experiments show that EquiTree can boost search efficiency up to two orders of magnitude at a small cost of index construction and maintenance.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"22 1","pages":"519-531"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75672006","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

Route Travel Time Estimation on A Road Network Revisited: Heterogeneity, Proximity, Periodicity and Dynamicity 道路网络的行程时间估计:异质性、邻近性、周期性和动态性

Proc. VLDB Endow. Pub Date : 2022-11-01 DOI: 10.14778/3570690.3570691

Haitao Yuan, Guoliang Li, Z. Bao

{"title":"Route Travel Time Estimation on A Road Network Revisited: Heterogeneity, Proximity, Periodicity and Dynamicity","authors":"Haitao Yuan, Guoliang Li, Z. Bao","doi":"10.14778/3570690.3570691","DOIUrl":"https://doi.org/10.14778/3570690.3570691","url":null,"abstract":"In this paper, we revisit the problem of route travel time estimation on a road network and aim to boost its accuracy by capturing and utilizing spatio-temporal features from four significant aspects: heterogeneity, proximity, periodicity and dynamicity.\u0000 Spatial-wise, we consider two forms of heterogeneity at link level in a road network: the turning ways between different links are heterogeneous which can make the travel time of the same link various; different links contain heterogeneous attributes and thereby lead to different travel time. In addition, we take into account the proximity: neighboring links have similar traffic patterns and lead to similar travel speeds. To this end, we build a link-connection graph to capture such heterogeneity and proximity.\u0000 Temporal-wise, the weekly/daily periodicity of temporal background information (e.g., rush hours) and dynamic traffic conditions have significant impact on the travel time, which result in static and dynamic spatio-temporal features respectively. To capture such impacts, we regard the travel time/speed as a combination of static and dynamic parts, and extract many spatio-temporal relevant features for the prediction task.\u0000 Talking about the methodology, it remains an open problem to build a generic learning model to boost the estimation accuracy. Hence, we design a novel encoder-decoder framework - The encoder uses the sequence attention model to encode dynamic features from the temporal-wise perspective. The decoder first uses the heterogeneous graph attention model to decode the static part of travel speed based on static spatio-temporal features, and then leverages the sequence attention model to decode the estimated travel time from spatial-wise perspective. Extensive experiments on real datasets verify the superiority of our method as well as the importance of the four aspects outlined above.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"5 1","pages":"393-405"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89506335","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

Computing Rule-Based Explanations by Leveraging Counterfactuals 利用反事实计算基于规则的解释

Proc. VLDB Endow. Pub Date : 2022-10-31 DOI: 10.48550/arXiv.2210.17071

Zixuan Geng, Maximilian Schleich, Dan Suciu

引用次数: 1

Keep CALM and CRDT On 保持冷静，继续前进

Proc. VLDB Endow. Pub Date : 2022-10-23 DOI: 10.48550/arXiv.2210.12605

Shadaj Laddad, Conor Power, Mae Milano, Alvin Cheung, Natacha Crooks, J. Hellerstein

引用次数: 1

Federated Calibration and Evaluation of Binary Classifiers 二值分类器的联邦校准与评价

Proc. VLDB Endow. Pub Date : 2022-10-22 DOI: 10.48550/arXiv.2210.12526

Graham Cormode, Igor L. Markov

引用次数: 1

Satisfying Complex Top-k Fairness Constraints by Preference Substitutions 用偏好替代满足复杂Top-k公平性约束

Proc. VLDB Endow. Pub Date : 2022-10-01 DOI: 10.14778/3565816.3565832

Md Mouinul Islam, Dong Wei, B. Schieber, Senjuti Basu Roy

{"title":"Satisfying Complex Top-k Fairness Constraints by Preference Substitutions","authors":"Md Mouinul Islam, Dong Wei, B. Schieber, Senjuti Basu Roy","doi":"10.14778/3565816.3565832","DOIUrl":"https://doi.org/10.14778/3565816.3565832","url":null,"abstract":"\u0000 Given\u0000 m\u0000 users (voters), where each user casts her preference for a single item (candidate) over\u0000 n\u0000 items (candidates) as a ballot, the preference aggregation problem returns\u0000 k\u0000 items (candidates) that have the\u0000 k\u0000 highest number of preferences (votes). Our work studies this problem considering\u0000 complex fairness constraints\u0000 that have to be satisfied via proportionate representations of different values of the group protected attribute(s) in the top-\u0000 k\u0000 results. Precisely, we study\u0000 the margin finding problem under single ballot substitutions\u0000 , where a single substitution amounts to removing a vote from candidate\u0000 i\u0000 and assigning it to candidate\u0000 j\u0000 and the goal is to\u0000 minimize the number of single ballot substitutions needed to guarantee that the top-k results satisfy the fairness constraints.\u0000 We study several variants of this problem considering how top-\u0000 k\u0000 fairness constraints are defined, (i) MFBinaryS and MFMultiS are defined when the fairness (proportionate representation) is defined over a single, binary or multivalued, protected attribute, respectively; (ii) MF-Multi2 is studied when top-\u0000 k\u0000 fairness is defined over two different protected attributes; (iii) MFMulti3+ investigates the margin finding problem, considering 3 or more protected attributes. We study these problems theoretically, and present a suite of algorithms with provable guarantees. We conduct rigorous large scale experiments involving multiple real world datasets by appropriately adapting multiple state-of-the-art solutions to demonstrate the effectiveness and scalability of our proposed methods.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"40 1","pages":"317-329"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77248988","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

Frequency Domain Data Encoding in Apache IoTDB Apache IoTDB的频域数据编码

Proc. VLDB Endow. Pub Date : 2022-10-01 DOI: 10.14778/3565816.3565829

Haoyu Wang, Shaoxu Song

{"title":"Frequency Domain Data Encoding in Apache IoTDB","authors":"Haoyu Wang, Shaoxu Song","doi":"10.14778/3565816.3565829","DOIUrl":"https://doi.org/10.14778/3565816.3565829","url":null,"abstract":"\u0000 Frequency domain analysis is widely conducted on time series. While online transforming from time domain to frequency domain is costly, e.g., by Fast Fourier Transform (FFT), it is highly demanded to store the frequency domain data for reuse. However, frequency domain data encoding for efficient storage is surprisingly untouched. We notice that (1) the precision of data value is unnecessarily high after transforming to frequency domain and (2) the data values are with skewed distribution leading to a very large bit width for encoding. To avoid such space waste in both precision and skewness, we devise a\u0000 descending bit-packing\u0000 encoding for frequency domain data. Specifically, we quantize the data values in proper precision referring to the signal-noise-ratio (SNR) in frequency domain analysis. Moreover, we sort the data values in descending order so that the bit width could be dynamically reduced in encoding. The method has been deployed in Apache IoTDB, an open-source time-series database, not only for directly encoding frequency domain data, but also as a lossy compression of the time domain data. The extensive experiments on the system demonstrate the superiority of our encoding for both frequency domain and time domain data.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"17 1","pages":"282-290"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81861333","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: 3