2020 IEEE 36th International Conference on Data Engineering (ICDE)最新文献_第7页

HBP: Hotness Balanced Partition for Prioritized Iterative Graph Computations 优先迭代图计算的热度平衡划分

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00209

Shufeng Gong, Yanfeng Zhang, Ge Yu

引用次数: 5

StructSim: Querying Structural Node Similarity at Billion Scale StructSim:在十亿尺度上查询结构节点相似度

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00211

Xiaoshuang Chen, Longbin Lai, Lu Qin, Xuemin Lin

引用次数: 15

Indoor Mobility Semantics Annotation Using Coupled Conditional Markov Networks 基于耦合条件马尔可夫网络的室内移动语义标注

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00128

Huan Li, Hua Lu, M. A. Cheema, L. Shou, Gang Chen

{"title":"Indoor Mobility Semantics Annotation Using Coupled Conditional Markov Networks","authors":"Huan Li, Hua Lu, M. A. Cheema, L. Shou, Gang Chen","doi":"10.1109/ICDE48307.2020.00128","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00128","url":null,"abstract":"Indoor mobility semantics analytics can greatly benefit many pertinent applications. Existing semantic annotation methods mainly focus on outdoor space and require extra knowledge such as POI category or human activity regularity. However, these conditions are difficult to meet in indoor venues with relatively small extents but complex topology. This work studies the annotation of indoor mobility semantics that describe an object’s mobility event (what ) at a semantic indoor region (where ) during a time period (when ). A coupled conditional Markov network (C2MN) is proposed with a set of feature functions carefully designed by incorporating indoor topology and mobility behaviors. C2MN is able to capture probabilistic dependencies among positioning records, semantic regions, and mobility events jointly. Nevertheless, the correlation of regions and events hinders the parameters learning. Therefore, we devise an alternate learning algorithm to enable the parameter learning over correlated variables. The extensive experiments demonstrate that our C2MN-based semantic annotation is efficient and effective on both real and synthetic indoor mobility data.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"19 1","pages":"1441-1452"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81988838","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: 7

Contribution Maximization in Probabilistic Datalog 概率数据中的贡献最大化

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00076

T. Milo, Y. Moskovitch, Brit Youngmann

{"title":"Contribution Maximization in Probabilistic Datalog","authors":"T. Milo, Y. Moskovitch, Brit Youngmann","doi":"10.1109/ICDE48307.2020.00076","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00076","url":null,"abstract":"The use of probabilistic datalog programs has been recently advocated for applications that involve recursive computation and uncertainty. While using such programs allows for a flexible knowledge derivation, it makes the analysis of query results a challenging task. Particularly, given a set O of output tuples and a number k, one would like to understand which k-size subset of the input tuples have contributed the most to the derivation of O. This is useful for multiple tasks, such as identifying the critical sources of errors and understanding surprising results. Previous works have mainly focused on the quantification of tuples contribution to a query result in non-recursive SQL queries, very often disregarding probabilistic inference. To quantify the contribution in probabilistic datalog programs, one must account for the recursive relations between input and output data, and the uncertainty. To this end, we formalize the Contribution Maximization (CM) problem. We then reduce CM to the well-studied Influence Maximization (IM) problem, showing that we can harness techniques developed for IM to our setting. However, we show that such naïve adoption results in poor performance. To overcome this, we propose an optimized algorithm which injects a refined variant of the classic Magic Sets technique, integrated with a sampling method, into IM algorithms, achieving a significant saving of space and execution time. Our experiments demonstrate the effectiveness of our algorithm, even where the naïve approach is infeasible.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"72 1","pages":"817-828"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78747207","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

vCBIR: A Verifiable Search Engine for Content-Based Image Retrieval 基于内容的图像检索的可验证搜索引擎vCBIR

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00156

Shangwei Guo, Yang Ji, Ce Zhang, Cheng Xu, Jianliang Xu

引用次数: 1

Cool, a COhort OnLine analytical processing system Cool，一个队列在线分析处理系统

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00056

Zhongle Xie, Hongbin Ying, Cong Yue, Meihui Zhang, Gang Chen, B. Ooi

引用次数: 2

Deciding When to Trade Data Freshness for Performance in MongoDB-as-a-Service 在mongodb即服务中决定何时以数据新鲜度换取性能

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00207

Chenhao Huang, Michael J. Cahill, A. Fekete, Uwe Röhm

引用次数: 5

Efficiently Answering Span-Reachability Queries in Large Temporal Graphs 大时间图中跨度可达性查询的有效回答

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00104

Dong Wen, Yilun Huang, Ying Zhang, Lu Qin, W. Zhang, Xuemin Lin

引用次数: 13

UniKV: Toward High-Performance and Scalable KV Storage in Mixed Workloads via Unified Indexing UniKV:通过统一索引在混合工作负载中实现高性能和可扩展的KV存储

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00034

Qiang Zhang, Yongkun Li, P. Lee, Yinlong Xu, Qiu Cui, L. Tang

引用次数: 11

Task Allocation in Dependency-aware Spatial Crowdsourcing 依赖感知空间众包中的任务分配

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00090

Wangze Ni, Peng Cheng, Lei Chen, Xuemin Lin

{"title":"Task Allocation in Dependency-aware Spatial Crowdsourcing","authors":"Wangze Ni, Peng Cheng, Lei Chen, Xuemin Lin","doi":"10.1109/ICDE48307.2020.00090","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00090","url":null,"abstract":"Ubiquitous smart devices and high-quality wireless networks enable people to participate in spatial crowdsourcing tasks easily, which require workers to physically move to specific locations to conduct their assigned tasks. Spatial crowdsourcing has attracted much attention from both academia and industry. In this paper, we consider a spatial crowdsourcing scenario, where the tasks may have some dependencies among them. Specifically, one task can only be dispatched when its dependent tasks have already been assigned. In fact, task dependencies are quite common in many real-life applications, such as house repairing and holding sports games. We formally define the dependency-aware spatial crowdsourcing (DA-SC), which focuses on finding an optimal worker-and-task assignment under the constraints of dependencies, skills of workers, moving distances and deadlines to maximize the successfully assigned tasks. We prove that the DA-SC problem is NP-hard and thus intractable. Therefore, we propose two approximation algorithms, including a greedy approach and a game-theoretic approach, which can guarantee the approximate bounds of the results in each batch process. Through extensive experiments on both real and synthetic data sets, we demonstrate the efficiency and effectiveness of our DA-SC approaches.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"61 1","pages":"985-996"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90996187","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: 24