2018 IEEE 34th International Conference on Data Engineering (ICDE)最新文献

Redundancy Reduction for Prevalent Co-Location Patterns 流行的协同定位模式的冗余减少

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-10-25 DOI: 10.1109/ICDE.2018.00246

Lizhen Wang, Xuguang Bao, Lihua Zhou

引用次数: 8

Price-and-Time-Aware Dynamic Ridesharing 有价格和时间意识的动态拼车

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-10-24 DOI: 10.1109/ICDE.2018.00099

Lu Chen, Qilu Zhong, Xiaokui Xiao, Yunjun Gao, Pengfei Jin, Christian S. Jensen

{"title":"Price-and-Time-Aware Dynamic Ridesharing","authors":"Lu Chen, Qilu Zhong, Xiaokui Xiao, Yunjun Gao, Pengfei Jin, Christian S. Jensen","doi":"10.1109/ICDE.2018.00099","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00099","url":null,"abstract":"Ridesharing refers to a transportation scenario where travellers with similar itineraries and time schedules share a vehicle for a trip and split the travel cost, which may include fuel, tolls, and parking fees. Ridesharing is popular among travellers because it can reduce their travel costs, and it also holds the potential to reduce travel time, congestion, air pollution, and overall fuel consumption. However, existing ridesharing systems often offer each traveller only one choice that aims to minimize system-wide vehicle travel distance or time. We propose a solution that offers more options. Specifically, we do this by considering both pick-up time and price, so that travellers are able to choose the vehicle that matches their preferences best. In order to identify quickly vehicles that satisfy incoming ridesharing requests, we propose two efficient matching algorithms that follow the single-side and dual-side search paradigms, respectively. To further accelerate the matching, indexes on the road network and vehicles are developed, based on which several pruning heuristics are designed. Extensive experiments on a large Shanghai taxi dataset offer insights into the performance of our proposed techniques and compare with a baseline that extends the state-of-the art method.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127255270","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: 38

Human-Database Interaction: A Holistic Approach 人-数据库交互:一个整体的方法

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-10-24 DOI: 10.1109/ICDE.2018.00209

Mohamed Sarwat, Venkata Vamsikrishna Meduri

引用次数: 0

KDE-Track: An Efficient Dynamic Density Estimator for Data Streams (Extended Abstract) KDE-Track:一种高效的数据流动态密度估计器(扩展摘要)

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-10-24 DOI: 10.1109/ICDE.2018.00237

A. Qahtan, Suojin Wang, Xiangliang Zhang

引用次数: 1

M2TD: Multi-Task Tensor Decomposition for Sparse Ensemble Simulations 稀疏集成模拟的多任务张量分解

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-10-24 DOI: 10.1109/ICDE.2018.00106

Xinsheng Li, K. Candan, M. Sapino

{"title":"M2TD: Multi-Task Tensor Decomposition for Sparse Ensemble Simulations","authors":"Xinsheng Li, K. Candan, M. Sapino","doi":"10.1109/ICDE.2018.00106","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00106","url":null,"abstract":"Data-and model-driven computer simulations are increasingly critical in many application domains. These simulations may track 10s or 100s of parameters, affected by complex inter-dependent dynamic processes. Moreover, decision makers usually need to run large simulation ensembles, containing 1000s of simulations. In this paper, we rely on a tensor-based framework to represent and analyze patterns in large simulation ensemble data sets to obtain a high-level understanding of the dynamic processes implied by a given ensemble of simulations.We, further, note that the inherent sparsity of the simulation ensembles (relative to the space of potential simulations one can run) constitutes a significant problem in discovering these underlying patterns. To address this challenge, we propose a partition-stitch sampling scheme, which divides the parameter space into subspaces to collect several lower modal ensembles, and complement this with a novel Multi-Task Tensor Decomposition (M2TD), technique which helps effectively and efficiently stitch these subensembles back. Experiments showed that, for a given budget of simulations, the proposed structured sampling scheme leads to significantly better overall accuracy relative to traditional sampling approaches, even when the user does not have a perfect information to help guide the structured partitioning process.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130791003","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: 11

Finding Top-k Optimal Sequenced Routes 寻找Top-k最优排序路径

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-10-24 DOI: 10.1109/ICDE.2018.00058

Huiping Liu, Cheqing Jin, B. Yang, Aoying Zhou

{"title":"Finding Top-k Optimal Sequenced Routes","authors":"Huiping Liu, Cheqing Jin, B. Yang, Aoying Zhou","doi":"10.1109/ICDE.2018.00058","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00058","url":null,"abstract":"Motivated by many practical applications in logistics and mobility-as-a-service, we study the top-k optimal sequenced routes (KOSR) querying on large, general graphs where the edge weights may not satisfy the triangle inequality, e.g., road network graphs with travel times as edge weights. The KOSR querying strives to find the top-k optimal routes (i.e., with the top-k minimal total costs) from a given source to a given destination, which must visit a number of vertices with specific vertex categories (e.g., gas stations, restaurants, and shopping malls) in a particular order (e.g., visiting gas stations before restaurants and then shopping malls). To efficiently find the top-k optimal sequenced routes, we propose two algorithms PruningKOSR and StarKOSR. In PruningKOSR, we define a dominance relationship between two partially-explored routes. The partially-explored routes that can be dominated by other partially-explored routes are postponed being extended, which leads to a smaller searching space and thus improves efficiency. In StarKOSR, we further improve the efficiency by extending routes in an A manner. With the help of a judiciously designed heuristic estimation that works for general graphs, the cost of partially explored routes to the destination can be estimated such that the qualified complete routes can be found early. In addition, we demonstrate the high extensibility of the proposed algorithms by incorporating Hop Labeling, an effective label indexing technique for shortest path queries, to further improve efficiency. Extensive experiments on multiple real-world graphs demonstrate that the proposed methods significantly outperform the baseline method. Furthermore, when k = 1, StarKOSR also outperforms the state-of-the-art method for the optimal sequenced route queries.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130660684","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: 39

Interactive Probabilistic Post-Mining of User-Preferred Spatial Co-Location Patterns 用户偏好空间协同定位模式的交互式概率后挖掘

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-10-24 DOI: 10.1109/ICDE.2018.00124

Lizhen Wang, Xuguang Bao, Longbing Cao

引用次数: 7

A Graph-Based Database Partitioning Method for Parallel OLAP Query Processing 并行OLAP查询处理中基于图的数据库分区方法

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-19 DOI: 10.1109/ICDE.2018.00096

Yoon-Min Nam, Min-Soo Kim, Donghyoung Han

引用次数: 3

SpaceKey: Exploring Patterns in Spatial Databases SpaceKey:探索空间数据库中的模式

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00180

Yixiang Fang, Reynold Cheng, Jikun Wang, Lukito Budiman, G. Cong, N. Mamoulis

引用次数: 6

LevelHeaded: A Unified Engine for Business Intelligence and Linear Algebra Querying LevelHeaded:商业智能和线性代数查询的统一引擎

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00048

Christopher R. Aberger, Andrew Lamb, K. Olukotun, C. Ré

{"title":"LevelHeaded: A Unified Engine for Business Intelligence and Linear Algebra Querying","authors":"Christopher R. Aberger, Andrew Lamb, K. Olukotun, C. Ré","doi":"10.1109/ICDE.2018.00048","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00048","url":null,"abstract":"Pipelines combining SQL-style business intelligence (BI) queries and linear algebra (LA) are becoming increasingly common in industry. As a result, there is a growing need to unify these workloads in a single framework. Unfortunately, existing solutions either sacrifice the inherent benefits of ex-clusively using a relational database (e.g. logical and physical independence) or incur orders of magnitude performance gaps compared to specialized engines (or both). In this work, we study applying a new type of query processing architecture to standard BI and LA benchmarks. To do this, we present a new in-memory query processing engine called LevelHeaded. LevelHeaded uses worst-case optimal joins as its core execution mechanism for both BI and LA queries. With LevelHeaded, we show how crucial optimizations for BI and LA queries can be captured in a worst-case optimal query architecture. Using these optimizations, LevelHeaded outperforms other relational database engines (LogicBlox, MonetDB, and HyPer) by orders of magnitude on standard LA benchmarks, while performing on average within 31% of the best-of-breed BI (HyPer) and LA (Intel MKL) solutions on their own benchmarks. Our results show that such a single query processing architecture can be efficient on both BI and LA queries.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126753724","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: 22