Proceedings of the 2018 International Conference on Management of Data最新文献_第9页

Session details: Research 5: Graph Data Management 会议详情:研究5:图数据管理

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3258009

S. Bhowmick

引用次数: 0

FastQRE: Fast Query Reverse Engineering FastQRE:快速查询逆向工程

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3183713.3183727

D. Kalashnikov, L. Lakshmanan, D. Srivastava

引用次数: 45

DITA: A Distributed In-Memory Trajectory Analytics System 分布式内存轨迹分析系统

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3183713.3193553

Zeyuan Shang, Guoliang Li, Z. Bao

引用次数: 13

Session details: Research 3: Transactions and Indexing 会议细节:研究3:交易和索引

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3258007

Pınar Tözün

引用次数: 0

Session details: Research 2: Usability and Security/Privacy 研究2:可用性和安全性/隐私

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3258005

Ashwin Machanavajjhala

引用次数: 0

Qetch: Time Series Querying with Expressive Sketches Qetch:时间序列查询与表达草图

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3183713.3193547

M. Mannino, A. Abouzeid

引用次数: 16

IoT-Detective: Analyzing IoT Data Under Differential Privacy 物联网检测:在差异隐私下分析物联网数据

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3183713.3193571

Sameera Ghayyur, Yan Chen, Roberto Yus, Ashwin Machanavajjhala, Michael Hay, G. Miklau, S. Mehrotra

{"title":"IoT-Detective: Analyzing IoT Data Under Differential Privacy","authors":"Sameera Ghayyur, Yan Chen, Roberto Yus, Ashwin Machanavajjhala, Michael Hay, G. Miklau, S. Mehrotra","doi":"10.1145/3183713.3193571","DOIUrl":"https://doi.org/10.1145/3183713.3193571","url":null,"abstract":"Emerging IoT technologies promise to bring revolutionary changes to many domains including health, transportation, and building management. However, continuous monitoring of individuals threatens privacy. The success of IoT thus depends on integrating privacy protections into IoT infrastructures. This demonstration adapts a recently-proposed system, PeGaSus, which releases streaming data under the formal guarantee of differential privacy, with a state-of-the-art IoT testbed (TIPPERS) located at UC Irvine. PeGaSus protects individuals' data by introducing distortion into the output stream. While PeGaSuS has been shown to offer lower numerical error compared to competing methods, assessing the usefulness of the output is application dependent. The goal of the demonstration is to assess the usefulness of private streaming data in a real-world IoT application setting. The demo consists of a game, IoT-Detective, in which participants carry out visual data analysis tasks on private data streams, earning points when they achieve results similar to those on the true data stream. The demo will educate participants about the impact of privacy mechanisms on IoT data while at the same time generating insights into privacy-utility trade-offs in IoT applications.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73807204","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

Dynamic Pricing in Spatial Crowdsourcing: A Matching-Based Approach 空间众包的动态定价:基于匹配的方法

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3183713.3196929

Yongxin Tong, Libin Wang, Zimu Zhou, Lei Chen, Bowen Du, Jieping Ye

{"title":"Dynamic Pricing in Spatial Crowdsourcing: A Matching-Based Approach","authors":"Yongxin Tong, Libin Wang, Zimu Zhou, Lei Chen, Bowen Du, Jieping Ye","doi":"10.1145/3183713.3196929","DOIUrl":"https://doi.org/10.1145/3183713.3196929","url":null,"abstract":"In spatial crowdsourcing, requesters submit their task-related locations and increase the demand of a local area. The platform prices these tasks and assigns spatial workers to serve if the prices are accepted by requesters. There exist mature pricing strategies which specialize in tackling the imbalance between supply and demand in a local market. However, in global optimization, the platform should consider the mobility of workers; that is, any single worker can be the potential supply for several areas, while it can only be the true supply of one area when assigned by the platform. The hardness lies in the uncertainty of the true supply of each area, hence the existing pricing strategies do not work. In the paper, we formally define this Global Dynamic Pricing(GDP) problem in spatial crowdsourcing. And since the objective is concerned with how the platform matches the supply to areas, we let the matching algorithm guide us how to price. We propose a MAtching-based Pricing Strategy (MAPS) with guaranteed bound. Extensive experiments conducted on the synthetic and real datasets demonstrate the effectiveness of MAPS.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83957391","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: 98

GeoFlux: Hands-Off Data Integration Leveraging Join Key Knowledge GeoFlux:利用连接关键知识的数据集成

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3183713.3193546

Jie Song, Danai Koutra, Murali Mani, H. Jagadish

{"title":"GeoFlux: Hands-Off Data Integration Leveraging Join Key Knowledge","authors":"Jie Song, Danai Koutra, Murali Mani, H. Jagadish","doi":"10.1145/3183713.3193546","DOIUrl":"https://doi.org/10.1145/3183713.3193546","url":null,"abstract":"Data integration is frequently required to obtain the full value of data from multiple sources. In spite of extensive research on tools to assist users, data integration remains hard, particularly for users with limited technical proficiency. To address this barrier, we study how much we can do with no user guidance. Our vision is that the user should merely specify two input datasets to be joined and get a meaningful integrated result. It turns out that our vision can be realized if the system can correctly determine the join key, for example based on domain knowledge. We demonstrate this notion by considering a broad domain: socioeconomic data aggregated by geography, a widespread category that accounts for 80% of the data published by government agencies. Intuitively two such datasets can be integrated by joining on the geographic unit column. Although it sounds easy, this task has many challenges: How can we automatically identify columns corresponding to geographic units, other dimension variables and measure variables, respectively? If multiple geographic types exist, which one should be chosen for the join? How to join tables with idiosyncratic schema, different geographic units of aggregation or no aggregation at all? We have developed GeoFlux, a data integration system that handles all these challenges and joins tabular data by automatically aggregating geographic information with a new, advanced crosswalk algorithm. In this demo paper, we overview the architecture of the system and its user-friendly interfaces, and then demonstrate via a real-world example that it is general, fully automatic and easy-to-use. In the demonstration, we invite users to interact with GeoFlux to integrate more sample socioeconomic data from data.ny.gov.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84169369","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

Pipelined Query Processing in Coprocessor Environments 协处理器环境中的流水线查询处理

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3183713.3183734

Henning Funke, S. Breß, Stefan Noll, V. Markl, J. Teubner

引用次数: 75