2018 IEEE 34th International Conference on Data Engineering (ICDE)最新文献_第2页

Building Data Civilizer Pipelines with an Advanced Workflow Engine 构建数据文明管道与先进的工作流引擎

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00184

Essam Mansour, Dong Deng, R. Fernandez, A. Qahtan, Wenbo Tao, Ziawasch Abedjan, A. Elmagarmid, I. Ilyas, S. Madden, M. Ouzzani, M. Stonebraker, N. Tang

引用次数: 10

Wadjet: Finding Outliers in Multiple Multi-Dimensional Heterogeneous Data Streams Wadjet:在多个多维异构数据流中发现异常值

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00118

Md. Shiblee Sadik, L. Gruenwald, Eleazar Leal

引用次数: 8

An Empirical Evaluation of Techniques for Ranking Semantic Associations (Extended Abstract) 语义关联排序技术的实证评价(扩展摘要)

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00254

Gong Cheng, Fei Shao, Yuzhong Qu

引用次数: 0

Range Optimistic Concurrency Control for a Composite OLTP and Bulk Processing Workload 复合OLTP和批量处理工作负载的范围乐观并发控制

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00061

Jiahao Wang, Peng Cai, Jinwei Guo, Weining Qian, Aoying Zhou

{"title":"Range Optimistic Concurrency Control for a Composite OLTP and Bulk Processing Workload","authors":"Jiahao Wang, Peng Cai, Jinwei Guo, Weining Qian, Aoying Zhou","doi":"10.1109/ICDE.2018.00061","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00061","url":null,"abstract":"This work addresses the need for efficient key-range validation for a composite OLTP and bulk processing workload characterized by modern enterprise applications. In-memory database system (IMDB), mostly adopting the optimistic concurrency control (OCC) mechanism, performs well if the contention of conventional OLTP workloads is low and each transaction only contains point read/write query with primary key. In this work we present the performance problem of IMDBs under mixed OLTP and bulk processing workloads. The reason is that existing OCC protocols take expensive cost to generate a serializable schedule if the OLTP workload contains bulk processing operations with key-range scan. To this end, we develop an efficient and scalable range optimistic concurrency control (ROCC) which uses logical ranges to track the potential conflicting transactions and to reduce the number of transactions to be validated. At the read phase, a transaction keeps a set of predicates to remember the version and precise scope of scanned ranges, which eliminates the cost of maintaining scanned records. Before entering the validation phase, if the transaction intends to update records in the logical range, it needs to register to the corresponding lock-free list implemented by a circular array. Finally, ROCC filters out unrelated transactions and validates the bulk operation at range level. Experimental results show that ROCC has good performance and scalability under heterogeneous workloads mixed with point access and bulk processing.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"72 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114004311","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

Blockchains and Databases: A New Era in Distributed Computing 区块链和数据库:分布式计算的新时代

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00227

C. Mohan

引用次数: 12

Supporting Set Operations in Temporal-Probabilistic Databases 支持时间概率数据库中的集合操作

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00109

K. Papaioannou, M. Theobald, Michael H. Böhlen

{"title":"Supporting Set Operations in Temporal-Probabilistic Databases","authors":"K. Papaioannou, M. Theobald, Michael H. Böhlen","doi":"10.1109/ICDE.2018.00109","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00109","url":null,"abstract":"In temporal-probabilistic (TP) databases, the combination of the temporal and the probabilistic dimension adds significant overhead to the computation of set operations. Although set queries are guaranteed to yield linearly sized output relations, all of the existing solutions exhibit a quadratic runtime complexity. They suffer from redundant interval comparisons and additional joins for the formation of lineage expressions. In this paper, we formally define TP set operations and study their properties. For their efficient computation, we introduce the lineage-aware temporal window, a mechanism that binds intervals with lineage expressions. We suggest the lineage-aware window advancer (LAWA) for producing lineage-aware temporal windows, which enable direct filtering of irrelevant intervals and finalization of output lineage expressions. This way, we compute TP set operations in linearithmic time. A series of experiments over both synthetic and real-world datasets show that (a) our approach has predictable performance, which depends only on the size of the input relations and not on the number of time intervals per fact or the overlap of the time intervals, and that (b) it outperforms state-of-the-art approaches.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"24 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":"121650262","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: 12

GPH: Similarity Search in Hamming Space GPH: Hamming空间的相似性搜索

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00013

Jianbin Qin, Yaoshu Wang, Chuan Xiao, Wei Wang, Xuemin Lin, Y. Ishikawa

{"title":"GPH: Similarity Search in Hamming Space","authors":"Jianbin Qin, Yaoshu Wang, Chuan Xiao, Wei Wang, Xuemin Lin, Y. Ishikawa","doi":"10.1109/ICDE.2018.00013","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00013","url":null,"abstract":"A similarity search in Hamming space finds binary vectors whose Hamming distances are no more than a threshold from a query vector. It is a fundamental problem in many applications, including image retrieval, near-duplicate Web page detection, and machine learning. State-of-the-art approaches to answering such queries are mainly based on the pigeonhole principle to generate a set of candidates and then verify them. We observe that the constraint based on the pigeonhole principle is not always tight and hence may bring about unnecessary candidates. We also observe that the distribution in real data is often skew, but most existing solutions adopt a simple equiwidth partitioning and allocate the same threshold to all the partitions, and hence fail to exploit the data skewness to optimize the query processing. In this paper, we propose a new form of the pigeonhole principle which allows variable partition size and threshold. Based on the new principle, we first develop a tight constraint of candidates, and then devise cost-aware methods for dimension partitioning and threshold allocation to optimize query processing. Our evaluation on datasets with various data distributions shows the robustness of our solution and its superior query processing performance to the state-of-the-art methods.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"32 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":"115961913","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: 13

Curating Variational Data in Application Development 在应用程序开发中管理变分数据

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00187

U. Störl, Daniel Müller, A. Tekleab, Stephane Tolale, Julian Stenzel, Meike Klettke, Stefanie Scherzinger

引用次数: 17

Test Data Generation for Database Applications 数据库应用程序的测试数据生成

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00191

Pooja Agrawal, B. Chandra, K. V. Emani, N. Garg, S. Sudarshan

引用次数: 2

Finding Diverse Neighbors in High Dimensional Space 在高维空间中寻找不同的邻居

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00086

Qi Guo, H. Jagadish, A. Tung, Yu Xin Zheng

引用次数: 1