2016 IEEE 32nd International Conference on Data Engineering (ICDE)最新文献_第3页

Leveraging non-volatile memory for instant restarts of in-memory database systems 利用非易失性内存立即重新启动内存中的数据库系统

2016 IEEE 32nd International Conference on Data Engineering (ICDE) Pub Date : 2016-05-16 DOI: 10.1109/ICDE.2016.7498351

David Schwalb, Martin Faust, Markus Dreseler, Pedro Flemming, H. Plattner

引用次数: 6

Quality-driven disorder handling for m-way sliding window stream joins m-way滑动窗口流连接的质量驱动无序处理

2016 IEEE 32nd International Conference on Data Engineering (ICDE) Pub Date : 2016-05-16 DOI: 10.1109/ICDE.2016.7498265

Yuanzhen Ji, Jun Sun, A. Nica, Zbigniew Jerzak, Gregor Hackenbroich, C. Fetzer

{"title":"Quality-driven disorder handling for m-way sliding window stream joins","authors":"Yuanzhen Ji, Jun Sun, A. Nica, Zbigniew Jerzak, Gregor Hackenbroich, C. Fetzer","doi":"10.1109/ICDE.2016.7498265","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498265","url":null,"abstract":"Sliding window join is one of the most important operators for stream applications. To produce high quality join results, a stream processing system must deal with the ubiquitous disorder within input streams which is caused by network delay, parallel processing, etc. Disorder handling involves an inevitable tradeoff between the latency and the quality of produced join results. To meet different requirements of stream applications, it is desirable to provide a user-configurable result-latency vs. result-quality tradeoff. Existing disorder handling approaches either do not provide such configurability, or support only user-specified latency constraints. In this work, we advocate the idea of quality-driven disorder handling, and propose a buffer-based disorder handling approach for sliding window joins, which minimizes sizes of input-sorting buffers, thus the result latency, while respecting user-specified result-quality requirements. The core of our approach is an analytical model which directly captures the relationship between sizes of input buffers and the produced result quality. Our approach is generic. It supports m-way sliding window joins with arbitrary join conditions. Experiments on real-world and synthetic datasets show that, compared to the state of the art, our approach can reduce the result latency incurred by disorder handling by up to 95% while providing the same level of result quality.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"7 1","pages":"493-504"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85935076","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

Edge classification in networks 网络中的边缘分类

2016 IEEE 32nd International Conference on Data Engineering (ICDE) Pub Date : 2016-05-16 DOI: 10.1109/ICDE.2016.7498311

C. Aggarwal, Gewen He, Peixiang Zhao

{"title":"Edge classification in networks","authors":"C. Aggarwal, Gewen He, Peixiang Zhao","doi":"10.1109/ICDE.2016.7498311","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498311","url":null,"abstract":"We consider in this paper the edge classification problem in networks, which is defined as follows. Given a graph-structured network G(N, A), where N is a set of vertices and A ⊆ N ×N is a set of edges, in which a subset Al ⊆ A of edges are properly labeled a priori, determine for those edges in Au = AAl the edge labels which are unknown. The edge classification problem has numerous applications in graph mining and social network analysis, such as relationship discovery, categorization, and recommendation. Although the vertex classification problem has been well known and extensively explored in networks, edge classification is relatively unknown and in an urgent need for careful studies. In this paper, we present a series of efficient, neighborhood-based algorithms to perform edge classification in networks. To make the proposed algorithms scalable in large-scale networks, which can be either disk-resident or streamlike, we further devise efficient, cost-effective probabilistic edge classification methods without a significant compromise to the classification accuracy. We carry out experimental studies in a series of real-world networks, and the experimental results demonstrate both the effectiveness and efficiency of the proposed methods for edge classification in large networks.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"5 1","pages":"1038-1049"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85067625","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: 20

Computing Connected Components with linear communication cost in pregel-like systems 类预凝胶系统中具有线性通信代价的连通组件计算

2016 IEEE 32nd International Conference on Data Engineering (ICDE) Pub Date : 2016-05-16 DOI: 10.1109/ICDE.2016.7498231

Xing Feng, Lijun Chang, Xuemin Lin, Lu Qin, W. Zhang

{"title":"Computing Connected Components with linear communication cost in pregel-like systems","authors":"Xing Feng, Lijun Chang, Xuemin Lin, Lu Qin, W. Zhang","doi":"10.1109/ICDE.2016.7498231","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498231","url":null,"abstract":"The paper studies two fundamental problems in graph analytics: computing Connected Components (CCs) and computing BiConnected Components (BCCs) of a graph. With the recent advent of Big Data, developing effcient distributed algorithms for computing CCs and BCCs of a big graph has received increasing interests. As with the existing research efforts, in this paper we focus on the Pregel programming model, while the techniques may be extended to other programming models including MapReduce and Spark. The state-of-the-art techniques for computing CCs and BCCs in Pregel incur O(m × #supersteps) total costs for both data communication and computation, where m is the number of edges in a graph and #supersteps is the number of supersteps. Since the network communication speed is usually much slower than the computation speed, communication costs are the dominant costs of the total running time in the existing techniques. In this paper, we propose a new paradigm based on graph decomposition to reduce the total communication costs from O(m×#supersteps) to O(m), for both computing CCs and computing BCCs. Moreover, the total computation costs of our techniques are smaller than that of the existing techniques in practice, though theoretically they are almost the same. Comprehensive empirical studies demonstrate that our approaches can outperform the existing techniques by one order of magnitude regarding the total running time.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"63 1","pages":"85-96"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84335508","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: 10

Answering why-not questions on metric probabilistic range queries 回答关于度量概率范围查询的why-not问题

2016 IEEE 32nd International Conference on Data Engineering (ICDE) Pub Date : 2016-05-16 DOI: 10.1109/ICDE.2016.7498288

Lu Chen, Yunjun Gao, Kai Wang, Christian S. Jensen, Gang Chen

引用次数: 17

Accelerating database workloads by software-hardware-system co-design 通过软件-硬件系统协同设计加速数据库工作负载

2016 IEEE 32nd International Conference on Data Engineering (ICDE) Pub Date : 2016-05-16 DOI: 10.1109/ICDE.2016.7498362

R. Bordawekar, Mohammad Sadoghi

引用次数: 4

Context-aware advertisement recommendation for high-speed social news feeding 基于上下文感知的广告推荐，支持高速社交新闻推送

2016 IEEE 32nd International Conference on Data Engineering (ICDE) Pub Date : 2016-05-16 DOI: 10.1109/ICDE.2016.7498266

Yuchen Li, Dongxiang Zhang, Ziquan Lan, K. Tan

{"title":"Context-aware advertisement recommendation for high-speed social news feeding","authors":"Yuchen Li, Dongxiang Zhang, Ziquan Lan, K. Tan","doi":"10.1109/ICDE.2016.7498266","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498266","url":null,"abstract":"Social media advertising is a multi-billion dollar market and has become the major revenue source for Facebook and Twitter. To deliver ads to potentially interested users, these social network platforms learn a prediction model for each user based on their personal interests. However, as user interests often evolve slowly, the user may end up receiving repetitive ads. In this paper, we propose a context-aware advertising framework that takes into account the relatively static personal interests as well as the dynamic news feed from friends to drive growth in the ad click-through rate. To meet the real-time requirement, we first propose an online retrieval strategy that finds k most relevant ads matching the dynamic context when a read operation is triggered. To avoid frequent retrieval when the context varies little, we propose a safe region method to quickly determine whether the top-k ads of a user are changed. Finally, we propose a hybrid model to combine the merits of both methods by analyzing the dynamism of news feed to determine an appropriate retrieval strategy. Extensive experiments conducted on multiple real social networks and ad datasets verified the efficiency and robustness of our hybrid model.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"43 1","pages":"505-516"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86511555","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: 35

MuVE: Efficient Multi-Objective View Recommendation for Visual Data Exploration 面向可视化数据探索的高效多目标视图推荐

2016 IEEE 32nd International Conference on Data Engineering (ICDE) Pub Date : 2016-05-16 DOI: 10.1109/ICDE.2016.7498285

Humaira Ehsan, M. Sharaf, Panos K. Chrysanthis

{"title":"MuVE: Efficient Multi-Objective View Recommendation for Visual Data Exploration","authors":"Humaira Ehsan, M. Sharaf, Panos K. Chrysanthis","doi":"10.1109/ICDE.2016.7498285","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498285","url":null,"abstract":"To support effective data exploration, there is a well-recognized need for solutions that can automatically recommend interesting visualizations, which reveal useful insights into the analyzed data. However, such visualizations come at the expense of high data processing costs, where a large number of views are generated to evaluate their usefulness. Those costs are further escalated in the presence of numerical dimensional attributes, due to the potentially large number of possible binning aggregations, which lead to a drastic increase in the number of possible visualizations. To address that challenge, in this paper we propose the MuVE scheme for Multi-Objective View Recommendation for Visual Data Exploration. MuVE introduces a hybrid multi-objective utility function, which captures the impact of binning on the utility of visualizations. Consequently, novel algorithms are proposed for the efficient recommendation of data visualizations that are based on numerical dimensions. The main idea underlying MuVE is to incrementally and progressively assess the different benefits provided by a visualization, which allows an early pruning of a large number of unnecessary operations. Our extensive experimental results show the significant gains provided by our proposed scheme.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"41 1","pages":"731-742"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79845279","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: 55

A column store engine for real-time streaming analytics 用于实时流分析的列存储引擎

2016 IEEE 32nd International Conference on Data Engineering (ICDE) Pub Date : 2016-05-16 DOI: 10.1109/ICDE.2016.7498332

Alex Skidanov, Anders J. Papito, A. Prout

引用次数: 10

Hobbes3: Dynamic generation of variable-length signatures for efficient approximate subsequence mappings 动态生成有效的近似子序列映射的变长签名

2016 IEEE 32nd International Conference on Data Engineering (ICDE) Pub Date : 2016-05-16 DOI: 10.1109/ICDE.2016.7498238

Jongik Kim, Chen Li, Xiaohui Xie

{"title":"Hobbes3: Dynamic generation of variable-length signatures for efficient approximate subsequence mappings","authors":"Jongik Kim, Chen Li, Xiaohui Xie","doi":"10.1109/ICDE.2016.7498238","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498238","url":null,"abstract":"Recent advances in DNA sequencing have enabled a flood of sequencing-based applications for studying biology and medicine. A key requirement of these applications is to rapidly and accurately map DNA subsequences to a reference genome. This DNA subsequence mapping problem shares core technical challenges with the similarity query processing problem studied in the database research literature. To solve this problem, existing techniques first extract signatures from a query, then retrieve candidate mapping positions from an index using the extracted signatures, and finally verify the candidate positions. The efficiency of these techniques depends critically on signatures selected from queries, while signature selection relies on an indexing scheme of a reference genome. The q-gram inverted indexing, one of the most widely used indexing schemes, can discover candidate positions quickly, but has the limitation that signatures of queries are restricted to fixed-length q-grams. To address the problem, we propose a flexible way to generate variable-length signatures using a fixed-length q-gram index. The proposed technique groups a few q-grams into a variable-length signature, and generates candidate positions for the variable-length signature using the inverted lists of the q-grams. We also propose a novel dynamic programming algorithm to balance between the filtering power of signatures and the overhead of generating candidate positions for the signatures. Through extensive experiments on both simulated and real genomic data, we show that our technique substantially improves the performance of read mapping in terms of both mapping speed and accuracy.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"26 1","pages":"169-180"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81746037","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: 19