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

Joint repairs for web wrappers 织网机的接缝修补

2016 IEEE 32nd International Conference on Data Engineering (ICDE) Pub Date : 2016-05-16 DOI: 10.1109/ICDE.2016.7498320

Stefano Ortona, G. Orsi, Tim Furche, Marcello Buoncristiano

{"title":"Joint repairs for web wrappers","authors":"Stefano Ortona, G. Orsi, Tim Furche, Marcello Buoncristiano","doi":"10.1109/ICDE.2016.7498320","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498320","url":null,"abstract":"Automated web scraping is a popular means for acquiring data from the web. Scrapers (or wrappers) are derived from either manually or automatically annotated examples, often resulting in under/over segmented data, together with missing or spurious content. Automatic repair and maintenance of the extracted data is thus a necessary complement to automatic wrapper generation. Moreover, the extracted data is often the result of a long-term data acquisition effort and thus jointly repairing wrappers together with the generated data reduces future needs for data cleaning. We study the problem of computing joint repairs for XPath-based wrappers and their extracted data. We show that the problem is NP-complete in general but becomes tractable under a few natural assumptions. Even tractable solutions to the problem are still impractical on very large datasets, but we propose an optimal approximation that proves effective across a wide variety of domains and sources. Our approach relies on encoded domain knowledge, but require no per-source supervision. An evaluation spanning more than 100k web pages from 100 different sites of a wide variety of application domains, shows that joint repairs are able to increase the quality of wrappers between 15% and 60% independently of the wrapper generation system, eliminating all errors in more than 50% of the cases.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"191 1","pages":"1146-1157"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74461790","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

pSCAN: Fast and exact structural graph clustering pSCAN:快速和精确的结构图聚类

2016 IEEE 32nd International Conference on Data Engineering (ICDE) Pub Date : 2016-05-16 DOI: 10.1109/ICDE.2016.7498245

Lijun Chang, Wei Li, Xuemin Lin, Lu Qin, W. Zhang

{"title":"pSCAN: Fast and exact structural graph clustering","authors":"Lijun Chang, Wei Li, Xuemin Lin, Lu Qin, W. Zhang","doi":"10.1109/ICDE.2016.7498245","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498245","url":null,"abstract":"In this paper, we study the problem of structural graph clustering, a fundamental problem in managing and analyzing graph data. Given a large graph G = (V, E), structural graph clustering is to assign vertices in V to clusters and to identify the sets of hub vertices and outlier vertices as well, such that vertices in the same cluster are densely connected to each other while vertices in different clusters are loosely connected to each other. Firstly, we prove that the existing SCAN approach is worst-case optimal. Nevertheless, it is still not scalable to large graphs due to exhaustively computing structural similarity for every pair of adjacent vertices. Secondly, we make three observations about structural graph clustering, which present opportunities for further optimization. Based on these observations, in this paper we develop a new two-step paradigm for scalable structural graph clustering. Thirdly, following this paradigm, we present a new approach aiming to reduce the number of structural similarity computations. Moreover, we propose optimization techniques to speed up checking whether two vertices are structure-similar to each other. Finally, we conduct extensive performance studies on large real and synthetic graphs, which demonstrate that our new approach outperforms the state-of-the-art approaches by over one order of magnitude. Noticeably, for the twitter graph with 1 billion edges, our approach takes 25 minutes while the state-of-the-art approach cannot finish even after 24 hours.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"12 1","pages":"253-264"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77053800","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: 72

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

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

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

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

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

An interval join optimized for modern hardware 为现代硬件优化的间隔连接

2016 IEEE 32nd International Conference on Data Engineering (ICDE) Pub Date : 2016-05-16 DOI: 10.1109/ICDE.2016.7498316

Danila Piatov, S. Helmer, Anton Dignös

引用次数: 51