2018 IEEE 34th International Conference on Data Engineering (ICDE)最新文献_第5页

Efficient Top-k Dominating Computation on Massive Data (Extended Abstract) 海量数据的高效Top-k支配计算(扩展摘要)

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00243

Xixian Han, Jianzhong Li, Hong Gao

引用次数: 0

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00066

F. Schuhknecht, J. Dittrich, Laurent Linden

{"title":"Adaptive Adaptive Indexing","authors":"F. Schuhknecht, J. Dittrich, Laurent Linden","doi":"10.1109/ICDE.2018.00066","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00066","url":null,"abstract":"In nature, many species became extinct as they could not adapt quickly enough to their environment. They were simply not fit enough to adapt to more and more challenging circumstances. Similar things happen when algorithms are too static to cope with particular challenges of their \"environment\", be it the workload, the machine, or the user requirements. In this regard, in this paper we explore the well-researched and fascinating family of adaptive indexing algorithms. Classical adaptive indexes solely adapt the indexedness of the data to the workload. However, we will learn that so far we have overlooked a second higher level of adaptivity, namely the one of the indexing algorithm itself. We will coin this second level of adaptivity meta-adaptivity. Based on a careful experimental analysis, we will develop an adaptive index, which realizes meta-adaptivity by (1) generalizing the way reorganization is performed, (2) reacting to the evolving indexedness and varying reorganization effort, and (3) defusing skewed distributions in the input data. As we will demonstrate, this allows us to emulate the characteristics of a large set of specialized adaptive indexing algorithms. In an extensive experimental study we will show that our meta-adaptive index is extremely fit in a variety of environments and outperforms a large amount of specialized adaptive indexes under various query access patterns and key distributions.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"1 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":"121169456","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

Query Independent Scholarly Article Ranking 查询独立学术文章排名

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00090

Shuai Ma, Chen Gong, Renjun Hu, Dongsheng Luo, Chunming Hu, J. Huai

{"title":"Query Independent Scholarly Article Ranking","authors":"Shuai Ma, Chen Gong, Renjun Hu, Dongsheng Luo, Chunming Hu, J. Huai","doi":"10.1109/ICDE.2018.00090","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00090","url":null,"abstract":"Ranking query independent scholarly articles is a practical and difficult task, due to the heterogeneous, evolving and dynamic nature of entities involved in scholarly articles. To do this, we first propose a scholarly article ranking model by assembling the importance of involved entities (i.e., articles, venues and authors) such that the importance is a combination of prestige and popularity to capture the evolving nature of entities. To compute the prestige of articles and venues, we propose a novel Time-Weighted PageRank that extends traditional PageRank with a time decaying factor. We then develop a batch algorithm for scholarly article ranking, in which we propose a block-wise method for Time-Weighted PageRank in terms of an analysis of the citation characteristics of scholarly articles. We further develop an incremental algorithm for dynamic scholarly article ranking, which partitions graphs into affected and unaffected areas, and employs different updating strategies for nodes in different areas. Using real-life data, we finally conduct an extensive experimental study, and show that our approach is both effective and efficient for ranking scholarly articles.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"44 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":"121452164","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: 7

Continuous Top-k Monitoring on Document Streams (Extended Abstract) 文档流的连续Top-k监控(扩展摘要)

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00259

Leong Hou U, Junjie Zhang, K. Mouratidis, Ye Li

引用次数: 0

MGTag: a Multi-Dimensional Graph Labeling Scheme for Fast Reachability Queries MGTag:用于快速可达性查询的多维图标记方案

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00153

Shuang Zhou, Pingpeng Yuan, Ling Liu, Hai Jin

{"title":"MGTag: a Multi-Dimensional Graph Labeling Scheme for Fast Reachability Queries","authors":"Shuang Zhou, Pingpeng Yuan, Ling Liu, Hai Jin","doi":"10.1109/ICDE.2018.00153","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00153","url":null,"abstract":"Reachability query asks whether a vertex can reach another vertex on large directed graphs. It is one of the most fundamental graph operators and has attracted many researchers to study it. Although there are many approaches solving this problem, it still remains a challenging problem when it comes to leverage the three main costs: the index construction time, the index size, and the query time on large and dense graphs. In this paper, we propose a High Dimension Graph Labeling approach to answer reachability queries. First, we recursively partition a graph into disjoint non-shared graphs, among which there are no common vertices, and cross edges. Second, we build a four dimensional label - one dimension of layer, one dimension of sub-graph and two dimensions of interval for each vertex. With the layer label and the sub-graph label, we can determine the positions (non-shared graphs) of any two vertices. Two dimensional interval label is used to assist to answer the reachability queries for vertex pair in those non-shared graphs. Finally, we design algorithms to answer researchability queries efficiently. In order to speed up query answering, we also build two directional labels: up and down labels to filter vertices quickly. The extensive experiments on 28 large/small and dense/sparse graphs show that building the high dimensional index is quickly and the index size is also competitive compared with most of the state of the art approaches. The results also show that our approach is more scalable and efficient than the state-of-the-art approaches in answering reachability queries.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"114 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":"123985209","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: 5

PrivTrie: Effective Frequent Term Discovery under Local Differential Privacy 局部差分隐私下的有效频繁项发现

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00079

Ning Wang, Xiaokui Xiao, Y. Yang, T. Hoang, Hyejin Shin, Junbum Shin, Ge Yu

{"title":"PrivTrie: Effective Frequent Term Discovery under Local Differential Privacy","authors":"Ning Wang, Xiaokui Xiao, Y. Yang, T. Hoang, Hyejin Shin, Junbum Shin, Ge Yu","doi":"10.1109/ICDE.2018.00079","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00079","url":null,"abstract":"A mobile operating system often needs to collect frequent new terms from users in order to build and maintain a comprehensive dictionary. Collecting keyboard usage data, however, raises privacy concerns. Local differential privacy (LDP) has been established as a strong privacy standard for collecting sensitive information from users. Currently, the best known solution for LDP-compliant frequent term discovery transforms the problem into collecting n-grams under LDP, and subsequently reconstructs terms from the collected n-grams by modelling the latter into a graph, and identifying cliques on this graph. Because the transformed problem (i.e., collecting n-grams) is very different from the original one (discovering frequent terms), the end result has poor utility. Further, this method is also rather expensive due to clique computation on a large graph. In this paper we tackle the problem head on: our proposal, PrivTrie, directly collects frequent terms from users by iteratively constructing a trie under LDP. While the methodology of building a trie is an obvious choice, obtaining an accurate trie under LDP is highly challenging. PrivTrie achieves this with a novel adaptive approach that conserves privacy budget by building internal nodes of the trie with the lowest level of accuracy necessary. Experiments using real datasets confirm that PrivTrie achieves high accuracy on common privacy levels, and consistently outperforms all previous methods.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"2020 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":"121978440","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: 65

Anna: A KVS for Any Scale 安娜:任何比例的KVS

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00044

Chenggang Wu, Jose M. Faleiro, Yihan Lin, J. Hellerstein

引用次数: 76

Time-Aware Boolean Spatial Keyword Queries (Extended Abstract) 时间感知布尔空间关键字查询(扩展抽象)

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00248

Gang Chen, Jingwen Zhao, Yunjun Gao, Lei Chen, R. Chen

引用次数: 1

Efficient Learning Interpretable Shapelets for Accurate Time Series Classification 有效的学习可解释的Shapelets精确的时间序列分类

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00052

Zicheng Fang, Peng Wang, Wen Wang

{"title":"Efficient Learning Interpretable Shapelets for Accurate Time Series Classification","authors":"Zicheng Fang, Peng Wang, Wen Wang","doi":"10.1109/ICDE.2018.00052","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00052","url":null,"abstract":"Recently, time series classification with shapelets, due to their high discriminative ability and good interpretability, has attracted considerable interests within the research community. Previously, shapelet generating approaches extracted shapelets from training time series or learned shapelets with many parameters. Although they can achieve higher accuracy than other approaches, they still confront some challenges. First, searching or learning shapelets in the raw time series space incurs a huge computation cost. For example, it may cost several hours to deal with only hundreds of time series. Second, they must determine how many shapelets are needed beforehand, which is difficult without prior knowledge. To overcome these challenges, in this paper, we propose a novel algorithm to learn shapelets. We first discover shapelet candidates from the Piecewise Aggregate Approximation (PAA) word space, which is much more efficient than searching in the raw time series space. Moreover, the concept of coverage is proposed to measure the quality of candidates, based on which we design a method to compute the optimal number of shapelets. After that, we apply the logistic regression classifier to adjust the shapelets. Extensive experimentation on 15 datasets demonstrates that our algorithm is more accurate against 6 baselines and outperforms 2 orders of magnitude in terms of efficiency. Moreover, our algorithm has fewer redundant shape-like shapelets and is more convenient to interpret classification decisions.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"166 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":"130085195","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: 45

Learning Association Relationship and Accurate Geometric Structures for Multi-Type Relational Data 多类型关系数据的关联关系学习与精确几何结构

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00053

Khanh Luong, R. Nayak

引用次数: 7