2020 IEEE 36th International Conference on Data Engineering (ICDE)最新文献_第4页

On the Integration of Machine Learning and Array Databases 论机器学习与阵列数据库的集成

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00170

Sebastián Villarroya, P. Baumann

引用次数: 12

A Novel Approach to Learning Consensus and Complementary Information for Multi-View Data Clustering 一种新的多视图数据聚类共识和互补信息学习方法

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00080

Khanh Luong, R. Nayak

引用次数: 16

Message from the ICDE 2020 Chairs ICDE 2020主席致辞

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/icde48307.2020.00005

引用次数: 0

Two-Level Data Compression using Machine Learning in Time Series Database 时间序列数据库中使用机器学习的两级数据压缩

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00119

Xinyang Yu, Yanqing Peng, Feifei Li, Sheng Wang, Xiaowei Shen, Huijun Mai, Yue Xie

{"title":"Two-Level Data Compression using Machine Learning in Time Series Database","authors":"Xinyang Yu, Yanqing Peng, Feifei Li, Sheng Wang, Xiaowei Shen, Huijun Mai, Yue Xie","doi":"10.1109/ICDE48307.2020.00119","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00119","url":null,"abstract":"The explosion of time series advances the development of time series databases. To reduce storage overhead in these systems, data compression is widely adopted. Most existing compression algorithms utilize the overall characteristics of the entire time series to achieve high compression ratio, but ignore local contexts around individual points. In this way, they are effective for certain data patterns, and may suffer inherent pattern changes in real-world time series. It is therefore strongly desired to have a compression method that can always achieve high compression ratio in the existence of pattern diversity.In this paper, we propose a two-level compression model that selects a proper compression scheme for each individual point, so that diverse patterns can be captured at a fine granularity. Based on this model, we design and implement AMMMO framework, where a set of control parameters is defined to distill and categorize data patterns. At the top level, we evaluate each sub-sequence to fill in these parameters, generating a set of compression scheme candidates (i.e., major mode selection). At the bottom level, we choose the best scheme from these candidates for each data point respectively (i.e., sub-mode selection). To effectively handle diverse data patterns, we introduce a reinforcement learning based approach to learn parameter values automatically. Our experimental evaluation shows that our approach improves compression ratio by up to 120% (with an average of 50%), compared to other time-series compression methods.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"8 1","pages":"1333-1344"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83698489","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

SFour: A Protocol for Cryptographically Secure Record Linkage at Scale 一种大规模加密安全记录链接协议

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00031

Basit Khurram, F. Kerschbaum

引用次数: 7

Optimizing Knowledge Graphs through Voting-based User Feedback 通过基于投票的用户反馈优化知识图谱

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00043

Ruida Yang, Xin Lin, Jianliang Xu, Yan Yang, Liang He

{"title":"Optimizing Knowledge Graphs through Voting-based User Feedback","authors":"Ruida Yang, Xin Lin, Jianliang Xu, Yan Yang, Liang He","doi":"10.1109/ICDE48307.2020.00043","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00043","url":null,"abstract":"Knowledge graphs have been used in a wide range of applications to support search, recommendation, and question answering (Q&A). For example, in Q&A systems, given a new question, we may use a knowledge graph to automatically identify the most suitable answers based on similarity evaluation. However, such systems may suffer from two major limitations. First, the knowledge graph constructed based on source data may contain errors. Second, the knowledge graph may become out of date and cannot quickly adapt to new knowledge. To address these issues, in this paper, we propose an interactive framework that refines and optimizes knowledge graphs through user votes. We develop an efficient similarity evaluation notion, called extended inverse P-distance, based on which the graph optimization problem can be formulated as a signomial geometric programming problem. We then propose a basic single-vote solution and a more advanced multi-vote solution for graph optimization. We also propose a split-and-merge optimization strategy to scale up the multi-vote solution. Extensive experiments based on real-life and synthetic graphs demonstrate the effectiveness and efficiency of our proposed framework.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"1 1","pages":"421-432"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77809538","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: 0

Data Sentinel: A Declarative Production-Scale Data Validation Platform 数据哨兵:声明式生产规模数据验证平台

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00140

A. Swami, Sriram Vasudevan, Joojay Huyn

{"title":"Data Sentinel: A Declarative Production-Scale Data Validation Platform","authors":"A. Swami, Sriram Vasudevan, Joojay Huyn","doi":"10.1109/ICDE48307.2020.00140","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00140","url":null,"abstract":"Many organizations process big data for important business operations and decisions. Hence, data quality greatly affects their success. Data quality problems continue to be widespread, costing US businesses an estimated $600 billion annually. To date, addressing data quality in production environments still poses many challenges: easily defining properties of high-quality data; validating production-scale data in a timely manner; debugging poor quality data; designing data quality solutions to be easy to use, understand, and operate; and designing data quality solutions to easily integrate with other systems. Current data validation solutions do not comprehensively address these challenges. To address data quality in production environments at LinkedIn, we developed Data Sentinel, a declarative production-scale data validation platform. In a simple and well-structured configuration, users declaratively specify the desired data checks. Then, Data Sentinel performs these data checks and writes the results to an easily understandable report. Furthermore, Data Sentinel provides well-defined schemas for the configuration and report. This makes it easy for other systems to interface or integrate with Data Sentinel. To make Data Sentinel even easier to use, understand, and operate in production environments, we provide Data Sentinel Service (DSS), a complementary system to help specify data checks, schedule, deploy, and tune data validation jobs, and understand data checking results. The contributions of this paper include the following: 1) Data Sentinel, a declarative production-scale data validation platform successfully deployed at LinkedIn 2) A generic design to build and deploy similar systems for production environments 3) Experiences and lessons learned that can benefit practitioners with similar objectives.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"13 1","pages":"1579-1590"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83360330","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

CrashSim: An Efficient Algorithm for Computing SimRank over Static and Temporal Graphs CrashSim:一个在静态和时间图上计算simmrank的有效算法

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00103

Mo Li, F. Choudhury, Renata Borovica-Gajic, Zhiqiong Wang, Junchang Xin, Jianxin Li

{"title":"CrashSim: An Efficient Algorithm for Computing SimRank over Static and Temporal Graphs","authors":"Mo Li, F. Choudhury, Renata Borovica-Gajic, Zhiqiong Wang, Junchang Xin, Jianxin Li","doi":"10.1109/ICDE48307.2020.00103","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00103","url":null,"abstract":"SimRank is a significant metric to measure the similarity of nodes in graph data analysis. The problem of SimRank computation has been studied extensively, however there is no existing work that can provide one unified algorithm to support the SimRank computation both on static and temporal graphs. In this work, we first propose CrashSim, an index-free algorithm for single-source SimRank computation in static graphs. CrashSim can provide provable approximation guarantees for the computational results in an efficient way. In addition, as the reallife graphs are often represented as temporal graphs, CrashSim enables efficient computation of SimRank in temporal graphs. We formally define two typical SimRank queries in temporal graphs, and then solve them by developing an efficient algorithm based on CrashSim, called CrashSim-T. From the extensive experimental evaluation using five real-life and synthetic datasets, it can be seen that the CrashSim algorithm and CrashSim-T algorithm substantially improve the efficiency of the state-of-the-art SimRank algorithms by about 30%, while achieving the precision of the result set with about 97%.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"25 1","pages":"1141-1152"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89364839","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

SVkNN: Efficient Secure and Verifiable k-Nearest Neighbor Query on the Cloud Platform* 云平台上高效、安全、可验证的k近邻查询方法*

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00029

Ningning Cui, Xiaochun Yang, Bin Wang, Jianxin Li, Guoren Wang

{"title":"SVkNN: Efficient Secure and Verifiable k-Nearest Neighbor Query on the Cloud Platform*","authors":"Ningning Cui, Xiaochun Yang, Bin Wang, Jianxin Li, Guoren Wang","doi":"10.1109/ICDE48307.2020.00029","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00029","url":null,"abstract":"With the boom in cloud computing, data outsourcing in location-based services is proliferating and has attracted increasing interest from research communities and commercial applications. Nevertheless, since the cloud server is probably both untrusted and malicious, concerns of data security and result integrity have become on the rise sharply. However, there exist little work that can commendably assure the data security and result integrity using a unified way. In this paper, we study the problem of secure and verifiable k nearest neighbor query (SVkNN). To support SVkNN, we first propose a novel unified structure, called verifiable and secure index (VSI). Based on this, we devise a series of secure protocols to facilitate query processing and develop a compact verification strategy. Given an SVkNN query, our proposed solution can not merely answer the query efficiently while can guarantee: 1) preserving the privacy of data, query, result and access patterns; 2) authenticating the correctness and completeness of the results without leaking the confidentiality. Finally, the formal security analysis and complexity analysis are theoretically proven and the performance and feasibility of our proposed approaches are empirically evaluated and demonstrated.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"7 1","pages":"253-264"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86526204","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: 28

Practical Anonymous Subscription with Revocation Based on Broadcast Encryption 基于广播加密的实用可撤销匿名订阅

2020 IEEE 36th International Conference on Data Engineering (ICDE) Pub Date : 2020-04-01 DOI: 10.1109/ICDE48307.2020.00028

X. Yi, Russell Paulet, E. Bertino, Fang-Yu Rao

{"title":"Practical Anonymous Subscription with Revocation Based on Broadcast Encryption","authors":"X. Yi, Russell Paulet, E. Bertino, Fang-Yu Rao","doi":"10.1109/ICDE48307.2020.00028","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00028","url":null,"abstract":"In this paper we consider the problem where a client wishes to subscribe to some product or service provided by a server, but maintain their anonymity. At the same time, the server must be able to authenticate the client as a genuine user and be able to discontinue (or revoke) the client’s access if the subscription fees are not paid. Current solutions for this problem are typically constructed using some combination of blind signature or zero-knowledge proof techniques, which do not directly support client revocation (that is, revoking a user before expiry of their secret value). In this paper, we present a solution for this problem on the basis of the broadcast encryption scheme, suggested by Boneh et al., by which the server can broadcast a secret to a group of legitimate clients. Our solution allows the registered client to log into the server anonymously and also supports client revocation by the server. Our solution can be used in many applications, such as location-based queries. We formally define a model for our anonymous subscription protocol and prove the security of our solution under this model. In addition, we present experimental results from an implementation of our protocol. These experimental results demonstrate that our protocol is practical.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"3 1","pages":"241-252"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87917424","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: 2