2018 IEEE 34th International Conference on Data Engineering (ICDE)最新文献_第3页

Cross-Platform Data Processing: Use Cases and Challenges 跨平台数据处理:用例和挑战

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00223

Zoi Kaoudi, Jorge-Arnulfo Quiané-Ruiz

{"title":"Cross-Platform Data Processing: Use Cases and Challenges","authors":"Zoi Kaoudi, Jorge-Arnulfo Quiané-Ruiz","doi":"10.1109/ICDE.2018.00223","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00223","url":null,"abstract":"There is a zoo of data processing platforms which help users and organizations to extract value out of their data. Although each of these platforms excels in specific aspects, users typically end up running their data analytics on suboptimal platforms. This is not only because choosing the right platform among the myriad of big data platforms is a daunting task, but also due to the fact that today's data analytics are moving beyond the limits of a single platform. Thus, there is an urgent need for cross-platform data processing, i.e., using more than one data processing platform to perform a data analytics task. Despite the need, achieving this is still a dreadful process where developers have to get intimate with many systems and write ad hoc scripts for integrating them. This tutorial is motivated by this need. We will discuss the importance of supporting cross-platform data processing in a systematic way as well as the current efforts to achieve that. In particular, we will introduce a classification of the different cases where an application needs or benefits from cross-platform data processing and the challenges of each case. Along with this classification, we will also present the efforts known up to date to support cross-platform data processing. We will conclude this tutorial with a discussion of several important open problems.","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":"114947652","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

Effective and Efficient User Account Linkage across Location Based Social Networks 跨基于位置的社交网络的有效和高效的用户帐户链接

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00101

Wei Chen, Hongzhi Yin, Weiqing Wang, Lei Zhao, Xiaofang Zhou

{"title":"Effective and Efficient User Account Linkage across Location Based Social Networks","authors":"Wei Chen, Hongzhi Yin, Weiqing Wang, Lei Zhao, Xiaofang Zhou","doi":"10.1109/ICDE.2018.00101","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00101","url":null,"abstract":"Sources of complementary information are connected when we link the user accounts belonging to the same user across different domains or devices. The expanded information promotes the development of a wide range of applications, such as cross-domain prediction, cross-domain recommendation, and advertisement. Due to the great significance of user account linkage, there are increasing research works on this study. With the widespread popularization of GPS-enabled mobile devices, linking user accounts with location data has become an important and promising research topic. Being different from most existing studies in this domain that only focus on the effectiveness, we propose novel approaches to improve both effectiveness and efficiency of user account linkage. In this paper, a kernel density estimation (KDE) based method has been proposed to improve the accuracy by alleviating the data sparsity problem in measuring users' similarities. To improve the efficiency, we develop a grid-based structure to organize location data to prune the search space. The extensive experiments conducted on two real-world datasets demonstrate the superiority of the proposed approach in terms of both effectiveness and efficiency compared with the state-of-art methods.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"103 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":"116976261","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: 32

RheemStudio: Cross-Platform Data Analytics Made Easy RheemStudio:跨平台数据分析变得简单

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00179

J. Lucas, Yasser Idris, Bertty Contreras-Rojas, Jorge-Arnulfo Quiané-Ruiz, S. Chawla

引用次数: 12

Continuous Proximity Detection via Predictive Safe Region Construction 基于预测安全区域构建的连续接近检测

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00063

Ying Xu, Dongxiang Zhang, Meihui Zhang, Dongsheng Li, Xiaoling Wang, Heng Tao Shen

引用次数: 7

OCTOPUS: An Online Topic-Aware Influence Analysis System for Social Networks OCTOPUS:一个面向社交网络的在线话题感知影响分析系统

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00178

Ju Fan, J. Qiu, Yuchen Li, Qingfei Meng, Dongxiang Zhang, Guoliang Li, K. Tan, Xiaoyong Du

引用次数: 23

Incentive-Based Entity Collection Using Crowdsourcing 使用众包的基于激励的实体收集

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00039

Chengliang Chai, Ju Fan, Guoliang Li

{"title":"Incentive-Based Entity Collection Using Crowdsourcing","authors":"Chengliang Chai, Ju Fan, Guoliang Li","doi":"10.1109/ICDE.2018.00039","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00039","url":null,"abstract":"Crowdsourced entity collection leverages human's ability to collect entities that are missing in a database, which has many real-world applications, such as knowledge base enrichment and enterprise data collection. There are several challenges. First, it is hard to evaluate the workers' quality because a worker's quality depends on not only the correctness of her provided entities but also the distinctness of these entities compared with the collected ones by other workers. Second, crowd workers are likely to provide popular entities and different workers will provide many duplicated entities, leading to a waste of money and low coverage. To address these challenges, we propose an incentive-based crowdsourced entity collection framework CrowdEC that encourages workers to provide more distinct items using an incentive strategy. CrowdEC has fundamental differences from existing crowdsourcing collection methods. One the one hand, CrowdEC proposes a worker model and evaluates a worker's quality based on cross validation and entity checking. CrowdEC devises a worker utility model that considers both worker's quality and entities' distinctness provided by workers. CrowdEC proposes a worker elimination method to block workers with a low utility, which solves the first challenge. On the other hand, CrowdEC proposes an incentive pricing technique that encourages each qualified (i.e., non-eliminated) worker to provide distinct entities rather than duplicates. CrowdEC provides two types of tasks and judiciously assigns workers with appropriate tasks to address the second challenge. We have conducted both real and simulated experiments, and the results show that CrowdEC outperforms existing state-of-the-art works on both cost and quality.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"88 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":"125078459","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: 22

Online E-Commerce Fraud: A Large-Scale Detection and Analysis 网络电子商务欺诈:大规模的检测与分析

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00162

Haiqin Weng, Zhao Li, S. Ji, Chen Chu, Haifeng Lu, Tianyu Du, Qinming He

{"title":"Online E-Commerce Fraud: A Large-Scale Detection and Analysis","authors":"Haiqin Weng, Zhao Li, S. Ji, Chen Chu, Haifeng Lu, Tianyu Du, Qinming He","doi":"10.1109/ICDE.2018.00162","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00162","url":null,"abstract":"Nowadays, e-commerce has become prevalent world-wide. With the big success of e-commerce, many malicious promotion services also rise: with the goal of increasing sales, malicious merchants attempt to promote their target items by illegally optimizing the search results using fake visits, purchases, etc. In this paper, we study the fraud detection problem on large-scale e-commerce platforms. First, we develop an efficient and scalable AnTi-Fraud system (ATF) to detect e-commerce frauds for large-scale e-commerce platforms, and implement it in parallel on a large-scale computing platform, called Open Data Processing Service (ODPS). Then, we evaluate ATF using two real large-scale e-commerce datasets (with tens of millions users and items). The results demonstrate that both the precision and the recall of ATF can achieve 0.97+, which suggests that ATF is very effective. More importantly, we deploy ATF on the Taobao platform of Alibaba, which is one of the world's largest e-commerce platforms. The evaluation results show that ATF can also achieve an accuracy of 98.16% on Taobao, which again suggests that ATF is very effective and deployable in practice. Our study in this paper is expected to shed light on defending against online frauds for practical e-commerce platforms.","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":"130000298","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: 27

Scalable Hypergraph-Based Image Retrieval and Tagging System 基于可扩展超图的图像检索和标记系统

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00032

Lu Chen, Yunjun Gao, Yuanliang Zhang, Sibo Wang, Baihua Zheng

引用次数: 12

Efficient Computation of a Near-Maximum Independent Set over Evolving Graphs 演化图上近极大独立集的高效计算

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00083

Weiguo Zheng, Qichen Wang, J. Yu, Hong Cheng, Lei Zou

引用次数: 8

Rethinking Concurrency Control for In-Memory OLAP DBMSs 重新思考内存中OLAP dbms的并发控制

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00164

P. Pedreira, Yinghai Lu, Sergey Pershin, A. Dutta, Chris Croswhite

{"title":"Rethinking Concurrency Control for In-Memory OLAP DBMSs","authors":"P. Pedreira, Yinghai Lu, Sergey Pershin, A. Dutta, Chris Croswhite","doi":"10.1109/ICDE.2018.00164","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00164","url":null,"abstract":"Although OLTP and OLAP database systems have fundamentally disparate architectures, most research work on concurrency control is geared towards transactional systems and simply adopted by OLAP DBMSs. In this paper we describe a new concurrency control protocol specifically designed for analytical DBMSs that can provide Snapshot Isolation for distributed in-memory OLAP database systems, called Append-Only Snapshot Isolation (AOSI). Unlike previous work, which are either based on multiversion concurrency control (MVCC) or Two Phase Locking (2PL), AOSI is completely lock-free and always maintains a single version of each data item. In addition, it removes the need for per-record timestamps of traditional MVCC implementations and thus considerably reduces the memory overhead incurred by concurrency control. In order to support these characteristics, the protocol sacrifices flexibility and removes support for a few operations, particularly record updates and single record deletions; however, we argue that even though these operations are essential in a pure transactional system, they are not strictly required in most analytic pipelines and OLAP systems. We also present an experimental evaluation of AOSI's current implementation within the Cubrick in-memory OLAP DBMS at Facebook, and show that lock-free single-version Snapshot Isolation can be achieved with low memory overhead and minor impact in query latency.","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":"123742960","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