2018 IEEE 34th International Conference on Data Engineering (ICDE)最新文献_第7页

Machine Learning to Data Management: A Round Trip 机器学习到数据管理:往返

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00226

Laure Berti-Équille, Angela Bonifati, Tova Milo

{"title":"Machine Learning to Data Management: A Round Trip","authors":"Laure Berti-Équille, Angela Bonifati, Tova Milo","doi":"10.1109/ICDE.2018.00226","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00226","url":null,"abstract":"With the emergence of machine learning (ML) techniques in database research, ML has already proved a tremendous potential to dramatically impact the foundations, algorithms, and models of several data management tasks, such as error detection, data cleaning, data integration, and query inference. Part of the data preparation, standardization, and cleaning processes, such as data matching and deduplication for instance, could be automated by making a ML model \"learn\" and predict the matches routinely. Data integration can also benefit from ML as the data to be integrated can be sampled and used to design the data integration algorithms. After the initial manual work to setup the labels, ML models can start learning from the new incoming data that are being submitted for standardization, integration, and cleaning. The more data supplied to the model, the better the ML algorithm can perform and deliver accurate results. Therefore, ML is more scalable compared to traditional and time-consuming approaches. Nevertheless, many ML algorithms require an out-of-the-box tuning and their parameters and scope are often not adapted to the problem at hand. To make an example, in cleaning and integration processes, the window sizes of values used for the ML models cannot be arbitrarily chosen and require an adaptation of the learning parameters. This tutorial will survey the recent trend of applying machine learning solutions to improve data management tasks and establish new paradigms to sharpen data error detection, cleaning, and integration at the data instance level, as well as at schema, system, and user levels.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"11 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":"125352362","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

Adaptive Lightweight Regularization Tool for Complex Analytics 复杂分析的自适应轻量级正则化工具

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00051

Zhaojing Luo, Shaofeng Cai, Jinyang Gao, Meihui Zhang, K. Ngiam, Gang Chen, Wang-Chien Lee

{"title":"Adaptive Lightweight Regularization Tool for Complex Analytics","authors":"Zhaojing Luo, Shaofeng Cai, Jinyang Gao, Meihui Zhang, K. Ngiam, Gang Chen, Wang-Chien Lee","doi":"10.1109/ICDE.2018.00051","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00051","url":null,"abstract":"Deep Learning and Machine Learning models have recently been shown to be effective in many real world applications. While these models achieve increasingly better predictive performance, their structures have also become much more complex. A common and difficult problem for complex models is overfitting. Regularization is used to penalize the complexity of the model in order to avoid overfitting. However, in most learning frameworks, regularization function is usually set as some hyper parameters, and therefore the best setting is difficult to find. In this paper, we propose an adaptive regularization method, as part of a large end-to-end healthcare data analytics software stack, which effectively addresses the above difficulty. First, we propose a general adaptive regularization method based on Gaussian Mixture (GM) to learn the best regularization function according to the observed parameters. Second, we develop an effective update algorithm which integrates Expectation Maximization (EM) with Stochastic Gradient Descent (SGD). Third, we design a lazy update algorithm to reduce the computational cost by 4x. The overall regularization framework is fast, adaptive and easy-to-use. We validate the effectiveness of our regularization method through an extensive experimental study over 13 standard benchmark datasets and three kinds of deep learning/machine learning models. The results illustrate that our proposed adaptive regularization method achieves significant improvement over state-of-the-art regularization methods.","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":"125800979","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

A Sliding-Window Framework for Representative Subset Selection 代表性子集选择的滑动窗口框架

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00127

Yanhao Wang, Yuchen Li, K. Tan

引用次数: 5

Workload Management in Database Management System: A Taxonomy (Extended Abstract) 数据库管理系统中的工作量管理:一个分类(扩展摘要)

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00269

Mingyi Zhang, Patrick Martin, W. Powley, Jianjun Chen

{"title":"Workload Management in Database Management System: A Taxonomy (Extended Abstract)","authors":"Mingyi Zhang, Patrick Martin, W. Powley, Jianjun Chen","doi":"10.1109/ICDE.2018.00269","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00269","url":null,"abstract":"Workload management is the discipline of effectively monitoring, managing and controlling work flow across computing systems. In particular, workload management in database management systems (DBMSs) is the process or act of monitoring and controlling work (i.e., requests) executing on a database system in order to make efficient use of system resources in addition to achieving any performance objectives assigned to that work. In the past decade, workload management studies and practice have made considerable progress in both academia and industry. New techniques have been proposed by researchers, and new features of workload management facilities have been implemented in most commercial database products. In this paper, we provide a systematic study of workload management in today's DBMSs by developing a taxonomy of workload management techniques. We apply the taxonomy to evaluate and classify existing workload management techniques implemented in the commercial databases and available in the recent research literature. We also introduce the underlying principles of today's workload management technology for DBMSs, discuss open problems and outline some research opportunities in this research area.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"20 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":"126476931","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: 1

On Configuring a Hierarchy of Storage Media in the Age of NVM 论NVM时代存储介质层次结构的配置

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00155

Shahram Ghandeharizadeh, S. Irani, Jenny Lam

{"title":"On Configuring a Hierarchy of Storage Media in the Age of NVM","authors":"Shahram Ghandeharizadeh, S. Irani, Jenny Lam","doi":"10.1109/ICDE.2018.00155","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00155","url":null,"abstract":"Advances in storage technology have introduced Non-Volatile Memory, NVM, as a new storage medium. NVM, along with DRAM and Disk present a system designer with a wide array of options in designing caching middleware. Moreover, design decisions to replicate a data item in more than one level of a caching memory hierarchy may enhance the overall system performance with a faster recovery time in the event of a memory failure. Given a fixed budget, the key configuration questions are: Which storage media should constitute the memory hierarchy? What is the storage capacity of each hierarchy? Should data be replicated or partitioned across the different levels of the hierarchy? We study a model of these cache configuration questions and present results from a simple algorithm to evaluate design tradeoffs in the context of a memory hierarchy for a Key-Value Store, e.g., memcached. The results show selective replication is appropriate with certain failure rates and workload characteristics. With a slim failure rate and frequent data updates, tiering of data across the different storage media that constitute the cache is superior to replication.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"76 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":"130543240","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

Detection and Delineation of Events and Sub-Events in Social Networks 社会网络中事件和子事件的检测与描述

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00147

Antonia Saravanou, I. Katakis, G. Valkanas, D. Gunopulos

引用次数: 3

Predictive Maintenance in Aviation: Failure Prediction from Post-Flight Reports 航空预测性维修:来自飞行后报告的故障预测

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00160

Panagiotis Korvesis, Stephane Besseau, M. Vazirgiannis

{"title":"Predictive Maintenance in Aviation: Failure Prediction from Post-Flight Reports","authors":"Panagiotis Korvesis, Stephane Besseau, M. Vazirgiannis","doi":"10.1109/ICDE.2018.00160","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00160","url":null,"abstract":"In this paper we present an approach to tackle the problem of event prediction for the purpose of performing predictive maintenance in aviation. Given a collection of recorded events that correspond to equipment failures, our method predicts the next occurrence of one or more events of interest (target events or critical failures). Our objective is to develop an alerting system that would notify aviation engineers well in advance for upcoming aircraft failures, providing enough time to prepare the corresponding maintenance actions. We formulate a regression problem in order to approximate the risk of occurrence of a target event, given the past occurrences of other events. In order to achieve the best results we employed a multiple instance learning scheme (multiple instance regression) along with extensive data preprocessing. We applied our method on data coming from a fleet of aircraft and our predictions involve failures of components onboard, specifically components that are related to the landing gear. The event logs correspond to post flight reports retrieved from multiple aircraft during several years of operation. To the best of our knowledge, this paper is the first attempt on aircraft failure prediction using post flight report data and finally, our findings show high potential impact on the aviation industry.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"13 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":"133837300","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: 31

Multi-Example Search in Rich Information Graphs 富信息图中的多示例搜索

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00078

Matteo Lissandrini, D. Mottin, Themis Palpanas, Yannis Velegrakis

引用次数: 18

A Scalable Circular Pipeline Design for Multi-Way Stream Joins in Hardware 硬件中多路流连接的可扩展圆形管道设计

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00130

M. Najafi, Mohammad Sadoghi, H. Jacobsen

引用次数: 3

Clarifying Trust in Social Internet of Things (Extended Abstract) 澄清社会物联网中的信任(扩展摘要)

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00270

Zhiting Lin, Liangfei Dong

引用次数: 11