Proceedings of the 2018 International Conference on Management of Data最新文献_第2页

Session details: Research 4: Query Processing 会议详情:研究4:查询处理

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3258008

H. Pirk

引用次数: 0

Online Processing Algorithms for Influence Maximization 影响最大化的在线处理算法

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3183713.3183749

Jing Tang, Xueyan Tang, Xiaokui Xiao, Junsong Yuan

{"title":"Online Processing Algorithms for Influence Maximization","authors":"Jing Tang, Xueyan Tang, Xiaokui Xiao, Junsong Yuan","doi":"10.1145/3183713.3183749","DOIUrl":"https://doi.org/10.1145/3183713.3183749","url":null,"abstract":"Influence maximization is a classic and extensively studied problem with important applications in viral marketing. Existing algorithms for influence maximization, however, mostly focus on offline processing, in the sense that they do not provide any output to the user until the final answer is derived, and that the user is not allowed to terminate the algorithm early to trade the quality of solution for efficiency. Such lack of interactiveness and flexibility leads to poor user experience, especially when the algorithm incurs long running time. To address the above problem, this paper studies algorithms for online processing of influence maximization (OPIM), where the user can pause the algorithm at any time and ask for a solution (to the influence maximization problem) and its approximation guarantee, and can resume the algorithm to let it improve the quality of solution by giving it more time to run. (This interactive paradigm is similar in spirit to online query processing in database systems.) We show that the only existing algorithm for OPIM is vastly ineffective in practice, and that adopting existing influence maximization methods for OPIM yields unsatisfactory results. Motivated by this, we propose a new algorithm for OPIM with both superior empirical effectiveness and strong theoretical guarantees, and we show that it can also be extended to handle conventional influence maximization. Extensive experiments on real data demonstrate that our solutions outperform the state of the art for both OPIM and conventional influence maximization.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86170758","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: 122

Adaptive Optimization of Very Large Join Queries 超大型连接查询的自适应优化

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3183713.3183733

Thomas Neumann, Bernhard Radke

{"title":"Adaptive Optimization of Very Large Join Queries","authors":"Thomas Neumann, Bernhard Radke","doi":"10.1145/3183713.3183733","DOIUrl":"https://doi.org/10.1145/3183713.3183733","url":null,"abstract":"The use of business intelligence tools and other means to generate queries has led to great variety in the size of join queries. While most queries are reasonably small, join queries with up to a hundred relations are not that exotic anymore, and the distribution of query sizes has an incredible long tail. The largest real-world query that we are aware of accesses more than 4,000 relations. This large spread makes query optimization very challenging. Join ordering is known to be NP-hard, which means that we cannot hope to solve such large problems exactly. On the other hand most queries are much smaller, and there is no reason to sacrifice optimality there. This paper introduces an adaptive optimization framework that is able to solve most common join queries exactly, while simultaneously scaling to queries with thousands of joins. A key component there is a novel search space linearization technique that leads to near-optimal execution plans for large classes of queries. In addition, we describe implementation techniques that are necessary to scale join ordering algorithms to these extremely large queries. Extensive experiments with over 10 different approaches show that the new adaptive approach proposed here performs excellent over a huge spectrum of query sizes, and produces optimal or near-optimal solutions for most common queries.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88569765","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: 52

Approximate Triangle Count and Clustering Coefficient 近似三角形计数和聚类系数

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3183713.3183715

Siddharth Bhatia

引用次数: 2

Big Data Linkage for Product Specification Pages 产品规格页面的大数据联动

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3183713.3183757

Disheng Qiu, Luciano Barbosa, Valter Crescenzi, P. Merialdo, D. Srivastava

{"title":"Big Data Linkage for Product Specification Pages","authors":"Disheng Qiu, Luciano Barbosa, Valter Crescenzi, P. Merialdo, D. Srivastava","doi":"10.1145/3183713.3183757","DOIUrl":"https://doi.org/10.1145/3183713.3183757","url":null,"abstract":"An increasing number of product pages are available from thousands of web sources, each page associated with a product, containing its attributes and one or more product identifiers. The sources provide overlapping information about the products, using diverse schemas, making web-scale integration extremely challenging. In this paper, we take advantage of the opportunity that sources publish product identifiers to perform big data linkage across sources at the beginning of the data integration pipeline, before schema alignment. To realize this opportunity, several challenges need to be addressed: identifiers need to be discovered on product pages, made difficult by the diversity of identifiers; the main product identifier on the page needs to be identified, made difficult by the many related products presented on the page; and identifiers across pages need to beresolved, made difficult by the ambiguity between identifiers across product categories. We present our RaF (Redundancy as Friend) solution to the problem of big data linkage for product specification pages, which takes advantage of the redundancy of identifiers at a global level, and the homogeneity of structure and semantics at the local source level, to effectively and efficiently link millions of pages of head and tail products across thousands of head and tail sources. We perform a thorough empirical evaluation of our RaF approach using the publicly available Dexter dataset consisting of 1.9M product pages from 7.1k sources of 3.5k websites, and demonstrate its effectiveness in practice.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86953641","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

Transform-Data-by-Example (TDE): Extensible Data Transformation in Excel 数据按例转换(TDE): Excel中的可扩展数据转换

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3183713.3193539

Yeye He, Kris Ganjam, Kukjin Lee, Yue Wang, Vivek R. Narasayya, S. Chaudhuri, Xu Chu, Yudian Zheng

引用次数: 15

Data Integration and Machine Learning: A Natural Synergy 数据集成和机器学习:自然的协同作用

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.14778/3229863.3229876

X. Dong, Theodoros Rekatsinas

引用次数: 95

DPaxos: Managing Data Closer to Users for Low-Latency and Mobile Applications DPaxos:管理数据更接近用户的低延迟和移动应用程序

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3183713.3196928

Faisal Nawab, D. Agrawal, A. E. Abbadi

引用次数: 57

Special Session: A Technical Research Agenda in Data Ethics and Responsible Data Management 特别会议:数据伦理和负责任数据管理的技术研究议程

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3183713.3205185

Julia Stoyanovich, Bill Howe, H. Jagadish

{"title":"Special Session: A Technical Research Agenda in Data Ethics and Responsible Data Management","authors":"Julia Stoyanovich, Bill Howe, H. Jagadish","doi":"10.1145/3183713.3205185","DOIUrl":"https://doi.org/10.1145/3183713.3205185","url":null,"abstract":"SESSION DESCRIPTION Recently, there has begun a movement towards fairness, accountability, and transparency (FAT) in algorithmic decision making, and in data science more broadly [1–4]. The database community has not been significantly involved in this movement, despite “owning” the models, languages, and systems that produce the input to the machine learning applications that are often the focus in data science. If training data are biased, or have errors, it stands to reason that the algorithmic result will also be unfair or erroneous. Similarly, transparency of just the algorithm is usually insufficient to understand why certain results were obtained: one needs also to know the data used. In short, FAT depend not just on the algorithm, but also on the data. This observation raises several important questions: What are the core data management issues to which the objectives of fairness, accountability and transparency give rise? What role should the database community play in this movement? Will emphasis on these topics dilute our core competency in techniques and technologies for data, or can it reinforce our central role in technology stacks ranging from startups to the enterprise, and from local non-profits to the federal government? This special session features leading researchers from machine learning, software engineering, security and privacy, and natural language processing, who are doing exciting technical work in FAT. The goal of this session is to outline a technical research agenda in data management foundations and systems around data ethics.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78227358","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: 4

Session details: Industry 1: Adaptive Query Processing 行业1:自适应查询处理

Proceedings of the 2018 International Conference on Management of Data Pub Date : 2018-05-27 DOI: 10.1145/3258006

J. Dittrich

引用次数: 0