Proceedings of the 25th International Database Engineering & Applications Symposium最新文献_第3页

A Zone-Based Data Lake Architecture for IoT, Small and Big Data 基于区域的物联网、小数据和大数据数据湖架构

Proceedings of the 25th International Database Engineering & Applications Symposium Pub Date : 2021-07-14 DOI: 10.1145/3472163.3472185

Yan Zhao, I. Megdiche, F. Ravat, Vincent-nam Dang

{"title":"A Zone-Based Data Lake Architecture for IoT, Small and Big Data","authors":"Yan Zhao, I. Megdiche, F. Ravat, Vincent-nam Dang","doi":"10.1145/3472163.3472185","DOIUrl":"https://doi.org/10.1145/3472163.3472185","url":null,"abstract":"Data lakes are supposed to enable analysts to perform more efficient and efficacious data analysis by crossing multiple existing data sources, processes and analyses. However, it is impossible to achieve that when a data lake does not have a metadata governance system that progressively capitalizes on all the performed analysis experiments. The objective of this paper is to have an easily accessible, reusable data lake that capitalizes on all user experiences. To meet this need, we propose an analysis-oriented metadata model for data lakes. This model includes the descriptive information of datasets and their attributes, as well as all metadata related to the machine learning analyzes performed on these datasets. To illustrate our metadata solution, we implemented a web application of data lake metadata management. This application allows users to find and use existing data, processes and analyses by searching relevant metadata stored in a NoSQL data store within the data lake. To demonstrate how to easily discover metadata with the application, we present two use cases, with real data, including datasets similarity detection and machine learning guidance.","PeriodicalId":242683,"journal":{"name":"Proceedings of the 25th International Database Engineering & Applications Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130288822","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

Exploratory analysis of methods for automated classification of clinical diagnoses in Veterinary Medicine 兽医临床诊断自动分类方法的探索性分析

Proceedings of the 25th International Database Engineering & Applications Symposium Pub Date : 2021-07-14 DOI: 10.1145/3472163.3472165

Oscar Tamburis, E. Masciari, G. Fatone

引用次数: 0

Bringing Common Subexpression Problem from the Dark to Light: Towards Large-Scale Workload Optimizations 将公共子表达式问题从黑暗带到光明:走向大规模工作负载优化

Proceedings of the 25th International Database Engineering & Applications Symposium Pub Date : 2021-07-14 DOI: 10.1145/3472163.3472180

Mohamed Kechar, Ladjel Bellatreche, S. N. Bahloul

{"title":"Bringing Common Subexpression Problem from the Dark to Light: Towards Large-Scale Workload Optimizations","authors":"Mohamed Kechar, Ladjel Bellatreche, S. N. Bahloul","doi":"10.1145/3472163.3472180","DOIUrl":"https://doi.org/10.1145/3472163.3472180","url":null,"abstract":"Nowadays large-scale data-centric systems have become an essential element for companies to store, manipulate and derive value from large volumes of data. Capturing this value depends on the ability of these systems in managing large-scale workloads including complex analytical queries. One of the main characteristics of these queries is that they share computations in terms of selections and joins. Materialized views (MV) have shown their force in speeding up queries by exploiting these redundant computations. MV selection problem (VSP) is one of the most studied problems in the database field. A large majority of the existing solutions follow workload-driven approaches since they facilitate the identification of shared computations. Interesting algorithms have been proposed and implemented in commercial DBMSs. But they fail in managing large-scale workloads. In this paper, we presented a comprehensive framework to select the most beneficial materialized views based on the detection of the common subexpressions shared between queries. This framework gives the right place of the problem of selection of common subexpressions representing the causes of the redundancy. The utility of final MV depends strongly on the selected subexpressions. Once selected, a heuristic is given to select the most beneficial materialized views by considering different query ordering. Finally, experiments have been conducted to evaluate the effectiveness and efficiency of our proposal by considering large workloads.","PeriodicalId":242683,"journal":{"name":"Proceedings of the 25th International Database Engineering & Applications Symposium","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125140542","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

Optimizing Transaction Schedules on Universal Quantum Computers via Code Generation for Grover’s Search Algorithm 基于Grover搜索算法的代码生成优化通用量子计算机上的事务调度

Proceedings of the 25th International Database Engineering & Applications Symposium Pub Date : 2021-07-14 DOI: 10.1145/3472163.3472164

Sven Groppe, Jinghua Groppe

引用次数: 7

A Framework for Enhancing Deep Learning Based Recommender Systems with Knowledge Graphs 基于知识图增强深度学习推荐系统的框架

Proceedings of the 25th International Database Engineering & Applications Symposium Pub Date : 2021-07-14 DOI: 10.1145/3472163.3472183

S. Mudur, Serguei A. Mokhov, Yuhao Mao

{"title":"A Framework for Enhancing Deep Learning Based Recommender Systems with Knowledge Graphs","authors":"S. Mudur, Serguei A. Mokhov, Yuhao Mao","doi":"10.1145/3472163.3472183","DOIUrl":"https://doi.org/10.1145/3472163.3472183","url":null,"abstract":"Recommendation methods fall into three major categories, content based filtering, collaborative filtering and deep learning based. Information about products and the preferences of earlier users are used in an unsupervised manner to create models which help make personalized recommendations to a specific new user. The more information we provide to these methods, the more likely it is that they yield better recommendations. Deep learning based methods are relatively recent, and are generally more robust to noise and missing information. This is because deep learning models can be trained even when some of the information records have partial information. Knowledge graphs represent the current trend in recording information in the form of relations between entities, and can provide any available information about products and users. This information is used to train the recommendation model. In this work, we present a new generic recommender systems framework, that integrates knowledge graphs into the recommendation pipeline. We describe its design and implementation, and then show through experiments, how such a framework can be specialized, taking the domain of movies as an example, and the resulting improvements in recommendations made possible by using all the information obtained using knowledge graphs. Our framework, to be made publicly available, supports different knowledge graph representation formats, and facilitates format conversion, merging and information extraction needed for training recommendation models.","PeriodicalId":242683,"journal":{"name":"Proceedings of the 25th International Database Engineering & Applications Symposium","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124152186","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

Load Balanced Semantic Aware Distributed RDF Graph 负载平衡语义感知分布式RDF图

Proceedings of the 25th International Database Engineering & Applications Symposium Pub Date : 2021-07-14 DOI: 10.1145/3472163.347216

Ami Pandat, Nidhi Gupta, Minal Bhise

{"title":"Load Balanced Semantic Aware Distributed RDF Graph","authors":"Ami Pandat, Nidhi Gupta, Minal Bhise","doi":"10.1145/3472163.347216","DOIUrl":"https://doi.org/10.1145/3472163.347216","url":null,"abstract":"Modern day application development requires efficient management of huge RDF data. The major approaches for RDF data management are Relational and Graph based techniques. As the relational approach suffers from query joins, we propose a semantic aware graph based partitioning method. The partitioned fragments are further allocated in a load balanced way. For efficient query processing, partial replication is implemented. It reduces Inter node Communication thereby accelerating queries on distributed RDF Graph. This approach has been demonstrated in two phases partitioning and Distribution of Linked Observation Data (LOD). The time complexity for partitioning and distribution of Load Balanced Semantic Aware RDF Graph (LBSD) is O(n) where n is the number of triples which is demonstrated by linear increment in algorithm execution time (AET) for LOD data scaled from 1x to 5x. LBSD has been found to behave well till 4x. LBSD is compared with the state of the art relational and graph-based partitioning techniques. LBSD records 71% QET gain when averaged over all the four query types. For most frequent query types, Linear and Star, on an average 65% QET gain is recorded over original configuration for scaling experiments. The optimal replication level has been found to be 12% of original data.","PeriodicalId":242683,"journal":{"name":"Proceedings of the 25th International Database Engineering & Applications Symposium","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114330406","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

An Automatic Schema-Instance Approach for Merging Multidimensional Data Warehouses 多维数据仓库的自动模式-实例合并方法

Proceedings of the 25th International Database Engineering & Applications Symposium Pub Date : 2021-07-14 DOI: 10.1145/3472163.3472268

Yuzhao Yang, J. Darmont, F. Ravat, O. Teste

引用次数: 0

Designing a Business View of Enterprise Data: An approach based on a Decentralised Enterprise Knowledge Graph 设计企业数据的业务视图:一种基于分散企业知识图的方法

Proceedings of the 25th International Database Engineering & Applications Symposium Pub Date : 2021-07-14 DOI: 10.1145/3472163.3472276

Bastien Vidé, J. Marty, F. Ravat, Max Chevalier

引用次数: 5

IDEAS: the first quarter century 观念:前25年

Proceedings of the 25th International Database Engineering & Applications Symposium Pub Date : 2021-07-14 DOI: 10.1145/3472163.3472168

B. Desai

引用次数: 0

An In-Browser Collaborative System for Functional Annotations 一个用于功能注释的浏览器内协作系统

Proceedings of the 25th International Database Engineering & Applications Symposium Pub Date : 2021-07-14 DOI: 10.1145/3472163.3472275

Yui Saeki, Motomichi Toyama

引用次数: 0