Proceedings of the 24th Symposium on International Database Engineering & Applications最新文献_第2页

Data science for healthcare predictive analytics 医疗保健预测分析的数据科学

Proceedings of the 24th Symposium on International Database Engineering & Applications Pub Date : 2020-08-12 DOI: 10.1145/3410566.3410598

C. Leung, Daryl L. X. Fung, Saad B. Mushtaq, Owen T. Leduchowski, R. L. Bouchard, Hui Jin, A. Cuzzocrea, Christine Y. Zhang

{"title":"Data science for healthcare predictive analytics","authors":"C. Leung, Daryl L. X. Fung, Saad B. Mushtaq, Owen T. Leduchowski, R. L. Bouchard, Hui Jin, A. Cuzzocrea, Christine Y. Zhang","doi":"10.1145/3410566.3410598","DOIUrl":"https://doi.org/10.1145/3410566.3410598","url":null,"abstract":"Big data are everywhere nowadays. Many businesses possess big data for their success because big data are very useful and are considered as new oil. For instance, big data are very important in predicting the trends on what will happen in the future. Many researchers have generated or gathered data to further enhance their research and to apply them to numerous real-life applications. Examples of big data include healthcare patient data. To improve the detection of illnesses and diseases, researchers have gathered healthcare patient data, examined the diagnosis on healthcare patient data (e.g., cells, blood count, antibodies count), and compared with previous data to determine if a specific illness or disease exist. Having an automatic predictive method for healthcare and disease analytics would be desirable. In this paper, we focus on healthcare mining, which aims to computationally discover knowledge from healthcare data. In particular, we present a data science framework with two predictive analytic algorithms for accurate prediction on the trends of cancer cases. The algorithms predict cancerous cells based on the information of the cell data from some data samples. Evaluation results on several real-life datasets related to the breast cancer demosntrate the effectiveness of our data science framework and predictive algorithms in healthcare data analytics.","PeriodicalId":137708,"journal":{"name":"Proceedings of the 24th Symposium on International Database Engineering & Applications","volume":"247 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132983512","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

An Android-based mobile paratransit application for vulnerable road users 一个基于android的移动辅助交通应用程序，用于弱势道路使用者

Proceedings of the 24th Symposium on International Database Engineering & Applications Pub Date : 2020-08-12 DOI: 10.1145/3410566.3410596

Kelvin Kwakye, Younho Seong, Sun Yi

{"title":"An Android-based mobile paratransit application for vulnerable road users","authors":"Kelvin Kwakye, Younho Seong, Sun Yi","doi":"10.1145/3410566.3410596","DOIUrl":"https://doi.org/10.1145/3410566.3410596","url":null,"abstract":"Making an optimal travel plan is not an easy task, mostly for vulnerable road users like the elderly and people with mobility disabilities. This optimal travel plan is dependent on the time of day to travel, the route to ply, ways to navigate, and suitable mode of transportation as these vulnerable road users need to reserve paratransit ahead of time. Researchers and mobile app developers are faced with difficulties in incorporating all necessary information that would ease navigation and bus riding for vulnerable road users (that is, making a more assistive system for vulnerable road users). In response to these challenges, this research is designed to develop an interactive android mobile application to ease and significantly encourage vulnerable road users (VRUs) especially the elderly and the disabled to use paratransit to improve their traveling experiences. This paper assesses the challenges facing the paratransit service. It provides an improvement in areas of reservation, dispatch & routing, and user experience. In response to these challenges, an interactive android mobile application has been developed to ease and significantly encourage vulnerable road users (VRUs) especially older adults and the disabled to use paratransit services and improve their traveling experiences.","PeriodicalId":137708,"journal":{"name":"Proceedings of the 24th Symposium on International Database Engineering & Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122569635","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: 8

The web: a hacker's heaven and an on-line system 网络:黑客的天堂和在线系统

Proceedings of the 24th Symposium on International Database Engineering & Applications Pub Date : 2020-08-12 DOI: 10.1145/3410566.3410589

B. Desai, A. L. Kipling, Reethu Navale, Jainhu Zhu

引用次数: 1

Benchmarking a distributed database design that supports patient cohort identification 对支持患者队列识别的分布式数据库设计进行基准测试

Proceedings of the 24th Symposium on International Database Engineering & Applications Pub Date : 2020-08-12 DOI: 10.1145/3410566.3410608

J. Schäfer, U. Sax, L. Wiese

引用次数: 1

DRLindex

Proceedings of the 24th Symposium on International Database Engineering & Applications Pub Date : 2020-08-12 DOI: 10.1145/3410566.3410603

Zahra Sadri, L. Gruenwald, Eleazar Lead

{"title":"DRLindex","authors":"Zahra Sadri, L. Gruenwald, Eleazar Lead","doi":"10.1145/3410566.3410603","DOIUrl":"https://doi.org/10.1145/3410566.3410603","url":null,"abstract":"Cloud database providers provision different architectures to guarantee high availability. One of these architectures is a cluster database that consists of several database engine nodes, where data is replicated among the nodes. Although the cloud database providers provide various auto-indexing tools, these tools mostly address characteristics of a database deployed on a single node, not a cluster. It is possible to install an index advisor on each node, which recommends an index set for that node. The problem with this approach is that the current index advisors for a single node aim to minimize the processing cost of the workload; however, on a cluster database, other goals such as load balancing can be considered. Hence, the better solution could be an index advisor which has a comprehensive view of the cluster node. In this paper, we propose an index advisor for a replicated database on a database cluster for a read-only workload. The advisor considers both query processing cost and load balancing. It utilizes a Deep Reinforcement Learning (DRL) approach in which a DRL agent learns to select a set of index configurations for nodes in a cluster. We describe the components of the DRL-advisor such as the agent, the environment, a set of actions, the reward function, and other modules. Experimental results validate the effectiveness of the algorithm.","PeriodicalId":137708,"journal":{"name":"Proceedings of the 24th Symposium on International Database Engineering & Applications","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116653820","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

Speculative query execution in RDBMS based on analysis of query stream multigraphs 基于查询流多图分析的RDBMS推测查询执行

Proceedings of the 24th Symposium on International Database Engineering & Applications Pub Date : 2020-08-12 DOI: 10.1145/3410566.3410604

A. Sasak-Okon, M. Tudruj

{"title":"Speculative query execution in RDBMS based on analysis of query stream multigraphs","authors":"A. Sasak-Okon, M. Tudruj","doi":"10.1145/3410566.3410604","DOIUrl":"https://doi.org/10.1145/3410566.3410604","url":null,"abstract":"The paper presents an insight into a speculative execution model of queries in RDBMS based on the analysis of the stream of current queries appearing at the database input. A specific multigraph representation of input query stream is created and used to determine the speculative queries for execution. A group of worker threads execute the chosen speculative queries in parallel with the execution of the standard input stream of user queries. The obtained speculative results are then used to support faster query execution. First, the paper briefly reminds the assumed graph modelling and analysis methods. Then, additional rules are presented which enable combining results of multiple speculative queries in execution of a single user input query. The quality of executed and used speculations is then analysed based on the defined quality metrics and structural details of speculative queries. Conclusions from this analysis are used to modify the selection method of target queries for speculative execution. It aims at intensification of the use of multiple speculative query results and further reduction of the user query execution time. Experimental results are presented in a multi-threaded speculative experimental environment cooperating with a SQLite database. They show that with the improved algorithm we can obtain more varied speculative query results, and thus, more intensive use of multiple speculative query results by the stream of user queries sent to the database.","PeriodicalId":137708,"journal":{"name":"Proceedings of the 24th Symposium on International Database Engineering & Applications","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125492533","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

Spatio-temporal event discovery in the big social data era 社会大数据时代的时空事件发现

Proceedings of the 24th Symposium on International Database Engineering & Applications Pub Date : 2020-08-12 DOI: 10.1145/3410566.3410568

Imad Afyouni, A. Khan, Z. Aghbari

{"title":"Spatio-temporal event discovery in the big social data era","authors":"Imad Afyouni, A. Khan, Z. Aghbari","doi":"10.1145/3410566.3410568","DOIUrl":"https://doi.org/10.1145/3410566.3410568","url":null,"abstract":"Social networks have been transforming the way people express opinions, post and react to events, and share ideas. Over the last decade, several studies on event detection from social media have been proposed, with the aim of extracting specific types of events, such as, social gatherings, natural disasters, and emergency situations, among others. However, these works do not consider the continuous processing of events over the social data streams, and therefore, cannot determine the spatial and temporal evolution of such events. This paper introduces a big data platform for event discovery, while tracking their evolution over space and time. We propose a scalable and efficient architecture that can manage and mine a huge data flow of unstructured streams, in order to detect geo-social events. The extracted clusters of events are indexed by a spatio-temporal index structure. We conduct experiments over twitter datasets to measure the effectiveness and efficiency of our system with respect to the existing major event detection techniques. An initial demonstration of our platform highlights its major advantage for detecting and tracking events spatially and temporally, thus allowing for great opportunities from application perspectives.","PeriodicalId":137708,"journal":{"name":"Proceedings of the 24th Symposium on International Database Engineering & Applications","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127807802","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: 6

A pattern-based approach for an early detection of popular Twitter accounts 一种基于模式的方法，用于早期检测流行的Twitter帐户

Proceedings of the 24th Symposium on International Database Engineering & Applications Pub Date : 2020-08-12 DOI: 10.1145/3410566.3410600

Jonathan Debure, S. Brunessaux, Camélia Constantin, C. Mouza

引用次数: 0

Avoiding blocking by scheduling transactions using quantum annealing 利用量子退火调度事务，避免阻塞

Proceedings of the 24th Symposium on International Database Engineering & Applications Pub Date : 2020-08-12 DOI: 10.1145/3410566.3410593

Tim Bittner, Sven Groppe

引用次数: 13

Patent prior art search using deep learning language model 利用深度学习语言模型进行专利现有技术检索

Proceedings of the 24th Symposium on International Database Engineering & Applications Pub Date : 2020-08-12 DOI: 10.1145/3410566.3410597

Dylan Myungchul Kang, Charles Cheolgi Lee, Suan Lee, Wookey Lee

{"title":"Patent prior art search using deep learning language model","authors":"Dylan Myungchul Kang, Charles Cheolgi Lee, Suan Lee, Wookey Lee","doi":"10.1145/3410566.3410597","DOIUrl":"https://doi.org/10.1145/3410566.3410597","url":null,"abstract":"A patent is one of the essential indicators of new technologies and business processes, which becomes the main driving force of the companies and even the national competitiveness as well, that has recently been submitted and exploited in a large scale of quantities of information sources. Since the number of patent processing personnel, however, can hardly keep up with the increasing number of patents, and thus may have been worried about from deteriorating the quality of examinations. In this regard, the advancement of deep learning for the language processing capabilities has been developed significantly so that the prior art search by the deep learning models also can be accomplished for the labor-intensive and expensive patent document search tasks. The prior art search requires differentiation tasks, usually with the sheer volume of relevant documents; thus, the recall is much more important than the precision, which is the primary difference from the conventional search engines. This paper addressed a method to effectively handle the patent documents using BERT, one of the major deep learning-based language models. We proved through experiments that our model had outperformed the conventional approaches and the combinations of the key components with the recall value of up to '94.29%' from the real patent dataset.","PeriodicalId":137708,"journal":{"name":"Proceedings of the 24th Symposium on International Database Engineering & Applications","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122018178","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: 7