Proceedings of the 27th International Database Engineered Applications Symposium最新文献

Approach Considering Collective Intelligence for Interactive Web Services Composition 考虑交互式Web服务组合集体智能的方法

Proceedings of the 27th International Database Engineered Applications Symposium Pub Date : 2023-05-05 DOI: 10.1145/3589462.3589500

Ameni Youssfi Nouira, Y. Jamoussi

{"title":"Approach Considering Collective Intelligence for Interactive Web Services Composition","authors":"Ameni Youssfi Nouira, Y. Jamoussi","doi":"10.1145/3589462.3589500","DOIUrl":"https://doi.org/10.1145/3589462.3589500","url":null,"abstract":"Service-oriented computing (SOC) is a new computing paradigm that creates new semantic Web services composition. Moreover, some current interactive (WS) composition approaches have produced multiple aspects of interactivity and collaborative processes with a growing interest in collective intelligence and collaborative procedures to improve and enrich ontological knowledge. Several researchers have attempted to benefit from Web information to improve discovery, selection, and Web services composition. The emergence of the social networking paradigm and the lack of research works dealing with the importance of social networking in knowledge building have led us to think about how to exploit in an enrichment and construction ontology. In this paper, we present a novel ontology with high precision, based on the information existing on social networking sites. We propose an approach that consists of an interactive WS composition relying on an ontology constructed by mining social networking sites (Twitter and Facebook). Our approach is composed of the following three steps: First, information is extracted from social networking sites. Second, a new ontology is built on the basis of the extracted facts. Finally, a semantic WS composition is executed by providing the benefits of the constructed ontology. To validate the proposed approach, we present a method of constructing the proposed ontology and show how it can be utilized for semantic Web services composition. We also demonstrate that the constructed ontology positively affects the process.","PeriodicalId":255231,"journal":{"name":"Proceedings of the 27th International Database Engineered Applications Symposium","volume":"684 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115116055","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

Enhancing Declarative Temporal Model Mining in Relational Databases: A Preliminary Study 关系型数据库中增强声明性时间模型挖掘的初步研究

Proceedings of the 27th International Database Engineered Applications Symposium Pub Date : 2023-05-05 DOI: 10.1145/3589462.3589491

Samuel Appleby, Giacomo Bergami, G. Morgan

引用次数: 3

AdapterEM: Pre-trained Language Model Adaptation for Generalized Entity Matching using Adapter-tuning AdapterEM:使用AdapterEM调优进行广义实体匹配的预训练语言模型自适应

Proceedings of the 27th International Database Engineered Applications Symposium Pub Date : 2023-05-05 DOI: 10.1145/3589462.3589498

John Bosco Mugeni, S. Lynden, T. Amagasa, Akiyoshi Matono

{"title":"AdapterEM: Pre-trained Language Model Adaptation for Generalized Entity Matching using Adapter-tuning","authors":"John Bosco Mugeni, S. Lynden, T. Amagasa, Akiyoshi Matono","doi":"10.1145/3589462.3589498","DOIUrl":"https://doi.org/10.1145/3589462.3589498","url":null,"abstract":"Entity Matching (EM) involves identifying different data representations referring to the same entity from multiple data sources and is typically formulated as a binary classification problem. It is a challenging problem in data integration due to the heterogeneity of data representations. State-of-the-art solutions have adopted NLP techniques based on pre-trained language models (PrLMs) via the fine-tuning paradigm, however, sequential fine-tuning of overparameterized PrLMs can lead to catastrophic forgetting, especially in low-resource scenarios. In this study, we propose a parameter-efficient paradigm for fine-tuning PrLMs based on adapters, small neural networks encapsulated between layers of a PrLM, by optimizing only the adapter and classifier weights while the PrLMs parameters are frozen. Adapter-based methods have been successfully applied to multilingual speech problems achieving promising results, however, the effectiveness of these methods when applied to EM is not yet well understood, particularly for generalized EM with heterogeneous data. Furthermore, we explore using (i) pre-trained adapters and (ii) invertible adapters to capture token-level language representations and demonstrate their benefits for transfer learning on the generalized EM benchmark. Our results show that our solution achieves comparable or superior performance to full-scale PrLM fine-tuning and prompt-tuning baselines while utilizing a significantly smaller computational footprint of the PrLM parameters.","PeriodicalId":255231,"journal":{"name":"Proceedings of the 27th International Database Engineered Applications Symposium","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115871056","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

Information visualisation for industrial process monitoring 用于工业过程监控的信息可视化

Proceedings of the 27th International Database Engineered Applications Symposium Pub Date : 2023-05-05 DOI: 10.1145/3589462.3595631

Elodie Toufaili, C. Bortolaso, Youssef Miloudi, Jean-Marc Petit, Vasile-Marian Scuturici

引用次数: 0

Trust on Personalised Electronic Commerce 个性化电子商务的信任

Proceedings of the 27th International Database Engineered Applications Symposium Pub Date : 2023-05-05 DOI: 10.1145/3589462.3589503

Covadonga Gijón, Fernando Fernández-Bonilla, J. Cuadrado-Gallego, L. Usero

引用次数: 0

Very fast variations of training set size reduction algorithms for instance-based classification 基于实例分类的训练集大小缩减算法的快速变化

Proceedings of the 27th International Database Engineered Applications Symposium Pub Date : 2023-05-05 DOI: 10.1145/3589462.3589493

Stefanos Ougiaroglou, Georgios Evangelidis

{"title":"Very fast variations of training set size reduction algorithms for instance-based classification","authors":"Stefanos Ougiaroglou, Georgios Evangelidis","doi":"10.1145/3589462.3589493","DOIUrl":"https://doi.org/10.1145/3589462.3589493","url":null,"abstract":"Reduction through Homogeneous Clustering (RHC) and its editing variant (ERHC) are effective data reduction techniques for the k-NN classifier. They are based on an iterative k-means clustering task that discovers homogeneous clusters. The centers of the resulting homogeneous clusters constitute the instances of the reduced training set. Although RHC and ERHC are quite fast compared to several well-known data reduction techniques, the iterative execution of k-means clustering renders both of them inappropriate for data reduction tasks that need to be performed quickly, especially, when run over large training datasets. The present paper proposes simple and very fast variations of the algorithms, which are appropriate for such environments. The variations are called RHC2 and ERHC2 and replace the complete execution of k-means clustering with a fast task that assigns instances to the class centers. The experimental study based on fourteen datasets, and, the corresponding statistical tests, show that the proposed RHC2 and ERHC2 variations are very fast and, at the cost of a small penalty on classification accuracy, they achieve higher reduction rates than their predecessors and other two well-known data reduction techniques. They are good candidates when fast reduction on large datasets is required.","PeriodicalId":255231,"journal":{"name":"Proceedings of the 27th International Database Engineered Applications Symposium","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116719623","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

HGQL: Supporting Schematic Hypergraphs in GraphQL 在GraphQL中支持原理图超图

Proceedings of the 27th International Database Engineered Applications Symposium Pub Date : 2023-05-05 DOI: 10.1145/3589462.3589481

Arun Bansal

引用次数: 0

Condensed Nearest Neighbour Rules for Multi-Label Datasets 多标签数据集的精简近邻规则

Proceedings of the 27th International Database Engineered Applications Symposium Pub Date : 2023-05-05 DOI: 10.1145/3589462.3589492

P. Filippakis, Stefanos Ougiaroglou, Georgios Evangelidis

{"title":"Condensed Nearest Neighbour Rules for Multi-Label Datasets","authors":"P. Filippakis, Stefanos Ougiaroglou, Georgios Evangelidis","doi":"10.1145/3589462.3589492","DOIUrl":"https://doi.org/10.1145/3589462.3589492","url":null,"abstract":"Reducing the size of the training set, that is, replacing it with a condensing set, while maintaining the classification accuracy as much as possible is a very common practice to speed up instance-based classifiers. Data reduction techniques, also known as prototype selection or generation algorithms, can be used to accomplish this. There are numerous such algorithms that can be found in the literature that are effective for single-label classification problems, but the majority of them cannot be used for multi-label data where an instance may belong to multiple classes. Due to the numerous binary condensing sets it creates, the well-known Binary Relevance transformation method cannot be combined with a Data Reduction algorithm. Condensed Nearest Neighbor is a well-known parameter-free single-label prototype selection algorithm. This study proposes three variations of that algorithm for training datasets with multiple labels. An experimental study that we conducted over nine distinct datasets shows that our three proposed approaches provide good reduction rates while not tampering with the classification rates.","PeriodicalId":255231,"journal":{"name":"Proceedings of the 27th International Database Engineered Applications Symposium","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128953784","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

A Generalization of the Chomsky-Halle Phonetic Representation using Real Numbers for Robust Speech Recognition in Noisy Environments 基于实数的乔姆斯基-哈勒语音表示在噪声环境下鲁棒语音识别中的推广

Proceedings of the 27th International Database Engineered Applications Symposium Pub Date : 2023-05-05 DOI: 10.1145/3589462.3589488

P. Revesz

引用次数: 1

Bitcoin Price Prediction Considering Sentiment Analysis on Twitter and Google News 考虑Twitter和谷歌新闻的情绪分析的比特币价格预测

Proceedings of the 27th International Database Engineered Applications Symposium Pub Date : 2023-05-05 DOI: 10.1145/3589462.3589494

Ameni Youssfi Nouira, Mariam Bouchakwa, Y. Jamoussi

引用次数: 0