Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services最新文献_第4页

A Comparison of Two Database Partitioning Approaches that Support Taxonomy-Based Query Answering 支持基于分类的查询应答的两种数据库分区方法的比较

Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services Pub Date : 2020-11-30 DOI: 10.1145/3428757.3429108

J. Schäfer, L. Wiese

引用次数: 0

Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services 第22届信息集成与基于网络的应用与服务国际会议论文集

Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services Pub Date : 2020-11-30 DOI: 10.1145/3428757

引用次数: 1

KNNAC KNNAC

Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services Pub Date : 2020-11-30 DOI: 10.1145/3428757.3429135

Yao Zhang, Yifeng Lu, Thomas Seidl

引用次数: 0

A new Multi-Agents System based on Blockchain for Prediction Anomaly from System Logs 基于区块链的多智能体系统日志异常预测

Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services Pub Date : 2020-11-30 DOI: 10.1145/3428757.3429149

Arwa Binlashram, Hajer Bouricha, L. Hsairi, Haneen Al Ahmadi

{"title":"A new Multi-Agents System based on Blockchain for Prediction Anomaly from System Logs","authors":"Arwa Binlashram, Hajer Bouricha, L. Hsairi, Haneen Al Ahmadi","doi":"10.1145/3428757.3429149","DOIUrl":"https://doi.org/10.1145/3428757.3429149","url":null,"abstract":"The execution traces generated by an application contain information that the developers believed would be useful in debugging or monitoring the application, it contains application states and significant events at various critical points that help them gain insight into failures and identify and predict potential problems before they occur. Despite the ubiquity of these traces universally in almost all computer systems, they are rarely exploited because they are not readily machine-parsable. In this paper, we propose a Multi-Agents approach for prediction process using Blockchain technology, which allows automatically analysis of execution traces and detects early warning signals for system failure prediction during executing. The proposed prediction approach is constructed using a four-layer Multi-Agents system architecture. The proposed prediction approach performance is based on data prepossessing and supervised learning algorithms for prediction. Blockchain was used to coordinate collaboration between agents, and to synchronize prediction between agents and the administrators. We validated our approach by applying it to real-world distributed systems, where we predicted problems before they occurred with high accuracy. In this paper we will focus on the Architecture of our prediction approach.","PeriodicalId":212557,"journal":{"name":"Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132863325","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

Analysis and Comparison of Block-Splitting-Based Load Balancing Strategies for Parallel Entity Resolution 基于块分割的并行实体解析负载均衡策略分析与比较

Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services Pub Date : 2020-11-30 DOI: 10.1145/3428757.3429140

Xiao Chen, Nishanth Entoor Venkatarathnam, Kirity Rapuru, David Broneske, Gabriel Campero Durand, Roman Zoun, G. Saake

{"title":"Analysis and Comparison of Block-Splitting-Based Load Balancing Strategies for Parallel Entity Resolution","authors":"Xiao Chen, Nishanth Entoor Venkatarathnam, Kirity Rapuru, David Broneske, Gabriel Campero Durand, Roman Zoun, G. Saake","doi":"10.1145/3428757.3429140","DOIUrl":"https://doi.org/10.1145/3428757.3429140","url":null,"abstract":"Entity resolution (ER) is a process to identify records that refer to the same real-world entity. In recent years, facing the ever-increasing data volume, both blocking techniques and parallel computation have been proposed for ER to reduce its running time and improve efficiency. It is popular and convenient to apply the MapReduce programming model for parallel computation. With the default load balancing strategy, if the block sizes are skewed, an imbalanced reducer load will occur and significantly increase the runtime. One possible solution is block-splitting: breaking the overpopulated blocks into smaller sub-blocks, to improve efficiency. In this paper we analyze the advantages and disadvantages of state-of-the-art block splitting methods (BlockSplit and BlockSlicer), and we propose two approaches: TLS and BOS to overcome the identified drawbacks. We comprehensively evaluate and compare our proposed solutions, with Spark implementations, using real-world and synthetic datasets with different properties. The results show that all of them can balance the reducer load with the help of the greedy partition assignment strategy. When memory of used cluster is not abundant given a dataset, a high number of reducers is required to reduce the GC time to improve efficiency. Partitcularly, our TLS and BOS have overwelmingly lower overhead due to the ability of block-wise composite key assignment.","PeriodicalId":212557,"journal":{"name":"Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126964513","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

A Patten Matcher for English Idioms on Web IndeX 网络索引英语习语的模式匹配器

Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services Pub Date : 2020-11-30 DOI: 10.1145/3428757.3429136

Takumi Shinzato, Jun Nemoto, Motomichi Toyama

引用次数: 1

Music Discovery as Differentiation Strategy for Streaming Providers 音乐发现作为流媒体提供商的差异化策略

Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services Pub Date : 2020-11-30 DOI: 10.1145/3428757.3429151

Andreas Raff, Andreas Mladenow, C. Strauss

引用次数: 1

Rammed, or What RAM3S Taught Us RAM3S教给我们什么

Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services Pub Date : 2020-11-30 DOI: 10.1145/3428757.3429098

Ilaria Bartolini, M. Patella

引用次数: 0

Transfer Learning in Classifying Prescriptions and Keyword-based Medical Notes 迁移学习在处方分类和基于关键词的医学笔记中的应用

Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services Pub Date : 2020-11-30 DOI: 10.1145/3428757.3429139

Mir Moynuddin Ahmed Shibly, Tahmina Akter Tisha, K. Islam, Md. Mohsin Uddin

{"title":"Transfer Learning in Classifying Prescriptions and Keyword-based Medical Notes","authors":"Mir Moynuddin Ahmed Shibly, Tahmina Akter Tisha, K. Islam, Md. Mohsin Uddin","doi":"10.1145/3428757.3429139","DOIUrl":"https://doi.org/10.1145/3428757.3429139","url":null,"abstract":"Medical text classification is one of the primary steps of health care automation. Diagnosing disease at the right time, and going to the right doctor is important for patients. To do that, two types of medical texts were classified into some medical specialties in this study. The first one is the keywords-based medical notes and the second one is the prescriptions. There are many methods and techniques to classify texts from any domain. But, textual resources of a specific domain can be inadequate to build a sustainable and accurate classifier. This problem can be solved by incorporating transfer learning. The objective of this study is to analyze the prospects of transfer learning in medical text classification. To do that, a transfer learning system has been created for classification tasks by fine-tuning Bidirectional Encoder Representations from Transformers aka the BERT language model, and its performance has been compared with three deep learning models - multi-layer perceptron, long short-term memory, and convolutional neural network. The fine-tuned BERT model has shown the best performance among all the other models in both classification tasks. It has 0.84 and 0.96 weighted f1-score in classifying medical notes and prescriptions respectively. This study has proved that transfer learning can be used in medical text classification, and significant improvement in performance can be achieved through it.","PeriodicalId":212557,"journal":{"name":"Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services","volume":"4647 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122696031","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

Mitigating Effect of Dictionary Matching Errors in Distantly Supervised Named Entity Recognition 字典匹配错误在远程监督命名实体识别中的缓解作用

Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services Pub Date : 2020-11-30 DOI: 10.1145/3428757.3429142

Koga Kobayashi, Kei Wakabayashi

引用次数: 0