2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)最新文献_第7页

A Comparative Study of System Virtualization Performance 系统虚拟化性能的比较研究

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00064

Anthony K. Tsetse, Samuel Tweneboah-Koduah, B. Rawal, Zhihao Zheng, Manoah Prattipati

{"title":"A Comparative Study of System Virtualization Performance","authors":"Anthony K. Tsetse, Samuel Tweneboah-Koduah, B. Rawal, Zhihao Zheng, Manoah Prattipati","doi":"10.1109/IRI.2019.00064","DOIUrl":"https://doi.org/10.1109/IRI.2019.00064","url":null,"abstract":"Virtualization presents an abstraction between the bare computer hardware (physical resource set) and the application running on top of it. Virtualization technology has become the defacto technology in cloud computing systems. The technique has become an enabling technology in cloud computing, which helps in the provisioning of resources on an on-demand basis, thereby addressing elastic resource requirements of organizations in their quest to scale. This paper seeks to investigate the effect of virtualization on system performance. The primary objective is to conduct an empirical analysis to compare and contrast the performance of Virtual Machines (VMs) and a Host machine running on different Operating Systems (OS). The effect of the use of different OS and VM configurations (Host and Guest systems) on system performance is also explored. The study shows that depending on the metric (i.e., throughput, jitter, and response time and packet loss ratio) used, virtualization technique results in a performance degradation between 21% and 39%. In particular, communication between systems with Linux (being either the Host or the Guest OS) performs better than Windows (as either the Guest or the Host) OS. Additionally, it is also observed that there is a performance loss ranging between 12% and 20% when communicating devices run heterogeneous Guest OS.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128750113","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

GO-FOR: A Goal-Oriented Framework for Ontology Reuse 追求:面向目标的本体重用框架

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00028

C. Reginato, J. S. Salamon, Gabriel G. Nogueira, M. Barcellos, V. Souza, M. Monteiro

引用次数: 3

A Sentiment Classification in Bengali and Machine Translated English Corpus 孟加拉语和机器翻译英语语料库中的情感分类

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00029

Salim Sazzed, S. Jayarathna

{"title":"A Sentiment Classification in Bengali and Machine Translated English Corpus","authors":"Salim Sazzed, S. Jayarathna","doi":"10.1109/IRI.2019.00029","DOIUrl":"https://doi.org/10.1109/IRI.2019.00029","url":null,"abstract":"The resource constraints in many languages have made the multi-lingual sentiment analysis approach a viable alternative for sentiment classification. Although a good amount of research has been conducted using a multi-lingual approach in languages like Chinese, Italian, Romanian, etc. very limited research has been done in Bengali. This paper presents a bilingual approach to sentiment analysis by comparing machine translated Bengali corpus to its original form. We apply multiple machine learning algorithms: Logistic Regression (LR), Ridge Regression (RR), Support Vector Machine (SVM), Random Forest (RF), Extra Randomized Trees (ET) and Long Short-Term Memory (LSTM) to a collection of Bengali corpus and corresponding machine translated English version. The results suggest that using machine translation improves classifiers performance in both datasets. Moreover, the results show that the unigram model performs better than higher-order n-gram model in both datasets due to linguistic variations and presence of misspelled words results from complex typing system of Bengali language; sparseness and noise in the machine translated data, and because of small datasets.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125249379","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: 28

Context-Based Knowledge Discovery and Querying for Social Media Data 基于上下文的社交媒体数据知识发现与查询

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00056

J. Phengsuwan, N. Thekkummal, Tejal Shah, Philip James, D. Thakker, Rui Sun, Divya Pullarkatt, H. Thirugnanam, M. Ramesh, R. Ranjan

{"title":"Context-Based Knowledge Discovery and Querying for Social Media Data","authors":"J. Phengsuwan, N. Thekkummal, Tejal Shah, Philip James, D. Thakker, Rui Sun, Divya Pullarkatt, H. Thirugnanam, M. Ramesh, R. Ranjan","doi":"10.1109/IRI.2019.00056","DOIUrl":"https://doi.org/10.1109/IRI.2019.00056","url":null,"abstract":"Modern Early Warning Systems (EWS) rely on scientific methods to analyse a variety of Earth Observation (EO) and ancillary data provided by multiple and heterogeneous data sources for the prediction and monitoring of hazard events. Furthermore, through social media, the general public can also contribute to the monitoring by reporting warning signs related to hazardous events. However, the warning signs reported by people require additional processing to verify the possibility of the occurrence of hazards. Such processing requires potential data sources to be discovered and accessed. However, the complexity and high variety of these data sources makes this particularly challenging. Moreover, sophisticated domain knowledge of natural hazards and risk management are also required to enable dynamic and timely decision making about serious hazards. In this paper we propose a data integration and analytics system which allows social media users to contribute to hazard monitoring and supports decision making for its prediction. We prototype the system using landslides as an example hazard. Essentially, the system consists of background knowledge about landslides as well as information about data sources to facilitate the process of data integration and analysis. The system also consists of an interactive agent that allows social media users to report their observations. Using the knowledge modelled within the system, the agent can raise an alert about a potential occurrence of landslides and perform new processes using the data sources suggested by the knowledge base to verify the event.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129153125","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

A Framework for Airfare Price Prediction: A Machine Learning Approach 机票价格预测框架:一种机器学习方法

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00041

Tianyi Wang, Samira Pouyanfar, Haiman Tian, Yudong Tao, M. Alonso, Steven Luis, Shu‐Ching Chen

引用次数: 17