{"title":"Simulation Testbed for Evaluating Distributed Querying and Searching of Mass Spectrometry Big Data in a Network-based Infrastructure.","authors":"Umair Mohammad, Fahad Saeed","doi":"10.1109/bigdataservice52369.2021.00022","DOIUrl":"10.1109/bigdataservice52369.2021.00022","url":null,"abstract":"<p><p>Advance access and reuse mechanisms for large-scale Mass Spectrometry (MS) data are essential for democratizing data for the omics research community and making it adhere to FAIR (Findable, Accessible, Interoperable, Reusable) principles. Although a number of centralized data repositories have been established, they have been limited to search mechanisms that depend on the meta-data associated with these MS datasets. Furthermore, they require constant influx of resources for maintenance. In this paper, we proposed an alternative novel distributed infrastructure for direct MS/MS spectral search. We designed and developed a simulation testbed using concepts from computer networks, queuing theory, and stochastic simulation methods. Results show that a distributed MS search based on raw MS/MS spectra can scale gracefully for up-to 2000 participating nodes, while simultaneously processing queries using the proposed networked infrastructure on the order of milliseconds to a few seconds for up-to a total of fifty billion MS/MS spectra.</p>","PeriodicalId":93613,"journal":{"name":"Proceedings. IEEE International Conference on Big Data Computing Service and Applications","volume":"2021 ","pages":"137-142"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007159/pdf/nihms-1794436.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49686121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The 5I Model of Smart City: A Case of Shanghai, china","authors":"Xixi Lin, H. Quan, Hong Zhang, Yinghua Huang","doi":"10.1109/BigDataService.2015.34","DOIUrl":"https://doi.org/10.1109/BigDataService.2015.34","url":null,"abstract":"Smart City has become the new developing target of modern city. Analyzing in depth the characteristics and connotation of Smart City and listing analysis dimensions for Smart City construction have the vital practical significance. This paper builds the 5I model of Smart City from four perspectives of information infrastructure construction, information perception and intelligent application, new generation of information technology industry and information security assurance. To have a more clear exposition of the present situation of Smart City construction from 5I model, this paper takes Shanghai for example to provide rational analysis perspectives and scientific references for the development of Smart City all over the world.","PeriodicalId":93613,"journal":{"name":"Proceedings. IEEE International Conference on Big Data Computing Service and Applications","volume":"41 1","pages":"329-332"},"PeriodicalIF":0.0,"publicationDate":"2015-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90497914","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}
Xinyan Wang, L. Tian, Bo Xu, Xueliang Wang, Wenjun Wu
{"title":"MOOC for Medical Big Data Research: An Important Role in Hypertension Big Data Research","authors":"Xinyan Wang, L. Tian, Bo Xu, Xueliang Wang, Wenjun Wu","doi":"10.1109/BIGDATASERVICE.2015.37","DOIUrl":"https://doi.org/10.1109/BIGDATASERVICE.2015.37","url":null,"abstract":"Due to limited technical and social resources, many physician practices fall short on accurate blood pressure measurement to carry out large-scale hypertension research projects. The accuracy and standard of data acquisition are very important when data sources are diverse in medical big data research. This paper proposes Massive Online Open Course (MOOC) is appropriate approach to teach volunteers necessary knowledge and skills of blood pressure measurement for hypertension research. It introduces a new citizen science \"paradigm\" to support big data research such as hypertension. MOOC is a new type online course that provides a combination of short video lectures, frequent comprehension quizzes and active participation in discussion forum. The well-trained data collectors by MOOC will be granted to collect and publish data of hypertension research. The process of medical big data research based on MOOC was introduced.","PeriodicalId":93613,"journal":{"name":"Proceedings. IEEE International Conference on Big Data Computing Service and Applications","volume":"124 1","pages":"453-455"},"PeriodicalIF":0.0,"publicationDate":"2015-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77335966","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}
H. Alaka, Lukumon O. Oyedele, M. Bilal, Olúgbénga O. Akinadé, H. Owolabi, Saheed Ajayi
{"title":"Bankruptcy Prediction of Construction Businesses: Towards a Big Data Analytics Approach","authors":"H. Alaka, Lukumon O. Oyedele, M. Bilal, Olúgbénga O. Akinadé, H. Owolabi, Saheed Ajayi","doi":"10.1109/BigDataService.2015.30","DOIUrl":"https://doi.org/10.1109/BigDataService.2015.30","url":null,"abstract":"","PeriodicalId":93613,"journal":{"name":"Proceedings. IEEE International Conference on Big Data Computing Service and Applications","volume":"251 1","pages":"347-352"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75757591","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}