Fu, Xiaoming

[["dc.bibliographiccitation.artnumber","11"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","The Journal of Chinese Sociology"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Luo, Jar-Der"],["dc.contributor.author","Liu, Jifan"],["dc.contributor.author","Yang, Kunhao"],["dc.contributor.author","Fu, Xiaoming"],["dc.date.accessioned","2019-07-09T11:51:58Z"],["dc.date.available","2019-07-09T11:51:58Z"],["dc.date.issued","2019"],["dc.description.abstract","Abstract Computational social science has integrated social science theories and methodology with big data analysis. It has opened a number of new topics for big data analysis and enabled qualitative and quantitative sociological research to provide the ground truth for testing the results of data mining. At the same time, threads of evidence obtained by data mining can inform the development of theory and thereby guide the construction of predictive models to infer and explain more phenomena. Using the example of the Internet data of China’s venture capital industry, this paper shows the triadic dialogue among data mining, sociological theory, and predictive models and forms a methodology of big data analysis guided by sociological theories."],["dc.identifier.doi","10.1186/s40711-019-0102-4"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16250"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/60053"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","Springer"],["dc.relation.orgunit","Fakultät für Mathematik und Informatik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Big data research guided by sociological theory: a triadic dialogue among big data analysis, theory, and predictive models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Zhu, Konglin"],["dc.contributor.author","Fu, Xiaoming"],["dc.contributor.author","Li, Wenzhong"],["dc.contributor.author","Lu, Sanglu"],["dc.contributor.author","Nagler, Jan"],["dc.contributor.editor","Fu, Xiaoming"],["dc.contributor.editor","Luo, Jar-Der"],["dc.contributor.editor","Boos, Margarete"],["dc.date.accessioned","2017-09-07T11:50:48Z"],["dc.date.available","2017-09-07T11:50:48Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1371/journal.pone.0100023"],["dc.identifier.gro","3147824"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10488"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5150"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.publisher","Taylor & Francis Group"],["dc.publisher.place","Boca Raton"],["dc.relation.isbn","1-4987-3664-5"],["dc.relation.ispartof","Social Network Analysis: Interdisciplinary Approaches and Case"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","How Do Online Social Networks Grow?"],["dc.type","book_chapter"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","351"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Peer-to-Peer Networking and Applications"],["dc.bibliographiccitation.lastpage","362"],["dc.bibliographiccitation.volume","3"],["dc.contributor.author","Lei, Jun"],["dc.contributor.author","Shi, Lei"],["dc.contributor.author","Fu, Xiaoming"],["dc.date.accessioned","2017-09-07T11:44:51Z"],["dc.date.available","2017-09-07T11:44:51Z"],["dc.date.issued","2010"],["dc.identifier.doi","10.1007/s12083-009-0063-5"],["dc.identifier.gro","3148998"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/5165"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5638"],["dc.notes.intern","Fu Crossref Import"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.publisher","Springer Nature"],["dc.relation.issn","1936-6442"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","An experimental analysis of Joost peer-to-peer VoD service"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","669"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Wireless Personal Communications"],["dc.bibliographiccitation.lastpage","692"],["dc.bibliographiccitation.volume","52"],["dc.contributor.author","Le, Deguang"],["dc.contributor.author","Fu, Xiaoming"],["dc.contributor.author","Hogrefe, Dieter"],["dc.date.accessioned","2017-09-07T11:44:54Z"],["dc.date.available","2017-09-07T11:44:54Z"],["dc.date.issued","2010"],["dc.identifier.doi","10.1007/s11277-008-9624-9"],["dc.identifier.gro","3149010"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/4048"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5651"],["dc.notes.intern","Fu Crossref Import"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.publisher","Springer Nature"],["dc.relation.issn","0929-6212"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","A Cross-Layer Approach for Improving TCP Performance in Mobile Environments"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","76098"],["dc.bibliographiccitation.journal","IEEE Access"],["dc.bibliographiccitation.lastpage","76110"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Yang, Song"],["dc.contributor.author","Wieder, Philipp"],["dc.contributor.author","Aziz, Muzzamil"],["dc.contributor.author","Yahyapour, Ramin"],["dc.contributor.author","Fu, Xiaoming"],["dc.contributor.author","Chen, Xu"],["dc.date.accessioned","2021-11-22T14:31:45Z"],["dc.date.available","2021-11-22T14:31:45Z"],["dc.date.issued","2018"],["dc.description.abstract","Customers often suffer from the variability of data access time in (edge) cloud storage service, caused by network congestion, load dynamics, and so on. One ef cient solution to guarantee a reliable latency-sensitive service (e.g., for industrial Internet of Things application) is to issue requests with multiple download/upload sessions which access the required data (replicas) stored in one or more servers, and use the earliest response from those sessions. In order to minimize the total storage costs, how to optimally allocate data in a minimum number of servers without violating latency guarantees remains to be a crucial issue for the cloud provider to deal with. In this paper, we study the latency-sensitive data allocation problem, the latency-sensitive data reallocation problem and the latency-sensitive workload consolidation problem for cloud storage. We model the data access time as a given distribution whose cumulative density function is known, and prove that these three problems are NP-hard. To solve them, we propose an exact integer nonlinear program (INLP) and a Tabu Search-based heuristic. The simulation results reveal that the INLP can always achieve the best performance in terms of lower number of used nodes and higher storage and throughput utilization, but this comes at the expense of much higher running time. The Tabu Searchbased heuristic, on the other hand, can obtain close-to-optimal performance, but in a much lower running time."],["dc.identifier.doi","10.1109/ACCESS.2018.2883674"],["dc.identifier.eissn","2169-3536"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15891"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/93400"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.notes.status","final"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/607584/EU//CLEANSKY"],["dc.relation.eissn","2169-3536"],["dc.relation.orgunit","Gesellschaft für wissenschaftliche Datenverarbeitung"],["dc.rights.access","openAccess"],["dc.subject","Data Allocation; Workload Consolidation; Cloud Storage"],["dc.subject.ddc","510"],["dc.title","Latency-Sensitive Data Allocation and Workload Consolidation for Cloud Storage"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","14"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Complex Adaptive Systems Modeling"],["dc.bibliographiccitation.volume","1"],["dc.contributor.author","Zhu, Konglin"],["dc.contributor.author","Li, Wenzhong"],["dc.contributor.author","Fu, Xiaoming"],["dc.date.accessioned","2017-09-07T11:44:55Z"],["dc.date.available","2017-09-07T11:44:55Z"],["dc.date.issued","2013"],["dc.description.abstract","Purpose Online social networks (OSNs) are now among the most popular applications on the web offering platforms for people to interact, communicate and collaborate with others. The rapid development of OSNs provides opportunities for people’s daily communication, but also brings problems such as burst network traffic and overload of servers. Studying the population growth pattern in online social networks helps service providers to understand the people communication manners in OSNs and facilitate the management of network resources. In this paper, we propose a population growth model for OSNs based on the study of population distribution and growth in spatiotemporal scale-space. Methods We investigate the population growth in three data sets which are randomly sampled from the popular OSN web sites including Renren, Twitter and Gowalla. We find out that the number of population follows the power-law distribution over different geographic locations, and the population growth of a location fits a power function of time. An aggregated population growth model is conducted by integrating the population growth over geographic locations and time. Results We use the data sets to validate our population growth model. Extensive experiments also show that the proposed model fits the population growth of Facebook and Sina Weibo well. As an application, we use the model to predict the monthly population in three data sets. By comparing the predicted population with ground-truth values, the results show that our model can achieve a prediction accuracy between 86.14% and 99.89%. Conclusions With our proposed population growth model, people can estimate the population size of an online social network in a certain time period and it can also be used for population prediction for a future time."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2013"],["dc.identifier.doi","10.1186/2194-3206-1-14"],["dc.identifier.gro","3149016"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10428"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5658"],["dc.language.iso","en"],["dc.notes.intern","Fu Crossref Import"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","2194-3206"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Modeling population growth in online social networks"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","470"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Nature Medicine"],["dc.bibliographiccitation.lastpage","474"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Rudolph, Volker"],["dc.contributor.author","Andrié, René P."],["dc.contributor.author","Rudolph, Tanja K."],["dc.contributor.author","Friedrichs, Kai"],["dc.contributor.author","Klinke, Anna"],["dc.contributor.author","Hirsch-Hoffmann, Birgit"],["dc.contributor.author","Schwoerer, Alexander P."],["dc.contributor.author","Lau, Denise"],["dc.contributor.author","Fu, XiaoMing"],["dc.contributor.author","Klingel, Karin"],["dc.contributor.author","Sydow, Karsten"],["dc.contributor.author","Didié, Michael"],["dc.contributor.author","Seniuk, Anika"],["dc.contributor.author","von Leitner, Eike-Christin"],["dc.contributor.author","Szoecs, Katalin"],["dc.contributor.author","Schrickel, Jan W."],["dc.contributor.author","Treede, Hendrik"],["dc.contributor.author","Wenzel, Ulrich"],["dc.contributor.author","Lewalter, Thorsten"],["dc.contributor.author","Nickenig, Georg"],["dc.contributor.author","Zimmermann, Wolfram-Hubertus"],["dc.contributor.author","Meinertz, Thomas"],["dc.contributor.author","Böger, Rainer H."],["dc.contributor.author","Reichenspurner, Hermann"],["dc.contributor.author","Freeman, Bruce A."],["dc.contributor.author","Eschenhagen, Thomas"],["dc.contributor.author","Ehmke, Heimo"],["dc.contributor.author","Hazen, Stanley L."],["dc.contributor.author","Willems, Stephan"],["dc.contributor.author","Baldus, Stephan"],["dc.date.accessioned","2017-09-07T11:46:08Z"],["dc.date.available","2017-09-07T11:46:08Z"],["dc.date.issued","2010"],["dc.description.abstract","Observational clinical and ex vivo studies have established a strong association between atrial fibrillation and inflammation(1). However, whether inflammation is the cause or the consequence of atrial fibrillation and which specific inflammatory mediators may increase the atria's susceptibility to fibrillation remain elusive. Here we provide experimental and clinical evidence for the mechanistic involvement of myeloperoxidase (MPO), a heme enzyme abundantly expressed by neutrophils, in the pathophysiology of atrial fibrillation. MPO-deficient mice pretreated with angiotensin II (AngII) to provoke leukocyte activation showed lower atrial tissue abundance of the MPO product 3-chlorotyrosine, reduced activity of matrix metalloproteinases and blunted atrial fibrosis as compared to wild-type mice. Upon right atrial electrophysiological stimulation, MPO-deficient mice were protected from atrial fibrillation, which was reversed when MPO was restored. Humans with atrial fibrillation had higher plasma concentrations of MPO and a larger MPO burden in right atrial tissue as compared to individuals devoid of atrial fibrillation. In the atria, MPO colocalized with markedly increased formation of 3-chlorotyrosine. Our data demonstrate that MPO is a crucial prerequisite for structural remodeling of the myocardium, leading to an increased vulnerability to atrial fibrillation."],["dc.identifier.doi","10.1038/nm.2124"],["dc.identifier.gro","3142946"],["dc.identifier.isi","000276446800053"],["dc.identifier.pmid","20305660"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6269"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/406"],["dc.language.iso","en"],["dc.notes.intern","WoS Import 2017-03-10. FU_Med"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.relation.issn","1078-8956"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Myeloperoxidase acts as a profibrotic mediator of atrial fibrillation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","6320"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","Sustainability"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Chen, Hui"],["dc.contributor.author","Voigt, Sven"],["dc.contributor.author","Fu, Xiaoming"],["dc.date.accessioned","2021-07-05T15:00:50Z"],["dc.date.available","2021-07-05T15:00:50Z"],["dc.date.issued","2021"],["dc.description.abstract","Understanding commuters’ behavior and influencing factors becomes more and more important every day. With the steady increase of the number of commuters, commuter traffic becomes a major bottleneck for many cities. Commuter behavior consequently plays an increasingly important role in city and transport planning and policy making. Although prior studies investigated a variety of potential factors influencing commuting decisions, most of them are constrained by the data scale in terms of limited time duration, space and number of commuters under investigation, largely owing to their dependence on questionnaires or survey panel data; as such only small sets of features can be explored and no predictions of commuter numbers have been made, to the best of our knowledge. To fill this gap, we collected inter-city commuting data in Germany between 1994 and 2018, and, along with other data sources, analyzed the influence of GDP, housing and the labor market on the decision to commute. Our analysis suggests that the access to employment opportunities, housing price, income and the distribution of the location’s industry sectors are important factors in commuting decisions. In addition, different age, gender and income groups have different commuting patterns. We employed several machine learning algorithms to predict the commuter number using the identified related features with reasonably good accuracy."],["dc.description.abstract","Understanding commuters’ behavior and influencing factors becomes more and more important every day. With the steady increase of the number of commuters, commuter traffic becomes a major bottleneck for many cities. Commuter behavior consequently plays an increasingly important role in city and transport planning and policy making. Although prior studies investigated a variety of potential factors influencing commuting decisions, most of them are constrained by the data scale in terms of limited time duration, space and number of commuters under investigation, largely owing to their dependence on questionnaires or survey panel data; as such only small sets of features can be explored and no predictions of commuter numbers have been made, to the best of our knowledge. To fill this gap, we collected inter-city commuting data in Germany between 1994 and 2018, and, along with other data sources, analyzed the influence of GDP, housing and the labor market on the decision to commute. Our analysis suggests that the access to employment opportunities, housing price, income and the distribution of the location’s industry sectors are important factors in commuting decisions. In addition, different age, gender and income groups have different commuting patterns. We employed several machine learning algorithms to predict the commuter number using the identified related features with reasonably good accuracy."],["dc.description.sponsorship","Fundamental Research Funds for the Central Universities"],["dc.description.sponsorship","EU H2020 COSAFE project"],["dc.identifier.doi","10.3390/su13116320"],["dc.identifier.pii","su13116320"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/87915"],["dc.language.iso","en"],["dc.notes.intern","DOI Import DOI-Import GROB-441"],["dc.publisher","MDPI"],["dc.relation.eissn","2071-1050"],["dc.rights","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Data-Driven Analysis on Inter-City Commuting Decisions in Germany"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]