Micorsoft Azure和Techila技术在某商业人文研究中的应用

DeIC Conference 2015,6-7 October, Middelfart, DenmarkUtilization of the Cloud in Matlab Analytic: a case from the Business Humanities ResearchFumiko Kano Glückstad, Assistant Professor, PhDDepartment of International Business CommunicationCopenhagen Business School(Sponsored by Microsoft Denmark)Agenda-Brief introduction-Research interests and examples-Solution proposed by Microsoft + Techila Technologies-Results of the PoC project3Brief introduction(Department of International Business Communication at CBS)I n t e r n a t i o n a l B u s i n e s s C o m m u n i c a t i o nProfessional Communication Communication managementCommunication across boundariesCommunication technology Corporate CommunicationIntercultural encounters Inter-lingual encountersMachine-mediated communicationBrief introduction (My background)E ducated as Psychologist (experimental psychology: perception and cognition) Panasonic Corporation, Home Appliances Company, Japan (7 years) Business Development (bridging between R&D and consumers)Marketing & Consumer Behavior Research(Have lived in Denmark since 1997)Phase One A/S (4 years)Senior coordinator, responsible for the Japanese marketResearch Associate for WIP Japan (Consultant agency) (4 years)PhD in Cross-Cultural Cognition from IBC@CBS (2012)Attended “Introduction to Machine Learning and Data Modeling” @DTU4Brief introduction (My background)Interested in the interdisciplinary approach bridging between:•Academic – Industrial research•Humanities/Social – Technical science•Danish/European – Japanese/Asian cultures5Agenda-Brief introduction-Research interests and examples-Solution proposed by Microsoft + Techila Technologies-Results of the PoC project7 Research interests (intro)Analysis of individual conceptualization towards collective conceptual modellingCross-categorization aross boundariesDevelopment and application of an automatic cross-cultural ontology alignment- and learning framework based on non-parametric Bayesian relational modelsOntology alignment and learning (logic-based) Specialized in non-parametricBayesian relational modeling ofResearch interests (DTU collaborators)Assoc. Profs. Morten Mørup and Mikkel N. Schmidt from the Section for Cognitive Systems, DTU Computewho are specialized in the non-parametric relational modeling applicable not only to brain connectivity, but also to social network analysis and others.8Application 1: the complex relational modeling applied to cross-cultural communicationExamples of research questions:1.How does a human’s conceptualization differ according to thedifferent types of mother languages, but also other factors such as culture, social status, gender, job-function, age, educationalbackground etc., when domain-specific knowledge is expressed in English?2.How can the identified individual/group conceptualization berepresented, e.g. ontologically (taxonomically) structured?→ Mapping semantics/definitions of words across cultures910Application 1: alignment of Educational systems across culturesCross-categorization aross bordersFigure adopted and modified from:Glückstad, F.K., Herlau, T., Schmidt, N. M., & Mørup, M. (2014) Cross-categorization of legal concepts across boundaries of legal systems: in consideration of inferential links . In: ArtificialCountry specific educational terms (e.g . ”elementary school ”) defined by UNESCO are mapped across twodifferent educational systemsInput data:Terms (objects) and definitions (features) of the terms defined by UNESCO11Application 1:Alignment and structuring of three or more ontologiesAn algorithm and a program has already been developed, andimplemented with a smaller dataset. Need implementation with a larger data.Figure adopted and modified from:Mørup, M., Glückstad, F.K., Herlau, T. & Schmidt, N. M. (2014) Non parametric statistical structuring of knowledgesystems using binary feature matches. In: Porceedings of 2014 IEEE International Workshop on Machine Learning for Signal ProcessingKT1KT2KT3KT4KT5KT6KT7KT8KT9KT1055, 54, 60 terms x 100 featuresComputation time 24 hours12Background:The identity formation and thereby value formation of modern consumers are becoming increasingly complex due to their belonging to local, national and global communities accessible via e.g. contemporary media technologies.For example, consumers in China or Japan cannot simply be categorized by a standardized consumer segmentation model universally used in the Western marketing practice.The new trend of so-called Big Data mining is in this respect meaningless without fully comprehending the core insights of interpersonal values connecting people around the globe.Hence, it is required to comprehend similarities and differences of consumer segments across boundaries of societies.Application 2: Cross-cultural ontology alignment framework applied to consumer survey analysesCited from Glückstad. (In Print) Application of an automatic dataalignment & structuring system for intercultural consumer segmentation analysis. In Proc. KEOD 201513Conventional cross-cultural dataanalysis:•Cross Table Analysis•Multi-Group Confirmatory Factor Analysis•Multi-Group Latent Class AnalysisTraditional approach:Comparison between the predefined cultural groupsOur approach: Cross-cultural ontologyalignment framework based onthe non-parametric Bayesian relational modelingAdded value:Enables to extract fine-grained heterogeneous segment structures across datasetsApplication 2: Mapping out heterogeneous consumer segment structures across culturesUtilization of the existing World Value Survey & European Social Survey database covering more than 400,000 respondents from around 100 countriesWVS/ESS communities: analysis of cross-cultural data(e.g. Davidov et al. 2011. Cross-Cultural Analysis, Routledge Academic)14Application 2: the cross-cultural ontology alignment framework applied to consumer survey analysisJapanDenmarkChinaExtracting consumer segments common across culturesNational characteristics:Preferences of Japanese, Danish, Chinese Segment specific characteristics: Preferences of each segmentClassified in an international sample databaseA new customer In JapanPossible to infer preference of thisJapanese customer C o n s u m e r s u r v e y S c h w a r t z v a l u e i t e m sExtractingconsumer segments specific to cultures J o i n t l y c l u s t e r i n gOther consumer data linked with the consumer segmentation modelAdopted from Glückstad. (In Print) Application of an automatic data alignment & structuring system for intercultural consumer segmentation analysis. Proc. KEOD 201515 Preliminary study: Analysis of World Value Survey database (publically available)1.Thinking up new ideas and being creative is important to him. He likes to do things in his own original way. (V80:Self-direction ) 2.It is important for him to be rich. He wants to have a lot of moneyand expensive things. (V81:Power ) 3.It is important for him to live in secure surroundings. He avoidsanything that might endanger his safety. (V82:Security ) 4.Having a good time is important to him. He likes to “spoil” himself.(V83:Hedonism ) 5.It’s very important for him to help the people around him. He wantsto care for their well-being. (V84:Benevolence ) 6.Being very successful is important to him. He hopes people willrecognize his achievements. (V85:Achievement ) 7.He looks for adventures and likes to take risks. He wants to have anexciting life (V86:Stimulation ) 8.It is important for him to always behave properly. He wants to avoiddoing anything people would way is wrong. (V87:Conformity ) 9.He strongly believes that people should care for nature. Lookingafter the environment is important to him. (V88:Universalism ) 10.Tradition is important to him. He tries to follow the customs handeddown by his religion or his family (V89:Tradition ) 1.Very much like me; 2. Like me; 3. Somewhat like me; 4. Little like me; 5. Not like me; 6. Not at all like me. Schwartz theory of 10 basic values: questions included in the WVSWorld Value Survey Official Data File v. 20090901, 2009. / ASEP/JDS, MadridSchwartz, S. H. 2012. An overview of theSchwartz theory of basic values. In OnlineReadings in Psychology and Culture, 2(1)16Four types of coding methods:Six combinations of datasets: JF vs. SF JF vs.JM JM vs. SM SF vs. SM JF vs. SM SF vs. JMCoding of datasets1. V e r y m u c h l i k e m e2. L i k e m e3. S o m e w h a t l i k e m e4. L i k e m e5. N o t l i k e m e6. N o t a t a l l l i k e m eTwo categoriesThree categoriesSix categories 1 (Yes) / 0 (No)1 (Yes) / 0 (No)1 (Yes) / 0 (No)1 (Yes) / 0 (No)1 (Yes) / 0 (No)1 (Yes) / 0 (No)5 digit coding 0-0-0-0-01-0-0-0-01-1-0-0-01-1-1-0-01-1-1-1-01-1-1-1-11 (Positive)0 (Negative)1 (Yes) / 0 (No) 1 (Yes) / 0 (No) 1 (Yes) / 0 (No)Japanese Females (JF): 908 respondentsJapanese Males (JM): 954 respondentsSwedish Females (SF): 612 respondentsSwedish Males (SM): 557 respondentsPreliminary study: Analysis of World ValueSurvey database (publically available)17Adopted from Glückstad. (In Print) Application of an automatic data alignment & structuring system for intercultural consumer segmentation analysis. Proc. KEOD 2015Agenda-Brief introduction-Research interests and examples-Solution proposed by Microsoft + Techila Technologies -Results of the PoC projectCase at CBS: Solution proposed byMicrosoft Azure + Techila TechnologiesPrevious environment at CBS-Bayesian relational models developed by DTU are programmed in Matlab.-I am one of the few of Matlab users at CBS and the only one (or very few) demanding high-performance computing solutions.-The aforementioned experiment (one combination such as JF vs. SF) generates 1GB output, which requires considerablecomputation hours with my laptop PC (Non parallel computing). -Only possibility has been to ask our DTU colleagues to perform the computation with the super computer at DTU Compute.-However, DTU collaboration is on a volunteer basis (not funded yet), means that conducting experimental computations ischallenging.1920•Techila Computingenvironment in Microsoft Azure–Virtual machines in Azure provide computational power–Techila HPC middleware distributes computations and manages results •Results downloaded to the End-User's computer where they can be post-processed in MatlabSolution proposed by Microsoft + Techila TechnologiesOn-premises Administrator(via Techila SDK or Browser)On-premisesEnd-Users (via Techila SDK)Techila Server instanceTechila Worker instancesStorage Server(s)MultithreadedHTTPS MultithreadedHTTPSSignal Channel 2Data Channel 1Management Channel 3Management Channel 3Web Access 4Storage Connection (Optional)Agenda-Brief introduction-Research interests and examples-Solution proposed by Microsoft + Techila Technology-Results of the PoC projectResults of the PoC project“Application 1” aligning the educational systems across three countriesThree datasets respectively contain 55, 54, 60 objects x 100 features3000 iterations for 20 times runs• Conventional environment* : 24 hours and 32 minutes (estimate)• Azure + Techila: 1 hour and 23 minutes“Application 2” aligning the Schwartz value survey items between two datasets Each dataset contains 500-1000 objects x 20 features2000 iterations for 20 times runs• Conventional environment: 178 hours (estimate)• Azure + Techila: 39 minutes* CPU: Intel Core i5 M 480 @ 2.67 GHz, Memory: 4 GBFrom the humanities/social scientific research point of view, having access to faster computing gives more flexibility when designing/implementing experiments at CBS. (without learning new skills to operate the Linux system etc.)22Thank you very much!Any questions?Torben Marcussen: torbenm@ (Microsoft Denmark)Tuomas Eerola: tuomas.eerola@ (Techila Technologies) Rolf Lunn: rl.it@cbs.dk (CBS IT)Fumiko Kano Glückstad: fkg.ibc@cbs.dk。