01531nas a2200133   4500008004100000245010800041210006900149260000900218520098900227653002301216100001501239700001901254856012401273       2022                        eng d00aCombating False Information by Sharing the Truth:  A Study on the Spread of Fact-checks on Social Media0 aCombating False Information by Sharing the Truth A Study on the   c20223 aMisinformation on social media has become a horrendous problem in our society. Fact-checks on information often fall behind the diffusion of misinformation, which can lead to negative impacts on society. This research studies how different factors may affect the spread of fact-checks over the internet. We collected a dataset of fact-checks in a six-month period and analyzed how they spread on Twitter. The spread of fact-checks is measured by the total retweet count. The factors/variables include the truthfulness rating, topic of information, source credibility, etc. The research identifies truthfulness rating as a significant factor: conclusive fact-checks (either true or false) tend to be shared more than others. In addition, the source credibility, political leaning, and the sharing count also affect the spread of fact-checks. The findings of this research provide practical insights into accelerating the spread of the truth in the battle against misinformation online.10aBusiness Analytics1 aLi, Jiexun1 aChang, Xiaohui  u/biblio/combating-false-information-sharing-truth-study-spread-fact-checks-social-media01524nas a2200133   4500008004100000245010400041210006900145260000900214520098900223653002301212100001501235700001901250856012101269       2022                        eng d00aCombating Misinformation by Sharing the Truth: a Study on the Spread of Fact-Checks on Social Media0 aCombating Misinformation by Sharing the Truth a Study on the Spr  c20223 aMisinformation on social media has become a horrendous problem in our society. Fact-checks on information often fall behind the diffusion of misinformation, which can lead to negative impacts on society. This research studies how different factors may affect the spread of fact-checks over the internet. We collected a dataset of fact-checks in a six-month period and analyzed how they spread on Twitter. The spread of fact-checks is measured by the total retweet count. The factors/variables include the truthfulness rating, topic of information, source credibility, etc. The research identifies truthfulness rating as a significant factor: conclusive fact-checks (either true or false) tend to be shared more than others. In addition, the source credibility, political leaning, and the sharing count also affect the spread of fact-checks. The findings of this research provide practical insights into accelerating the spread of the truth in the battle against misinformation online.10aBusiness Analytics1 aLi, Jiexun1 aChang, Xiaohui  u/biblio/combating-misinformation-sharing-truth-study-spread-fact-checks-social-media01140nas a2200121   4500008004100000245010500041210006900146260000900215520069800224653002300922100001900945856005400964       2022                        eng d00aImproving Student Engagement and Connection in Online Learning: Part II, Methodologies and Practices0 aImproving Student Engagement and Connection in Online Learning P  c20223 aThe first article in the series appeared last December. Since then, we have received plenty of feedback from other instructors who are actively engaged in online education. Almost all of them agreed that teaching well online remains a challenging task. In this article, I discussed six specific practices that I have found particularly helpful for online teaching and learning.<br><br>Practice 1: Adopt a variety of communication methods<br>Practice 2: Create a Q&A Discussion Board<br>Practice 3: Estimate the amount of time taken for each assignment<br>Practice 4: Ensure timely replies<br>Practice 5: Synchronize assignments with the Canvas calendar<br>Practice 6: Reorganize course content10aBusiness Analytics1 aChang, Xiaohui  uhttps://blogs.oregonstate.edu/inspire/2021/12/07/00601nas a2200145   4500008004100000245011500041210006900156260000900225653000800234653002300242100002000265700001500285700001700300856013800317       2022                        eng d00aNeed for Speed, but How Much Does It Cost? Unpacking the Fee-Speed Relationship in Cryptocurrency Transactions0 aNeed for Speed but How Much Does It Cost Unpacking the FeeSpeed   c202210aBIS10aBusiness Analytics1 aShang, Guangzhi1 aIlk, Noyan1 aFan, Shaokun  u/biblio/need-speed-how-much-does-it-cost-unpacking-fee-speed-relationship-cryptocurrency-transactions00633nas a2200169   4500008004100000020001400041245010700055210006900162260000900231300000800240653000800248653002300256100002100279700001300300700002400313856012600337       2022                        eng d  a1865-134800aPixel Importance: The Impact of Saturation and Brightness on the Spread of Information on Social Media0 aPixel Importance The Impact of Saturation and Brightness on the   c2022  a10510aBIS10aBusiness Analytics1 aKaskela, Timothy1 aZhu, Bin1 aSayali, Dhamapurkar  u/biblio/pixel-importance-impact-saturation-and-brightness-spread-information-social-media02094nas a2200193   4500008004100000245007900041210006900120260000900189520150600198653002301704653001501727100001501742700001601757700001801773700002101791700001901812700002401831856004501855       2021                        eng d00aBias in context: Small biases in hiring evaluations have big consequences.0 aBias in context Small biases in hiring evaluations have big cons  c20213 aIt is widely acknowledged that subgroup bias can influence hiring evaluations. However, the notion that bias still threatens equitable hiring outcomes in modern employment contexts continues to be debated, even among organizational scholars. In this study, we sought to contextualize this debate by estimating the practical impact of bias on real-world hiring outcomes (a) across a wide range of hiring scenarios and (b) in the presence of diversity-oriented staffing practices. Toward this end, we conducted a targeted meta-analysis of recent hiring experiments that manipulated both candidate gender and qualifications to couch our investigation within ongoing debates surrounding the impact of small amounts of bias in otherwise meritocratic hiring contexts. Consistent with prior research, we found evidence of small gender bias effects (d = −0.30) and large qualification effects (d = 1.61) on hiring managers’ evaluations of candidate hireability. We then used these values to inform the starting parameters of a large-scale computer simulation designed to model conventional processes by which candidates are recruited, evaluated, and selected for open positions. Collectively, our simulation findings empirically substantiate assertions that even seemingly trivial amounts of subgroup bias can produce practically significant rates of hiring discrimination and productivity loss. Furthermore, we found contextual factors can alter but cannot obviate the consequences of biased evaluations,10aBusiness Analytics10aManagement1 aHardy, Jay1 aTey, K., S.1 aWilson, Cyrus1 aMartell, Richard1 aOlstad, Andrew1 aUhlmann, Eric, Luis  uhttps://doi.org/10.1177/014920632098265400700nas a2200193   4500008004100000245012100041210006900162260000900231300001200240490000700252653000800259653002300267653001200290653003200302100001600334700001500350700001700365856012400382       2021                        eng d00aDestabilization and Consolidation: Conceptualizing, Measuring, and Validating the Dual Characteristics of Technology0 aDestabilization and Consolidation Conceptualizing Measuring and   c2021  a104-1150 v5010aBIS10aBusiness Analytics10aFinance10aStrategy & Entrepreneurship1 aChen, Jiyao1 aShao, Rong1 aFan, Shaokun  u/biblio/destabilization-and-consolidation-conceptualizing-measuring-and-validating-dual00610nas a2200169   4500008004100000245009000041210006900131260000900200300000900209653000800218653002300226100001600249700001500265700001700280700001900297856012400316       2021                        eng d00aDividend or No Dividend in Delegated Blockchain Governance: A Game Theoretic Analysis0 aDividend or No Dividend in Delegated Blockchain Governance A Gam  c2021  a1-1910aBIS10aBusiness Analytics1 aPan, Dapeng1 aZhao, Leon1 aFan, Shaokun1 aZhang, Ziqiong  u/biblio/dividend-or-no-dividend-delegated-blockchain-governance-game-theoretic-analysis00477nas a2200145   4500008004100000245005700041210005600098260000900154490000700163653000800170653002300178100002500201700001300226856009200239       2021                        eng d00aEnhancing decision-making with data quality metadata0 aEnhancing decisionmaking with data quality metadata  c20210 v2310aBIS10aBusiness Analytics1 aShankaranarayanan, G1 aZhu, Bin  u/biblio/enhancing-decision-making-data-quality-metadata00850nas a2200121   4500008004100000245009300041210006900134260000900203520033100212653002300543100001900566856014300585       2021                        eng d00aImproving Student Engagement and Connection in Online Learning through Proactive Support0 aImproving Student Engagement and Connection in Online Learning t  c20213 aXiaohui Chang associate professor of Business Analytics, doesn't hold office hours. She holds "ask me anything hours" as a part of her methods to engage, connect and show empathy for her online students. Her first essay on the Ecampus teaching journey has great tips for all of our increased interactions in the virtual space.10aBusiness Analytics1 aChang, Xiaohui  uhttps://blogs.oregonstate.edu/inspire/2021/12/07/improving-student-engagement-and-connection-in-online-learning-through-proactive-support/00541nas a2200157   4500008004100000245006700041210006700108260000900175300001200184490000600196653002300202653001700225100002200242700001500264856010400279       2021                        eng d00aStability conditions of coupled autonomous vehicles formations0 aStability conditions of coupled autonomous vehicles formations  c2021  a513-5220 v810aBusiness Analytics10aOSU-Cascades1 aBaldivieso, Pablo1 aVeerman, J  u/biblio/stability-conditions-coupled-autonomous-vehicles-formations00613nas a2200181   4500008004100000245007800041210006900119260001700188300001200205490000700217653000800224653002300232100001500255700002000270700001700290700001500307856010900322       2021                        eng d00aStability of Transaction Fees in Bitcoin: A Supply and Demand Perspective0 aStability of Transaction Fees in Bitcoin A Supply and Demand Per  aCanyonc2021  a563-6920 v4510aBIS10aBusiness Analytics1 aIlk, Noyan1 aShang, Guangzhi1 aFan, Shaokun1 aZhao, Leon  u/biblio/stability-transaction-fees-bitcoin-supply-and-demand-perspective00567nas a2200133   4500008004100000245011800041210006900159260000900228653000800237653002300245100001500268700001700283856013300300       2020                        eng d00aCombining Textual Cues with Social Clues: Utilizing Social Features to Improve Sentiment Analysis in Social Media0 aCombining Textual Cues with Social Clues Utilizing Social Featur  c202010aBIS10aBusiness Analytics1 aIlk, Noyan1 aFan, Shaokun  u/biblio/combining-textual-cues-social-clues-utilizing-social-features-improve-sentiment-analysis00673nas a2200181   4500008004100000245009400041210006900135260002300204653000800227653002300235653001200258653003200270100001600302700001500318700001700333700001500350856012600365       2020                        eng d00aImpact of Team Size on Technological Contributions:  Unpacking Disruption and Development0 aImpact of Team Size on Technological Contributions Unpacking Dis  aVancouver CAc202010aBIS10aBusiness Analytics10aFinance10aStrategy & Entrepreneurship1 aChen, Jiyao1 aShao, Rong1 aFan, Shaokun1 aLi, Jiexun  u/biblio/impact-team-size-technological-contributions-unpacking-disruption-and-development02320nas a2200157   4500008004100000245009900041210006900140260000900209300001400218490000700232520173700239653002301976100001501999700001902014856012902033       2020                        eng d00aImproving Mobile Health Apps Usage: A Quantitative Study on mPower Data of Parkinson's Disease0 aImproving Mobile Health Apps Usage A Quantitative Study on mPowe  c2020  a399–4200 v343 a<b>Purpose</b><br>The emergence of mobile health (mHealth) products has created a capability of monitoring and managing the health of patients with chronic diseases. These mHealth technologies would not be beneficial unless they are adopted and used by their target users. This study identifies key factors affecting the usage of mHealth apps based on user usage data collected from an mHealth app.<br><b><br>Design/methodology/approach</b><br>Using a data set collected from an mHealth app named mPower, developed for patients with Parkinson’s disease (PD), this paper investigated the effects of disease diagnosis, disease progression, and mHealth app difficulty level on app usage, while controlling for user information. App usage is measured by five different activity counts of the app.<br><br><b>Findings</b><br>The results across five measures of mHealth app usage vary slightly. On average, previous professional diagnosis and high user performance scores encourage user participation and engagement, while disease progression hinders app usage.<br><br><b>Research limitations/implications</b><br>The findings potentially provide insights into better design and promotion of mHealth products and improve the capability of health management of patients with chronic diseases.<br><br><b>Originality/value</b><br>Studies on the mHealth app usage are critical but sparse because large-scale and reliable mHealth app usage data are limited. Unlike earlier works based solely on survey data, this research used a large user usage data collected from an mHealth app to study key factors affecting app usage. The methods presented in this study can serve as a pioneering work for the design and promotion of mHealth technologies.10aBusiness Analytics1 aLi, Jiexun1 aChang, Xiaohui  u/biblio/improving-mobile-health-apps-usage-quantitative-study-mpower-data-parkinsons-disease00618nas a2200169   4500008004100000245009000041210006900131260000900200300001400209490000800223653002300231100001700254700001800271700001900289700001500308856012500323       2020                        eng d00aModeling and Regionalization of China's PM2.5 Using Spatial-Functional Mixture Models0 aModeling and Regionalization of Chinas PM25 Using SpatialFunctio  c2020  a116–1320 v11610aBusiness Analytics1 aLiang, Decai1 aZhang, Haozhe1 aChang, Xiaohui1 aHuang, Hui  u/biblio/modeling-and-regionalization-chinas-pm25-using-spatial-functional-mixture-models01955nas a2200181   4500008004100000245008100041210006900122260000900191300000900200490000700209520140300216653002301619100001801642700002101660700001901681700001901700856005401719       2020                        eng d00aNoise Accumulation in High Dimensional Classification and Total Signal Index0 aNoise Accumulation in High Dimensional Classification and Total   c2020  a1-230 v213 aGreat attention has been paid to Big Data in recent years. Such data hold promise for scientific discoveries but also pose challenges to analyses. One potential challenge is noise accumulation. In this paper, we explore noise accumulation in high dimensional two-group classification. First, we revisit a previous assessment of noise accumulation with principal component analyses, which yields a different threshold for discriminative ability than originally identified. Then we extend our scope to its impact on classifiers developed with three common machine learning approaches—random forest, support vector machine, and boosted classification trees. We simulate four scenarios with differing amounts of signal strength to evaluate each method. After determining noise accumulation may affect the performance of these classifiers, we assess factors that impact it. We<br>conduct simulations by varying sample size, signal strength, signal strength proportional to the number predictors, and signal magnitude with random forest classifiers. These simulations suggest that noise accumulation affects the discriminative ability of high-dimensional classifiers developed using common machine learning methods, which can be modified by sample size, signal strength, and signal magnitude. We developed the measure total signal index (TSI) to track the trends of total signal and noise accumulation.10aBusiness Analytics1 aElman, Miriam1 aMinnier, Jessica1 aChang, Xiaohui1 aChoi, Dongseok  uhttp://jmlr.org/papers/volume21/19-117/19-117.pdf01488nas a2200181   4500008004100000245011200041210006900153260000900222300001600231490000700247520082500254653002301079100001801102700001401120700001901134700001501153856013801168       2020                        eng d00aRealized Volatility Forecasting and Volatility Spillovers: Evidence from Chinese Non-Ferrous Metals Futures0 aRealized Volatility Forecasting and Volatility Spillovers Eviden  c2020  a2713–27310 v263 aWe study the prediction of realized volatility of non-ferrous metals futures traded on the Shanghai Futures Exchange from March 2011 to December 2017. A dynamic model averaging model is employed to combine multiple prediction models using time-varying weights based on individual model performance. Empirical results also reveal that models incorporating volatility spillovers across metals are important for forecast combinations, and short-term spillovers have a stronger impact than long-term spillovers. This approach offers the best forecasting performance and allows users to identify the most dominant model at any given time and demonstrate when and how volatility transmission from another metal is valuable for forecasting. We also find evidence of distinct trading behaviors in emerging and developed markets.10aBusiness Analytics1 aWang, Donghua1 aXin, Yang1 aChang, Xiaohui1 aSu, Xingze  u/biblio/realized-volatility-forecasting-and-volatility-spillovers-evidence-chinese-non-ferrous-metals00521nas a2200145   4500008004100000245007600041210006900117260000900186490000700195653000800202653002300210100001700233700001500250856011000265       2020                        eng d00aA text analytics framework for automated communication pattern analysis0 atext analytics framework for automated communication pattern ana  c20200 v5710aBIS10aBusiness Analytics1 aFan, Shaokun1 aIlk, Noyan  u/biblio/text-analytics-framework-automated-communication-pattern-analysis01160nas a2200157   4500008004100000245007000041210006900111260000900180300001200189490000800201520062900209653002300838100001900861700001500880856010700895       2019                        eng d00aBusiness Performance Prediction in Location-based Social Commerce0 aBusiness Performance Prediction in Locationbased Social Commerce  c2019  a112-1230 v1263 aSocial commerce and location-based services provide a data platform for coexisting and competing businesses in geographical neighborhoods. Our research is aimed at mining data from such platforms to gain valuable insights for better support to strategic and operational business decisions. We develop a computational framework for predicting business performance that takes into account both intrinsic (e.g., attributes) and extrinsic (e.g., competitions) factors. Our experiments on synthetic and real datasets demonstrated superiority of a hybrid prediction model that adopts both link-based and context-based assumptions.10aBusiness Analytics1 aChang, Xiaohui1 aLi, Jiexun  u/biblio/business-performance-prediction-location-based-social-commerce00607nas a2200157   4500008004100000245010800041210006900149260000900218300001000227653000800237653002300245100001600268700001500284700001700299856013300316       2019                        eng d00aImpacts of Consensus Algorithms in Cryptocurrency: A Theoretical Analysis of PoW versus PoS in Ethereum0 aImpacts of Consensus Algorithms in Cryptocurrency A Theoretical   c2019  a16-2210aBIS10aBusiness Analytics1 aPan, Dapeng1 aZhao, Leon1 aFan, Shaokun  u/biblio/impacts-consensus-algorithms-cryptocurrency-theoretical-analysis-pow-versus-pos-ethereum00614nas a2200157   4500008004100000245009200041210006900133260001700202653000800219653002300227653003200250100001600282700001400298700001700312856012700329       2019                        eng d00aKnowledge Networks, Collaboration Networks, and Innovation: A Replication and Extension0 aKnowledge Networks Collaboration Networks and Innovation A Repli  aBostonc201910aBIS10aBusiness Analytics10aStrategy & Entrepreneurship1 aChen, Jiyao1 aShen, Jia1 aFan, Shaokun  u/biblio/knowledge-networks-collaboration-networks-and-innovation-replication-and-extension00513nas a2200109   4500008004100000245009700041210006900138260002400207653002300231100001900254856013000273       2019                        eng d00aLocation-based Data on Social Commerce Platforms can Provide Insights for Business Decisions0 aLocationbased Data on Social Commerce Platforms can Provide Insi  aCorvallis, ORc201910aBusiness Analytics1 aChang, Xiaohui  u/biblio/location-based-data-social-commerce-platforms-can-provide-insights-business-decisions00547nas a2200133   4500008004100000245009200041210006900133260002500202653000800227653002300235100001500258700001700273856012300290       2019                        eng d00aA Process Mining Framework for Communication Pattern Analysis in Online Contact Centers0 aProcess Mining Framework for Communication Pattern Analysis in O  aSalt lake cityc201910aBIS10aBusiness Analytics1 aIlk, Noyan1 aFan, Shaokun  u/biblio/process-mining-framework-communication-pattern-analysis-online-contact-centers00521nas a2200145   4500008004100000245006900041210006500110260001700175653000800192653002300200100001500223700002000238700001700258856010000275       2019                        eng d00aA Supply and Demand Model for Bitcoin’s Data Space Marketplace0 aSupply and Demand Model for Bitcoin s Data Space Marketplace  aMunichc201910aBIS10aBusiness Analytics1 aIlk, Noyan1 aShang, Guangzhi1 aFan, Shaokun  u/biblio/supply-and-demand-model-bitcoins-data-space-marketplace00503nas a2200145   4500008004100000245006200041210005900103260002200162653000800184653002300192100001500215700001700230700001600247856009400263       2018                        eng d00aBlockchain-Enabled Trust: The Case of Inter-Firm Dataflow0 aBlockchainEnabled Trust The Case of InterFirm Dataflow  aNew Orleansc201810aBIS10aBusiness Analytics1 aZhao, Leon1 aFan, Shaokun1 aZheng, Eric  u/biblio/blockchain-enabled-trust-case-inter-firm-dataflow00571nas a2200169   4500008004100000245007800041210006900119260000900188300001200197490000700209653000800216653002300224100001100247700001700258700001500275856011100290       2018                        eng d00aCommunity Engagement and Online Word of Mouth: An Empirical Investigation0 aCommunity Engagement and Online Word of Mouth An Empirical Inves  c2018  a258-2700 v5510aBIS10aBusiness Analytics1 aWu, Ji1 aFan, Shaokun1 aZhao, Leon  u/biblio/community-engagement-and-online-word-mouth-empirical-investigation00651nas a2200169   4500008004100000245009800041210006900139260002200208653000800230653002300238653001200261653003200273100001600305700001500321700001700336856012800353       2018                        eng d00aDevelopment of Context-based Indices for Measuring Dynamic and Dualistic Nature of Innovation0 aDevelopment of Contextbased Indices for Measuring Dynamic and Du  aChicago, ILc201810aBIS10aBusiness Analytics10aFinance10aStrategy & Entrepreneurship1 aChen, Jiyao1 aShao, Rong1 aFan, Shaokun  u/biblio/development-context-based-indices-measuring-dynamic-and-dualistic-nature-innovation00528nas a2200145   4500008004100000245006900041210006600110260001700176653000800193653002300201100001900224700001700243700001600260856010600276       2018                        eng d00aAn Efficient Recommender System Using Locality Sensitive Hashing0 aEfficient Recommender System Using Locality Sensitive Hashing  aHawaiic201810aBIS10aBusiness Analytics1 aZhang, Kunpeng1 aFan, Shaokun1 aWang, Harry  u/biblio/efficient-recommender-system-using-locality-sensitive-hashing01294nas a2200169   4500008004100000245007300041210006900114260000900183300001000192490000800202520073100210653002300941100001400964700001900978700001800997856010901015       2018                        eng d00aFlexible and Efficient Estimating Equations for Variogram Estimation0 aFlexible and Efficient Estimating Equations for Variogram Estima  c2018  a45-580 v1223 aVariogram estimation plays a vastly important role in spatial modeling. Different methods<br>for variogram estimation can be largely classified into least squares methods and likelihood<br>based methods. A general framework to estimate the variogram through a set of estimating<br>equations is proposed. This approach serves as an alternative approach to likelihood based<br>methods and includes commonly used least squares approaches as its special cases. The<br>proposed method is highly efficient as a low dimensional representation of the weight<br>matrix is employed. The statistical efficiency of various estimators is explored and the lag<br>effect is examined. An application to a hydrology data set is also presented.10aBusiness Analytics1 aSun, Ying1 aChang, Xiaohui1 aGuan, Yongtao  u/biblio/flexible-and-efficient-estimating-equations-variogram-estimation00606nas a2200145   4500008004100000245011100041210006900152260002200221653000800243653002300251100001500274700001700289700002000306856013400326       2018                        eng d00aInvestigating the Fee-Delay Relationship in Cryptocurrency Transactions: Evidence from the Bitcoin Network0 aInvestigating the FeeDelay Relationship in Cryptocurrency Transa  aSanta Clarac201810aBIS10aBusiness Analytics1 aIlk, Noyan1 aFan, Shaokun1 aShang, Guangzhi  u/biblio/investigating-fee-delay-relationship-cryptocurrency-transactions-evidence-bitcoin-network00552nas a2200169   4500008004100000245006300041210006200104260000900166653000800175653002300183653001700206100001500223700001600238700001300254700001600267856009900283       2018                        eng d00aMaking Sense of Organization Dynamics Using Text Analysis.0 aMaking Sense of Organization Dynamics Using Text Analysis  c201810aBIS10aBusiness Analytics10aSupply Chain1 aLi, Jiexun1 aWu, Zhaohui1 aZhu, Bin1 aXu, Kaiquan  u/biblio/making-sense-organization-dynamics-using-text-analysis00441nas a2200121   4500008004100000245005200041210005200093260002600145653002300171100002100194700001200215856009200227       2018                        eng d00aSocial Media Response Based Upon Media Richness0 aSocial Media Response Based Upon Media Richness  aNew Orleans, LAc201810aBusiness Analytics1 aKaskela, Timothy1 aSong, J  u/biblio/social-media-response-based-upon-media-richness00470nas a2200121   4500008004100000245006100041210006100102260002800163653002300191100002100214700001200235856010100247       2018                        eng d00aSocial Media Responses Based Upon Frames and Symbol Sets0 aSocial Media Responses Based Upon Frames and Symbol Sets  aSan Francisco, CAc201810aBusiness Analytics1 aKaskela, Timothy1 aSong, J  u/biblio/social-media-responses-based-upon-frames-and-symbol-sets00530nas a2200169   4500008004100000245005000041210005000091260000900141300001200150490000800162653002300170653001700193100002200210700001900232700002200251856008700273       2018                        eng d00aSpectra of certain large tridiagonal matrices0 aSpectra of certain large tridiagonal matrices  c2018  a123-1470 v54810aBusiness Analytics10aOSU-Cascades1 aBaldivieso, Pablo1 aVeerman, JJ, P1 aHammond, David, K  u/biblio/spectra-certain-large-tridiagonal-matrices00511nas a2200145   4500008004100000245007500041210006900116260000900185653002300194653001700217100002100234700001900255700001900274856007200293       2018                        eng d00aUsing a Q Matrix to Assess Students' Latent Skills in an Online Course0 aUsing a Q Matrix to Assess Students Latent Skills in an Online C  c201810aBusiness Analytics10aSupply Chain1 aHsieh, Ping-Hung1 aChang, Xiaohui1 aOlstad, Andrew  uhttps://ecampus.oregonstate.edu/research/publications/white-papers/00484nas a2200145   4500008004100000245005700041210005700098260000900155653000800164653002300172100001500195700001800210700001300228856009700241       2017                        eng d00aBEHAVIOR THEORY ENABLED GENDER CLASSIFICATION METHOD0 aBEHAVIOR THEORY ENABLED GENDER CLASSIFICATION METHOD  c201710aBIS10aBusiness Analytics1 aWang, Jing1 aYan, Xiangbin1 aZhu, Bin  u/biblio/behavior-theory-enabled-gender-classification-method00625nas a2200169   4500008004100000245011000041210006900151260000900220300001200229490000700241653000800248653002300256100001700279700001200296700001500308856013200323       2017                        eng d00aCollaboration Process Pattern Approach to Improving Teamwork Performance: A Data Mining-Based Methodology0 aCollaboration Process Pattern Approach to Improving Teamwork Per  c2017  a438-4560 v2910aBIS10aBusiness Analytics1 aFan, Shaokun1 aLi, Xin1 aZhao, Leon  u/biblio/collaboration-process-pattern-approach-improving-teamwork-performance-data-mining-based00526nas a2200145   4500008004100000245007600041210006900117260000900186653000800195653002300203100001300226700001300239700001600252856011200268       2017                        eng d00aThe Different Behaviors between Product Searchers and Website Searchers0 aDifferent Behaviors between Product Searchers and Website Search  c201710aBIS10aBusiness Analytics1 aZun, Kai1 aZhu, Bin1 aZuo, Meiyun  u/biblio/different-behaviors-between-product-searchers-and-website-searchers00646nas a2200157   4500008004100000245012900041210006900170260001600239653002300255100001200278700001300290700001300303700002100316700001400337856013700351       2017                        eng d00aDiscovery of the Optimal Visualization for Representing Three Dimensions of Data Using Functional Magnetic Resonance Imaging0 aDiscovery of the Optimal Visualization for Representing Three Di  aSeoulc201710aBusiness Analytics1 aBina, S1 aGraue, W1 aJones, D1 aKaskela, Timothy1 aWalden, E  u/biblio/discovery-optimal-visualization-representing-three-dimensions-data-using-functional-magnetic00587nas a2200181   4500008004100000245006900041210006700110260000900177300001000186490000700196653000800203653002300211100001700234700001700251700001600268700001500284856010600299       2017                        eng d00aEnabling effective workflow model reuse: A data-centric approach0 aEnabling effective workflow model reuse A datacentric approach  c2017  a11-250 v9310aBIS10aBusiness Analytics1 aLiu, Zhiyong1 aFan, Shaokun1 aWang, Harry1 aZhao, Leon  u/biblio/enabling-effective-workflow-model-reuse-data-centric-approach00562nas a2200157   4500008004100000245008400041210006900125260000900194653000800203653002300211100001700234700001800251700001400269700001500283856010600298       2017                        eng d00aIntroduction to the special issue of ECR on E-business innovation with big data0 aIntroduction to the special issue of ECR on Ebusiness innovation  c201710aBIS10aBusiness Analytics1 aFan, Shaokun1 aXiao, JInghua1 aXie, Kang1 aZhao, Leon  u/biblio/introduction-special-issue-ecr-e-business-innovation-big-data01377nas a2200181   4500008004100000245007600041210006900117260000900186300001600195490000700211520077900218653002300997100001801020700001701038700001901055700001601074856010501090       2017                        eng d00aThe Lead-Lag Relationship between the Spot and Futures Markets in China0 aLeadLag Relationship between the Spot and Futures Markets in Chi  c2017  a1447–14560 v173 aBased on daily and one-minute high-frequency returns, this paper examines the<br>lead-lag dependence between the CSI 300 index spot and futures markets from 2010 to 2014. The<br>nonparametric and nonlinear thermal optimal path method is adopted. Empirical results of the<br>daily data indicate that the lead-lag relationship between the two markets is within one day but<br>this relationship is volatile since neither of the two possible situations (the futures leads or lags<br>behind the spot market) takes a dominant place. Besides, our results from high-frequency data<br>demonstrate that there is a price discovery in the Chinese futures market: the intraday one-minute<br>futures return leads the cash return by 0~5 minutes regardless of the price trend of the market.10aBusiness Analytics1 aWang, Donghua1 aTu, Jingqing1 aChang, Xiaohui1 aLi, Saiping  u/biblio/lead-lag-relationship-between-spot-and-futures-markets-china00568nas a2200145   4500008004100000245009400041210006900135260000900204653000800213653002300221100001800244700001300262700001600275856013100291       2016                        eng d00aDifferences between Younger and Senior Information Providers in Senior Online Communities0 aDifferences between Younger and Senior Information Providers in   c201610aBIS10aBusiness Analytics1 aWang, Changyu1 aZhu, Bin1 aZuo, Meiyun  u/biblio/differences-between-younger-and-senior-information-providers-senior-online-communities00558nas a2200145   4500008004100000245007700041210006900118260002200187653002300209653001700232100002100249700001900270700001900289856010400308       2016                        eng d00aEarly Detection of Placement for Success in an Online Quantitative Class0 aEarly Detection of Placement for Success in an Online Quantitati  aChicago, ILc201610aBusiness Analytics10aSupply Chain1 aHsieh, Ping-Hung1 aChang, Xiaohui1 aOlstad, Andrew  u/biblio/early-detection-placement-success-online-quantitative-class03641nas a2200157   4500008004100000245009500041210006900136260000900205520306600214653000803280653002303288100001803311700001303329700001603342856012503358       2016                        eng d00aHelping Senior Participants Acquire the Right Type of Social Support in Online Communities0 aHelping Senior Participants Acquire the Right Type of Social Sup  c20163 aSenior citizens could greatly be benefited from the social support received from a community(Choi et al. 2014; Goswami et al. 2010). Social support denotes to the
interaction/communication with others, verbal or nonverbal, reducing the uncertainty or
enhancing the self-perception of in control of one’s own life (Albrecht and Adelman 1987). All
participants of online communities are motivated by their desire of seeking social support. And
such support occurs when community members form relational links among them and have
interactions that intend to help (Heaney and Israel 2002). A network member can receive/send
different types of social supports from/to others. Informational support transmits information
and provides guidance related to the task/question a community member has (Krause 1986);
emotional support expresses understanding, encouragement, empathy affection, affirming,
validation, sympathy, caring and concern (House 1981; Wang et al. 2014); companionship or
network support gives the recipient a sense of belonging (Keating 2013; Wang et al. 2014); and
appraisal support enhances the self-evaluation of the recipient (House 1981). Studies have
shown that people are usually motivated by their desire of seeking one or more types of social
supports to participate in an online community (Goswami et al. 2010; Kanayama 2003; Pfeil
2007; Pfeil and Zaphiris 2009; Wright 2000; Xie 2008). And such social support can only be
acquired during the interaction with others. For senior citizens, even though they can be greatly
benefited from the social support received through participation, the obstacles they need to
overcome in order to feel engaged could be larger than that of younger people (Charness and
Boot 2009; Lee et al. 2011), especially when they come to the community for the first time. They
could be easily overwhelmed by the content that has been generated by other existing members,
finding it difficult to identify an appropriate member to initiate a meaningful interaction. It
therefore is critical for an online community system to help senior participants identify other
existing members who are more likely to supply the type of support they are seeking. While many
previous studies have uncovered the variety factors, contextual (Pfeil and Zaphiris 2009; Wang
et al. 2015; Xie 2008) or individual (Wang et al. 2014, 2015, 2012; Wright 1999), that impact
the degree to which a senior citizen receives social support needed from an online community, it
remains unclear what the characteristics of existing community members who are more likely to
provide a new comer the kind of support, informational, emotional, companionship, or appraisal
are. And the answer to this question may have significant academic and practical implications.
This study thus proposes to fulfil the gap by utilizing data collected from a senior community
website to investigate the links between the characteristics of existing senior members and the
amount and the type of support they provided to new comers.
10aBIS10aBusiness Analytics1 aWang, Changyu1 aZhu, Bin1 aZuo, Meiyun  u/biblio/helping-senior-participants-acquire-right-type-social-support-online-communities00614nas a2200157   4500008004100000245011600041210006900157260000900226490000600235653000800241653002300249100001500272700001700287700001500304856013700319       2016                        eng d00aOverview of business innovations and research opportunities in blockchain and introduction to the special issue0 aOverview of business innovations and research opportunities in b  c20160 v210aBIS10aBusiness Analytics1 aZhao, Leon1 aFan, Shaokun1 aYan, Jiaqi  u/biblio/overview-business-innovations-and-research-opportunities-blockchain-and-introduction-special00613nas a2200169   4500008004100000245009600041210006900137260000900206490000800215653000800223653002300231100001700254700001200271700001500283700001700298856012800315       2016                        eng d00aA Process Ontology Based Approach to Easing Semantic Ambiguity in Business Process Modeling0 aProcess Ontology Based Approach to Easing Semantic Ambiguity in   c20160 v10210aBIS10aBusiness Analytics1 aFan, Shaokun1 aHua, Z.1 aStorey, V.1 aZhao, J., L.  u/biblio/process-ontology-based-approach-easing-semantic-ambiguity-business-process-modeling00555nas a2200169   4500008004100000245007000041210006900111260000900180300001000189490000600199653000800205653002300213100001700236700001200253700001700265856010300282       2015                        eng d00aDemystifying big data analytics through the lens of marketing mix0 aDemystifying big data analytics through the lens of marketing mi  c2015  a28-320 v210aBIS10aBusiness Analytics1 aFan, Shaokun1 aLau, R.1 aZhao, J., L.  u/biblio/demystifying-big-data-analytics-through-lens-marketing-mix00558nas a2200145   4500008004100000245009300041210006900134260000900203653000800212653002300220100001500243700001700258700001500275856012200290       2015                        eng d00aThe Design of IdeaWorks: Applying Social Learning Networks to Support Tertiary Education0 aDesign of IdeaWorks Applying Social Learning Networks to Support  c201510aBIS10aBusiness Analytics1 aKang, Lele1 aFan, Shaokun1 aZhao, Leon  u/biblio/design-ideaworks-applying-social-learning-networks-support-tertiary-education01790nas a2200217   4500008004100000245011300041210006900154260000900223300001200232490000700244520105400251653002301305100001901328700002201347700001101369700001101380700002001391700001301411700001301424856013501437       2015                        eng d00aDisease Risk Estimation by Combining Case-Control Data with Aggregated Information on the Population at Risk0 aDisease Risk Estimation by Combining CaseControl Data with Aggre  c2015  a114-1210 v713 aWe propose a novel statistical framework by supplementing case-control data with summary statistics on the population at risk for a subset of risk factors. Our approach is to first form two unbiased estimating equations, one based on the case-control data and the other on both the case data and the summary statistics, and then optimally combine them to derive another estimating equation to be used for the estimation. The proposed method is computationally simple and more efficient than standard approaches based on case-control data alone. We also establish asymptotic properties of the resulting estimator, and investigate its finite-sample performance through simulation. As a substantive application, we apply the proposed method to investigate risk factors for endometrial cancer, by using data from a recently completed population-based case-control study and summary statistics from the Behavioral Risk Factor Surveillance System, the Population Estimates Program of the US Census Bureau, and the Connecticut Department of Transportation.10aBusiness Analytics1 aChang, Xiaohui1 aWaagepetersen, R.1 aYu, H.1 aMa, X.1 aHolford, T., R.1 aWang, R.1 aGuan, Y.  u/biblio/disease-risk-estimation-combining-case-control-data-aggregated-information-population-risk02960nas a2200181   4500008004100000245015600041210006900197260000900266300001200275490000600287520226000293653002302553100001802576710001802594700001302612700001902625856013402644       2015                        eng d00aDynamic relation of Chinese stock price-volume pre- and post- the Split Share Structure Reform: New evidence from a two-state Markov-switching approach0 aDynamic relation of Chinese stock pricevolume pre and post the S  c2015  a386-4010 v53 aPurpose – The purpose of this paper is to identify the bull and bear regimes in Chinese stock market and empirically analyze the dynamic relation of Chinese stock price-volume pre- and post- the Split Share Structure Reform.<br><br>Design/methodology/approach – The authors investigate the price-volume relationship in the Chinese stock market before and after the Split Share Structure Reform using Shanghai Composite Index daily data from July 1994 to April 2013. Using a two-state Markov-switching autoregressive model and a modified two-state Markov-switching vector autoregression model, this study identifies bull or bear market and also examine the existence of regime-dependent Granger causality.<br><br>Findings – Using a two-state Markov-switching autoregressive model, the authors detect structural changes in the market volatility due to the reform, and find evidence of a positive rather than an asymmetric price-volume contemporaneous correlation. There is a strong dynamic Granger causal relation from stock returns to trading volume before and after the reform regardless of the market conditions, but the causal effects of volume on returns are only seen in the bear markets before the reform. The model is robust when using different stock indices and time periods.<br><br>Originality/value – The work is different from previous studies in the following aspects: most of the existing empirical literature focus on the well-developed economies, but our interest lies in the emerging Chinese market that has witnessed rapid growth in the past decade; in contrast to many works in the literature that examine the price-volume relationship during one market condition, the authors compare the relationship in a bull market with that in a bear market, using a two-state MS-AR model; the authors also employ a modified two-state Markov-switching vector autoregression model to examine the existence of regime-dependent Granger causality; as the most massive systematic reform for the Chinese stock market since its inception in 2005, the Split Share Structure Reform has a profound impact on the Chinese stock market, thus it is of vital importance to explore its effects on both the price-volume relationship and the market structure.10aBusiness Analytics1 aWang, Donghua1 aEmptyAuthNode1 aLei, Man1 aChang, Xiaohui  u/biblio/dynamic-relation-chinese-stock-price-volume-pre-and-post-split-share-structure-reform-new00543nas a2200157   4500008004100000245007200041210006900113260000900182490000600191653000800197653002300205100001200228700002100240700001700261856010700278       2015                        eng d00aHarnessing Internet finance with innovative cyber credit management0 aHarnessing Internet finance with innovative cyber credit managem  c20150 v110aBIS10aBusiness Analytics1 aLin, Z.1 aWhinston, A., B.1 aFan, Shaokun  u/biblio/harnessing-internet-finance-innovative-cyber-credit-management00623nas a2200145   4500008004100000245011800041210006900159260002600228653002300254100001600277700001700293700001900310700001900329856012900348       2015                        eng d00aImpact of Pharmacy Student Involvement in Medical Reconciliation on Samaritan  Health Services Outpatient Clinics0 aImpact of Pharmacy Student Involvement in Medical Reconciliation  aNew Orleans, LAc201510aBusiness Analytics1 aNguyen, Thi1 aFahey, Colin1 aOlstad, Stacey1 aOlstad, Andrew  u/biblio/impact-pharmacy-student-involvement-medical-reconciliation-samaritan-health-services00633nas a2200145   4500008004100000245012500041210006900166260002600235653002300261100001700284700001600301700001900317700001900336856013200355       2015                        eng d00aInvolvement of Pharmacy in Admission and Discharge Medication Review to Improve Patient Outcomes in an Inpatient Setting0 aInvolvement of Pharmacy in Admission and Discharge Medication Re  aNew Orleans, LAc201510aBusiness Analytics1 aFahey, Colin1 aNguyen, Thi1 aOlstad, Stacey1 aOlstad, Andrew  u/biblio/involvement-pharmacy-admission-and-discharge-medication-review-improve-patient-outcomes00544nas a2200157   4500008004100000245006500041210006500106260002300171653000800194653002300202653001700225100001500242700001600257700001300273856010000286       2015                        eng d00aMining Hidden Organizational Structures from Meeting Records0 aMining Hidden Organizational Structures from Meeting Records  aPhiladelphiac201510aBIS10aBusiness Analytics10aSupply Chain1 aLi, Jiexun1 aWu, Zhaohui1 aZhu, Bin  u/biblio/mining-hidden-organizational-structures-meeting-records00522nas a2200157   4500008004100000245007800041210006900119260002000188653000800208653002300216100001400239700001600253700001300269700001800282856006400300       2015                        eng d00aPREDICTING HABITUAL CONTINUING SOCIAL NETWORKING SITES USE OF THE ELDERLY0 aPREDICTING HABITUAL CONTINUING SOCIAL NETWORKING SITES USE OF TH  aSingaporec201510aBIS10aBusiness Analytics1 aChai, Wen1 aZuo, Meiyun1 aZhu, Bin1 aTian, Xuesong  uhttp://pacis2015.comp.nus.edu.sg/pages/workshops_cnais.html00566nas a2200145   4500008004100000245009100041210006900132260001700201653000800218653002300226100001700249700001500266700001900281856012000300       2015                        eng d00aSentiment Analysis in Social Media Platforms: The Contribution of Social Relationships0 aSentiment Analysis in Social Media Platforms The Contribution of  aDallasc201510aBIS10aBusiness Analytics1 aFan, Shaokun1 aIlk, Noyan1 aZhang, Kunpeng  u/biblio/sentiment-analysis-social-media-platforms-contribution-social-relationships00532nas a2200169   4500008004100000245005500041210005400096260000900150653001500159653000800174653002300182100001500205700001500220700001300235700002000248856009400268       2015                        eng d00aWeather Factors and Online Product/Service Reviews0 aWeather Factors and Online ProductService Reviews  c201510aAccounting10aBIS10aBusiness Analytics1 aFeng, Jiao1 aYao, Zhong1 aZhu, Bin1 aMarshall, Byron  u/biblio/weather-factors-and-online-productservice-reviews00574nas a2200169   4500008004100000245007400041210006900115260000900184300001600193490000700209653000800216653002300224100001700247700001200264700001700276856011100293       2015                        eng d00aWorkflow-Aware Attention Tracking to Enhance Collaboration Management0 aWorkflowAware Attention Tracking to Enhance Collaboration Manage  c2015  a1253–12640 v1710aBIS10aBusiness Analytics1 aFan, Shaokun1 aKang, L1 aZhao, J., L.  u/biblio/workflow-aware-attention-tracking-enhance-collaboration-management00597nas a2200169   4500008004100000245009200041210006900133260000900202300001200211490000600223653000800229653002300237100001700260700001700277700001100294856012200305       2014                        eng d00aBusiness Challenges and Research Directions of Management Analytics in the Big Data Era0 aBusiness Challenges and Research Directions of Management Analyt  c2014  a169-1740 v110aBIS10aBusiness Analytics1 aZhao, J., L.1 aFan, Shaokun1 aHu, D.  u/biblio/business-challenges-and-research-directions-management-analytics-big-data-era00559nas a2200145   4500008004100000245008200041210006900123260003100192653000800223653002300231100001500254700001200269700001300281856011900294       2014                        eng d00aCollective opinion classification: A global consistency maximization approach0 aCollective opinion classification A global consistency maximizat  aAukland, New Zealandc201410aBIS10aBusiness Analytics1 aLi, Jiexun1 aLi, Xin1 aZhu, Bin  u/biblio/collective-opinion-classification-global-consistency-maximization-approach00535nas a2200157   4500008004100000245006000041210005900101260002600160653000800186653002300194100001700217700001700234700002100251700001500272856009000287       2014                        eng d00aCredit Risk Assessment of POS-Loans in the Big Data Era0 aCredit Risk Assessment of POSLoans in the Big Data Era  aHongkong, Chinac201410aBIS10aBusiness Analytics1 aBian, Yiyang1 aFan, Shaokun1 aYe, Ryan, Liying1 aZhao, Leon  u/biblio/credit-risk-assessment-pos-loans-big-data-era00535nas a2200133   4500008004100000245008900041210006900130260001900199653000800218653002300226100001600249700001300265856012300278       2014                        eng d00aEnsuring Positive Impact of Data Quality Metadata: Implications for Decision Support0 aEnsuring Positive Impact of Data Quality Metadata Implications f  aSavannahc201410aBIS10aBusiness Analytics1 aShankar, G.1 aZhu, Bin  u/biblio/ensuring-positive-impact-data-quality-metadata-implications-decision-support-000616nas a2200157   4500008004100000245010300041210006900144260002500213653000800238653002300246100001100269700001700280700001400297700001500311856013200326       2014                        eng d00aFormation and effect of Social Interactions in Online Brand Community: an Empirical Investigation.0 aFormation and effect of Social Interactions in Online Brand Comm  aChengdu, Chinac201410aBIS10aBusiness Analytics1 aWu, Ji1 aFan, Shaokun1 aWu, Manli1 aZhao, Leon  u/biblio/formation-and-effect-social-interactions-online-brand-community-empirical-investigation01798nas a2200181   4500008004100000245007500041210006800116260000900184300001400193490000700207520122500214653000801439653002301447100001201470700001801482700001301500856010301513       2014                        eng d00aThe Hl-index: Improvement of H-index Based on Quality of Citing Papers0 aHlindex Improvement of Hindex Based on Quality of Citing Papers  c2014  a1021-10310 v983 aThis paper proposes hl-index as an improvement of the h-index, a popular measurement for the research quality of academic researchers. Although the h-index integrates the number of publications and the academic impact of each publication to evaluate the productivity of a researcher, it assumes that all papers that cite an academic article contribute equally to the academic impact of this article. This assumption, of course, could not be true in most times. The citation from a well-cited paper certainly brings more attention to the article than the citation from a paper that people do not pay attention to. It therefore becomes important to integrate the impact of papers that cite a researcher’s work into the evaluation of the productivity of the researcher. Constructing a citation network among academic papers, this paper therefore proposes hl-index that integrating the h-index with the concept of lobby index, a measures that has been used to evaluate the impact of a node in a complex network based on the impact of other nodes that the focal node has direct link with. This paper also explores the characteristics of the proposed hl-index by comparing it with citations, h-index and its variant g-index.10aBIS10aBusiness Analytics1 aZai, Li1 aYan, Xiangbin1 aZhu, Bin  u/biblio/hl-index-improvement-h-index-based-quality-citing-papers-000597nas a2200157   4500008004100000245007900041210006900120260003100189653000800220653002300228100001900251700002100270700001700291700001900308856011200327       2014                        eng d00aScalable Audience Targeted Models for Brand Advertising on Social Networks0 aScalable Audience Targeted Models for Brand Advertising on Socia  aFoster City, CA, USAc201410aBIS10aBusiness Analytics1 aZhang, Kunpeng1 aOuksel, Aris, M.1 aFan, Shaokun1 aLiu, Hengchang  u/biblio/scalable-audience-targeted-models-brand-advertising-social-networks01615nas a2200157   4500008004100000245008100041210006900122260000900191300001200200490000600212520105900218653002301277100001901300700002301319856011501342       2014                        eng d00aWavelet Methods in Interpolation of High-Frequency Spatial-Temporal Pressure0 aWavelet Methods in Interpolation of HighFrequency SpatialTempora  c2014  a52–680 v83 aThe location-scale and whitening properties of wavelets make them more favorable for interpolating high-frequency monitoring data than Fourier-based methods. In the past, wavelets have been used to simplify the dependence structure in multiple time or spatial series, but little has been done to apply wavelets as a modeling tool in a space–time setting, or, in particular, to take advantage of the localization of wavelets to capture the local dynamic characteristics of high-frequency meteorological data. This paper analyzes minute-by-minute atmospheric pressure data from the Atmospheric Radiation Measurement program using different wavelet coefficient structures at different scales and incorporating spatial structure into the model. This approach of modeling space–time processes using wavelets produces accurate point predictions with low uncertainty estimates, and also enables interpolation of available data from sparse monitoring stations to a high density grid and production of meteorological maps on large spatial and temporal scales.10aBusiness Analytics1 aChang, Xiaohui1 aStein, Michael, L.  u/biblio/wavelet-methods-interpolation-high-frequency-spatial-temporal-pressure02077nas a2200253   4500008004100000245011700041210006900158260000900227300001400236490000700250520125000257653000801507653002301515100001401538700001701552700001901569700001301588700001701601700001801618700001801636700001801654700001501672856013601687       2013                        eng d00aAn ACP Approach to Public Health Emergency Management: Using a Campus Outbreak of H1N1 Influenza as a Case Study0 aACP Approach to Public Health Emergency Management Using a Campu  c2013  a1028-10410 v433 aIn order to tackle the infeasibility of building mathematical models and conducting physical experiments for public health emergencies in a real world, we apply the ACP (Artificial societies, Computational experiments, and Parallel execution) approach to public health emergency management. We conducted a case study on the largest collective outbreak of H1N1 influenza at a Chinese university in 2009. We built an artificial society to reproduce H1N1 influenza outbreaks. In computational experiments, aiming to obtain comparable results with the real data, we applied the same intervention strategy as that was used during the real outbreak. Then we compared experiment results with real data to verify our models, including spatial models, population distribution, weighted social networks, contact patterns, students’ behaviors, and models of H1N1 influenza disease, in the artificial society. We then applied alternative intervention strategies to the artificial society. The simulation results suggested that alternative strategies controlled the outbreak of H1N1 influenza more effectively. Our models and their application to intervention strategy improvement show that the ACP approach is useful for public health emergency management10aBIS10aBusiness Analytics1 aDuan, Wei1 aCao, Zhidong1 aWang, Youzhong1 aZhu, Bin1 aZeng, Daniel1 aWang, Fei-Yue1 aQiu, Xiaogang1 aSong, Hongbin1 aWang, Yong  u/biblio/acp-approach-public-health-emergency-management-using-campus-outbreak-h1n1-influenza-case-001675nas a2200157   4500008004100000245011300041210006900154260000900223520115900232653000801391653002301399100001801422700002601440700001301466856003801479       2013                        eng d00aA cognitive-neural approach to explaining market oscillations in a fully recurrent adaptive agent population0 acognitiveneural approach to explaining market oscillations in a   c20133 aRecreating market oscillations to study the markets often makes use of induced activity reversal via finite share or auction thresholds, strategically replacing agents via bankruptcy or genetic algorithm rules, heavily data specific network parameterization, or stochastic randomness. However, such techniques do not shed any additional light on how and why intelligent individual scale agents may spontaneously and rationally decide to endogenously change from a buying to a selling posture within a population. This paper introduces Social Netmap, an agent based population of general purpose, parameter-free, adaptive agents adjusting their behavior in real time to the directly observed aggregate and individual behaviors of their neighbors much like real intelligent actors might in a population. Without relying on random processes, validated parameters, turning-point thresholds, or agent replacement, Social Netmap was able to endogenously create typical market oscillations in 21 out of 30 cases of real Dow Jones Industrial Average data. Social Netmap points towards future work in more realistic group behavior of intelligent, rational agents.10aBIS10aBusiness Analytics1 aWong, Charles1 aVersace, Massimiliano1 aZhu, Bin  uhttp://www.dmi.unict.it/ecal2013/00495nas a2200133   4500008004100000245006300041210006100104260003400165653000800199653002300207100001300230700001900243856009900262       2013                        eng d00aData/Knowledge Management for LIDAR Data Users/Researchers0 aDataKnowledge Management for LIDAR Data UsersResearchers  a����������c201310aBIS10aBusiness Analytics1 aZhu, Bin1 aOlson, Michael  u/biblio/dataknowledge-management-lidar-data-usersresearchers-001375nas a2200157   4500008004100000245008800041210006900129260000900198300001400207490000700221520079900228653002301027100001901050700002301069856012501092       2013                        eng d00aDecorrelation Property of Discrete Wavelet Transform Under Fixed-Domain Asymptotics0 aDecorrelation Property of Discrete Wavelet Transform Under Fixed  c2013  a8001-80130 v593 aTheoretical aspects of the decorrelation property of the discrete wavelet transform when applied to stochastic processes have been studied exclusively from the increasing-domain perspective, in which the distance between neighboring observations stays roughly constant as the number of observations increases. To understand the underlying data-generating process and to obtain good interpolations, fixed-domain asymptotics, in which the number of observations increases in a fixed region, is often more appropriate than increasing-domain asymptotics. In the fixed-domain setting, we prove that, for a general class of inhomogeneous covariance functions, with suitable choice of wavelet filters, the wavelet transform of a nonstationary process has mostly asymptotically uncorrelated components.10aBusiness Analytics1 aChang, Xiaohui1 aStein, Michael, L.  u/biblio/decorrelation-property-discrete-wavelet-transform-under-fixed-domain-asymptotics00581nas a2200145   4500008004100000245009800041210006900139260002300208653000800231653002300239100001700262700001300279700001500292856012800307       2013                        eng d00aEver-Changing Workarounds: A Model for Workaround Management Lifecycle in Healthcare Workflow0 aEverChanging Workarounds A Model for Workaround Management Lifec  aMilan, Italyc201310aBIS10aBusiness Analytics1 aFan, Shaokun1 aTong, Yu1 aZhao, Leon  u/biblio/ever-changing-workarounds-model-workaround-management-lifecycle-healthcare-workflow01181nas a2200157   4500008004100000245008100041210006900122260000900191520069300200653000800893653002300901100001300924700001800937700001500955856005300970       2013                        eng d00aGender Classification for Product Reviewers in China: A Data-Driven Approach0 aGender Classification for Product Reviewers in China A DataDrive  c20133 aWhile it is crucial for organizations to automatically identify the gender of participants in product discussion forums, they may have difficulties adopting existing gender classification methods because the associations between the linguistic features used in those studies and gender type usually varies with context. The prototype system we propose to demo validates a framework for the development of gender classification that uses a more “data-driven” approach. It constantly extracts content-specific features from the discussion content. And the system could automatically adjust itself to accommodate the contextual changes in order to achieve better classification accuracy.10aBIS10aBusiness Analytics1 aZhu, Bin1 aYan, Xiangbin1 aWang, Jing  uhttp://www.som.buffalo.edu/isinterface/wits2013/00529nas a2200133   4500008004100000245007500041210006900116260003400185653000800219653002300227100001300250700002300263856010900286       2013                        eng d00aMapping User requirements to Design Alternatives: The Whole Nine yards0 aMapping User requirements to Design Alternatives The Whole Nine   a����������c201310aBIS10aBusiness Analytics1 aZhu, Bin1 aHoyle, Christopher  u/biblio/mapping-user-requirements-design-alternatives-whole-nine-yards-000579nas a2200145   4500008004100000245009400041210006900135260002600204653000800230653002300238100001700261700001200278700001500290856012800305       2012                        eng d00aCollaboration Process Patterns and Efficiency of Issue Resolution in Software Development0 aCollaboration Process Patterns and Efficiency of Issue Resolutio  aDenver, CO, USAc201210aBIS10aBusiness Analytics1 aFan, Shaokun1 aLi, Xin1 aZhao, Leon  u/biblio/collaboration-process-patterns-and-efficiency-issue-resolution-software-development00489nas a2200145   4500008004100000245004600041210004600087260003800133300001400171653000800185653002300193100002600216700001300242856008800255       2012                        eng d00aData Quality Metadata and Decision Making0 aData Quality Metadata and Decision Making  aGrand Wailea, Maui, HI, USAc2012  a1434-144310aBIS10aBusiness Analytics1 aShankaranarayanan, G.1 aZhu, Bin  u/biblio/data-quality-metadata-and-decision-making-000448nas a2200133   4500008004100000245004500041210004500086260003400131653000800165653002300173100001300196700001800209856008700227       2012                        eng d00aFinding People Who Forward Your Messages0 aFinding People Who Forward Your Messages  a����������c201210aBIS10aBusiness Analytics1 aZhu, Bin1 aChau, Michael  u/biblio/finding-people-who-forward-your-messages-200439nas a2200133   4500008004100000245004500041210004500086260002500131653000800156653002300164100001300187700001800200856008700218       2012                        eng d00aFinding People Who Forward Your Messages0 aFinding People Who Forward Your Messages  aSnowbird, Utahc201210aBIS10aBusiness Analytics1 aZhu, Bin1 aChau, Michael  u/biblio/finding-people-who-forward-your-messages-100367nas a2200121   4500008004100000245003100041210003100072260002500103653000800128653002300136100001300159856007300172       2012                        eng d00aFinding People Who Retweet0 aFinding People Who Retweet  aBeijing, Chinac201210aBIS10aBusiness Analytics1 aZhu, Bin  u/biblio/finding-people-who-retweet-000595nas a2200181   4500008004100000245008200041210006900123260000900192300001200201490000700213653000800220653002300228100001700251700001700268700001200285700001200297856010400309       2012                        eng d00aA Framework for the Transformation from Conceptual to Logical Workflow Models0 aFramework for the Transformation from Conceptual to Logical Work  c2012  a781-7940 v5510aBIS10aBusiness Analytics1 aFan, Shaokun1 aZhao, J., L.1 aLiu, M.1 aDou, W.  u/biblio/framework-transformation-conceptual-logical-workflow-models00555nas a2200145   4500008004100000245008000041210006900121260002700190653001500217653000800232653002300240100001300263700002000276856011300296       2012                        eng d00aIs It You or the Message: Why Do People Pass Along Micro-Blogging Messages?0 aIt You or the Message Why Do People Pass Along MicroBlogging Mes  aOrlando, Floridac201210aAccounting10aBIS10aBusiness Analytics1 aZhu, Bin1 aMarshall, Byron  u/biblio/it-you-or-message-why-do-people-pass-along-micro-blogging-messages-000568nas a2200169   4500008004100000245007200041210006900113260000900182300001200191490000700203653000800210653002300218100001900241700001700260700001300277856010800290       2012                        eng d00aPatterns of News Dissemination through Online News Network in China0 aPatterns of News Dissemination through Online News Network in Ch  c2012  a557-5700 v1610aBIS10aBusiness Analytics1 aWang, Youzhong1 aZeng, Daniel1 aZhu, Bin  u/biblio/patterns-news-dissemination-through-online-news-network-china-100490nas a2200145   4500008004100000245004800041210004800089260003600137653000800173653002300181100001700204700002000221700001500241856008800256       2012                        eng d00aTowards Collaboration Virtualization Theory0 aTowards Collaboration Virtualization Theory  aHo Chi Minh City, Vietnamc201210aBIS10aBusiness Analytics1 aFan, Shaokun1 aSia, Choon-Ling1 aZhao, Leon  u/biblio/towards-collaboration-virtualization-theory00540nas a2200121   4500008004100000245008900041210006900130260002200199653002300221100002800244700001900272856012700291       2011                        eng d00aComparing Item Nonresponse and Responses Across Modes in General Population Surveys.0 aComparing Item Nonresponse and Responses Across Modes in General  aPhoenix, AZc201110aBusiness Analytics1 aLesser, Dr., Virginia M1 aOlstad, Andrew  u/biblio/comparing-item-nonresponse-and-responses-across-modes-general-population-surveys-000525nas a2200145   4500008004100000245007200041210006900113260000900182653000800191653002300199100001900222700001700241700001300258856010800271       2011                        eng d00aPatterns of News Dissemination through Online News Network in China0 aPatterns of News Dissemination through Online News Network in Ch  c201110aBIS10aBusiness Analytics1 aWang, Youzhong1 aZeng, Daniel1 aZhu, Bin  u/biblio/patterns-news-dissemination-through-online-news-network-china-200573nas a2200157   4500008004100000245008900041210006900130260000900199490000700208653000800215653002300223100001200246700001700258700001700275856012300292       2010                        eng d00aA Collaborative Scheduling Approach for Service-Driven Scientific Workflow Execution0 aCollaborative Scheduling Approach for ServiceDriven Scientific W  c20100 v7610aBIS10aBusiness Analytics1 aDou, W.1 aZhao, J., L.1 aFan, Shaokun  u/biblio/collaborative-scheduling-approach-service-driven-scientific-workflow-execution00445nas a2200157   4500008004100000245003700041210003600078260000900114653000800123653002300131100001300154700001300167700001400180700001800194856007500212       2010                        eng d00aFinding Treasures in Your Trash,0 aFinding Treasures in Your Trash  c201010aBIS10aBusiness Analytics1 aZhu, Bin1 aLuo, Xin1 aMa, James1 aChau, Michael  u/biblio/finding-treasures-your-trash-200451nas a2200157   4500008004100000245003600041210003600077260002500113653000800138653002300146100001300169700001200182700001100194700001300205856007500218       2010                        eng d00aFinding Treasures in Your Trash0 aFinding Treasures in Your Trash  aChengdu, Chinac201010aBIS10aBusiness Analytics1 aZhu, Bin1 aLuo, X.1 aMa, J.1 aChau, M.  u/biblio/finding-treasures-your-trash-100472nas a2200145   4500008004100000245005300041210005200094260000900146653000800155653002300163100001300186700001800199710001800217856009100235       2010                        eng d00aUnderstanding Awareness Diffusion at Twitter.com0 aUnderstanding Awareness Diffusion at Twittercom  c201010aBIS10aBusiness Analytics1 aZhu, Bin1 aChau, Michael1 aEmptyAuthNode  u/biblio/understanding-awareness-diffusion-twittercom-200448nas a2200133   4500008004100000245005300041210005200094260002100146653000800167653002300175100001300198700001200211856009100223       2010                        eng d00aUnderstanding Awareness Diffusion at Twitter.com0 aUnderstanding Awareness Diffusion at Twittercom  aLima, Peruc201010aBIS10aBusiness Analytics1 aZhu, Bin1 aChau, M  u/biblio/understanding-awareness-diffusion-twittercom-100561nas a2200157   4500008004100000245008900041210006900130260000900199300001200208490000700220653000800227653002300235100001300258700001400271856011800285       2010                        eng d00aVisualization of network concepts: The impact of working memory capacity differences0 aVisualization of network concepts The impact of working memory c  c2010  a327-3440 v2110aBIS10aBusiness Analytics1 aZhu, Bin1 aWatts, S.  u/biblio/visualization-network-concepts-impact-working-memory-capacity-differences00472nas a2200169   4500008004100000245004000041210004000081260000900121300001200130490000700142653000800149653002300157100001300180700001400193700001300207856008200220       2010                        eng d00aVisualizing Social Network Concepts0 aVisualizing Social Network Concepts  c2010  a151-1610 v4910aBIS10aBusiness Analytics1 aZhu, Bin1 aWatts, S.1 aChen, H.  u/biblio/visualizing-social-network-concepts-000520nas a2200133   4500008004100000245008700041210006900128260000900197653000800206653002300214100001300237700001600250856012000266       2009                        eng d00aCommunication Clique Evolution Graph: A Tool to Monitor Conflicts in Virtual Teams0 aCommunication Clique Evolution Graph A Tool to Monitor Conflicts  c200910aBIS10aBusiness Analytics1 aZhu, Bin1 aQin, Jialun  u/biblio/communication-clique-evolution-graph-tool-monitor-conflicts-virtual-teams-200584nas a2200157   4500008004100000245008700041210006900128260002200197653000800219653002300227100001200250700001300262700001500275700001600290856012000306       2009                        eng d00aCommunication Clique Evolution Graph: A Tool to Monitor Conflicts in Virtual Teams0 aCommunication Clique Evolution Graph A Tool to Monitor Conflicts  aPhoenix, AZc200910aBIS10aBusiness Analytics1 aQin, J.1 aZhu, Bin1 aGaynor, M.1 aBradner, S.  u/biblio/communication-clique-evolution-graph-tool-monitor-conflicts-virtual-teams-100456nas a2200121   4500008004100000245006300041210006300104260002100167653000800188653002300196100001300219856010200232       2009                        eng d00aExpanding Research Using System Development as Methodology0 aExpanding Research Using System Development as Methodology  aTucson, AZc200910aBIS10aBusiness Analytics1 aZhu, Bin  u/biblio/expanding-research-using-system-development-methodology-000429nas a2200133   4500008004100000245005000041210005000091260000900141653000800150653002300158100001300181700001300194856008800207       2009                        eng d00aInformation Visualization for Decision Making0 aInformation Visualization for Decision Making  c200910aBIS10aBusiness Analytics1 aZhu, Bin1 aChen, H.  u/biblio/information-visualization-decision-making-000603nas a2200181   4500008004100000245007800041210006900119260000900188490000600197520001700203653000800220653002300228100001200251700001700263700001700280700001200297856011200309       2009                        eng d00aTransformation Framework of Conceptual to Logical Business Process Models0 aTransformation Framework of Conceptual to Logical Business Proce  c20090 v33 a(In Chinese)10aBIS10aBusiness Analytics1 aLiu, M.1 aFan, Shaokun1 aZhao, J., L.1 aDou, W.  u/biblio/transformation-framework-conceptual-logical-business-process-models00526nas a2200133   4500008004100000245007800041210006900119260002700188653000800215653002300223100001300246700001300259856012000272       2009                        eng d00aUnderstanding How Product Information Traverses Across Online Communities0 aUnderstanding How Product Information Traverses Across Online Co  aGuangzhou, Chinac200910aBIS10aBusiness Analytics1 aZhu, Bin1 aYang, C.  u/biblio/understanding-how-product-information-traverses-across-online-communities-100541nas a2200145   4500008004100000245007800041210006900119260000900188653000800197653002300205100001300228700001600241710001800257856012000275       2009                        eng d00aUnderstanding How Product Information Traverses Across Online Communities0 aUnderstanding How Product Information Traverses Across Online Co  c200910aBIS10aBusiness Analytics1 aZhu, Bin1 aYang, Chris1 aEmptyAuthNode  u/biblio/understanding-how-product-information-traverses-across-online-communities-200575nas a2200145   4500008004100000245008700041210006900128260002800197653000800225653002300233100001300256700002600269700001200295856012200307       2009                        eng d00aVisualizing Data Quality Metadata for Decision Support: A Prototype and Evaluation0 aVisualizing Data Quality Metadata for Decision Support A Prototy  aSan Francisco, CAc200910aBIS10aBusiness Analytics1 aZhu, Bin1 aShankaranarayanan, G.1 aCai, Y.  u/biblio/visualizing-data-quality-metadata-decision-support-prototype-and-evaluation-100562nas a2200145   4500008004100000245008700041210006900128260000900197653000800206653002300214100002600237700001300263710001800276856012200294       2009                        eng d00aVisualizing Data Quality Metadata for Decision Support: A Prototype and Evaluation0 aVisualizing Data Quality Metadata for Decision Support A Prototy  c200910aBIS10aBusiness Analytics1 aShankaranarayanan, G.1 aZhu, Bin1 aEmptyAuthNode  u/biblio/visualizing-data-quality-metadata-decision-support-prototype-and-evaluation-200608nas a2200157   4500008004100000245012000041210006900161260000900230300001200239490000700251653000800258653002300266100001300289700001300302856013500315       2008                        eng d00aCommunicationGarden System: Visualizing a Computer Mediated Communication System to Facilitate Knowledge Management0 aCommunicationGarden System Visualizing a Computer Mediated Commu  c2008  a778-7940 v4510aBIS10aBusiness Analytics1 aZhu, Bin1 aChen, H.  u/biblio/communicationgarden-system-visualizing-computer-mediated-communication-system-facilitate-000542nas a2200157   4500008004100000245007000041210006900111260000900180490000700189520001700196653000800213653002300221100001700244700001600261856010700277       2008                        eng d00aComplex Problem Solving based on Complex Problem Definition Model0 aComplex Problem Solving based on Complex Problem Definition Mode  c20080 v253 a(In Chinese)10aBIS10aBusiness Analytics1 aFan, Shaokun1 aWanchun, D.  u/biblio/complex-problem-solving-based-complex-problem-definition-model00595nas a2200181   4500008004100000245007100041210006700112260000900179300001400188490000700202653000800209653002300217100001600240700001300256700001700269700001900286856010800305       2008                        eng d00aA context- and role-driven scientific workflow development pattern0 acontext and roledriven scientific workflow development pattern  c2008  a1741-17570 v2010aBIS10aBusiness Analytics1 aWanchun, D.1 aChen, J.1 aFan, Shaokun1 aCheung, S., C.  u/biblio/context-and-role-driven-scientific-workflow-development-pattern00557nas a2200169   4500008004100000245006600041210006500107260000900172490000700181520001700188653000800205653002300213100001700236700001600253700001500269856010300284       2008                        eng d00aContext-aware Resource Access Control in Scientific Workflows0 aContextaware Resource Access Control in Scientific Workflows  c20080 v293 a(In Chinese)10aBIS10aBusiness Analytics1 aFan, Shaokun1 aWanchun, D.1 aXiping, L.  u/biblio/context-aware-resource-access-control-scientific-workflows00471nas a2200145   4500008004100000245004800041210004800089260002100137653000800158653002300166100002600189700001300215700001200228856008500240       2008                        eng d00aDecision support with data quality metadata0 aDecision support with data quality metadata  aBoston, MAc200810aBIS10aBusiness Analytics1 aShankaranarayanan, G.1 aZhu, Bin1 aCai, Y.  u/biblio/decision-support-data-quality-metadata-000500nas a2200133   4500008004100000245006900041210006800110260002500178653000800203653002300211100001300234700001400247856010500261       2008                        eng d00aMonitoring Conflicts in Virtual Teams: A Social Network Approach0 aMonitoring Conflicts in Virtual Teams A Social Network Approach  aKunming, Chinac200810aBIS10aBusiness Analytics1 aZhu, Bin1 aGaynor, M  u/biblio/monitoring-conflicts-virtual-teams-social-network-approach-000539nas a2200145   4500008004100000245007500041210006900116260002400185653000800209653002300217100001300240700001400253700001600267856011000283       2008                        eng d00aMonitoring Team Conflicts through the Visualization of Social Networks0 aMonitoring Team Conflicts through the Visualization of Social Ne  aParis, Francec200810aBIS10aBusiness Analytics1 aZhu, Bin1 aGaynor, M1 aBradner, S.  u/biblio/monitoring-team-conflicts-through-visualization-social-networks-000657nas a2200193   4500008004100000245009100041210006900132260000900201490000700210653000800217653002300225100001600248700001300264700001100277700001900288700001300307700001700320856012600337       2008                        eng d00aA Workflow Engine-Driven SOA-Based Cooperative Computing Paradigm in Grid Environments0 aWorkflow EngineDriven SOABased Cooperative Computing Paradigm in  c20080 v2210aBIS10aBusiness Analytics1 aWanchun, D.1 aChen, J.1 aLiu, J1 aCheung, S., C.1 aChen, G.1 aFan, Shaokun  u/biblio/workflow-engine-driven-soa-based-cooperative-computing-paradigm-grid-environments00670nas a2200193   4500008004100000245010100041210006900142260000900211490000700220653000800227653002300235100001200258700001600270700001300286700001700299700001900316700001200335856012900347       2007                        eng d00aOn Design, Verification, and Dynamic Modification of the Problem-Based Scientific Workflow Model0 aDesign Verification and Dynamic Modification of the ProblemBased  c20070 v1510aBIS10aBusiness Analytics1 aLiu, X.1 aWanchun, D.1 aChen, J.1 aFan, Shaokun1 aCheung, S., C.1 aCai, S.  u/biblio/design-verification-and-dynamic-modification-problem-based-scientific-workflow-model00526nas a2200157   4500008004100000245006700041210006400108260000900172490000700181653000800188653002300196100001500219700001600234700001700250856010100267       2007                        eng d00aA Fuzzy Directed Graph-based QoS Model for Service Composition0 aFuzzy Directed Graphbased QoS Model for Service Composition  c20070 v1210aBIS10aBusiness Analytics1 aSanjun, G.1 aWanchun, D.1 aFan, Shaokun  u/biblio/fuzzy-directed-graph-based-qos-model-service-composition00600nas a2200145   4500008004100000245010200041210006900143260002500212653000800237653002300245100001300268700002600281700001200307856013500319       2007                        eng d00aIntegrating Data Quality Data into Decision-Making Process: an Information Visualization Approach0 aIntegrating Data Quality Data into DecisionMaking Process an Inf  aBeijing, Chinac200710aBIS10aBusiness Analytics1 aZhu, Bin1 aShankaranarayanan, G.1 aCai, Y.  u/biblio/integrating-data-quality-data-decision-making-process-information-visualization-approach-000424nas a2200121   4500008004100000245005100041210005100092260002500143653000800168653002300176100001300199856009000212       2006                        eng d00aManagement Information Systems Research is USA0 aManagement Information Systems Research is USA  aChengdu, Chinac200610aBIS10aBusiness Analytics1 aZhu, Bin  u/biblio/management-information-systems-research-usa-000392nas a2200145   4500008004100000245003000041210003000071260000900101490000700110653000800117653002300125100001300148700001300161856007200174       2005                        eng d00aInformation Visualization0 aInformation Visualization  c20050 v3910aBIS10aBusiness Analytics1 aZhu, Bin1 aChen, H.  u/biblio/information-visualization-000504nas a2200181   4500008004100000245004500041210004400086260000900130300001200139490000700151653000800158653002300166100001400189700001300203700001300216700001300229856008000242       2005                        eng d00aNewsMap: A Knowledge Map for Online News0 aNewsMap A Knowledge Map for Online News  c2005  a583-5970 v3910aBIS10aBusiness Analytics1 aOng, T-H.1 aChen, H.1 aSung, WK1 aZhu, Bin  u/biblio/newsmap-knowledge-map-online-news-000427nas a2200121   4500008004100000245004700041210004700088260004000135653000800175653002300183100001300206856008600219       2005                        eng d00aResearch in Management Information Systems0 aResearch in Management Information Systems  aNanchang, Jiangxi, P.R. Chinac200510aBIS10aBusiness Analytics1 aZhu, Bin  u/biblio/research-management-information-systems-000599nas a2200157   4500008004100000245011600041210006900157260000900226300001200235490000700247653000800254653002300262100001300285700001300298856013000311       2005                        eng d00aUsing 3D Interfaces to Facilitate the Spatial Knowledge Retrieval: A Geo-referenced Knowledge Repository System0 aUsing 3D Interfaces to Facilitate the Spatial Knowledge Retrieva  c2005  a167-1820 v4010aBIS10aBusiness Analytics1 aZhu, Bin1 aChen, H.  u/biblio/using-3d-interfaces-facilitate-spatial-knowledge-retrieval-geo-referenced-knowledge-000465nas a2200133   4500008004100000245006000041210005600101260002600157653000800183653002300191100001300214700001300227856009100240       2003                        eng d00aThe Design for an Effective Knowledge Repository System0 aDesign for an Effective Knowledge Repository System  aMinneapolis, MNc200310aBIS10aBusiness Analytics1 aZhu, Bin1 aIyer, B.  u/biblio/design-effective-knowledge-repository-system-000599nas a2200181   4500008004100000245008000041210006900121260000900190300001200199490000700211653000800218653002300226100001300249700001600262700001300278700001300291856011300304       2003                        eng d00aHelpfulMed: Intelligent Searching for Medical Information over the Internet0 aHelpfulMed Intelligent Searching for Medical Information over th  c2003  a683-6940 v5410aBIS10aBusiness Analytics1 aChen, H.1 aLally, A.M.1 aZhu, Bin1 aChau, M.  u/biblio/helpfulmed-intelligent-searching-medical-information-over-internet-000542nas a2200157   4500008004100000245006700041210006400108260002300172653000800195653002300203100001300226700001400239700001300253700001300266856010500279       2002                        eng d00aMedTextus: an intelligent web-based medical meta-search system0 aMedTextus an intelligent webbased medical metasearch system  aPortland, ORc200210aBIS10aBusiness Analytics1 aZhu, Bin1 aLeory, G.1 aChen, 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4500008004100000245005600041210005500097260000900152653000800161653002300169100001300192856009800205       2002                        eng d00aVisualizing Computer-Mediated Communication Process0 aVisualizing ComputerMediated Communication Process  c200210aBIS10aBusiness Analytics1 aZhu, Bin  u/biblio/visualizing-computer-mediated-communication-process-400539nas a2200133   4500008004100000245009700041210006900138260000900207653000800216653002300224100001300247700001300260856013200273       2001                        eng d00aSocial Visualization for Computer-Mediated Communication: A Knowledge Management Perspective0 aSocial Visualization for ComputerMediated Communication A Knowle  c200110aBIS10aBusiness Analytics1 aZhu, Bin1 aChen, H.  u/biblio/social-visualization-computer-mediated-communication-knowledge-management-perspective-000440nas a2200121   4500008004100000245005600041210005500097260002400152653000800176653002300184100001300207856009800220       2001                        eng d00aVisualizing Computer-Mediated Communication Process0 aVisualizing ComputerMediated Communication Process  aRochester, NYc200110aBIS10aBusiness Analytics1 aZhu, Bin  u/biblio/visualizing-computer-mediated-communication-process-600447nas a2200121   4500008004100000245005600041210005500097260003100152653000800183653002300191100001300214856009800227       2001                        eng d00aVisualizing Computer-Mediated Communication Process0 aVisualizing ComputerMediated Communication Process  aColorado Springs, COc200110aBIS10aBusiness Analytics1 aZhu, Bin  u/biblio/visualizing-computer-mediated-communication-process-700442nas a2200121   4500008004100000245005600041210005500097260002600152653000800178653002300186100001300209856009800222       2001                        eng d00aVisualizing Computer-Mediated Communication Process0 aVisualizing ComputerMediated Communication Process  aBloomington, INc200110aBIS10aBusiness Analytics1 aZhu, Bin  u/biblio/visualizing-computer-mediated-communication-process-500565nas a2200169   4500008004100000245007300041210006900114260000900183300001200192490000600204653000800210653002300218100001300241700001500254700001300269856011300282       2000                        eng d00aCreating a Large-Scale Content-Based Air Photo Image Digital Library0 aCreating a LargeScale ContentBased Air Photo Image Digital Libra  c2000  a163-1670 v910aBIS10aBusiness Analytics1 aZhu, Bin1 aRamsey, M.1 aChen, H.  u/biblio/creating-large-scale-content-based-air-photo-image-digital-library-000483nas a2200157   45000080041000002450053000412100053000942600009001