Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS

[EN] Cloud computing instruction requires hands-on experience with a myriad of distributed computing services from a public cloud provider. Tracking the progress of the students, especially for online courses, requires one to automatically gather evidence and produce learning analytics in order to further determine the behavior and performance of students. With this aim, this paper describes the experience from an online course in cloud computing with Amazon Web Services on the creation of an open-source data processing tool to systematically obtain learning analytics related to the hands-on activities carried out throughout the course. These data, combined with the data obtained from the learning management system, have allowed the better characterization of the behavior of students in the course. Insights from a population of more than 420 online students through three academic years have been assessed, the dataset has been released for increased reproducibility. The results corroborate that course length has an impact on online students dropout. In addition, a gender analysis pointed out that there are no statistically significant differences in the final marks between genders, but women show an increased degree of commitment with the activities planned in the course.This research was funded by the Spanish "Ministerio de Economia, Industria y Competitividad through grant number TIN2016-79951-R (BigCLOE)", the "Vicerrectorado de Estudios, Calidad y Acreditacion" of the Universitat Politecnica de Valencia (UPV) to develop the PIME B29 and PIME/19-20/166, and by the Conselleria d'Innovacio, Universitat, Ciencia i Societat Digital for the project "CloudSTEM" with reference number AICO/2019/313.Moltó, G.; Naranjo-Delgado, DM.; Segrelles Quilis, JD. (2020). Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS. Applied Sciences. 10(24):1-13. https://doi.org/10.3390/app10249148S1131024Motiwalla, L., Deokar, A. V., Sarnikar, S., & Dimoka, A. (2019). Leveraging Data Analytics for Behavioral Research. Information Systems Frontiers, 21(4), 735-742. doi:10.1007/s10796-019-09928-8Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12. doi:10.1145/2330601.2330661Blikstein, P. (2013). Multimodal learning analytics. Proceedings of the Third International Conference on Learning Analytics and Knowledge - LAK ’13. doi:10.1145/2460296.2460316Hewson, E. R. F. (2018). Students’ Emotional Engagement, Motivation and Behaviour Over the Life of an Online Course: Reflections on Two Market Research Case Studies. Journal of Interactive Media in Education, 2018(1). doi:10.5334/jime.472Kahan, T., Soffer, T., & Nachmias, R. (2017). Types of Participant Behavior in a Massive Open Online Course. The International Review of Research in Open and Distributed Learning, 18(6). doi:10.19173/irrodl.v18i6.3087Cross, S., & Whitelock, D. (2016). Similarity and difference in fee-paying and no-fee learner expectations, interaction and reaction to learning in a massive open online course. Interactive Learning Environments, 25(4), 439-451. doi:10.1080/10494820.2016.1138312Charleer, S., Klerkx, J., & Duval, E. (2014). Learning Dashboards. Journal of Learning Analytics, 1(3), 199-202. doi:10.18608/jla.2014.13.22Worsley, M. (2012). Multimodal learning analytics. Proceedings of the 14th ACM international conference on Multimodal interaction - ICMI ’12. doi:10.1145/2388676.2388755Spikol, D., Prieto, L. P., Rodríguez-Triana, M. J., Worsley, M., Ochoa, X., Cukurova, M., … Ringtved, U. L. (2017). Current and future multimodal learning analytics data challenges. Proceedings of the Seventh International Learning Analytics & Knowledge Conference. doi:10.1145/3027385.3029437Ochoa, X., Worsley, M., Weibel, N., & Oviatt, S. (2016). Multimodal learning analytics data challenges. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK ’16. doi:10.1145/2883851.2883913Aguilar, J., Sánchez, M., Cordero, J., Valdiviezo-Díaz, P., Barba-Guamán, L., & Chamba-Eras, L. (2017). Learning analytics tasks as services in smart classrooms. Universal Access in the Information Society, 17(4), 693-709. doi:10.1007/s10209-017-0525-0Lu, O. H. T., Huang, J. C. H., Huang, A. Y. Q., & Yang, S. J. H. (2017). Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course. Interactive Learning Environments, 25(2), 220-234. doi:10.1080/10494820.2016.1278391Drachsler, H., & Kalz, M. (2016). The MOOC and learning analytics innovation cycle (MOLAC): a reflective summary of ongoing research and its challenges. Journal of Computer Assisted Learning, 32(3), 281-290. doi:10.1111/jcal.12135Ruiperez-Valiente, J. A., Munoz-Merino, P. J., Gascon-Pinedo, J. A., & Kloos, C. D. (2017). Scaling to Massiveness With ANALYSE: A Learning Analytics Tool for Open edX. IEEE Transactions on Human-Machine Systems, 47(6), 909-914. doi:10.1109/thms.2016.2630420Er, E., Gómez-Sánchez, E., Dimitriadis, Y., Bote-Lorenzo, M. L., Asensio-Pérez, J. I., & Álvarez-Álvarez, S. (2019). Aligning learning design and learning analytics through instructor involvement: a MOOC case study. Interactive Learning Environments, 27(5-6), 685-698. doi:10.1080/10494820.2019.1610455Tabaa, Y., & Medouri, A. (2013). LASyM: A Learning Analytics System for MOOCs. International Journal of Advanced Computer Science and Applications, 4(5). doi:10.14569/ijacsa.2013.040516Shorfuzzaman, M., Hossain, M. S., Nazir, A., Muhammad, G., & Alamri, A. (2019). Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment. Computers in Human Behavior, 92, 578-588. doi:10.1016/j.chb.2018.07.002Klašnja-Milićević, A., Ivanović, M., & Budimac, Z. (2017). Data science in education: Big data and learning analytics. Computer Applications in Engineering Education, 25(6), 1066-1078. doi:10.1002/cae.21844Logglyhttps://www.loggly.com/Molto, G., & Caballer, M. (2014). On using the cloud to support online courses. 2014 IEEE Frontiers in Education Conference (FIE) Proceedings. doi:10.1109/fie.2014.7044041Caballer, M., Blanquer, I., Moltó, G., & de Alfonso, C. (2014). Dynamic Management of Virtual Infrastructures. Journal of Grid Computing, 13(1), 53-70. doi:10.1007/s10723-014-9296-5AWS CloudTrailhttps://aws.amazon.com/cloudtrail/Amazon Simple Storage Service (Amazon S3)http://aws.amazon.com/s3/Naranjo, D. M., Prieto, J. R., Moltó, G., & Calatrava, A. (2019). A Visual Dashboard to Track Learning Analytics for Educational Cloud Computing. Sensors, 19(13), 2952. doi:10.3390/s19132952Baldini, I., Castro, P., Chang, K., Cheng, P., Fink, S., Ishakian, V., … Suter, P. (2017). Serverless Computing: Current Trends and Open Problems. Research Advances in Cloud Computing, 1-20. doi:10.1007/978-981-10-5026-8_1Zimmerman, D. W. (1987). Comparative Power of StudentTTest and Mann-WhitneyUTest for Unequal Sample Sizes and Variances. The Journal of Experimental Education, 55(3), 171-174. doi:10.1080/00220973.1987.10806451Kruskal, W. H., & Wallis, W. A. (1952). Use of Ranks in One-Criterion Variance Analysis. Journal of the American Statistical Association, 47(260), 583-621. doi:10.1080/01621459.1952.10483441Voyer, D., & Voyer, S. D. (2014). Gender differences in scholastic achievement: A meta-analysis. Psychological Bulletin, 140(4), 1174-1204. doi:10.1037/a0036620Ellemers, N., Heuvel, H., Gilder, D., Maass, A., & Bonvini, A. (2004). The underrepresentation of women in science: Differential commitment or the queen bee syndrome? British Journal of Social Psychology, 43(3), 315-338. doi:10.1348/0144666042037999Sheard, M. (2009). Hardiness commitment, gender, and age differentiate university academic performance. British Journal of Educational Psychology, 79(1), 189-204. doi:10.1348/000709908x30440

Density kernel depth for outlier detection in functional data

In this paper, we propose a novel approach to address the problem of functional outlier detection. Our method leverages a low-dimensional and stable representation of functions using Reproducing Kernel Hilbert Spaces (RKHS).We define a depth measure based on density kernels that satisfy desirable properties.We also address the challenges associated with estimating the density kernel depth. Throughout aMonte Carlo simulation we assess the performance of our functional depth measure in the outlier detection task under different scenarios. To illustrate the effectiveness of our method, we showcase the proposed method in action studying outliers in mortality rate curves.Este artículo se encuentra publicado en International Journal of Data Science and Analytics (Springer Nature)https://doi.org/10.1007/s41060-023-00420-

CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research.

Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes

Using Image Transformations to Learn Network Structure

Many learning tasks require observing a sequence of images and making a decision. In a transportation problem of designing and planning for shipping boxes between nodes, we show how to treat the network of nodes and the flows between them as images. These images have useful structural information that can be statistically summarized. Using image compression techniques, we reduce an image down to a set of numbers that contain interpretable geographic information that we call geographic signatures. Using geographic signatures, we learn network structure that can be utilized to recommend future network connectivity. We develop a Bayesian reinforcement algorithm that takes advantage of statistically summarized network information as priors and user-decisions to reinforce an agent's probabilistic decision.Comment: 11 pages, 6 figures, 5 tables, In Submission with International Journal of Data Science and Analytics, Special Issue: Domain Driven Data Minin

A Visual Dashboard to Track Learning Analytics for Educational Cloud Computing

[EN] Cloud providers such as Amazon Web Services (AWS) stand out as useful platforms to teach distributed computing concepts as well as the development of Cloud-native scalable application architectures on real-world infrastructures. Instructors can benefit from high-level tools to track the progress of students during their learning paths on the Cloud, and this information can be disclosed via educational dashboards for students to understand their progress through the practical activities. To this aim, this paper introduces CloudTrail-Tracker, an open-source platform to obtain enhanced usage analytics from a shared AWS account. The tool provides the instructor with a visual dashboard that depicts the aggregated usage of resources by all the students during a certain time frame and the specific use of AWS for a specific student. To facilitate self-regulation of students, the dashboard also depicts the percentage of progress for each lab session and the pending actions by the student. The dashboard has been integrated in four Cloud subjects that use different learning methodologies (from face-to-face to online learning) and the students positively highlight the usefulness of the tool for Cloud instruction in AWS. This automated procurement of evidences of student activity on the Cloud results in close to real-time learning analytics useful both for semi-automated assessment and student self-awareness of their own training progress.This research was funded by the Spanish Ministerio de Economia, Industria y Competitividad, grant number TIN2016-79951-R (BigCLOE) and by the Vicerrectorado de Estudios, Calidad y Acreditacion of the Universitat Politecnica de Valencia (UPV) to develop the PIME B29.Naranjo, DM.; Prieto, JR.; Moltó, G.; Calatrava Arroyo, A. (2019). A Visual Dashboard to Track Learning Analytics for Educational Cloud Computing. Sensors. 19(13):1-15. https://doi.org/10.3390/s19132952S1151913Porter, W. W., Graham, C. R., Spring, K. A., & Welch, K. R. (2014). Blended learning in higher education: Institutional adoption and implementation. Computers & Education, 75, 185-195. doi:10.1016/j.compedu.2014.02.011Thai, N. T. T., De Wever, B., & Valcke, M. (2017). The impact of a flipped classroom design on learning performance in higher education: Looking for the best «blend» of lectures and guiding questions with feedback. Computers & Education, 107, 113-126. doi:10.1016/j.compedu.2017.01.003Chen, Y., Wang, Y., Kinshuk, & Chen, N.-S. (2014). Is FLIP enough? Or should we use the FLIPPED model instead? Computers & Education, 79, 16-27. doi:10.1016/j.compedu.2014.07.004Baepler, P., Walker, J. D., & Driessen, M. (2014). It’s not about seat time: Blending, flipping, and efficiency in active learning classrooms. Computers & Education, 78, 227-236. doi:10.1016/j.compedu.2014.06.006Molto, G., & Caballer, M. (2014). On using the cloud to support online courses. 2014 IEEE Frontiers in Education Conference (FIE) Proceedings. doi:10.1109/fie.2014.7044041González-Martínez, J. A., Bote-Lorenzo, M. L., Gómez-Sánchez, E., & Cano-Parra, R. (2015). Cloud computing and education: A state-of-the-art survey. Computers & Education, 80, 132-151. doi:10.1016/j.compedu.2014.08.017AWS Cloudtrailhttps://aws.amazon.com/cloudtrail/?nc1=h_lsFerguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304. doi:10.1504/ijtel.2012.051816Schwendimann, B. A., Rodriguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., … Dillenbourg, P. (2017). Perceiving Learning at a Glance: A Systematic Literature Review of Learning Dashboard Research. IEEE Transactions on Learning Technologies, 10(1), 30-41. doi:10.1109/tlt.2016.2599522Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A. (2020). Linking learning behavior analytics and learning science concepts: Designing a learning analytics dashboard for feedback to support learning regulation. Computers in Human Behavior, 107, 105512. doi:10.1016/j.chb.2018.05.004Tabaa, Y., & Medouri, A. (2013). LASyM: A Learning Analytics System for MOOCs. International Journal of Advanced Computer Science and Applications, 4(5). doi:10.14569/ijacsa.2013.040516Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Van Assche, F., Parra, G., & Klerkx, J. (2013). Learning dashboards: an overview and future research opportunities. Personal and Ubiquitous Computing. doi:10.1007/s00779-013-0751-2Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12. doi:10.1145/2330601.2330666Ali, L., Hatala, M., Gašević, D., & Jovanović, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers & Education, 58(1), 470-489. doi:10.1016/j.compedu.2011.08.030Leony, D., Pardo, A., de la Fuente Valentín, L., de Castro, D. S., & Kloos, C. D. (2012). GLASS. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12. doi:10.1145/2330601.2330642Vieira, C., Parsons, P., & Byrd, V. (2018). Visual learning analytics of educational data: A systematic literature review and research agenda. Computers & Education, 122, 119-135. doi:10.1016/j.compedu.2018.03.018Jivet, I., Scheffel, M., Specht, M., & Drachsler, H. (2018). License to evaluate. Proceedings of the 8th International Conference on Learning Analytics and Knowledge. doi:10.1145/3170358.3170421Amazon CloudWatchhttps://aws.amazon.com/cloudwatch/?nc1=h_lsSpectrumhttps://spectrumapp.io/Opsview Monitorhttps://www.opsview.com/SignalFxhttps://signalfx.com/AWS Cloud Monitoringhttps://www.solarwinds.com/topics/aws-monitoringLonn, S., Aguilar, S. J., & Teasley, S. D. (2015). Investigating student motivation in the context of a learning analytics intervention during a summer bridge program. Computers in Human Behavior, 47, 90-97. doi:10.1016/j.chb.2014.07.013Pintrich, P. R. (2004). A Conceptual Framework for Assessing Motivation and Self-Regulated Learning in College Students. Educational Psychology Review, 16(4), 385-407. doi:10.1007/s10648-004-0006-xButler, D. L., & Winne, P. H. (1995). Feedback and Self-Regulated Learning: A Theoretical Synthesis. Review of Educational Research, 65(3), 245-281. doi:10.3102/00346543065003245Knight, S., Buckingham Shum, S., & Littleton, K. (2014). Epistemology, Assessment, Pedagogy: Where Learning Meets Analytics in the Middle Space. Journal of Learning Analytics, 1(2). doi:10.18608/jla.2014.12.3Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness Is Not Enough: Pitfalls of Learning Analytics Dashboards in the Educational Practice. Lecture Notes in Computer Science, 82-96. doi:10.1007/978-3-319-66610-5_

CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical research.

Big data is important to new developments in global clinical science that aim to improve the lives of patients. Technological advances have led to the regular use of structured electronic health-care records with the potential to address key deficits in clinical evidence that could improve patient care. The COVID-19 pandemic has shown this potential in big data and related analytics but has also revealed important limitations. Data verification, data validation, data privacy, and a mandate from the public to conduct research are important challenges to effective use of routine health-care data. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including representation from patients, clinicians, scientists, regulators, journal editors, and industry members. In this Review, we propose the CODE-EHR minimum standards framework to be used by researchers and clinicians to improve the design of studies and enhance transparency of study methods. The CODE-EHR framework aims to develop robust and effective utilisation of health-care data for research purposes

The Journal of Apicultural Research welcomes the publication of research findings from around the globe

The Journal of Apicultural Research (JAR) is a peer-reviewed, scientific journal dedicated to examining and publishing the latest research on bees from around the world. JAR publishes many different types of articles to reach different international audiences, from career scientists to students and well-informed beekeepers. These comprise original, theoretical, and experimental research papers, as well as authoritative notes, comments, and reviews on scientific aspects of all types of bees (superfamily Apoidea). As of 2021, JAR has an Impact Factor of 2.407 and is ranked 33rd out of 100 in the Entomology category (© InCites Journal Citation Reports®, Clarivate Analytics, 2022). Five regular issues are published per year and special issues are added when timely topics arise, the latest being a special issue on stingless bees (2022) and review papers (2023). In the last decade, COLOSS BEEBOOK chapters are published in JAR. These open-access chapters are a collection of the Standard Methods used in honey bee research, including the study of parasites, pests, and hive products. They are a primary reference resource for bee researchers across the globe and facilitate new projects that might not otherwise be undertaken by laboratories that are new to apidology (236,516 downloads - Taylor & Francis 3,028 citations - Web of Science, 2022). The Journal of Apicultural Research was founded by the International Bee Research Association (IBRA) in 1962. The very first issue included a Note from the first Editors, Dr. Eva Crane & Dr. James Simpson, who introduced JAR as a new opportunity for publication: “The journal will cover all aspects of bees, Apis and non-Apis, and substances used or produced by them, their pollinating activities, and organisms causing diseases or injuries to them.” Since the first issue, this legacy has been maintained in more than 2,800 scientific articles, co-authored by some 1,900 researchers, published so far in JAR, making our journal a key forum for the international exchange of scientific data in apidology. We encourage colleagues from around the globe to continue to participate in sharing their research with the scientific community by publishing in JAR.info:eu-repo/semantics/publishedVersio

BITOUR: A Business Intelligence Platform for Tourism Analysis

[EN] Integrating collaborative data in data-driven Business Intelligence (BI) system brings an opportunity to foster the decision-making process towards improving tourism competitiveness. This article presents BITOUR, a BI platform that integrates four collaborative data sources (Twitter, Openstreetmap, Tripadvisor and Airbnb). BITOUR follows a classical BI architecture and provides functionalities for data transformation, data processing, data analysis and data visualization. At the core of the data processing, BITOUR offers mechanisms to identify tourists in Twitter, assign tweets to attractions and accommodation sites from Tripadvisor and Airbnb, analyze sentiments in opinions issued by tourists, and all this using geolocation objects in Openstreetmap. With all these ingredients, BITOUR enables data analysis and visualization to answer questions like the most frequented places by tourists, the average stay length or the view of visitors of some particular destination.This work has been supported by COLCIENCIAS through a PhD scholarship. This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R.Bustamante, A.; Sebastiá Tarín, L.; Onaindia De La Rivaherrera, E. (2020). BITOUR: A Business Intelligence Platform for Tourism Analysis. ISPRS International Journal of Geo-Information. 9(11):1-23. https://doi.org/10.3390/ijgi9110671S123911Nakahira, K. T., Akahane, M., & Fukami, Y. (2012). The Difference and Limitation of Cognition for Piano Playing Skill with Difference Educational Design. Smart Innovation, Systems and Technologies, 609-617. doi:10.1007/978-3-642-29934-6_59Chua, A., Servillo, L., Marcheggiani, E., & Moere, A. V. (2016). Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy. Tourism Management, 57, 295-310. doi:10.1016/j.tourman.2016.06.013Karagiannakis, N., Giannopoulos, G., Skoutas, D., & Athanasiou, S. (2015). OSMRec Tool for Automatic Recommendation of Categories on Spatial Entities in OpenStreetMap. Proceedings of the 9th ACM Conference on Recommender Systems. doi:10.1145/2792838.2796555Burcher, M., & Whelan, C. (2017). Social network analysis as a tool for criminal intelligence: understanding its potential from the perspectives of intelligence analysts. Trends in Organized Crime, 21(3), 278-294. doi:10.1007/s12117-017-9313-8Alcabnani, S., Oubezza, M., & Elkafi, J. (2019). An approach for the implementation of semantic Big Data Analytics in the Social Business Intelligence process on distributed environments (Cloud computing). Proceedings of the 4th International Conference on Big Data and Internet of Things. doi:10.1145/3372938.3373003Zeng, B., & Gerritsen, R. (2014). What do we know about social media in tourism? A review. Tourism Management Perspectives, 10, 27-36. doi:10.1016/j.tmp.2014.01.001Lalicic, L. (2018). Open innovation platforms in tourism: how do stakeholders engage and reach consensus? International Journal of Contemporary Hospitality Management, 30(6), 2517-2536. doi:10.1108/ijchm-04-2016-0233Dwyer, L., & Kim, C. (2003). Destination Competitiveness: Determinants and Indicators. Current Issues in Tourism, 6(5), 369-414. doi:10.1080/13683500308667962Gomezelj, D. O., & Mihalič, T. (2008). Destination competitiveness—Applying different models, the case of Slovenia. Tourism Management, 29(2), 294-307. doi:10.1016/j.tourman.2007.03.009Zhong, L., Deng, J., & Xiang, B. (2008). Tourism development and the tourism area life-cycle model: A case study of Zhangjiajie National Forest Park, China. Tourism Management, 29(5), 841-856. doi:10.1016/j.tourman.2007.10.002Fernández, J. I. P., & Rivero, M. S. (2009). Measuring Tourism Sustainability: Proposal for a Composite Index. Tourism Economics, 15(2), 277-296. doi:10.5367/000000009788254377Cibinskiene, A., & Snieskiene, G. (2015). Evaluation of City Tourism Competitiveness. Procedia - Social and Behavioral Sciences, 213, 105-110. doi:10.1016/j.sbspro.2015.11.411Business Intelligence (BI)—Glossaryhttps://www.gartner.com/it-glossary/business-intelligence-bi/Mariani, M., Baggio, R., Fuchs, M., & Höepken, W. (2018). Business intelligence and big data in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management, 30(12), 3514-3554. doi:10.1108/ijchm-07-2017-0461Maeda, T. N., Yoshida, M., Toriumi, F., & Ohashi, H. (2016). Decision Tree Analysis of Tourists’ Preferences Regarding Tourist Attractions Using Geotag Data from Social Media. Proceedings of the Second International Conference on IoT in Urban Space. doi:10.1145/2962735.2962745Guy, I., Mejer, A., Nus, A., & Raiber, F. (2017). Extracting and Ranking Travel Tips from User-Generated Reviews. Proceedings of the 26th International Conference on World Wide Web. doi:10.1145/3038912.3052632Peng, M. Y.-P., Tuan, S.-H., & Liu, F.-C. (2017). Establishment of Business Intelligence and Big Data Analysis for Higher Education. Proceedings of the International Conference on Business and Information Management - ICBIM 2017. doi:10.1145/3134271.3134296Castellanos, M., Gupta, C., Wang, S., Dayal, U., & Durazo, M. (2012). A platform for situational awareness in operational BI. Decision Support Systems, 52(4), 869-883. doi:10.1016/j.dss.2011.11.011Cohen, L. (2017). Impacts of business intelligence on population health. 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