Conceptual design framework for information visualization to support multidimensional datasets in higher education institutions

Conceptual design framework for information visualization to support multidimensional datasets in higher education institutions

Information Visualization (InfoVis) enjoys diverse adoption and applicability because of its strength in solving the problem of information overload inherent in institutional data. Policy and decision makers of higher education institutions (HEIs) are also experiencing information overload while in...

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Main Author: Ayobami, Akanmu Semiu
Format: Thesis
Language:eng
eng
Published: 2016
Subjects:
TK7885-7895 Computer engineering
Computer hardware
LB2300 Higher Education
Online Access:https://etd.uum.edu.my/6046/1/s95110_01.pdf
https://etd.uum.edu.my/6046/2/s95110_02.pdf
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id my-uum-etd.6046
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institution Universiti Utara Malaysia
collection UUM ETD
language eng
eng
advisor Jamaludin, Zulikha
topic TK7885-7895 Computer engineering
Computer hardware
LB2300 Higher Education
spellingShingle TK7885-7895 Computer engineering
Computer hardware
LB2300 Higher Education
Ayobami, Akanmu Semiu
Conceptual design framework for information visualization to support multidimensional datasets in higher education institutions
description Information Visualization (InfoVis) enjoys diverse adoption and applicability because of its strength in solving the problem of information overload inherent in institutional data. Policy and decision makers of higher education institutions (HEIs) are also experiencing information overload while interacting with students‟ data, because of its multidimensionality. This constraints decision making processes, and therefore requires a domain-specific InfoVis conceptual design framework which will birth the domain‟s InfoVis tool. This study therefore aims to design HEI Students‟ data-focused InfoVis (HSDI) conceptual design framework which addresses the content delivery techniques and the systematic processes in actualizing the domain specific InfoVis. The study involved four phases: 1) a users‟ study to investigate, elicit and prioritize the students‟ data-related explicit knowledge preferences of HEI domain policy. The corresponding students‟ data dimensions are then categorised, 2) exploratory study through content analysis of InfoVis design literatures, and subsequent mapping with findings from the users‟ study, to propose the appropriate visualization, interaction and distortion techniques for delivering the domain‟s explicit knowledge preferences, 3) conceptual development of the design framework which integrates the techniques‟ model with its design process–as identified from adaptation of software engineering and InfoVis design models, 4) evaluation of the proposed framework through expert review, prototyping, heuristics evaluation, and users‟ experience evaluation. For an InfoVis that will appropriately present and represent the domain explicit knowledge preferences, support the students‟ data multidimensionality and the decision making processes, the study found that: 1) mouse-on, mouse-on-click, mouse on-drag, drop down menu, push button, check boxes, and dynamics cursor hinting are the appropriate interaction techniques, 2) zooming, overview with details, scrolling, and exploration are the appropriate distortion techniques, and 3) line chart, scatter plot, map view, bar chart and pie chart are the appropriate visualization techniques. The theoretical support to the proposed framework suggests that dictates of preattentive processing theory, cognitive-fit theory, and normative and descriptive theories must be followed for InfoVis to aid perception, cognition and decision making respectively. This study contributes to the area of InfoVis, data-driven decision making process, and HEI students‟ data usage process.
format Thesis
qualification_name Ph.D.
qualification_level Doctorate
author Ayobami, Akanmu Semiu
author_facet Ayobami, Akanmu Semiu
author_sort Ayobami, Akanmu Semiu
title Conceptual design framework for information visualization to support multidimensional datasets in higher education institutions
title_short Conceptual design framework for information visualization to support multidimensional datasets in higher education institutions
title_full Conceptual design framework for information visualization to support multidimensional datasets in higher education institutions
title_fullStr Conceptual design framework for information visualization to support multidimensional datasets in higher education institutions
title_full_unstemmed Conceptual design framework for information visualization to support multidimensional datasets in higher education institutions
title_sort conceptual design framework for information visualization to support multidimensional datasets in higher education institutions
granting_institution Universiti Utara Malaysia
granting_department Awang Had Salleh Graduate School of Arts & Sciences
publishDate 2016
url https://etd.uum.edu.my/6046/1/s95110_01.pdf
https://etd.uum.edu.my/6046/2/s95110_02.pdf
_version_ 1747828014430814208
spelling my-uum-etd.60462021-04-05T01:59:24Z Conceptual design framework for information visualization to support multidimensional datasets in higher education institutions 2016 Ayobami, Akanmu Semiu Jamaludin, Zulikha Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences TK7885-7895 Computer engineering. Computer hardware LB2300 Higher Education Information Visualization (InfoVis) enjoys diverse adoption and applicability because of its strength in solving the problem of information overload inherent in institutional data. Policy and decision makers of higher education institutions (HEIs) are also experiencing information overload while interacting with students‟ data, because of its multidimensionality. This constraints decision making processes, and therefore requires a domain-specific InfoVis conceptual design framework which will birth the domain‟s InfoVis tool. This study therefore aims to design HEI Students‟ data-focused InfoVis (HSDI) conceptual design framework which addresses the content delivery techniques and the systematic processes in actualizing the domain specific InfoVis. The study involved four phases: 1) a users‟ study to investigate, elicit and prioritize the students‟ data-related explicit knowledge preferences of HEI domain policy. The corresponding students‟ data dimensions are then categorised, 2) exploratory study through content analysis of InfoVis design literatures, and subsequent mapping with findings from the users‟ study, to propose the appropriate visualization, interaction and distortion techniques for delivering the domain‟s explicit knowledge preferences, 3) conceptual development of the design framework which integrates the techniques‟ model with its design process–as identified from adaptation of software engineering and InfoVis design models, 4) evaluation of the proposed framework through expert review, prototyping, heuristics evaluation, and users‟ experience evaluation. For an InfoVis that will appropriately present and represent the domain explicit knowledge preferences, support the students‟ data multidimensionality and the decision making processes, the study found that: 1) mouse-on, mouse-on-click, mouse on-drag, drop down menu, push button, check boxes, and dynamics cursor hinting are the appropriate interaction techniques, 2) zooming, overview with details, scrolling, and exploration are the appropriate distortion techniques, and 3) line chart, scatter plot, map view, bar chart and pie chart are the appropriate visualization techniques. The theoretical support to the proposed framework suggests that dictates of preattentive processing theory, cognitive-fit theory, and normative and descriptive theories must be followed for InfoVis to aid perception, cognition and decision making respectively. This study contributes to the area of InfoVis, data-driven decision making process, and HEI students‟ data usage process. 2016 Thesis https://etd.uum.edu.my/6046/ https://etd.uum.edu.my/6046/1/s95110_01.pdf text eng public https://etd.uum.edu.my/6046/2/s95110_02.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia Alavi, M., & Joachimsthaler, E.A. (1992). Revisiting DSS implementation research: a meta-analysis of the literature and suggestions for researchers, MIS Quarterly, 16 (1), 95–116 Ankerst, M., Keim, D. A., & Kriegel, H.-P. (1996). Circle segments: A technique for visually exploring large multidimensional datasets, in Proc. Visualization 96, Hot Topic Session, San Francisco, CA, 1996. Ariffin, M. (2009). Conceptual Design of Reality learning Media (RLM) Model Based on Entertaining and Fun Construct. A PhD Thesis submitted to College of Arts and Science, Universiti Utara Malaysia. Aris, A., & Shneiderman, B. (2007). Designing semantic substrates for visual network exploration, Journal of Information Visualization, 6 (4), 281–300. Arkhipenkov, S., & Goluber, D. (2001). Oracle Express Olap., USA: Charles River Media. Ayres, L. (2007). Qualitative Research Proposal – Part I: Posing the problem. Journal of Wound Ostomy Continence Nursing, 34, 30–32. Babbie, E. (2010). The Practice of Social Research, 12th Edition. USA: Wadsworth Cengage Learning. Baker, D., Bridges, D., Hunter, R., Johnson, G., Krupa, J., Murphy, J. & Sorenson, (2002). Guidebook to Decision-Making Methods, WSRC-IM-2002-00002, USA: Department of Energy. Balakrishnam, N., Render, B. & Stair, M.R. (2012). Managerial Decision Making with Spread sheet, 2nd Edition. USA: Pearson International. Barab, S., & Squire, K. (2004). Design-based research: putting a stake in the ground. Journal of the Learning Sciences, 13, 1-14. DOI: 10.1.1.128.5080 Battista, V., & Cheng, E. (2011). Motion Charts: Telling Stories with Statistics. In Section on Statistical Graphics, JSM 2011. Retrieved from http://www.bls.gov/osmr/pdf/st110110.pdf on 3rd October, 2016. Benoit, G. (2012). Measuring Relevance Theory‟s Effects in an Image-Driven Explorative Information System, Proceedings of iConference, 2012, Toronto, Canada. ACM, pp. Pages 438 – 440. Bier, E. A., Stone, M. C., & Pier, K., Buxton, W., & DeRose, T. (1993). Toolglass and magic lenses: The see-through interface, in Proc. SIGGRAPH ‟93, Anaheim, CA, 1993, pp. 73–80. Bogard, M., & Green, B. (2013). A Data Driven Analytic Strategy for Increasing Yield and Retention at Western Kentucky University Using SAS Enterprise BI and SAS Enterprise Miner, Paper 044, presented at SAS Global Forum, 2013, pp. 1 – 12. Bonner, N. (2008). Acceptance of Systems Development Methodologies: Testing A Theoretically Integrated Model. PhD Thesis. The University of Texas Arlington. Bowen, J.P. (2013). Online Communities: Visualization and Formalization, Proceedings of the Second International Workshop on Cyberpatterns: Unifying Design Patterns with Security, Attack and Forensic Patterns. Abingdon, UK, 8-9 July 2013 Boy, J., Detienne, F., & Fekete, J-D. (2015). Can Initial Narrative VisualizationTechniques and Storytelling help Engage Online-Users with Exploratory Information Visualizations? 2015 Association for Computing Machinery, CHI 2015, April 18 - 23 2015, Seoul, Republic of Korea http://dx.doi.org/10.1145/2702123.2702452 Bresfelean, V.P., Ghisoiu, N., Lacaurezeanu, R., & Sitar-Taut, D.A (2009). Towards the Development of Decision Support in Academic Environments, Proceedings of International Conference on Information Technology Interfaces, ITI, ‟09, pp. 343- 348 Brooke, J. (2009). SUS: A quick and dirty usability Scale, In Jordan, P.W., Thomas, B., Weerdmeester, B.A., & McClelland, I.L. (Eds) Usability Evaluation In Industry. London: Taylor & Francis. Buja, A., Cook, D., & Swayne, D. F. (1996). Interactive High-Dimensional. UK: Wiley Card, S.T., Mackinlay, J. & Shneiderman, B. (1999). Readings in Information Visualization: Using Vision to Think, United States of America: Morgan Kaufmann Publishers. Carrasco, J-A., Hogan, B., Wellman, B., & Miller, E. J. (2006). Collecting Social Network Data yo Study Social Activity-Travel Behaviour: An Egocentric Approach, presented at the 85th Transportation Research Board Meeting, Washington DC, January 22-26, 2006. Carroll, J. M. (2000). Making use scenario-based design of human-computer interactions. Cambridge, MA: MIT Press. Chan, Y.H., Correa, C.D., & Ma, K-L. (2013). The Generalized Sensitivity Scatterplots. IEEE Transaction on Visualization and Computer Graphics, IEEE computer Society 19(10), pp. 1768 - 81 Chenail, R. J. (2012). Conducting Qualitative Data Analysis: Reading Line-by-Line, but Analyzing by Meaningful Qualitative Units. The Qualitative Report, 17 (1), 266 – 269. Retrieved from http://www.nova.edu/ssss/QR/QR17-1/chenailline.pdf on 4th December, 2015. Chi, h H. (2000). A taxonomy of visualization techniques using the data state reference model. In Proceedings of the IEEE Symposium on Information Visualizations 2000, INFOVIS ‟00, pages 69 – 76 Washington, DC, USA, 2000. IEEE Computer Society. Chin, J.P., Diehl., V.A., & Norman, K.L. (1988). Development of an Instrument Measuring User Satisfaction of the Human Computer Interface. ACM CHI ‟88 proceedings, pp. 213-218 Christina, N., Julian, A., & Micheal, S. (2016). Visual Exploration of Media Transparency for Data Journalists: Problem Characterization and Abstraction, Retrieved from: http://www.validproject.at/wp-content/uploads/2015/ 11/151031_AusserhoferGutounigOppermann-Dubrovnik_ Dubrovnik.pdf, 15 August, 2016. Chuah, M. C., & Roth, S. F. (1996). On the Semantics of Interactive Visualizations, Proceedings of IEEE Symposium on Information Visualization, San Francisco, CA, pp. 29- 36. Codd, E.F., Codd S.B., & Salley G.T. (1993). Providing OLAP (Online Analytical Processing) to User-Analysis: AN I.T Mandate. Hyperion Solution Corporation. Retrieved from: http://www.hyperion.com/solution/whitepapers.cfm on 15/08/13 Cohen, J. W. (1988). Statistical power analysis for behavioural sciences (2nd edn). Hillsdale, NJ: Lawrence Erlbaum Associates Collier, K. (2015). You‟ve Got the Data, Now What? Data Driven Decisions for School Improvement. Retrieved from http://engage.ok.gov/wpcontent/ uploads/2015/07/98-Data-Driven-Decision-Making-PPT-June-8-2015.pdf on 15th August, 2016. Connolly, T. & Begg, C. (2010). Database Systems: A Practical To Design, Implementation, and Management. Fifth Edition, Boston: Pearson Education Inc. Conrad, C., Gasman, M., Lundberg, T., Nguyen, T-H., Commodore, F., & Samayoa, A.C. (2013). Using Educational Data to Increase Learning, Retention, and Degree Attainment at Minority Serving Institutions (MSIs). A Research Report of Penn Graduate School of Education, GSE. Cooper, A., Reimann, R., Cronin, D., & Noessel, C. (2014). About Face: Essentials of Interaction Design, Fourth Edition. USA: Wiley. Cordeiro, R.L., Traina, C., Traina, A.J.M., Lopez, J., Kang, U. & Faloutsos, C. (2011). Clustering Very Large Multi-dimensional Datasets with MapReduce, in Proceedings of KDD, ACM, San-Diego, California, USA. Crapo, A.W., Waisel, L.B., Wallance, W.A. & Willemain, T.R. (2000). Visualization and The Process of Modelling: A Cognitive-theoretic View, Proceedings of KDD, USA, ACM. Creswell, J.W. (2009). Research Design: Qualitative, Quantitative and Mixed Methods Approaches. Third Edition, United States of America, Sage. Croll A. (2013). Big Data Is Our Generation‟s Civil Rights Issue and We Don‟t Know It (In Eds) Big Data Now: A Collection of O‟ Reilly Perspectives on Big Data. Delavari, N., Phon-Amnuaisuk, S., & Beikzadeh. M.R. (2008). Data Mining Application in Higher Learning Institutions, Vilnus, 7(1), 31-54. Denscomber, M. (2014). The Good Research Guide: For Small-Scale Social Research Projects. UK: Open University Press Dix, A. & Ellis, G. (1998). Starting simple: adding value to static visualisation through simple interaction, In the Working conference on Advanced visual interfaces (AVI '98), L'Aquila, Italy, pp. 124-134, 1998. Druin, A. (2002). The Role of Children in the Design of New Technology. Behaviour and Information Technology, 21(1), 1-25. DOI: 10.1080/01449290110108659 Druzdzel, M., & Flynn, R. (2002) Decision Support System. Encyclopedia of Library and Information Science, Second Edition, Allen Kent (ed.) New York: Marcel Dekker, Inc. Dumbill, E. (2012). Big Data Now: A Collection of O‟ Reilly Perspectives on Big Data In Eds). Eduventure Inc. (2013). Predictive Analytics in Higher Education: Data-Driven Decision Making for the Student Life Cycle, White Paper Publication by Eduventure Inc., Boston. Ehlers, K., Joubert, M., Kinghorn, J., & van Zyl, A. (2009). Decision Support System for Institutional Research Management in Higher Education: Data Mining to determine Research Focus, Intensity and Synergy, Proceedings of International Conference on Computational Science and Engineering, pp. 885-890 El-Fattah, M.A. (2012). Visual Model for Managing Educational Capacity Utilization in Egyptian Universities, in Proceedings of International Conference on Information Technology Based Higher Education and Training, pp. 1-4. Elmqvist, N., Stasko, J., & Tsigas, P. (2007). DataMeadow: A Visual Canvas for Analysis of Large-Scale Multivariate Data, Proc. IEEE Symposium in Visual Analytics Science and Technology (VAST ‟07), pp. 187-194 Elmqvist, N., Dragicevic, P., & Fekete, J.-D. (2008). Rolling the dice: Multidimensional visual exploration using scatterplot matrix navigation. IEEE Transaction on Visualization &. Computer Graphics, 14 (6), 56 – 60. Elo S, Kyngäs H. (2008). The Qualitative Content Analysis Process. Journal of Advanced Nursing, 62, 107–115. Ewell, P.T. (2012). Using Information in Higher Education Decision-Making: Modes, Obstacles, and Remedies. Retrieved from: www.spencer.org/resources/content/3/3/8/documents/ Ewell-paper.pdf on Aug. 18, 2013 Faisal, S., Craft, B., Cairns, P. & Blandford, A. (2008) Internalization, Qualitative methods, and Evaluation, In Proceedings of the 2008 conference on Beyond time and errors: novel evaluation methods for Information Visualization, pp. 1-8 Fekete, J-D. & Plaisant, C. (2002). Interactive Information Visualization of a Million Items, Proceedings of the IEEE Symposium on Information Visualization (InfoVis ‟02), pp. 117-124. Fekete, J-N., van Wijk, J.J., Stasko, J.T. & North, C. (2008). The Value of Information Visualization. Information Visualization: Human-Centred Issues and Perspectives (E.d.), pp. 1-18. Fishkin, K., & Stone, M. C. (1995). Enhanced dynamic queries via movable filters, Proceeding Human Factors in Computing Systems, CHI ‟95, pp. 415–420 Forsell, C. (2012). Evaluation in Information Visualization: Heuristic Evaluation, In Proceedings of 16th International Conference on Information Visualization, pp. 136-142 Frankel, L. & Racine, M. (2010). The Complex Field of Research: for Design, through Design, and about Design. In E.S. David Durling, Rabah Bousbaci, LinLin Chen, Philippe Gauthier, Tiu Poldma, Seymour Roworth-Stokes (Eds.), Design & Complexity: International Conference of the Design Research Society. Friendly, M. & Denis, D. (2005). The Early Origins and Development of the Scatter plot. Journal of the History of the Behavioural Sciences, 14 (2), 103-130. Gelman, A. & Unwin, A. (2012). Infovis and Statistical Graphics: Different Goals, Different Looks. Readings from a departmental seminar held at University of Kentucky. Gill, B., Borden, B.C., & Hallgren, K. (2014). A Conceptual Framework for Data-Driven Decision Making. Final Report of Research conducted by Mathematica Policy Research, Princeton, submitted to Bill & Melinda Gates Foundation, Seattle, WA. Goss, P., & Hunter, J. (2015). Targeted Teaching: How better use of data can improve student learning. A White Paper Publication of Gratian Institute. Retrieved from http://grattan.edu.au/wp-content/uploads/2015/07/827-Targeted-Teaching.pdf on 15 August, 2016 Graham, D. (2005). Information Visualization Theory and Practice, Proceedings of the Ninth International Conference on Information Visualization pp. 3-8 Greenberg, S. (2002) Working through Task-Centered System Design, in Diaper, D. and Stanton, N. (Eds) The Handbook of Task Analysis for Human-Computer Interaction. Lawrence Erlbaum Associates. Hackos, J. T., & Reish, J. C. (1998). User and Task Analysis for Interface Design. Canada: Wiley. Halverson, C. A. (2002). Activity Theory and Distributed Cognition: or What does CSCW need to do with Theories? Computer Supported Cooperative Work, 11, 243–267 Hao, M. C., Dayal, U., Sharma., Keim, D., & Janetzko, H. (2010). Visual Analytics of Large Multi-Dimensional Data Using Variable Binned Scatter Plots, in Visualization and Data Analysis Proceedings of IS & T/SPIE Electronic Imaging, Science and Technology, Belligham. Hao, M., Rohrdantz, C., Janetzko, H., Keim, D., Dayal, U., Haug, L. & Hsu, M. (2012). Integrating Sentiment Analysis and Term Association with Geo- Temporal Visualization on Customer Feedback Streams, Proceedings SPIE 8294, Visualization and Data Analysis, 2012 Harrison, K., Bowen, J.P. & Bowen, A.M. (2013). Electronic Visualisation in Chemistry: From Alchemy to Art, Proceedings of EVA London 2013 Conference, Electronic Workshops in Computing (eWiC), British Computer Society, 29-31 July 2013. Healey, C.G. (1996). Effective Visualization of Large Multidimensional Datasets. A PhD Thesis in Partial Fulfilment for the Degree of Doctor of Philosophy (Ph.D). The University of British Columbia. Hecksel, D. (2004). Methodology evaluation and selection. Sun Microsystems. Retrieved Jan 15, 2014 from http://www.davidhecksel.com/projectcontext/ whitepaper.html Hevner, A. R., March, T. S., Park, J., & Ram, S. (2004). Design science research in information systems. Management Information Systems Quarterly, 28 (1), 75-105. DOI: 10.2307/249422 Hollan, J. D., Hutchins, E., & Kirsh, D. (2000). Distributed cognition: Toward a new foundation for human-computer interaction. ACM Transactions on Computer-Human Interaction, 7 (2), 98 - 104 Hollingworth, L., Sullivan, A.M., Condon, C., Bhatt, M. & Brandt, W. C. (2012). Data-Driven Decision Making in Higher Education: One University‟s Process of Revamping the Superintendent Licensure Program, Journal of Research on Leadership Education, 7 (1), 78–97. IBM Data Profile (2012). IBM Big Data Success Stories. A White Paper Publication of IBM Inc. Inselberg, A., & Dimsdale, B. (1990). Parallel coordinates: A tool for visualizing multi-dimensional geometry, in Proc. Visualization 90, San Francisco, CA, pp. 361-370. Institute for Higher Education Policy (IHEP). (2009). Impact of College Rankings on Institutional Decision Making: Four Country Case Studies. Retrieved at: http://www.ihep.org/publications/ publications-detail.cfm?id=126 on August, 18, 2013. Isenberg, P., Tang, A., & Carpendale, S. (2008). An Exploratory Study of Visual Information Analysis, CHI Proceedings: Visualization to Support Information Work, pp. 1217-1226 Jackson, S. S. (2013). A continuous Improvement Framework: Data Driven Decision-Making in Mathematics Education, A White Paper Publication of Data-Driven Innovation Consulting, Inc. Janetzko, H., Hao, M.C., Mittelstadt, S., Dayal, U. & Keim, D. (2013). Enhancing Scatter Plots Using Ellipsoid Pixel Placement and Shading, Proceedings of the 46th Annual Hawaii International Conference on System Science, IEEE Computer Society, pp. 1522- 1531. Johansson, J., Nest, T.S. & Linner, B. (2010). Evaluating Climate Visualization: An Information Visualization Approach, Proceedings of IEEE 2010 14th International Conference on Information Visualization, pp. 156- 161. Julie, A., Marsh, J. F., & Laura S. H. (2006). Making Sense of Data-Driven Decision Making in Education. Rand Occasional Paper, RAND Corporation. Kaufman, T., Graham, C. R., Picciano, A. G., Wiley, D., & Popham, J. A. (2014). Data-driven decision making in the k12 classroom. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (4th ed., pp. 337–346). New York, NY: Springer Karine, T., Diane, L., & Deborah, R. (2012). Assessment of Higher Education Learning Outcomes. Feasibility Study Report. Volume 1. OECD Better Policies for Better Lives. Retrieved from http://www.oecd.org/education/skillsbeyond-school/ AHELOFSReportVolume1.pdf, on 3rd October, 2016 Keim, D. & Ward, M. (2002). Visual Data Mining Techniques. Published in Intelligent Data Analysis: An Introduction/ Micheal Berthod (eds), Berlin. Springer. Keim, D. A. (2002). Information Visualization and Visual Data Mining, IEEE Transactions on Visualization and Computer Graphics, 8, 1-8 Keim, D. A., Kriegel, H.-P., & Ankerst, M. (1995). Recursive pattern: A technique for visualizing very large amounts of data, in Proc. Visualization 95, Atlanta, GA, pp. 279–286. Keim, D., Andrienko, G., Fekete, J-D., Gorg, C., Kohlhammer, J. & Melancon, G. (2008). Visual Analytics: Definition, Process, and Challenges. A Kerren et al. (Eds): Information Visualization, LNCS 4950, pp. 154-175. Springer-Verlag Berlin Heidelberg. Keim, D., Hao, M.C., Daya., U., Janetzko, H., & Bak, P. (2009). Generalized Scatter Plots. Information Visualization, pp. 1-11, Macmillan Publishers Ltd. Keim, D., Kohlhamer, J., Ellis, G. & Mansmann, F. (2010a). Mastering the Information Age: Solving Problems with Visual Analytics. Germany, Eurographics Association. Keim, D., Pras, A., Schonwalder, J., Wong, P.C. & Mansmann, F. (2010b). Report on the Datgstuhl Seminar on Visualization and Monitoring of Network Traffic, Journal of Network and Systems Management, 18 (2), 232-236. Keim, D.A., Mansmann, F., Oelke, D. & Ziegler, H. (2008). Visual Analytics: Combining Automated Discovery with Interactive Visualization, Proceedings of the 11th International Conference on Discovery Science (DS,2008), pp. 2-14 Kerzner, H. (2006). Project Management: Best Practices: Achieving Global Excellence. USA: Wiley. Koh, L.C., Slingsby, A., Dykes, J., & Kam, T.S. (2011). Developing and Applying a User-centred Model for the Design and Implementation of Information Visualization Tools, Proceedings of 15th IEEE International Conference on Information Visualization, IEEE Computer Society, pp. 90-95. Kohlhammer, J., Ruppert, T., Darvey, J., Mansmann, F. & Keim, D. (2010). Information Visualization and Visual Analytics for Governance and Policy Modelling, CROSSROAD Call for Contribution on Future Internet on ICT for Governance and Policy Modelling, 2010, pp. 251-252 Kopáčková, H. & Škrobáčková M. (2006). Decision Support Systems or Business Intelligence: What Can Help In Decision Making? Krstajic, M., Najm-Araghi, M. Mansmann, F. & Keim, D. (2012). Incremental Visual Text Analytics of News Story Development, Proceedings of Conference on Visualization and Data Analysis (VDA ‟12), pp. 308-323 Kucuktunc, O., & Ferhatosmanoglu, H. (2013). -Diverse Nearest Neighbours Browsing for Multidimensional Data, IEEE Transaction on Knowledge and Data Engineering, 25 (3), 481-493 Lam, H., Bertini, E., Isenberg, P., Plaisant, C. & Carpendale, S. (2012). Empirical Studies in Information Visualization: Seven Scenario, IEEE Transaction on Visualization and Computer Graphics, 18 (9), 1520-1536 Lange, C., Range, B., & Welsh, K. (2012). Conditions for Effective Data Use to Improve Schools: Recommendations for School Leaders. Retrieved at: http://cnx.org/content/m45021/latest/ on August, 18, 2013 Lau, A. & Moere, A. V. (2007). Towards a Model of Information Aesthetics in Information Visualization, in Proceedings of 11th International Conference on Information Visualization, IEEE Computer Society LeBlanc, J., Ward, M. O., & Wittels, N. (1990). Exploring n-dimensional databases, in Proc. Visualization ‟90, San Francisco, pp. 230–239. Lee, B., Czerwinski, M., Roberston, G. & Bederston, B.B. (2005). Understanding Research Trend using PaperLens, Proceedings CHI 2005, Portland, Oregon, USA. Leventhal, L., & Barnes, J. (2007). Usability Engineering: Process, Products and Examples. USA: Pearson Lewis, J.R. (1995). IBM computer usability satisfaction questionnaire: Psychometric evaluation and instruction for use. International Journal of Human-Computer Interaction, 7 (1), 57-58 Li, I., Hook, K., Dey, A., Forlizzi, J., & Medynskiy, Y. (2011). Personal Informatics and HCI: Design, Theory and Social Implications, Proceedings of CHI, 2011, Vancouver, Canada. ACM. Lin, X. (1997). Map displays for information retrieval. Journal of the American Society for Information Science, 48 (1), 40–54 Lirong, X., Mengium, W. & Jing, F. (2011). A Visualization System for Web Retrieved Credit Information, Proceedings of seventh International Conference on Natural Computation, pp. 728-732. Liu, Z. & Stasko, J. (2010a). Theories in Information Visualization: What, Why and How, VisWeek 2010, The Role of Theory in Information Visualization. ACM Workshop. Liu, Z., & Stasko, J. (2010b). Mental Models, Visual Reasoning and Interaction in Information Visualization, IEEE Transaction on Visualization and Computer Graphics, 16 (6), 999- 1008 Liu, Z., Nersessian, N. J. & Stasko, J. T. (2008) Distributed cognition as a Theoretical Framework for Information Visualization, IEEE Transactions on Visualization and Computer Graphics, 14 (6), 1173 – 1180 Luna-Reyes, L. F., & Andersen, D. L. (2003). Collecting and Analysing qualitative data for system dynamics: methods and models. System Dynamics Review, 19(4), 271-296. DOI: 10.1002/sdr.280 Maciejewski, R., Rudolph, S., Hafen, R., Abusalah, A., Yakout, M., Ouzzani, M., Cleveland, W.S., Grannis, S.J., & Ebert, D.S. (2010). A Visual Analytics Approach to Understanding Spatiotemporal Hotspots, IEEE Transactions on Visualization and Computer Graphics, 16 (2), 205-220. Mandinach. E.B., Gummer, E.S & Muller, R.D. (2012). The Complexities of Integrating Data-Driven Decision Making into Professional Preparation in Schools of Education: It‟s Harder than You think. A Report Summary Mansmann, F., Fisher, F., Keim, D.A. & North, S.C. (2009). Visual Support for Analyzing Network Traffic and Intrusion Detection Events using TreeMap and Graph Representations, Proceedings of the 3rd ACM Symposium on Computer Human Interaction for the Management of Information Technology (CHiMiT ‟09), page 19-28. Matsui, K., Yamanouchi, M., & Sunahara, H. (2011). A Proposal of Framework for Information Visualization in Developing of Web Application, Proceedings of 2011 IEEE/IPSJ International Symposium on Application and the Internet, pp. 457- 462, IEEE Computer Society Mazza, R., & Berre, A. (2007). Focus Group Methodology Evaluating Information Visualization Techniques and Tools (2007). Proceedings of IEEE 11th International Conference on Information Visualization, 2007. IV '07, pg. 74-80., Zurich. Merriam-Webster, Inc., Merriam-Webster Online Dictionary, First published on 2007, retrieved from http://www.m-w.com/, on 22nd, September, 2013 Meyer, M. (2012). Designing Visualization for Biological Data, A Keynote talk at 3rd Leonardo Satellite Symposium of Arts, Humanities and Complex Networks (NetSci, 2012) Microsoft Inc. (2004). Data Driven Decision Making in Higher Education. Discover, A White paper publication of Microsoft Incorporation, released in February, 2004. Modified Assured Career Progression Scheme (MACPS) (2009). Government of lndia Ministry of Personnel, Public Grievances and Pensions (Department of Personnel and Training) North Block, New Delhi, the 19th May, 2009. Neuman, L.W. (2007). Basics of Social Research: Qualitative and Quantitative Approaches, 2nd Edition. Pearson International, USA. Nielsen, J. (1993). Usability Engineering, Boston, Massachusetts: Academic Press, Inc. Ning, Z., Jiaxin, W., Bing, W., & Shaolong, Z. (2008). A Visualization Model for Information Resources Management, in Proceedings of 12th International Conference on Information Visualization, pp. 57- 62, IEEE Computer Society Ning, Z., Wenxing, H., Siting, Z. & Li, T. (2012). A Solution for an Application of Information Visualization in Telemedicine. Proceedings of the 7th International Conference on Computer Science & Education (ICCSE, 2012), Melbourne, Australia, pp. 407-411. Norman, D. (2002). The Design of Everyday Things, New York: Basic Books. O‟Reilly Media (2012). Big Data Now: A Collection of O‟ Reilly Perspectives on Big Data. O‟Reilly Strata Making Data Work Oelke, D., Bak, P., Keim, D.A., Last, M. & Danon, G. (2008). Visual Evaluation of Text Features for Documents Summarization and Analysis, Proceedings of IEEE Symposium on Visual Analytics Science and Technology, Columbus, Ohio, USA Oelke, D., Hao, M., Rohrdantz, C., Keim, D.A., Dayal, U., Haug, L.E. & Janetzko, H. (2009). Visual Opinion Analysis of Customer Feedback Data, Proceedings of the 2009 IEEE Symposium on Visual Analytics Science and Technology (VAST ‟09), pp.187-194 Oelke, D., Janetzko, H., Simon, S., Neuhaus, K., & Keim, D. (2011). Visual Boosting in Pixel-based Visualizations, Eurographics/IEEE Symposium on Visualization, 30 (3), 6- 15. OLAP Council (1998). APB-1 OLAP Benchmark Release II. Retrieved from http://www.olapcouncil.org/research/bmarkco.html on August 15, 2013. Oracle Data Sheet (2011). Oracle Higher Education Constituent Hub (HECH). Retrieved on 6th June, 2013 from http://www.oracle.com/us/products/applications/ master-datamanagement/education-research-hech-ds- 164687.pdf Parsons, P., & Sedig, K. (2011). Human-information interaction: An emerging focus for educational cognitive tools, Proceedings of FORMATEX, in Education in a technological world: communicating current and emerging research and technological efforts, A.Medez-Vilas (Ed), pp. 245- 251 Pinto, M., & Carvalho, D. (2014). U-TRACER: The Use of Communication Technology in Higher Education, Universidade de Aveiro, Ano, 2014. Retrieved from https://ria.ua.pt/bitstream/10773/13140/1/u%20 tracer_%20o%20uso%20das%20tecnologias%20da%20 comunicacao%20no%20ensino%20superior.pdf on 3rd October, 2016. Pinto, M., Raposo, R. & Ramos, F. (2012). Comparison of Emerging Information Visualization Tools for Higher Education, Proceedings of 16th International Conference on Information Visualization, pp. 100-105 Playfair, W. (1801). The Statistical Breviary; Shewing, on a Principle Entirely New, the Resources of Every State and Kingdom in Europe, Wallis. Pocius, K. & Reklaitis, V. (2000). Web-based Services for Technology Education Management and Planning, Proceedings of International Workshop on Advanced Learning Technologies, IWALT, 2000, pp. 284-285. Protheroe, N. (2009). Improving Teaching and Learning with Data-Based Decisions: Asking the Right Questions and Acting on the Answers. Education Research Centre, Research Report. Protheroe, N. (2010). Schools as Effective Data Users. Research Report of www.naesp.org, October/November, 2010. Purao, S. (2002). Design research in the technology of Information system: Truth or Dare. GSU Department of CIS working paper. Atlanta, GA. Purchase, H. C., Andrienko, N., Jankun-Kelly, T., & Ward, M. (2008). Theoretical foundations of information visualization. In Information Visualization-Human-Centered Issues and Perspectives Quigley, A.J. (2002). Experience with FADE for the Visualization and Abstraction of Software Views, Proceedings in 10th International Workshop on Programme Comprehension, IWPC ‟02 Quinlan, P. T. & Humphreys, G. W. (1987). Visual search for targets defined by combinations of color, shape, and size: An examination of task constraints on feature and conjunction searches, Perception & Psychophysics, 41 (5), 455-472. Reigeluth, C. M. (2008). Instructional-design theories and models: Building a common knowledge base, pp. 83. Ren, L., Cui, J., Du, Y., & Dai, G. (2013). Multilevel Interaction Model For Hierarchical Tasks in Information Visualization, In VINCI 2013, Tianjin, China, pp. 11- 15 Reshef, D.N., Reshef, Y.A., Finucane, H.K., Grossman, S.R., McVean, G., Turnbaugh, P.J., Lander, E.S., Mitzenmacher, M., & Sabeti, P.C. (2011). Detecting Novel Associations in Large Data Sets. Journal of Science Mag, 33(4), 1518- 1524 Robertson, W., & DeFerrari, C. (1999). Systematic approaches to visualization: is a reference model needed? Retrieved from http://www.siggraph.org/education/materials/ HyperVis/concepts/ref_model.htm on 30th August, 2016 Robinson, A. C., Chen, J., Lengerich, E. J., Meyer, H. G. & MacEachren, A. M. (2005). Combining Usability Techniques to Design Geovisualization Tools for Epidemiology, Cartography and Geographic Information Science, 32 (4), 243–255 Rogers, Y., Sharp, H. & Preece, J. (2011). Interaction Design: Beyond Human Computer Interaction, 3rd Edition, United Kingdom. John Wiley & Sons, Limited Roth, R. E., Ross, K. S., Finch, B. G., Luo, W., & MacEachren, A. M. (2010). A user-centered approach for designing and developing spatiotemporal crime analysis tools, in Sixth International conference on Geographic Information Science Sabri, A., Hela, L., & Mounir, B. A. (2015). Towards an intelligent evaluation method of medical data visualizations, 2015 15th International Conference on Intelligent Systems DeSign and Applications (ISDA), pp. 673 - 678 Sarker, Farhana, Davis, Hugh, Tiropanis, & Thanasis (2010). The Role of Institutional Repositories in addressing Higher Education Challenges, The Second International Workshop on Semantic Web Application in Higher Education, University of Southampton, UK SAS (2011). Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics, SAS (The Power To Know), White Paper. Schaefer, M., Wanner, F., Kahl, R., Zhang, L., Schreck, T. & Keim, D.A. (2011). A Novel Explorative Visualization Tool for Financial Time Series Data Analysis, Proceedings of VAW, University College London, UK Schmidt, S., Sch¨ogl, O., Kirchberger, R., Doleisch, H., Muigg, P., Hauser, H., Grabner, M., Bornik, A. & Schmalstieg, D. (2005). Novel visualization and interaction techniques for gaining insight into fluid dynamics in internal combustion engines, In Proc. of the NAFEMS World Congress Conference 2005 Sedig, K., Parson, P. & Babanski, A. (2012). Towards a Charaterization of Interactivity in Visual Analytics. Journal of Multimedia Processing and Technologies, Special Issue on Theory and Application of Visual Analytics, 31), 12-28. Sedlmair, M., Meyer, M., & Munzner, T. (2012). Design Study Methodology: Reflections from the Trenches and the Stacks, IEEE TVCG, 18, 2431-2440 Semiu A. & Zulikha J. (2015a). Measuring InfoVis‟ Decision Support Effectiveness: From Theory to Practice. Proceeding of Science and Information Conference, pp. 560-564. July, 28 - 30, 2015, London, United Kingdom Semiu A., & Zulikha, J. (2015b). A Theoretical Approach towards Designing InfoVis for Decision Support Effectiveness. Special Issue of Jurnal Teknologi (In print) Semiu, A. & Zulikha J. (2014). Towards Developing An Instrument In Measuring The Need for InfoVis, in Proceeding of Knowledge Management International Conference, Langkawi, pp. 628-631, Malaysia- KMICe 2014, Aug., 12-15, 2014 Semiu, A. & Zulikha, J. (2013). Information Visualization Techniques Usage Model, in Proceeding of 4th International Conference on Computing and Informatics, Sarawak, Malaysia- ICOCI, 2013, pp.247 - 252, Aug., 28-30, 2013. Semiu, A. & Zulikha, J. (2015c). A Conceptual Framework for Students‟ Data-Driven Decision Making Process in Higher Education Institutions. Advanced Science Letters, 21 (7): 2256-2260. DOI:10.1166/asl.2015.6275, American Scientific Publishers. Semiu, A., & Zulikha, J. (2015d). Structural Components of Students‟ Data-focussed Information Visualization, in Proceeding of 5th International Conference on Computing and Informatics, Istanbul, Turkey- ICOCI, 2015, pp. 665 – 670. Aug. 22-24, 2015. Semiu, A., & Zulikha, J. (2016). Students' Data-driven Decision Making in HEI: The Explicit Knowledge Involved. International Journal of Information and Education Technology, 6 (1), 71 - 75. Sheneiderman, B. & Plaisant, C. (2010). Designing the User Interface: Strategies for Effective Human-Computer Interaction, 5th Edition, U.S.A. Pearson Higher Education. Sherwood, C., & Rout, T. (1998). A structured methodology for multimedia product and systems development, in Proceeding of the ASCILITE98 Conference Shneiderman, B. (1996). The eyes have it: a task by data type taxonomy for information visualizations, IEEE Symposium on Visual Languages, 1996, Boulder, CO, USA, pp. 336-343. Simon, S., Oelke, D., Landstorfer, R., Neuhaus, K. & Keim, D. (2011). Visual Analysis of Next-Generation Sequencing Data to Detect Overlapping Genes in Bacterial Genomes, Proceedings of IEEE Symposium on Biological Data Visualization, October 23-24, Providence Island, USA, page 47- 54 Soukup, T., & Davison, I. (2002). Visual Data Mining: Techniques and Tools for Data Visualization and Mining, United States of America, John Wiley & Sons, Inc. Spence, R. (2007). Information Visualization: Design for Interaction, 2nd Edition, England. Pearson Education Limited. Stacey, M., Salvatore, J. & Jorgensen, A. (2013). Visual Intelligence: Microsoft Tools and Techniques for Visualizing Data. Indianapolis, John Wiley & Sons, Inc Stasko, J. (2008). Visualization for Information Exploration and Analysis, Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC ‟08) Stoffel, A., Spretke, D., Kinnemann, H. & Keim, D.A. (2010). Enhancing Document Structure Analysis using Visual Analytics, Proceedings of the 2010 ACM Symposium on Applied Computing (SAC‟10), pp. 8-12, 2010 Stoffel, A., Zhang, L., Weber, S.H. & Keim, D.A. (2012). AMPLIO VQA-A Web-Based Visual Query Analysis System for Micro Grid Energy Mix Planning, Proceedings of International Workshop on Visual Analytics, the Eurographics Association, 2012 Strobelt, H., Oelke, D., Rohrdantz, C. & Stoffel, A. (2009). Document Cards: A Top Trumps Visualization for Documents. IEEE Transaction on Visualization and Computational Graph, 15 (6), 1145 - 52. doi: 10.1109/TVCG.2009.139. Syamsul, B.Z. (2011). Mobile Game-Based Learning (mGBL) Engineering Model, an Unpublished PhD Thesis submitted to Awang Had Salleh Graduate of Arts and Sciences, Universiti Utara Malaysia. Tan, P-N, Steinbach, M. & Kumar, V. (2006). Introduction to Data Mining, 1st Edition, U.S.A. Pearson Education, Inc Tatu, A., Albuqerque, G., Eisemann, M., & Bak, P. (2011). Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data, IEEE Transaction on Visualization and Computer Graphics, 17 (5), 584- 596 Teegavarapu, S., & Summers, J.D. (2008). Case Study Method for Design Research, Proceedings of International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, August 3-6, 2008, New York city, New York, USA, pp. 1-9 Teets, J.M., Tegarden, D.P., & Russell, R.S. (2010). Using Cognitive Fit Theory to Evaluate the Effectiveness of Information Visualizations: An Example Using Quality Assurance Data, IEEE Transactions on Visualization and Computer Graphics, 16 (5), 841-853 Tekir, S., Mansmann, F. & Keim, D. (2011). Geodesic Distances for Web Document Clustering, Proceedings of IEEE Symposium on Computational Intelligence and Data Mining (CIDM, 2011) Thacker, B.H., Doebling, S.W., Hemez, F., M., Anderson, M.C., Pepin, J. E., & Rodrigues, W., A. (2004). Concepts of Model Verification and Validation, South Research Institute & Los Alamos National Laboratory Thomas, J.J., & Cook, K.A. (2005). Illuminating the Path: The Research and Development Agenda for Visual Analytics, Richland, WA: IEEE Press. Thomsen, E. (2002). OLAP Solutions: Building Multi-dimensional Information Systems. John Wiley & Sons Toker, D., Conati, C., Steichen, B., & Carenini, G. (2013). Individual User Characteristics and Information Visualization: Connecting the Dots through Eye Tracking, In CHI 2013, pp. 295-303 Tongco, M.D.C (2007). Purposive Sampling as a Tool for Informant Selection, Ethnobotany Research & Applications, 5, 147-158 Tory, M. & Staub-French, S. (2008). Qualitative Analysis of Visualization: A Building Design Field Study, ACM Proceedings of BELIV, 2008, Florence, Italy, pp. 1-8. Tory, M., & Moller, T. (2005). Evaluating Visualizations: Do Expert Reviews Work? Visualization Viewpoints, IEEE Computer Graphics and Application, September/October, 2005 Edition pp. 8-11 Triesman, A. (1985). Preattentive Processing in Vision. Computer Vision, Graphics and Image Processing, 31, 156- 177 Truckenbrod, J. R. (1981). Effective Use of Color in Computer Graphics, Computer Graphics, 15 (2), 83-90 Tullis, T., & Albert, B. (2008). Measuring The User Experience: Collecting, Analyzing and Presenting Usability Metrics. USA: Morgan Kaufmann Publishers Turban, E. (1998). Decision Support Systems and Intelligent System. USA: Prentice Hall. Turban, E. Sharda, R. & Delen, D. (2010). Decision Support and Business Intelligent Systems, 9th Edition. USA: Prentice Hall. Vaishnavi, V., & Kuechler, W. (2007). Design Science Research Methods and Patterns: Innovating Information and Communication Technology. New York: Auerbach Publications. Wagner, E. & Ice, P. (2012). Data Changes Everything: Delivering on the Promise of Learning Analytics in Higher Education, EduCause Review, July/August pp. 33- 42. Wang, H. (2013). Asynchronous Visualization of Spatiotemporal Information for Multiple Moving Targets, an unpublished PhD Dissertation in School of Information Sciences, University of Pittsburgh. Wang, H., Zhao, G., Wang, W. & Chen, C. (2012). The Design and Implementation of the Platform of Interactive Information Visualization on Aircraft Product Development, Proceedings of 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization, pp. 180-184 Wanner, F., Rohrdantz, C., Mansmann, F., Stoffel, A., Oelke, D., Kristajic, M., Keim, D.A, Lou, D., Yang, J. & Atkinson, M. (2009). Large-Scale Comparative Sentiment Analysis of News Articles, Proceedings of InfoVis Conference ‟09. Ward, M., Gristein, G., & Keim, D. (2010). Interactive Visualization: Foundations, Techniques, and Applications. India: A.K. Peters, Ltd Ware, C. (2000). Information Visualization: Perception for Design. United States of America: Morgan Kaufmann Publishers Web Center for Social Research Publication. Available at: https://www.syr.edu/gradschool/pdf/assessmentpdfs/ Evaluation%20Defined.pdf. Retrieved on 19th January, 2015. Wen-ting, W., Heng-yu, Z., Yi, R., Mi-mi, T., & Li-rong, X. (2009). Study on Plant Landscape Information Visualization and Its Key Technologies, in Proceedings of International Conference on Management and Service Science, pp. 1- 4 White, S. (2008). Interaction and Presentation Techniques for Situated Visualization, UIST'08, pp. 3-7, October 19-22, 2008 Wiegers, K. E. (2002). Seven Truths about Peer Reviews. Cutter IT Journal, 15(7). Wilkinson, L. (2005). The Grammar of Graphics, 2nd ed. New York, NY, USA: Springer. Willems, N., H. van de Wetering, J.J., & van Wijk. (2009). Visualization of Vessel Movements, Proceedings of EuroVis, 28 (3), 959-966. Williams, N. & Amin, G. (2006). Higher Education in Sudan and Knowledge Management Application. Information and Communication Technologies, 2(1), 60-65 Wolfe, J. M. & Cave, K. R. (1989). Deploying Visual Attention: The Guided Search model, In AI and the Eye, Troscianko, T. and Blake, A., Eds., 79-103. John Wiley & Sons, Inc., Chichester, United Kingdom Wright, H. (2007). Introduction to Scientific Visualization. United Kingdom. Springer Science and Business Media, LLC. Wu, X. & Ren, F. (2010). Mechanism and Methods of Outdoor AR Spatial Information Visualization Representation, in Proceedings 2010 Second International Conference on Computer Modelling and Simulation, pp. 272-276, IEEE Computer Society Xie, Y., Hofmann, H., & Cheng, X. (2014). Reactive Programming for Interactive Graphics. Statistical Science, 29 (2), 201 – 213. DOI: 10.1214/14-STS477 Yanosky, R. (2009). Institutional Data Management in Higher Education. A Report from EduCause Centre for Applied Research. Retrieved on 6th June, 2013 from http://net.educause.edu/ir/library/pdf/EKF/ EKF0908.pdf Yi, J. S. (2010). Towards Practical Information Visualization Theory: Lessons from the History of Decision Science, VisWeek 2010, The Role of Theory in Information Visualization. ACM Workshop. Yi, J.S., Kang, Y., Stasko, J.T., & Jacko, J.A. (2007). Toward a deeper understanding of the Role of Interaction in Information Visualization, IEEE Transactions on Visualization and Computer Graphics, 13 (6), 1124–1231. Yu, C., Yurovsky, D. & Xu, T. (2012). Visual Data Mining: An Exploratory Approach to Analyzing Temporal Patterns of Eye Movement, Infancy, 17 (1), 33-60. Zadrozny, P. & Kodali, R. (2013). Big Data Analytics Using Splunk: Deriving Operational Intelligence from Social Media, Machine Data, Existing Data Warehouses and Other Real Time Streaming Sources. Apress Zalta, E. N., Nodelman, U., Allen, C. & Perry, J. (2013). The Problem of Induction, Standard Encyclopedia of Philosophy, 36 (7), 1-5 Zhao, D. & Zhou, N. (2008). Analysis of the Present Applications of Information Visualization in E-commerce Websites Zheng, X-Z., Wang, S-L., Luo, J-H., Cao, Y., & Qing, D-Z. (2011). Research on the Framework of Information Visualization System of Equipment Support Decision-making. Proceedings of 2011 International Conference on Computational and Information Science, pp. 780- 784. IEEE Computer Society Zhong, C., Wang, T., Cairns, S., & Arisona, S.M. (2012). Using an Interactive Dymaxion Map to Convey Research Information through Visualization, Proceedings of 16th International Conference on Information Visualization, IEEE Computer Society Ziegler, H., Nietzschmann, T. & Keim, D. (2008). Visual Analytics on the Financial Market: Pixel-based Analysis and Comparison of Long-Term Investments, Proceedings of the 12th Conference on Information Visualization (IV 2008), IV-VDM Symposium on Visualization and Data Mining, pp. 287- 295 Ziemkiewicz, C., Ottley, A., Crouser, R.J., Chauncey, K., Su, S.L., & Chang, R. (2012). Understanding Visualization by Understanding Individual Users, IEEE Journal of Computer Graphics and Applications, 32 (6), 88-94 Zikmund, W.G., Babin, B.J., Carr, C.J., & Griffin, M. (2010). Business Research Methods, 8th Edition, Canada. South-Western Cengage Learning Centre Zikopoulous, P., Eaton, C., Deutsch, T., Deroos, D., & Lapis, G. (2012). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. USA, IBM. Zins, C. (2007). Conceptual Approaches for Defining Data, Information, and Knowledge. Journal of the American Society for Information Science and Technology, 58(4), 479–493

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