Quality of Citizen Reporting Tools at Municipal Level
Hana Kopackova, Petra Libalova
J INFORM SYSTEMS ENG, Volume 4, Issue 3, Article No: em0092
https://doi.org/10.29333/jisem/5891
Research Article
[Abstract]
[PDF]
[References]
ABSTRACT
Quality of life in cities is highly dependent on the state of public infrastructure. Maintenance of the infrastructure is managed by the local government that needs to be informed about issues on the infrastructure. Although the duty of maintenance staff is to check the state of the infrastructure, it is difficult to know about all the problems in the city. Therefore, citizens can take the initiative and participate by reporting any issue on public infrastructure. Today, citizens have many options on how to report such issues, due to modern communication technologies. Citizen reporting systems, which are a special type of participatory technology, deserve deeper research due to its novelty in the information system research. In this article, we introduce the study of 13 Czech regional capitals and their reporting systems. In this study, we evaluate the existence of different types of communication channels (phone, e-mail, electronic form, WebGis, and mobile application) and their quality based on four criteria (searchability, coverage of issues, visual geolocation, and display of sent reports). Moreover, the results of regional capitals will be compared with previous research on cities in these regions.
Keywords: citizen reporting, smart cities, non-emergency, e-participation
REFERENCES
- Abu-Tayeh, G., Neumann, O. and Stuermer, M. (2018). Exploring the Motives of Citizen Reporting Engagement: Self-Concern and Other-Orientation. Business & Information Systems Engineering, 60(3), 215–226. https://doi.org/10.1007/s12599-018-0530-8
- Aitken, M. (2014). E-Planning and Public Participation: Addressing or aggravating the challenges of public participation in planning? International Journal of E-Planning Research (IJEPR), 3(2), 38-53. https://doi.org/10.4018/ijepr.2014040103
- Atzmanstorfer, K. and Blaschke, T. (2013). The geospatial web: A tool to support the empowerment of citizens through E-participation? In Citizen e-Participation in urban governance: Crowdsourcing and collaborative creativity, IGI Global, 144-171. https://doi.org/10.4018/978-1-4666-4169-3.ch009
- Borges, J., Budde, M., Peters, O., Riedel, T. and Beigl, M. (2016). Towards two-tier citizen sensing. In 2016 IEEE International Smart Cities Conference (ISC2), 1-4. https://doi.org/10.1109/ISC2.2016.7580771
- Boulos, M. N. K., Resch, B., Crowley, D. N., Breslin, J. G., Sohn, G., Burtner, R., ... and Chuang, K. Y. S. (2011). Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples. International journal of health geographics, 10(1), 67. https://doi.org/10.1186/1476-072X-10-67
- Campbell, A. T., Eisenman, S. B., Lane, N. D., Miluzzo, E. and Peterson, R. A. (2006). People-centric urban sensing. In Proceedings of the 2nd annual international workshop on Wireless internet, 18-26. https://doi.org/10.1145/1234161.1234179
- Caragliu, A., Del Bo, C., and Nijkamp, P. (2011). Smart cities in Europe. Journal of urban technology, 18(2), 65-82. https://doi.org/10.1080/10630732.2011.601117
- Citizens as Partners. OECD handbook on information, consultation and public participation in policy-making. (2001). [Online]. Available at: https://www.internationalbudget.org/wp-content/uploads/Citizens-as-Partners-OECD-Handbook.pdf (Accessed 10 March 2019)
- Clark, B. Y. and Brudney, J. (2017). Too Much of a Good Thing? Frequent Flyers and the Implications for the Coproduction of Public Service Delivery. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2942269
- Clark, B. Y. and Rokakis, M. (2014). Do 311 Systems Shape Citizen Satisfaction with Local Government? SSRN Electronic Journal, 2491034. https://doi.org/10.2139/ssrn.2491034
- Consoli, S., Recupero, D. R., Mongiovi, M., Presutti, V., Cataldi, G. and Patatu, W. (2015). An Urban Fault Reporting and Management Platform for Smart Cities. Assoc Computing Machinery, 535–540. https://doi.org/10.1145/2740908.2743910
- Crawford, S. P. and Walters D. (2013). Citizen-Centered Governance: The Mayor’s Office of New Urban Mechanics and the Evolution of CRM in Boston. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2307158
- De Filippi, F., Coscia, C., Boella, G., Antonini, A., Calafiore, A., Cantini, A. … and Schifanella, C. (2016). MiraMap: A We-Government Tool for Smart Peripheries in Smart Cities. IEEE Access, 4, 3824–3843. https://doi.org/10.1109/ACCESS.2016.2548558
- DeMeritt, M. and Writer, E. (2011). Simplifying citizen reporting. ArcUser, Magazine for ESRI Software User, 14(1), 26-27.
- Dirks, S. and Keeling, M. (2009). A vision of smarter cities: How cities can lead the way into a prosperous and sustainable future. Somers, NY: IBM Global Business Services
- Dutta, P., Aoki, P. M., Kumar, N., Mainwaring, A., Myers, C., Willett, W. and Woodruff, A. (2009). Common sense: participatory urban sensing using a network of handheld air quality monitors. In Proceedings of the 7th ACM conference on embedded networked sensor systems, 349-350. https://doi.org/10.1145/1644038.1644095
- Floreddu, P. B. and Cabiddu, F. (2012). Public decisions and citizen satisfaction: the potential role of public participation geographic information systems. International Journal of Electronic Commerce Studies, 3(1), 121-134.
- Gabrys, J. (2014). Programming environments: environmentality and citizen sensing in the smart city. Environment and Planning D: Society and Space, 32(1), 30-48. https://doi.org/10.1068/d16812
- Ganapati, S. (2011). Uses of Public Participation Geographic Information Systems Applications in E-Government. Public Administration Review, 71(3), 425-434. https://doi.org/10.1111/j.1540-6210.2011.02226.x
- Hartmann, S., Mainka, A. and Stock, W.G. (2017). Citizen relationship management in local governments: The potential of 311 for public service delivery. In Beyond Bureaucracy, Springer, 337-353. https://doi.org/10.1007/978-3-319-54142-6_18
- Ho, A. and Coates, P. (2004). Citizen-initiated performance assessment: The initial Iowa experience. Public Performance & Management Review, 27(3), 29–50.
- Kannabiran, G. M. J. X., Xavier, M. J. and Anantharaaj, A. (2004). Enabling e-governance through citizen relationship management-concept, model and applications. Journal of Services Research, 4(2).
- King, S. F. and Brown, P. (2007). Fix my street or else: using the internet to voice local public service concerns. In Proceedings of the 1st international conference on Theory and practice of electronic governance, ACM, pp. 72-80. https://doi.org/10.1145/1328057.1328076
- Kopackova, H. (2019). Reflexion of citizens’ needs in city strategies: The case study of selected cities of Visegrad group countries. Cities, 84, 159-171. https://doi.org/10.1016/j.cities.2018.08.004
- Kopackova, H. and Libalova, P. (2019). Citizen reporting as the form of e-participation in smart cities. In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1-6, IEEE. https://doi.org/10.23919/CISTI.2019.8760931
- Kopackova, H., Komarkova, J. and Jech, J. (2019). Technology helping citizens to express their needs and improve their neighborhood. In IEEE Information and Digital Technologies (IDT), pp. 225-231.
- Linders, D. (2012). From e-government to we-government: Defining a typology for citizen coproduction in the age of social media. Government Information Quarterly, 29(4), 446-454. https://doi.org/10.1016/j.giq.2012.06.003
- Lu, Q. and Johnson, P.A. (2016). Characterizing New Channels of Communication: A Case Study of Municipal 311 Requests in Edmonton, Canada. URBAN PLANNING, 1(2), 18-31. https://doi.org/10.17645/up.v1i2.621
- Michels, A. and De Graaf, L. (2010). Examining citizen participation: Local participatory policy making and democracy. Local Government Studies, 36(4), 477-491. https://doi.org/10.1080/03003930.2010.494101
- Motta, G., You, L., Sacco, D. and Ma, T. (2014). City feed: A crowdsourcing system for city governance. In 2014 IEEE 8th international symposium on service oriented system engineering, pp. 439-445. https://doi.org/10.1109/SOSE.2014.64
- Mulligan, C. E. and Olsson, M. (2013). Architectural implications of smart city business models: An evolutionary perspective. IEEE Communications Magazine, 51, 80-85. https://doi.org/10.1109/MCOM.2013.6525599
- Nabatchi, T., Sancino, A. and Sicilia, M. (2017). Varieties of participation in public services: The who, when, and what of coproduction. Public Administration Review, 77(5), 766–776. https://doi.org/10.1111/puar.12765
- Nam, T. (2012). Suggesting frameworks of citizen-sourcing via Government 2.0. Government Information Quarterly, 29(1), 12-20. https://doi.org/10.1016/j.giq.2011.07.005
- Nam, T. and Pardo, T. A. (2011). Smart city as urban innovation: Focusing onmanagement, policy, and context. Proceedings of the 5th International Conference on Theory and Practice of Electronic Governance, September 26–28, Tallinn, Estonia. https://doi.org/10.1145/2072069.2072100
- Nam, T. and Pardo, T.A. (2014). The changing face of a city government: A case study of Philly311. Government Information Quarterly, 31, 1-9. https://doi.org/10.1016/j.giq.2014.01.002
- Neshkova, M. I. and Guo, H. D. (2012). Public participation and organizational performance: Evidence from state agencies. Journal of Public Administration Research and Theory, 22(2), 267–288. https://doi.org/10.1093/jopart/mur038
- O’Brien, D. T., Offenhuber, D., Baldwin-Philippi, J., Sands, M. and Gordon, E. (2017). Uncharted territoriality in coproduction: The motivations for 311 reporting. Journal of Public Administration Research and Theory, 27(2), 320-335. https://doi.org/10.1093/jopart/muw046
- O’Byrne, J. C. (2015). The Diffusion and Evolution of 311 Citizen Service Centers in American Cities from 1996 to 2012-A Study to Identify the Catalysts for the Adoption of Citizen Engagement Technology (Doctoral dissertation, Virginia Tech).
- Offenhuber, D. (2014). Infrastructure legibility—a comparative analysis of open311-based citizen feedback systems. Cambridge Journal of Regions, Economy and Society, 8(1), 93-112. https://doi.org/10.1093/cjres/rsu001
- Ramasubramanian, L. (2016). Using Geo-Spatial Knowledge for Good Governance. In Advancing Geographic Information Science: The Past and Next Twenty Years, 307-311.
- Schmidthuber, L., et al. (2017). The emergence of local open government: Determinants of citizen participation in online service reporting. Government Information Quarterly, 34(3), 457-469. https://doi.org/10.1016/j.giq.2017.07.001
- Sheth, A. (2009). Citizen sensing, social signals, and enriching human experience. IEEE Internet Computing, 13(4), 87-92. https://doi.org/10.1109/MIC.2009.77
- Susanto, T. D., Diani, M. M. and Hafidz, I. (2017). User Acceptance of e-Government Citizen Report System (a Case Study of City113 App). Procedia Computer Science, 124, 560-568. https://doi.org/10.1016/j.procs.2017.12.190
- Swindell, D. and Kelly, J. M. (2000). Linking citizen satisfaction to performance measures: A preliminary evaluation. Public Performance and Management Review, 24(1), 30–52. https://doi.org/10.2307/3381075
- Thompson, M. M. (2016). Upside-down GIS: The future of citizen science and community participation. The Cartographic Journal, 53(4), 326-334. https://doi.org/10.1080/00087041.2016.1243863
- Torres, L. H. (2007). Citizen sourcing in the public interest. Knowledge Management for Development Journal, 3(1), 134-145.
- Winkler, T. J. Ziekow, H. and Weinberg, M. (2012b). Municipal benefits of participatory Urban sensing: A simulation approach and case validation. Journal of Theoretical and Applied Electronic Commerce Research, 7(3), 101-120. https://doi.org/10.4067/S0718-18762012000300010
- Winkler, T. J., Günther, O., Trouvilliez, G. and Hirsch, H (2012a). Participatory urban sensing: Citizens’ acceptance of a mobile reporting service. Association for Information Systems, paper 106.
- You, L., et al. (2016). CITY FEED: A Pilot System of Citizen-Sourcing for City Issue Management. ACM Transactions on Liget Systems and Technology, 7(4, SI), 53. https://doi.org/10.1145/2873064
Decision Support System for Business Ideas Competitions
Diana Martins, Rui Assis, Ricardo Coelho, Fernando Almeida
J INFORM SYSTEMS ENG, Volume 4, Issue 3, Article No: em0093
https://doi.org/10.29333/jisem/5892
Research Article
[Abstract]
[PDF]
[References]
ABSTRACT
Business ideas competitions have gained increasing importance in stimulating entrepreneurial activity mainly among highly qualified graduates. However, the operating model of these competitions is quite heterogeneous, complex and often confusing, since the perception of the merit of each project is assessed differently by each jury member. Therefore, it is important to propose a decision support system that simplifies the evaluation process of competing projects and ensures all the opinions of the jury members are considered and have the same importance. The developed application uses C# and Windows Forms technologies and the AHP method to serialize competing projects according to the individual evaluation of each jury member. The results of the study allowed testing the application considering four scenarios in which the relative importance of each criterion and the performance of each project according to these criteria are changed and evaluated.
Keywords: support decision system, AHP, business competition, ideas competition, entrepreneurship
REFERENCES
- Ács, Z., Szerb, L. and Lloyd, A. (2018). Global Entrepreneurship Index. Global Entrepreneurship Development Institute. Available at: https://thegedi.org/wp-content/uploads/dlm_uploads/2017/11/GEI-2018-1.pdf (Accessed February 20, 2019).
- Almeida, F., Silva, P. And Leite, J. (2017). Proposal of a carsharing system to improve urban mobility. Theoretical Empirical Researches in Urban Management (TERUM), 12(3), 32-44.
- Asih, A., Sopha, B. and Kriptaniadewa, G. (2017). Comparison study of metaheuristics: Empirical application of delivery problems. International Journal of Engineering Business Management, 9, 1-12. https://doi.org/10.1177/1847979017743603
- Assena, M., Peixe, J. and Almeida, F. (2013). MedAdvice24 – A Decision Support System for Medical Advice. Studies in System Science, 1(3), 38-49.
- Bergset, L. and Fichter, K. (2015). Green start-ups - a new typology for sustainable entrepreneurship and innovation research. Journal of Innovation Management, 3(3), 118-144. https://doi.org/10.24840/2183-0606_003.003_0009
- Bhushan, N. and Rai, K. (2004). Strategic decision making: applying the analytic hierarchy process. New York: Springer Publishing.
- Chabowski, B. and Mena, J. (2017). A Review of Global Competitiveness Research: Past Advances and Future Directions. Journal of International Marketing, 25(4), 1-24. https://doi.org/10.1509/jim.16.0053
- Comin, D. (2016). Drivers of Competitiveness. Singapore: World Scientific. https://doi.org/10.1142/9720
- Dulcic, Z., Pavlic, D. and Silic, I. (2012). Evaluating the intended use of Decision Support System (DSS) by applying Technology Acceptance Model (TAM) in business organizations in Croatia. Procedia – Social and Behavioral Sciences, 58, 1565-1575. https://doi.org/10.1016/j.sbspro.2012.09.1143
- Farshidi, S., Jansen, S., de Jong, R. and Brinkkemper, S. (2018). A decision support system for software technology selection. Journal of Decision Systems, 27, 98-110. https://doi.org/10.1080/12460125.2018.1464821
- Gigerenzer, G., Hertwig, R. and Pachur, T. (2015). Heuristics: The Foundations of Adaptive Behavior. Oxford, UK: Oxford University Press.
- Hassan, M., Eldin, A. and El-Ghazali, A. (2015). A Decision Support System for Subjective Forecasting of New Product Sales. International Journal of Computer Application, 126(2), 25-30. https://doi.org/10.5120/ijca2015905997
- Hilletofth, P., Hilmola, O. and Wang, Y. (2016). Simulation based decision support systems in the supply chain context. Industrial Management & Data Systems, 116(2), 1-3. https://doi.org/10.1108/IMDS-11-2015-0477
- HTGC (2019). Hello Tomorrow Global Challenge. Available at: https://hello-tomorrow.org/startups (Accessed February 22, 2019).
- Jain, S. (2017). The LINQ between XML and Database. International Research Journal of Engineering and Technology (IRJET), 4(6), 1938-1944.
- Karaman, E. and Ghaffarzadeh, M. (2014). Decision making based on Management Information System and Decision Support System. Bulletin of Environment, Pharmacology and Life Sciences, 3, 126-135.
- MC (2019). MassChallenge – More than an Accelerator. Available at: https://masschallenge.org (Accessed February 22, 2019).
- Palcic, I. and Lalic, B. (2009). Analytical Hierarchy Process as a Tool for Selecting and Evaluating Projects. International Journal of Simulation Model, 8(1), 16-26. https://doi.org/10.2507/IJSIMM08(1)2.112
- Qaiser, F., Ahmed, K., Sykora, M., Choudhary, A. and Simpson, M. (2017). Decision support systems for sustainable logistics: a review and bibliometric analysis. Industrial Management & Data Systems, 117(7), 1376-1388. https://doi.org/10.1108/IMDS-09-2016-0410
- Ries, E. (2011). The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. New York: Penguin Random House.
- Saaty, T. (2004). Decision making — the Analytic Hierarchy and Network Processes (AHP/ANP). Journal of Systems Science and Systems Engineering, 13(1), 1-35. https://doi.org/10.1007/s11518-006-0151-5
- Saaty, T. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98. https://doi.org/10.1504/IJSSCI.2008.017590
- Saaty, T. and Vargas, L. (2012). Models, methods, concepts & applications of the analytic hierarchy process. New York: Springer Publishing. https://doi.org/10.1007/978-1-4614-3597-6
- Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw Hill. https://doi.org/10.21236/ADA214804
- Sharda, R., Delen, D. and Turban, E. (2014). Business Intelligence and Analytics: Systems for Decision Support. London, UK: Pearson.
- Sharma, J. (2018). Economics of Decision Making: Exploring Analytical Hierarchical Process (AHP). Theoretical Economics Letters, 8, 3141-3156. https://doi.org/10.4236/tel.2018.814195
- Shim, J., Warkentin, M., Courtney, J., Power, D., Sharda, R. and Carlsson, C. (2002). Past, present, and future of decision support technology. Decision Support Systems, 33, 111-126. https://doi.org/10.1016/S0167-9236(01)00139-7
- Sommerville, I. (2015). Software Engineering. London, UK: Pearson.
- Spender, J., Corvello, V., Grimaldi, M. and Rippa, P. (2017). Startups and open innovation: a review of the literature. European Journal of Innovation Management, 20(1), 4-30. https://doi.org/10.1108/EJIM-12-2015-0131
- Tangil, G., Tapiador, J., Lopez, P. and Blasco, J. (2014). DENDROID: A text mining approach to analyzing and classifying code structures in Android malware families. Expert Systems with Applications, 41(4), 1104-1117. https://doi.org/10.1016/j.eswa.2013.07.106
- Turban, E., Aronson, J. and Liang, T. (2004). Decision Support Systems and Intelligent Systems. New Jersey: Prentice Hall.
- VC (2019). Venture Cup. Available at: http://www.venturecup.se/en/home/ (Accessed February 22, 2019).
- Vohra, R. and Das, N. (2011). Intelligent Decision Support Systems for Admission Management in Higher Education Institutes. International Journal of Artificial Intelligence & Applications (IJAIA), 2(4), 63-70. https://doi.org/10.5121/ijaia.2011.2406
- Xu, J. and Zhang, J. (2014). Exploration-exploitation tradeoffs in metaheuristics: Survey and analysis. In Proceedings of the 33rd Chinese Control Conference, Nanjing, China, pp. 8633–8638. https://doi.org/10.1109/ChiCC.2014.6896450
- Zhengmeng, C. and Haoxiang, J. (2011). A brief review on Decision Support Systems and it’s applications. In Proceedings of the IEEE International Symposium on IT in Medicine and Education, Cuangzhou, China, pp. 401-405. https://doi.org/10.1109/ITiME.2011.6132134
An Ensemble Predictive Model Based Prototype for Student Drop-out in Secondary Schools
Neema Mduma, Khamisi Kalegele, Dina Machuve
J INFORM SYSTEMS ENG, Volume 4, Issue 3, Article No: em0094
https://doi.org/10.29333/jisem/5893
Research Article
[Abstract]
[PDF]
[References]
ABSTRACT
When a student is absent from school for a continuous number of days as defined by the relevant authority, that student is considered to have dropped out of school. In Tanzania, for instance, drop-out is when a student is absent continuously for a period of 90 days. Despite the fact that several efforts have been made to improve the overall status of education at secondary level, the student drop-out problem still persists. Taking advantage of advancement in technology, several studies have used machine learning to address the student drop-out problem. However, most of the conducted studies have used datasets from developed countries, while developing countries are facing challenges on generating public datasets to be used to address this problem. Using a dataset from Tanzania which reflect a local scenario; this study presents an ensemble predictive model based prototype for student drop-out in secondary schools. The deployed model was developed by soft combining a tuned Logistic Regression and Multi-Layer Perceptron models. A feature engineering experiment was conducted to obtain the most important features for predicting student drop-out. Furthermore, a visualization module was integrated to assist interpretation of the machine learning results and we used flask framework in the development of the prototype.
Keywords: student drop-out, predictive model, machine learning, prototype, feature engineering experiment, visualization module
REFERENCES
- Alsaleh, S. and Haron, H. (2016). The Most Important Functional and Non-Functional Requirements of Knowledge Sharing System at Public Academic Institutions: A Case Study. Lecture Notes on Software Engineering, 4(2), 157–161. https://doi.org/10.7763/LNSE.2016.V4.242
- Ameri, S., Fard, M. J., Chinnam, R. B. and Reddy, C. K. (2016). Survival Analysis based Framework for Early Prediction of Student Dropouts. https://doi.org/10.1145/2983323.2983351
- Armash Aslam, F., Nabeel Mohammed, H., Jummal Musab, M. and Munir Murade Aaraf Gulamgaus, H. (2015). Efficient Way of Web Development Using Python and Flask. International Journal of Advanced Research in Computer Science, 6(2), 54–57.
- Aulck, L., Velagapudi, N., Blumenstock, J. and West, J. (2016). Predicting Student Dropout in Higher Education.
- Aung, A. M., Ramakrishnan, A. and Whitehill, J. R. (2018). Who are they looking at? Automatic Eye Gaze Following for Classroom Observation Video Analysis. Educational Data Mining.
- Basu, K., Basu, T., Buckmire, R. and Lal, N. (2019). Predictive Models of Student College Commitment Decisions Using Machine Learning. Data, 4(2), 65. https://doi.org/10.3390/data4020065
- Berens, J., Oster, S., Schneider, K. and Burghoff, J. (2018). Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods. Schumpeter School of Business and Economics, pp. 0–32.
- Elbadrawy, A., Polyzou, A., Ren, Z., Sweeney, M., Karypis, G. and Rangwala, H. (2016). Predicting student performance using personalized analytics. Computer, 49(4), 61–69. https://doi.org/10.1109/MC.2016.119
- Fei, M. and Yeung, D. Y. (2015). Temporal models for predicting student dropout in massive open online courses. In 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 256–263. https://doi.org/10.1109/ICDMW.2015.174
- Human Rights Watch (2017). I Had a Dream to Finish School: Barriers to Secondary Education in Tanzania.
- Hung, J. L., Wang, M. C., Wang, S., Abdelrasoul, M., Li, Y. and He, W. (2017). Identifying At-Risk Students for Early Interventions - A Time-Series Clustering Approach. IEEE Transactions on Emerging Topics in Computing, 5(1), 45–55. https://doi.org/10.1109/TETC.2015.2504239
- Iftikhar, W., Sheraz, M., Malik, A., Tariq, S., Saad, M., Ahmad, J. and Shareef, F. (2018). User Interface Design Issues In HCI. IJCSNS International Journal of Computer Science and Network Security, 18(8), 153–157.
- Iqbal, S. Z. (2017). Z-SDLC Model A New Model For Software Development Life Cycle (SDLC). International Journal of Engineering and Advanced Research Technology (IJEART), 3(2), 8.
- Jiang, Y. H., Javaad, S. S., and Golab, L. (2016). Data mining of undergraduate course evaluations. Informatics in Education, 15(3), 85–102. https://doi.org/10.15388/infedu.2016.05
- Kim, D. and Kim, S. (2018). Sustainable Education: Analyzing the Determinants of University Student Dropout by Nonlinear Panel Data Models. Sustainability, 10(4), 954. https://doi.org/10.3390/su10040954
- Lakkaraju, H., Aguiar, E., Shan, C., Miller, D., Bhanpuri, N., Ghani, R. and Addison, K. L. (2015). A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes. Kdd, 1909–1918. https://doi.org/10.1145/2783258.2788620
- Liang, J., Li, C. and Zheng, L. (2016). Machine learning application in MOOCs: Dropout prediction. ICCSE 2016 - 11th International Conference on Computer Science and Education, (Iccse), pp. 52–57. https://doi.org/10.1109/ICCSE.2016.7581554
- Maginga, T. J., Nordey, T. and Ally, M. (2018). Extension System for Improving the Management of Vegetable Cropping Systems. Journal of Information Systems Engineering & Management, 3(4). https://doi.org/10.20897/jisem/3940
- Mduma, N., Kalegele, K. and Machuve, D. (2019a). A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction. Data Science Journal, 18, 1–10. https://doi.org/10.5334/dsj-2019-014
- Mduma, N., Kalegele, K. and Machuve, D. (2019b). Machine learning approach for reducing students dropout rates. International Journal of Advanced Computer Research, 9(42). https://doi.org/10.19101/IJACR.2018.839045
- Mgala, M. (2016). Investigating Prediction Modelling of Academic Performance for Students in Rural Schools in Kenya. PhD thesis, University of Cape Town.
- Mitchell, T. (2015). Machine Learning With Python Scikit-Learn-Application to the Estimation of Occupancy and Human Activities. Number July.
- Nacheva, R. (2017). Prototyping Approach in User Interface. In 2nd Conference on Innovative Teaching Methods, number June, pp. 80–87, Bulgaria.
- Nargesian, F., Samulowitz, H., Khurana, U., Khalil, E. B. and Turaga, D. (2017). Learning feature engineering for classification. IJCAI International Joint Conference on Artificial Intelligence, (August), pp. 2529–2535. https://doi.org/10.24963/ijcai.2017/352
- Naz, Zafar and Khan (2019). Ensemble Based Classification of Sentiments Using Forest Optimization Algorithm. Data, 4(2), 76. https://doi.org/10.3390/data4020076
- Rovira, S., Puertas, E. and Igual, L. (2017). Data-driven system to predict academic grades and dropout. PLOS ONE, 12(2), 1–21. https://doi.org/10.1371/journal.pone.0171207
- Santana, M. A., Costa, E. B., Neto, B. F. S., Silva, I. C. L., and Rego, J. B. A. (2015). A predictive model for identifying students with dropout profiles in online courses. CEUR Workshop Proceedings, 1446.
- Shahul, S., Suneel, S., Rahaman, M. A. and Swathi, J. N. (2016). A Study of Data Pre-Processing Techniques for Machine Learning Algorithm to Predict Software Effort Estimation. Imperial Journal of Interdisciplinary Research, 2(6), 2454–1362.
- TAMISEMI (2004). The United Republic of Tanzania Ministry of Education and Culture. Pp. 2004–2009.
- Truta, C., Parv, L. and Topala, I. (2018). Academic engagement and intention to drop out: Levers for sustainability in higher education. Sustainability, 10(12), 1–11. https://doi.org/10.3390/su10124637
- Wizara ya Elimu na Mafunzo ya Ufundi (2014). Sera ya Elimu na Mafunzo. Technical report.
- Xin, Y., Claude, B., Rob, V., Bertoni, A. and Phillipe, E. (2018). Data Visualization in Conceptual De- sign: Developing a Prototype to Support Decision Making. In 12th International Conference on Modeling, Optimization and SIMulation - MOSIM’18, number July, page 12.
- Xu, J., Moon, K. H. and van der Schaar, M. (2017). A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs. IEEE Journal of Selected Topics in Signal Processing, 11(5), 742–753. https://doi.org/10.1109/JSTSP.2017.2692560
- Yu, J. (2018). Research Process on Software Development Model. IOP Conference Series: Materials Science and Engineering, 394(3).
Low-cost UAV as a Source of Image Data for Detection of Land Cover Changes
Pavel Sedlák, Jitka Komárková, Jakub Jech, Oldřich Mašín
J INFORM SYSTEMS ENG, Volume 4, Issue 3, Article No: em0095
https://doi.org/10.29333/jisem/5894
Research Article
[Abstract]
[PDF]
[References]
ABSTRACT
Data from remote sensing methods have been used successfully to monitor changes in the landscape. At present, unmanned aerial vehicles (UAVs) are also increasingly used. The article shows how to use the middle-class dron, specifically DJI Phantom 3 with a built-in camera, to monitor and document changes in the vicinity of small water bodies where a part of the shore is poorly accessible or totally unavailable. The obtained data can be further processed by conventional digital image processing methods such as image classification, highlighting through spectral indexes, or even visually comparing.
Keywords: image processing, UAV, land cover, detection of changes
REFERENCES
- Amici, S. and Turci, M. (2014). Monitoring Vulcanoes with UAS: Testing the Suitability of Thermal Infrared Sensors. Available at: https://www.gim-international.com/content/article/monitoring-vulcanoes-with-uas (Accessed 13 February 2019).
- Boak, E. H. and Turner, I. L. (2005). Shoreline Definition and Detection: A Review. Journal of Coastal Research, 21(4), 688-703. https://doi.org/10.2112/03-0071.1
- Dolan, R., et al. (1980). The reliability of shoreline change measurements from aerial photographs. Shore and Beach, 48(4), 22-29.
- Forman, R. T. T. and Godron, M. (1986). Landscape ecology. New York, New York, USA: John Wiley and Sons.
- Gorkovchuk, D., Gorkovchuk, J. and Hutnyk, B. (2017). Photogrammetry for Mining: Exploring Consumer-grade Copters for Quarry Mapping. Available at: https://www.gim-international.com/content/article/low-cost-uas-photogram metry-for-mining?utm_source=Newsletter+Superlist&utm_campaign=5afc1de9b6-EMAIL_CAMPAIGN_ 2017_10_19GIM&utm_medium=email&utm_term=0_9bcc6040d6-5afc1de9b6-46151793 (Accessed 13 February 2019).
- Karas J. and Šafář, V. (2017). UAS Photogrammetry and Railway Mapping: Pilot in the Czech Republic Attains High-precision Results. Available at: https://www.gim-international.com/content/article/uas-photogram metry-and-railway-mapping (Accessed 13 February 2019).
- Karas, J. (2013). UAS and Flooding: Monitoring Flooding and Assessing Damage in the Czech Republic. Available at: https://www.gim-international.com/content/article/uas-and-flooding (Accessed 13 February 2019).
- Komarkova, J. and Sedlak, P. (2018). UAV Spectral Image Mapping of Shoreline Vegetation. GIM International-The Worldwide Magazine for Geomatics, 32(6), 26-27.
- List, J. H. and Farris, A. S. (1999). Large-scale shoreline response to storms and fair weather. Proceedings of the Coastal Sediments, pp. 1324-1337.
- Martínez, D., Sarabia, A. and García, S. (2014). UAS in Farming: A Pilot Project in Cuba. Available at: https://www.gim-international.com/content/article/uas-in-farming (Accessed 13 February 2019).
- Morton, R. A. (1991). Accurate shoreline mapping: past, present, and future. Proceedings of the Coastal Sediments, pp. 997-1010.
- Sedlák, P., Komárková, J. Mašín, O. and Jech, J. (2019). The Procedure for Processing Images from a Low-cost UAV The Case of Monitoring Landscape Changes. In 14th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1-4, IEEE. https://doi.org/10.23919/CISTI.2019.8760952
- Smith, G. L. and Zarillo, G. A. (1990). Calculating long-term shoreline recession rates using aerial photographic and beach profiling techniques. Journal of Coastal Research, 6(1), 111-120.
- Tumlisan, G., Bronsveld, K. and Koeva, M. (2018). UAS-based Measurement of Crop Height and Biomass: Monitoring Crop Growth and Estimating Yield Production in Precision Farming. Available at: https://www.gim-international.com/content/article/uas-based-measurement-of-crop-height-and-biomass?utm_source= Newsletter+Superlist&utm_campaign=4b43b3d340-EMAIL_CAMPAIGN_2018_01_25GIM&utm_medium =email&utm_term=0_9bcc6040d6-4b43b3d340-46151793&mc_cid=4b43b3d340&mc_eid=8e06fb45f3 (Accessed 13 February 2019).
- Whitehead, K. (2010). Unmanned Aerial Vehicles for Glaciological Studies: Airborne Survey of Fountain Glacier’s Terminus Region. Available at: https://www.gim-international.com/content/article/unmanned-aerial-vehicles-for-glaciological-studies (Accessed 13 February 2019).
Big Data Analysis in Supply Chain Management in Portuguese SMEs “Leader Excellence”
Fábio Azevedo, José Luís Reis
J INFORM SYSTEMS ENG, Volume 4, Issue 3, Article No: em0096
https://doi.org/10.29333/jisem/5895
Research Article
[Abstract]
[PDF]
[References]
ABSTRACT
With the market becoming increasingly competitive, companies are looking for ways to differentiate themselves from competitors, thereby increasing the interest of organizations in analysing large data and their potential benefits to supply chain management. The objective of this study is to understand the involvement of the Portuguese SMEs “Leader Excellence” with the Big Data theme and their analysis as a function of the Supply Chain Management, as well as to understand if these companies are in the same line as the companies of worldwide reference, in what concerns to the topic in question. For this study, a survey was carried out with 18 questions and applied to 80 SMEs distinguished as “SMEs Leader Excellence” certified by IAPMEI. With this random sample, from the analysis of the results, it was possible to verify that the Portuguese SMEs are not yet at the level of the big world companies with respect to the use of Big Data in the management of the supply chain. Although the Big Data theme is often explored by the scientific community, since they are relatively recent concepts, when the focus is on SMEs, the number of available studies is scarce, hence this work is a further contribution to broadening the body knowledge in this area.
Keywords: big data analysis, logistics, supply chain management, SMEs
REFERENCES
- Akter, S. and S. Fosso Wamba (2016). Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26(2), 173-194. https://doi.org/10.1007/s12525-016-0219-0
- Ashton, K. (2009). That ‘Internet of Things’ Thing. In the real world, things matter more than ideas. RFID Journal. Available at: http://www.rfidjournal.com/articles/view?4986 (Accessed 14 June 2018)
- Assunção, M. D., Calheiros, R. N., Bianchi, S., Netto, M. A., Buyya, R. (2015). Big Data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing, 79, 3-15. https://doi.org/10.1016/j.jpdc.2014.08.003
- Barton, D. and Court, D. (2012). Making advanced analytics work for you. Harvard Business Review, 90(10), 78-83.
- Bryson, S., Kenwright, D., Cox, M., Ellsworth, D. and Haimes, R. (1999). Visually exploring gigabyte data sets in real time. Communications of the ACM, 42(8), 82-90. https://doi.org/10.1145/310930.310977
- Carvalho, J. and Ramos, T. (2009). Logística na saúde (3rd ed). Lisboa: Edições Sílabo.
- Castro Melo, D. and Alcântara, R. L. C. (2012). A gestão da demanda em cadeias de suprimentos: uma abordagem além da previsão de vendas. Gestão & Produção, 18(4), 809-824. https://doi.org/10.1590/S0104-530X2011000400009
- Chen, H., Chiang, R. H. and Storey, V. C. (2012) Business intelligence and analytics: from big data to big impact. MIS Quarterly, 36(4), 1165-1188. https://doi.org/10.2307/41703503
- Christopher, M. (2011). Logistics & supply chain management (4rd ed.). UK: Pearson. https://doi.org/10.1108/13598541111115338
- Cooper, M. C., Lambert, D. M. and Pagh, J. D. (1997). Supply chain management: more than a new name for logistics. The International Journal of Logistics Management, 8(1), 1-14. https://doi.org/10.1108/09574099710805556
- Davenport, T. (2014). Big data at work: dispelling the myths, uncovering the opportunities. Boston: Harvard Business Review Press. https://doi.org/10.15358/9783800648153
- DHL Trend Research. (2016). Logistics Trend Radar: Delivering Insight Today. Creating Value Tomorrow! Germany: DHL Trend Research.
- Douglas, L. (2001). 3dr data management: Controlling data volume, velocity and variety. Available at: https://blog s.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf (Accessed 12 May 2018)
- DRE Diário da República. (2007). Decreto-Lei n.º 372/2007. Available at: https://dre.pt/application/file/a/629347 (Accessed 10 Mars 2018)
- Dumbill, E. (2012). Planning for big data. Sebastopol: O’Reilly Media.
- Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G. and Gnanzou, D. (2015). How ‘Big Data’ Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2014.12.031
- Halaweh, M. and Massry, A. E. (2015). Conceptual model for successful implementation of big data in organizations. Journal of International Technology and Information Management, 24(2), 2.
- Henke, N., Bughin, J., Chui, M., Manyika, J., Saleh, T., Wiseman, B. and Sethupathy, G. (2016). The age of analytics: Competing in a data-driven world. McKinsey Global Institute report.
- Hill, M. and Hill, A. (2005). Investigação por questionário. Lisboa: Edições Sílabo.
- Howie, T. (2013) The Big Bang. How the Big Data Explosion Is Changing the World. Microsoft UK Enterprise Insights Blog. Available at: http://blogs.msdn.com/b/microsoftenterpriseinsight/archive/2013/04/15/big-bang-how-the-big-data-explosion-is-changing-theworld.aspx (Accessed 16 June 2018)
- IAPMEI. (2018). PME Líder e PME Excelência 2017. Available at: https://www.iapmei.pt/PRODUTOS-E-SERVICOS/Qualificacao-Certificacao/PME-Lider.aspx (Accessed 10 March 2018)
- INE - Instituto Nacional de Estatística. (2018). Empresas de Portugal 2016.
- Jun, S. P., Park, D. H. and Yeom, J. (2014). The possibility of using search traffic information to explore consumer product attitudes and forecast consumer preference. Technological Forecasting and Social Change, 86, 237-253. https://doi.org/10.1016/j.techfore.2013.10.021
- Kaminsky, P., Simchi-Levi, D. and Simchi-Levi, E. (2003). Designing and managing the supply chain: Concepts, strategies, and case studies (3rd Ed.). New York: McGraw-Hill.
- KPMG Capital. (2014). Going Beyond the Data: achieving actionable insights with data and analytics. Available at: https://assets.kpmg.com/content/dam/kpmg/pdf/2015/04/going-beyond-data-and-analytics-v4.pdf
- Leeflang, P. S., Verhoef, P. C., Dahlström, P. and Freundt, T. (2014). Challenges and solutions for marketing in a digital era. European Management Journal, 32(1), 1-12. https://doi.org/10.1016/j.emj.2013.12.001
- Loshin, D. (2013). Big data analytics: from strategic planning to enterprise integration with tools, techniques, NoSQL, and graph. USA: Elsevier Inc.
- Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A. (2011). Big data: The next frontier for innovation, competition, and productivity. Available at: https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation (Accessed 12 July 2018)
- Mcafee, A., Brynjolfsson, E. and Davenport, T. H. (2012). Big data: the management revolution. Harvard Business Review, 90(10), 60-68.
- Normandeau, K. (2013). Beyond Volume, Variety and Velocity is the Issue of Big Data Veracity. Available at: https://insidebigdata.com/2013/09/12/beyond-volume-variety-velocity-issue-big-data-veracity/ (Accessed 12 May 2018)
- Novo, R. and Neves, J. (2013). Inovação na inteligência analítica por meio do Big Data: características de diferenciação da abordagem tradicional. In Workshop de Pós-Graduação e Pesquisa do Centro Paula Souza. São Paulo. p. 32-44.
- Porter, M. E. and Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11), 64-88.
- Pozo, H. (2000). Administração de recursos materiais e patrimoniais: uma abordagem logística. São Paulo: Atlas.
- Quayle, M. (2003). A study of supply chain management practice in UK industrial SMEs. Supply Chain Management: An International Journal, 8(1), 79-86. https://doi.org/10.1108/13598540310463387
- Rowe S. and Pournader M. (2017). Supply Chain Big Data Series Part 1. Available at: https://assets.kpmg.com/ content/dam/kpmg/au/pdf/2017/big-data-analytics-supply-chain-performance.pdf (Accessed 10 February 2018)
- Sagaert, Y., Kourentzes, N., Aghezzaf, E. H. and Desmet, B. (2016) Sales Forecasting with Temporal Big Data: Avoiding Information Overload for Supply Chain Management. In Informs International, Technology and Engineering conference. 12 – 15 May. Hawaii: United States of America https://www.ine.pt/xportal/xmain?xpid=INE&xpgid =ine_publicacoes&PUBLICACOESpub_boui=318224733&PUBLICACOESmodo=2 (Accessed 10 March 2018)
- Stamford, C. (2013). Gartner Predicts Business Intelligence and Analytics Will Remain Top Focus for CIOs through 2017. Available at: www.gartner.com/newsroom/id/2637615 (Accessed 10 March 2018)
- Stevens, G. C. (1989). Integrating the supply chain. International Journal of Physical Distribution and Materials Management, 19(8), 3–8. https://doi.org/10.1108/EUM0000000000329
- Swaminathan, S. (2012). The Effects of Big Data on the Logistics. Available at: http://www.oracle.com/us/corporate/profit/archives/opinion/021512-sswaminathan-1523937.html (Accessed 20 March 2018)
- Thong, J. Y. (2001) Resource constraints and information systems implementation in Singaporean small businesses. Omega, 29(2), 143-156. https://doi.org/10.1016/S0305-0483(00)00035-9
- Turner, D., Schroeck, M. and Shockley, R. (2013). Analytics: The real-world use of big data in financial services. IBM Global Business Services, 1-12.
- Waller, M. A. and Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84. https://doi.org/10.1111/jbl.12010
- Wu, X., Zhu, X., Wu, G. Q. and Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97-107. https://doi.org/10.1109/TKDE.2013.109