|

Leveraging Context Awareness in Designing Mobile E-Government

Authors: Agbozo E., Medvedev A.N. Published: 18.12.2020
Published in issue: #4(133)/2020  
DOI: 10.18698/0236-3933-2020-4-4-21

 
Category: Informatics, Computer Engineering and Control | Chapter: Management in Social and Economic Systems  
Keywords: context-aware, e-government, ontological evaluation, PMJ model, cognitive computing, user experience

In the age of ubiquitous computing, smart systems, internet of things, and numerous modern technological advances in the smartphone world, context-aware programming has made progress within the past decade. Google's Awareness API is a great example of the available power to developers for optimizing user experience of mobile applications. Electronic Government (e-government) solutions, primarily mobile-based, which stand to gain maximum utility should integrate such innovations. This study, supported by the PMJ (Perception--Memory--Judgment) cognitive computing model, integrates context-aware models into e-government design in order to increase e-participation and user experience with respect to public service delivery. The study has employed the ontological evaluation technique which is a recommended non-empirical method of evaluating information systems. The purpose of using this technique is validating potency of the model in the real-world. The study conceptualizes the model and verifies it by the non-empirical technique of ontological evaluation using Protege. This study simulates a scenario using C# and illustrates the feasibility of the model by considering user-privacy and system health

References

[1] Faisal N., Talib F. E-government to m-government: a study in a developing economy. IJMC, 2016, vol. 14, no. 6, pp. 568--592. DOI: https://doi.org/10.1504/IJMC.2016.079301

[2] Meiyanti R., Utomo B., Sensuse D.I., et al. E-government challenges in developing countries: a literature review. CITSM, 2018. DOI: https://doi.org/10.1109/CITSM.2018.8674245

[3] Lessa L. Sustainability framework for e-government success: feasibility assessment. Proc. ICEGOV2019, 2019, pp. 231--239. DOI: https://doi.org/10.1145/3326365.3326396

[4] Bennett D. Factors influencing the success of an E-participation project in South Africa. PhD Thesis. University of Cape Town, 2015.

[5] Selker T., Burleson W. Context-aware design and interaction in computer systems. IBM Syst. J., 2000, vol. 39, no. 3-4, pp. 880--891. DOI: https://doi.org/10.1147/sj.393.0880

[6] Pradeep P., Krishnamoorthy S. The MOM of context-aware systems: a survey. Comput. Commun., 2019, vol. 137, pp. 44--69. DOI: https://doi.org/10.1016/j.comcom.2019.02.002

[7] Shafaei S., Muller F., Salzmann T., et al. Context prediction architectures in next generation of intelligent cars. ITSC, 2018, pp. 2923--2930. DOI: https://doi.org/10.1109/ITSC.2018.8569617

[8] Rakotonirainy A. Design of context-aware systems for vehicle using complex systems paradigms. Proc. CONTEXT-05 Workshop on Safety and Context, 2005. Available at: https://eprints.qut.edu.au/2700 (accessed: 18.05.2020).

[9] Irfan R., Bickler G., Khan S.U., et al. Survey on social networking services. IET Netw., 2013, vol. 2, no. 4, pp. 224--234.

[10] Primo A., Phoha V.V., Kumar R., et al. Context-aware active authentication using smartphone accelerometer measurements. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops, 2014, pp. 98--105. DOI: https://doi.org/10.1109/CVPRW.2014.20

[11] Truong H., Dustdar S. A survey on context-aware web service systems. Int. J. Web Inform. Syst., 2009, vol. 5, no. 1, pp. 5--31. DOI: https://doi.org/10.1108/17440080910947295

[12] Mongiello M., di Noia T., Nocera F., et al. Context-aware design of reflective middleware in the internet of everything. STAF, 2016, pp. 423--435. DOI: https://doi.org/10.1007/978-3-319-50230-4_33

[13] Vigliarolo B. Google awareness API. Еhe smart person’s guide. TechRepublic, 2017.

[14] Google Awareness API. developers.google.com: website. Available at: https://developers.google.com/awareness (accessed: 13.05.2020).

[15] Gedeon J., Himmelmann N., Felka P., et al. vStore: a context-aware framework for mobile micro-storage at the edge. MobiCASE, 2018, pp. 165--182. DOI: https://doi.org/10.1007/978-3-319-90740-6_10

[16] Sinha A., Mehta N., Bhakta P., et al. On the fly integration of applications using context aware approach. IJARIIT, 2019, vol. 5, no. 3, pp. 435--438. Available at: https://www.ijariit.com/manuscripts/v5i3/V5I3-1302.pdf

[17] Fu X., Cai L.H., Liu Y., et al. A computational cognition model of perception, memory, and judgment. Sс. China Inf. Sc., 2014, vol. 57, no. 3. DOI: https://doi.org/10.1007/s11432-013-4911-9

[18] Recker J. Conceptual model evaluation: towards more paradigmatic rigor. EMMSAD'05: Tenth International Workshop on Exploring Modeling Methods in Systems Analysis and Design. University of Porto, Portugal, pp. 569--580.

[19] Derguech W. Business capability-centric management of services and process models. PhD Thesis. Galway, National University of Ireland, 2014.

[20] Barcelos P.P.F., Guizzardi G., Garcia A.S., et al. Ontological evaluation of the ITU-T Recommendation G.805. 18th Int. Conf. Telecommunications, 2011, pp. 232--237. DOI: https://doi.org/10.1109/CTS.2011.5898926

[21] Aileen C.-S., Latif A.-H. Information systems research methods, epistemology, and applications. IGI Global, 2008.

[22] Barn R., Barn B. An ontological representation of a taxonomy for cybercrime. Proc. ECIS, 2016. Available at: https://aisel.aisnet.org/ecis2016_rp/45 (accessed: 18.05.2020).

[23] Evermann J. A UML and OWL description of Bunge’s upper-level ontology model. Softw. Syst. Model, 2009, vol. 8, no. 2, pp. 235--249. DOI: https://doi.org/10.1007/s10270-008-0082-3

[24] Falconer S. OntoGraf. Protege Wiki, 2010.

[25] Shimizu C., Hammar K. CoModIDE --- the comprehensive modular ontology engineering IDE. ISWC, 2019, vol. 2456, pp. 249--252.

[26] Noy N.F., McGuinness D.L. Ontology development 101: a guide to creating your first ontology. Available at: https://protege.stanford.edu/publications/ontology_development/ontology101.pdf (accessed: 18.05.2020).

[27] Wang W., Zhang Q. Privacy preservation for context sensing on smartphone. IEEE ACM Trans. Netw., 2016, vol. 24, no. 6, pp. 3235--3247. DOI: https://doi.org/10.1109/TNET.2015.2512301

[28] Yurur O., Liu C.H., Perera Ch., et al. Energy-efficient and context-aware smart-phone sensor employment. IEEE Trans. Veh. Technol, 2015, vol. 64, no. 9, pp. 4230--4244. DOI: https://doi.org/10.1109/TVT.2014.2364619

[29] Kansal A., Saponas S., Bernheim Brush A.J., et al. The latency, accuracy, and battery (LAB) abstraction: programmer productivity and energy efficiency for continuous mobile context sensing. ACM SIGPLAN Notices, 2013, vol. 48, no. 10, pp. 661--676. DOI: https://doi.org/10.1145/2509136.2509541

[30] Zhang M., Yin H. Efficient, context-aware privacy leakage confinement for Android applications without firmware modding. Proc. ASIA CCS, 2014, pp. 259--270. DOI: https://doi.org/10.1145/2590296.2590312