Information Technology Reference
In-Depth Information
5.6 Towards a Unifying Theory of CMA
Progress in the above challenges ismuchmore likelywhenCMAmanages to establish
a unifying theory. This topic contributes to the establishment of such a unifying theory
by providing a structured overview of concepts, techniques and their implications for
three important CMA aspects. As an umbrella research field, CMAmust persistently
continue its efforts to agree on ontological foundations and common basic tasks, and
intensify the sharing of data and methods. Initial community efforts in that direction
sparked the flame but must now be followed up. When researchers from the many
contributing fields manage to bundle their efforts, CMA can make a real contribution
to the present and future challenges of a world in motion.
References
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery
in databases.
AI Magazine
,
17
(3), 37-54.
Galton, A. (2005). Dynamic collectives and their collective dynamics. In A. Cohn & D. M. Mark
(Eds.),
Spatial information theory, proceedings
, (Vol. 3693, pp. 300-315)., Lecture Notes in
Computer Science Berlin: Springer.
Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of
geography.
Dialogues in Human Geography
,
3
(3), 255-261.
Kitchin, R. (2013). Big data and human geography: Opportunities, challenges and risks.
Dialogues
in Human Geography
,
3
(3), 262-267.
Krumm, J. (2009). A survey of computational location privacy.
Personal and Ubiquitous Computing
,
13
(6), 391-399.
Laube, P., Dennis, T., Walker, M., & Forer, P. (2007). Movement beyond the snapshot—dynamic
analysis of geospatial lifelines.
Computers, Environment and Urban Systems
,
31
(5), 481-501.
Laube, P., Duckham, M., Worboys, M., & Joyce, T. (2010). Decentralized spatial computing in
urban environments. In B. Jiang & X. Yao (Eds.),
Geospatial analysis and modelling of urban
structure and dynamics, geojournal library
(pp. 53-74). Berlin: Springer.
Mayer-Schönberger, V., & Cukier, K. (2013).
Big data: A revolution that will transform how we
live, work, and think
. Boston, MA: Houghton Mifflin Harcourt.
Sui, D., &Goodchild, M. (2011). The convergence of gis and social media: challenges for giscience.
International Journal of Geographical Information Science
,
25
(11), 1737-1748.
Wirz, M., Franke, T., Roggen, D., Mitleton-Kelly, E., Lukowicz, P., & Troster, G. (2012). Inferring
crowd conditions from pedestrians' location traces for real-time crowd monitoring during city-
scale mass gatherings.
IEEE 21st International Workshop on Enabling Technologies: Infrastruc-
ture for Collaborative Enterprises (WETICE)
, pp. 367-372.