TY - GEN
T1 - Rule selection for collaborative ubiquitous smart device development
T2 - 5th International Conference on Ubiquitous Intelligence and Computing, UIC 2008
AU - Kim, Kyoung Yun
AU - Choi, Keunho
AU - Kwon, Ohbyung
PY - 2008
Y1 - 2008
N2 - Comparing with general mobile devices, Ubiquitous Smart Device (USD) is characterized by its capability to generate or use context data for autonomous services, and it provides users with personalized and situation-aware interfaces. While the USD development requires more knowledge-intensive and collaborative environment, the capture, retrieval, accessibility, and reusability of that design knowledge are increasingly critical. In the design collaboration, the cumulative, evolutionary design information and design rules behind the USD design are infrequently captured and often difficult to hurdle due to its complexity. Rough set theory synthesizes approximation of concepts, analyzes data by discovering patterns, and classifies into certain decision classes. Such patterns can be extracted from data by means of methods based on Boolean reasoning and discernibility. In this paper, a rough set theory generates demanded rules and selects the appropriate minimal rules among the demanded rules associated to USD physical component design. The presented method shows the feasibility of rough-set based rule selection considering complex design data objects of USD physical components.
AB - Comparing with general mobile devices, Ubiquitous Smart Device (USD) is characterized by its capability to generate or use context data for autonomous services, and it provides users with personalized and situation-aware interfaces. While the USD development requires more knowledge-intensive and collaborative environment, the capture, retrieval, accessibility, and reusability of that design knowledge are increasingly critical. In the design collaboration, the cumulative, evolutionary design information and design rules behind the USD design are infrequently captured and often difficult to hurdle due to its complexity. Rough set theory synthesizes approximation of concepts, analyzes data by discovering patterns, and classifies into certain decision classes. Such patterns can be extracted from data by means of methods based on Boolean reasoning and discernibility. In this paper, a rough set theory generates demanded rules and selects the appropriate minimal rules among the demanded rules associated to USD physical component design. The presented method shows the feasibility of rough-set based rule selection considering complex design data objects of USD physical components.
UR - http://www.scopus.com/inward/record.url?scp=48349096789&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-69293-5_31
DO - 10.1007/978-3-540-69293-5_31
M3 - Conference contribution
AN - SCOPUS:48349096789
SN - 3540692924
SN - 9783540692928
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 386
EP - 396
BT - Ubiquitous Intelligence and Computing - 5th International Conference, UIC 2008, Proceedings
Y2 - 23 June 2008 through 25 June 2008
ER -