By Noureddine Mouaddib, Guillaume Raschia (auth.), Yahiko Kambayashi, Dik-Lun Lee, Ee-peng Lim, Mukesh Mohania, Yoshifumi Masunaga (eds.)
This publication offers the completely refereed joint post-proceedings of 3 workshops held throughout the seventeenth overseas convention on Conceptual Modeling, ER '98, in Singapore in November 1998. The 50 revised papers offered have undergone rounds of reviewing and revision. The ebook is split in sections on wisdom discovery, info mining, facts and internet warehousing, multidimensional databases, information warehouse layout, caching, information dissemination, replication, cellular networks, cellular structures, monitoring and tracking, collaborative paintings aid, temporal info modelling, relocating items and spatial indexing, spatio-temporal databases, and video database contents.
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Extra resources for Advances in Database Technologies: ER ’98 Workshops on Data Warehousing and Data Mining, Mobile Data Access, and Collaborative Work Support and Spatio-Temporal Data Management, Singapore, November 19-20, 1998. Proceedings
The size of the sample is determined based on the acceptable degree of error, and the acceptable probability that this degree of error will be exceeded. The formula for calculating the sample size (Step 1) is discussed in Section 3. Step 2: Assign Representations to All Attribute Groups. Each attribute is assigned a representation based on the analysis of its data type, length of the attribute, and values. The representations of attributes are then used to determine the representations of attribute groups.
Cercone, “Data-Driven Discovery of Quantitative Rules in Relational Databases,” IEEE Trans. on Knowledge and Data Engineering, vol. 5, no. 1, pp. 29-40, 1993. 14 13. J. Han, Y. Fu, Y. Huang, Y. Cai, and N. Cercone, “DBLearn: A System Prototype for Knowledge Discovery in Relational Databases,” Proc. of ACM SIGMOD’94, vol. 23, no. 2, pp. 516, 1994. 15 14. S. Nishio, H. Kawano, J. Han, “Knowledge Discovery in Object-Oriented Databases: The First Step,” Proc. of the AAAI Knowledge Discovery in Databases Workshop 1993, pp.
These problems include: 1) the identiﬁcation of the measurement scale of an attribute group, and 2) the need for more information than just the measurement scales to determine the appropriate correlation test. First, to identify the appropriate correlation test, the measurement scales of the attribute groups must be known. For example, one of the correlation tests for interval measurement scales, such as the coeﬃcient of determination should be used to measure the correlation of an attribute group pair with interval measurement scales.