Abstract
The big data analytics achieves wide application in a number of areas due to its capability in uncovering hidden patterns, correlations and insights through integrating multiple data sources. However, the interdependence and heterogeneity features of these data sources pose a big challenge in managing these data sources to support “last mile” analytics in decision making and value co-creation which are usually with multiple perspectives and at multiple granularities. In this paper, we propose a unified knowledge representation framework, namely, Cyber-Entity (Cyber-E) modeling, to capture and formalize selected behaviors of real entities in both the social and physical worlds to the cyber analytic space. Its special features include not only the stateful, intra- properties of a Cyber-E, but also the inter-relationship and dependence among them. A grouping mechanism, called Cyber-G, is also introduced to support flexible granularity adjustment in the knowledge management. It supports rapid on-demand self-service analytics. An illustrating example of applying this approach in academic research community is given, followed by a case study of two top conferences in service computing area– ICSOC and ICWS– to illustrate the effectiveness and potentials of our approach.
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Notes
- 1.
The CAF can take multiple inputs and gives one single output. More specifically, we have (a) the input of caf can be raw data, can also be property output of the same or other CAF, (b) different CAF can share the same input, (c) an algorithm of multiple outputs could be decomposed into multiple single-output algorithms. Correspondingly, the number of the input arrows could be 1 or many, while the number of output arrows could only be 1.
- 2.
There are two situations for the output of a potential inter-group CAF: (i) a property of a Cyber-G, or (ii) a property of a Cyber-E which belongs to certain Cyber-G. Suppose \(GS\ne \emptyset \), for each situation, the definition is given in Definition 10
- 3.
- 4.
- 5.
- 6.
Due to data limitations, the propagation through the relational properties (i.e., “Published In Venue”, “Cited By Author”, “Cited By Paper”) is broken as illustrated by line \(l_1\) and \(l_2\), as shown in Fig. 2.
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Han, H. et al. (2019). A Methodology for Resolving Heterogeneity and Interdependence in Data Analytics. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_2
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