# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 7 Algorithms for Synthesizing the Optimal Data Fusion Configuration
### Optimal-Fit
## 9 Discussion

This section elaborates on the following conditions which may violate the assumptions made in this article:

* **Data sets and incorrect assumptions of the effectiveness, cost, and timing values of data sources:** The data sets used in the examples of this article are based on the characteristics of the data sources in Appendices A and B. Each value is expressed as a probabilistic variable of uniform distribution within a certain range. Although carefully defined, these values may differ considerably from some of the commercially available data sources in the market. Moreover, with the advancement of technology, new products are introduced frequently. It is therefore advisable to consider concrete products instead of their abstract representations. In case of adoption of concrete products, the accuracy of the estimations can be improved by consulting to the catalogues, and if necessary, by carrying out dedicated experiments. If sufficient data are available, machine learning techniques can be adopted to improve these values as well. Nevertheless, the methods and techniques introduced in this article do not depend on the data values presented in Appendices A and B; the data values are used for illustration purposes and in the examples only. * **Inaccurate data fusion formulas:** The data fusion formulas presented in Section 6.2 are based on the following assumptions: (a) The relevancy factor of a data fusion for the objectives 1 to 3 is a probabilistic variable defined in the range of 0 to 1. (b) Attaching a new data source cannot degrade the effectiveness factors of the already attached data sources. (c) If a newly added data source contributes to the considered objective, the effectiveness function is a monotonously increasing function asymptotically approaching 1 or a value less than 1. We consider these assumptions reasonable. The formulas used for weak, medium, and strong contributions can be adapted to the needs, or new formulas can be introduced as plug-ins. A limitation to this approach is that data fusion is assumed to be realized at a geographical element only. In Section 8, more general fusion possibilities are discussed. New formulas can be defined in various ways, as long as they do not violate the assumptions made. The contribution factors of the data sources to each other as presented in Appendix B can be improved by experimentation. In addition, if sufficient data are available, machine learning techniques can be adopted to improve these values. * **Extensibility of the framework:** Due to evolution of the needs and technologies, it may be necessary to introduce new elements and/or data sources. The model-based architectural style as described in Section 5 provides flexibility. For example, to introduce a new geographical element, the following actions must be carried out: (1) A new class representing the element must be introduced in the GIS model, possibly by subclassing the existing classes. (2) The attributes of the instances of the class must be initialized including the command objects for the relevant queries. The menu items of the user interface can be automatically generated from the command objects by using the Command design pattern [44]. If a new data source is to be introduced, the following steps must be carried out: (1) The feature-model must be edited to introduce the new feature, which represents the new data source. (2) A new set of command objects must be added to the relevant element instances to enable attaching of the new data source, if necessary. (3) The tables used in computing the effectiveness, cost, and timing values must be updated. Changes to computations can be introduced as plug-ins. * **Complexity of automatic synthesis of data fusion**: If the number of possible data sources which can be attached to a selected element is large and if this element offers a large number of alternatives for data sources, the search space of the optimization algorithm can be too large to handle.