# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 7 Algorithms for Synthesizing the Optimal Data Fusion Configuration
### Optimal-Fit
Furthermore, a detailed specification of the relevant data sources from the perspective of data fusion is presented in Appendices A and B. A novel set of extensible object-oriented models and queries is introduced to give an answer to the research question 'How to compute the combined effectiveness, cost and timing values as a result of data fusion?'. These can be used for simulating the prospective data fusion alternatives. In addition, formulas are defined to determine whether the quality objectives are fulfilled or not. Novel automatic synthesis algorithms for data fusion are proposed to address the research question 'What kind of algorithms can be defined for automated synthesis of the optimal data fusion?'. The utility of the proposed approach is illustrated by a set of examples. Overall, the proposed approach may eliminate the risks of designing and installing irrelevant or less effective data fusion systems. We consider that the research questionspresented in Section 3 are adequately addressed within the limits of the assumptions, as discussed in the previous section. **Author Contributions:** [PERSON] had the original idea for this work. He has elaborated and written most of the sections. Students H.S. and M.A.E. and V.V.d.C. have concentrated their efforts on some background-related points, elaboration of the object-oriented model (Section 5), implementing the related code, and reviewing the paper. All authors have read and agreed on the published version of the manuscript. **Funding:** The TUBITAK BIDEB program 2232 International Fellowship for Outstanding Researchers. **Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article. **Acknowledgments:** [PERSON], as a student, contributed to an earlier version of the domain model. This work has been generously supported by the TUBITAK BIDEB program 2232 International Fellowship for Outstanding Researchers. **Conflicts of Interest:** The authors declare no conflict of interest. ## Appendix A Estimated Parameters of Data Sources

None of the algorithms of this article are dependent on the data values presented in this appendix. Data values are estimated based on the characteristics of some selected products available in the market. Since technology is rapidly evolving, detection accuracy, price, and response time of the presented data sources may vary in due time. When applying the method and techniques presented in this article, the data values must be obtained from the catalogs of the data sources to be purchased. Moreover, for brevity, not all the possible data source types are represented in Appendices A and B. ### Data Sources Attached to Geographical Areas

Table 11 is used to document the estimated characteristics of data sources that can be attached to geographical areas. Here, the first column lists the considered data sources. The remaining columns list the individual characteristics of these data sources with respect to the five objectives presented in Section 4. The objectives are abbreviated as Obj<i> where the index i is defined between 1 to 5. The min and max columns refer to the estimated range of the characteristics per objective. From Obj1 to Obj3, the values in cells indicate the estimated effectiveness values of data sources. These are expressed as probabilistic variables, where the corresponding min and max columns indicate their ranges.The columns Obj4 and Obj5 refer to the cost and timing characteristics of the data sources, respectively. These are expressed as probability distribution functions, where the corresponding min and max columns refer to their ranges.