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Holistic analysis of many real-world problems are based on data collectedfrom multiple sources contributing to some aspect of that problem. The wordfusion has also been used in the literature for such problems involvingdisparate data types. Holistically understanding traffic patterns, causes ofaccidents, bombings, terrorist planning and many natural phenomenon such asstorms, earthquakes fall into this category. Some may have real-timerequirements and some may need to be analyzed after the fact (post-mortem orforensic analysis.) What is common for all these problems is that the amountand types of data associated with the event. Data may also be incomplete andtrustworthiness of sources may also vary. Currently, manual and ad-hocapproaches are used in aggregating data in different ways for analyzing andunderstanding these problems.  In this paper, we approach this problem in a novel way using multilayerednetworks. We identify features of a central event and propose a network layerfor each feature. This approach allows us to study the effect of each featureindependently and its impact on the event. We also establish that the proposedapproach allows us to compose these features in arbitrary ways (without loss ofinformation) to analyze their combined effect. Additionally, formulation ofrelationships (e.g., distance measure for a single feature instead of severalat the same time) is simpler. Further, computations can be done once on eachlayer in this approach and reused for mixing and matching the features foraggregate impacts and "what if" scenarios to understand the problemholistically. This has been demonstrated by recreating the communities for theAND-Composed network by using the communities of the individual layers.  We believe that techniques proposed here make an important contribution tothe nascent yet fast growing area of data fusion.

Scalable Holistic Analysis of Multi-Source, Data-Intensive Problems Using Multilayered Networks