Data-flow languages have been hailed as the solution to the programmability of general-purpose multiprocessors. However, data-flow semantics introduce constructs that lead to much overhead at compilation, allocation, and execution time. Indeed, due to its functionality, the data-flow model of computation does not handle repetitive program constructs very efficiently. This is due to the fact that the cornerstone of data flow, namely the concept of single assignment, is opposed to the idea of reexecution of a portion of program as in a loop. A corollary of this problem is the effective representation, storage, and processing of data structures, as these will most often be used in loops. In this paper, various aspects of this issue are explailned in detail. Several solutions that have been put forward in the current literature are then surveyed and analyzed. In order to offset some of the disadvantages presented by these, we introduce new methods for handling arrays. In the first one, we raise the level of computation to that of arrays for more efficient operation. In the two others, the opposite approach is taken, and the notion of array is done away with entirely at the execution level in order to take advantage of the data-flow semantics at their best logical level of performance.