  We propose a new (theoretical) computational model for the study of massive
data processing with limited computational resources. Our model measures the
complexity of reading the very large data sets in terms of the data size N and
analyzes the computational cost in terms of a parameter k that characterizes
the computational power provided by limited local computing resources. We
develop new algorithmic techniques that implement algorithms for solving
well-known computational problems on the proposed model. In particular, we
present an algorithm that finds a k-matching in a general unweighted graph in
time O(N + k^{2.5}) and an algorithm that constructs a maximum weighted
k-matching in a general weighted graph in time O(N + k^3 log k). Both
algorithms have their space complexity bounded by O(k^2).
