  Traditional databases are not equipped with the adequate functionality to
handle the volume and variety of "Big Data". Strict schema definition and data
loading are prerequisites even for the most primitive query session. Raw data
processing has been proposed as a schema-on-demand alternative that provides
instant access to the data. When loading is an option, it is driven exclusively
by the current-running query, resulting in sub-optimal performance across a
query workload. In this paper, we investigate the problem of workload-driven
raw data processing with partial loading. We model loading as fully-replicated
binary vertical partitioning. We provide a linear mixed integer programming
optimization formulation that we prove to be NP-hard. We design a two-stage
heuristic that comes within close range of the optimal solution in a fraction
of the time. We extend the optimization formulation and the heuristic to
pipelined raw data processing, scenario in which data access and extraction are
executed concurrently. We provide three case-studies over real data formats
that confirm the accuracy of the model when implemented in a state-of-the-art
pipelined operator for raw data processing.
