Challenges in selecting paths for navigational queries: trade-off of benefit of path versus cost of plan

Life sciences sources are characterized by a complex graph of overlapping sources, and multiple alternate links between sources. A (navigational) query may be answered by traversing multiple alternate paths between a start source and a target source. Each of these paths may have dissimilar benefit, e.g., the cardinality of result objects that are reached in the target source. Paths may also have dissimilar costs of evaluation, i.e., the execution cost of a query evaluation plan for a path. In prior research, we developed ESearch, an algorithm based on a Deterministic Finite Automaton (DFA), which exhaustively enumerates all paths to answer a navigational query. The challenge is to develop heuristics that improve on the exhaustive ESearch solution and identify good utility functions that can rank the sources, the links between sources, and the sub-paths that are already visited, in order to quickly produce paths that have the highest benefit and the least cost. In this paper, we present a heuristic that uses local utility functions to rank sources, using either the benefit attributed to the source, the cost of a plan using the source, or both. The heuristic will limit its search to some Top XX% of the ranked sources. To compare ESearch and the heuristic, we construct a Pareto surface of all dominant solutions produced by ESearch, with respect to benefit and cost. We choose the Top 25% of the ESearch solutions that are in the Pareto surface. We compare the paths produced by the heuristic to this Top 25% of ESearch solutions with respect to precision and recall. This motivates the need for further research on developing a more efficient algorithm and better utility functions.