为导航查询选择路径的挑战:路径收益与计划成本的权衡

Maria-Esther Vidal, L. Raschid, Julián Mestre
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引用次数: 13

摘要

生命科学资源的特点是重叠资源的复杂图,以及资源之间的多个交替链接。(导航)查询可以通过遍历起始源和目标源之间的多个备选路径来回答。这些路径中的每一个都可能有不同的好处,例如,在目标源中到达的结果对象的基数。路径也可能具有不同的求值成本,即路径的查询求值计划的执行成本。在之前的研究中,我们开发了一种基于确定性有限自动机(DFA)的算法search,它详尽地枚举所有路径来回答导航查询。我们面临的挑战是开发启发式方法,改进穷尽式研究解决方案,并确定能够对资源、资源之间的链接和已访问的子路径进行排序的良好效用函数,以便快速生成具有最高收益和最低成本的路径。在本文中,我们提出了一种启发式方法,使用局部效用函数对资源进行排序,使用归因于资源的收益,使用资源的计划成本,或两者兼而有之。启发式将其搜索限制在排名前XX%的来源。为了比较研究和启发式,我们构建了一个关于收益和成本的研究产生的所有主导解决方案的帕累托曲面。我们选择在帕累托曲面上的前25%的研究解决方案。我们将启发式生成的路径与研究解决方案中精确度和召回率最高的25%的路径进行比较。这激发了进一步研究开发更有效的算法和更好的效用函数的需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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