Querying large models efficiently often imposes high demands on system resources such as memory, processing time, disk access or network latency. The situation becomes more complicated when data are highly interconnected, e.g. in the form of graph structures, and when data sources are heterogeneous, partly coming from dynamic systems and partly stored in databases. These situations are now common in many existing social networking applications and geo-location systems, which require specialized and efficient query algorithms in order to make informed decisions on time. In this paper, we propose an algorithm to improve the memory consumption and time performance of this type of queries by reducing the amount of elements to be processed, focusing only on the information that is relevant to the query but without compromising the accuracy of its results. To this end, the reduced subset of data is selected depending on the type of query and its constituent filters. Three case studies are used to evaluate the performance of our proposal, obtaining significant speedups in all cases.

有效查询大型模型通常会对系统资源（例如内存，处理时间，磁盘访问或网络延迟）提出高要求。当数据高度互连时（例如，以图形结构的形式），并且数据源是异构的（部分来自动态系统，部分存储在数据库中），则情况变得更加复杂。这些情况现在在许多现有的社交网络应用程序和地理位置系统中很常见，它们需要专门且高效的查询算法才能及时做出明智的决策。在本文中，我们提出了一种算法，可通过减少要处理的元素数量来提高此类查询的内存消耗和时间性能，仅关注与查询相关的信息，而不会影响其结果的准确性。为此，将根据查询的类型及其组成的过滤器来选择精简的数据子集。我们使用了三个案例研究来评估我们提案的效果，从而在所有案例中均获得了明显的提速。