Skyline query has attracted a great deal of interest during last years because of its ability to help decision makers when multi-criteria objectives are to be handled. Several authors have pointed the interest of multidimensional skylines, i.e., the set of criteria become a parameter of the query. In order to efficiently evaluate these queries, index structures have been proposed. In this paper, we address the problem of efficiently handling multidimensional skyline queries in the context of streaming data. The appended records have a validity time interval after which they become outdated and hence, can be discarded. To that end, we propose a framework that handles an index structure periodically updated. Then the queries consider just the indexed data. This is the price we pay to deal with the streaming nature of the data we consider.

Through extensive experiments, we demonstrate our framework’s ability to handle multidimensional skyline queries with challenging streaming data. The main criteria we consider to assess the performance of our solution are query execution time and both index structure maintenance time and its memory consumption.

过去几年中，天际线查询吸引了很多兴趣，因为它可以在处理多标准目标时帮助决策者。几位作者指出了多维天际线的重要性，即，标准集成为查询的参数。为了有效地评估这些查询，已经提出了索引结构。在本文中，我们解决了在流数据上下文中有效处理多维天际线查询的问题。附加记录具有有效时间间隔，在此间隔之后，它们将过时，因此可以丢弃。为此，我们提出了一个处理定期更新的索引结构的框架。然后查询仅考虑索引数据。这是我们为处理我们考虑的数据流性质而付出的代价。

通过广泛的实验，我们证明了我们的框架具有处理具有挑战性的流数据的多维天际线查询的能力。我们考虑评估解决方案性能的主要标准是查询执行时间，索引结构维护时间及其内存消耗。