In recent years, the concept of the Internet of Things (IoT) has been attracting attention from various fields as IoT devices can continuously monitor various environmental properties. While the number of IoT devices increases rapidly, managing large volume of IoT data faces a serious scalability issue. To address this issue, many studies have shown that the performance of key-value storages is better than traditional relational databases. However, IoT data have multi-dimensional attributes including spatial, temporal and thematic attributes. How to construct an efficient multi-attribute combined index is an important topic. In this research, we consider four main types of attributes and their corresponding queries, which are spatial, temporal, keyword, and value attributes. While each attribute has its own suitable index method, integrating the indexes into a combined index usually requires a certain sequence of indexes, which significantly decides the query performance. As many literatures directly present their designed combined index, this research proposes an adaptive method to decide the most efficient combined index by estimating the selectivity and query performance of individual query criterion. The main idea is that highly-selective queries should be performed first to reduce the number of intermediate results, which can improve the query performance of following queries. Hence, this research proposes an index framework considering every possible sequence and automatically identifying the most efficient combined index for each query. According to the result, the proposed system has 94–99% chance to save 25 to 51 times response time compared to using a single combined index, and is twice faster than PostGIS on average when querying a one-million-record real-world dataset.

近年来，物联网 (IoT) 的概念受到各个领域的关注，因为物联网设备可以持续监控各种环境属性。虽然物联网设备的数量迅速增加，但管理大量物联网数据面临着严重的可扩展性问题。为了解决这个问题，很多研究表明键值存储的性能优于传统的关系型数据库。然而，物联网数据具有多维属性，包括空间、时间和主题属性。如何构建高效的多属性组合索引是一个重要的课题。在本研究中，我们考虑了四种主要类型的属性及其相应的查询，即空间属性、时间属性、关键字属性和值属性。虽然每个属性都有自己合适的索引方法，将索引整合成组合​​索引通常需要一定的索引序列，这对查询性能有很大的影响。由于许多文献直接提出了他们设计的组合索引，本研究提出了一种自适应方法，通过估计单个查询标准的选择性和查询性能来确定最有效的组合索引。主要思想是先执行高选择性查询，减少中间结果的数量，从而提高后续查询的查询性能。因此，本研究提出了一个索引框架，考虑到每个可能的序列，并自动识别每个查询的最有效组合索引。根据结果​​，