The multi-way join query plays a fundamental role in many big data analytic scenarios. Recently, the hybrid join query is becoming increasingly important. However, the existing one-round and multi-round algorithms have limitations in the process of the hybrid query. In this paper, we present a novel hybrid structure-aware multi-way join algorithm called HyMJ, which combines the one-round and multi-round algorithms to compute the hybrid query efficiently. First, we propose the query structure graph (QSG) to represent the internal query structure of a given join query and the query structure decomposition tree (QSDT) to represent the structure-aware query plan. Each internal node of the QSDT denotes a subquery with a cyclic or acyclic query structure. Then, we design a graph contraction based algorithm to construct QSDT from QSG. Furthermore, to select the optimal join strategy for each subquery in the QSDT, we introduce a heuristic strategy selection model. Experimental results on Apache Spark reveal that HyMJ outperforms both the one-round and multi-round algorithms for hybrid multi-way join queries on real-world datasets.

Soil moisture retrieval is important in microwave remote sensing, but its high dimensional and non-linearity make it difficult to realize. In this paper, an improved Fruit Fly Optimization Algorithm (IFOA) to optimize the Least Squares Support Vector Machine (LSSVM) Model is used for soil moisture retrieval. The sampling data for training and test is generated by using Advanced Integral Equation Model (AIEM). To verify the performance of IFOA optimized LSSVM (IFOA-LSSVM), we apply it to the soil moisture retrieval in L and C bands and compare the results with those of FOA-LSSVM and LSSVM.