The traditional age estimation methods based on ranking and regression cannot effectively utilize the evolutionary characteristics of human faces and build correlation between different ranking labels. Moreover， using binary classification methods for age estimation may result in inconsistent ranking issues. To solve above problems， an age estimation method based on integrated ranking matrix encoding and consistency preserving was proposed to fully utilize the correlation between age and ranking value and suppress the problem of inconsistent ranking. A new indicator， the proportion of samples with inconsistent ranking， was proposed to evaluate the problem of inconsistent rankings in the two-class ranking method. First， age categories were converted into a ranking matrix form through a designed coding method. Then， the ResNet34 （Residual Network） feature extraction network was used to extract facial features， which were then learned through the proposed encoding learning module. Finally， the network prediction results were decoded into the predicted age of the image through a ranking decoder based on a metric method. The experimental results show that： the proposed method achieves a Mean Absolute Error （MAE） of 2.18 on MORPH Ⅱ dataset， and has better results on other publicly available datasets compared to methods also based on ranking and ordinal regression， such as OR-CNN （Ordinal Regression with CNN） and CORAL （COnsistent RAnk Logits）； at the same time， the proposed method decreases the proportion of samples with inconsistent ranking， and improves the measurement performance of ranking inconsistency by about 65% compared to the OR-CNN method.

In the era of big data and cloud computing， querying and analyzing temporal big data faces many important challenges. Focused on the issues such as poor query performance and ineffective utilization of indexes for temporal aggregation range query， a Distributed Temporal Index （DTI） for temporal aggregation range query was proposed. Firstly， random or round-robin strategy was used to partition the temporal data. Secondly， intra-partition index construction algorithm based on timestamp’s bit array prefix was used to build intra-partition index， and partition statistics including time span were recorded. Thirdly， the data partitions whose time span overlapped with the query time interval were selected by predicate pushdown operation， and were pre-aggregated by index scan. Finally， all pre-aggregated values obtained from each partition were merged and aggregated by time. The experimental results show that the execution time of intra-partition index construction algorithm of the index for processing data with density of 2 400 entries per unit of time is similar to the execution time for processing data with density of 0.001 entries per unit of time. Compared to ParTime， the temporal aggregation range query algorithm with index takes at least 22% less time for each step when querying the data in the first 75% of timeline and at least 11% less time for each step when executing selective aggregation. Therefore， the algorithm with index is faster in most temporal aggregate range query tasks and its intra-partition index construction algorithm is capable to solve data sparsity problem with high efficiency.