Aiming at the problem of multi-path query in tree-spatial index and not considering temporal index, A Hilbert-R tree index construction scheme combining time and clustering results was proposed. Firstly, according to the periodicity of data collection, the spatial-temporal dataset was divided, and on this basis, a time index was established. The spatial data was partitioned and encoded by the Hilbert curve, and the spatial coordinates were mapped to one-dimensional intervals. Secondly, according to the distribution of the feature object in space, a clustering algorithm using dynamic determination of K value was adopted, to build an efficient Hilbert-R tree spatial index. Finally, based on several common key-value data structures of Redis, the hierarchical indexing mechanism of time attributes and clustering results was built. Compared with the Cache Conscious R+tree (CCR+), the proposed algorithm can effectively reduce the time overhead, and the query time is shortened by about 25% on average in the experiment of spatial-temporal range and target vector object query. It has good adaptability to different intensive data and can better support Redis for massive spatio-temporal data queries.

Focusing on the issue that the only one Hash/Range partitioning strategy in Spark usually results in unbalanced data load at Reduce phase and increases job duration sharply, an Iterative Filling data Partitioning and Mapping algorithm (IFPM) which include several innovative approaches was proposed. First of all, according to the analysis of job execute scheme of Spark, the job efficiency model and partition mapping model were established, the definitions of job execute timespan and allocation incline degree were given. Moreover, the Extendible Partitioning Algorithm (EPA) and Iterative Mapping Algorithm (IMA) were proposed, which reserved partial data into extend region by one-to-many partition function at Map phase. Data in extended region would be mapped by extra iterative allocation until the approximate data distribution was obtained, and the adaptive mapping function was executed by awareness of calculated data size at Reduce phase to revise the unbalanced data load in original region allocation. Experimental results demonstrate that for any distribution of the data, IFPM promotes the rationality of data load allocation from Map phase to Reduce phase and optimize the job efficiency of in-memory computing framework.

Aiming at the fact that the traditional collaborative filtering algorithm cannot deeply mine user relationship and recommend new items to users, a Trust and Item Preference Collaborative Filtering (TIPCF) recommendation algorithm was proposed. Firstly, in order to mine the latent trust relationship of the users, the user reliability was gotten and the trust degree between users was quantified by analyzing user ratings. Secondly, by considering that the difference of users' preference for different target items has an effect on user similarity, user preference was added to the traditional user similarity algorithm to improve the similarity algorithm. Thirdly, the choice of nearest neighbor set was more accurate by incorporating user reliability and improved similarity. Finally, the users' preference on item attribute was used to recommend new items. Experimental results show that, compared with traditional collaborative algorithm, the Mean Absolute Error (MAE) of TIPCF was decreased by 6.7%, and the MAE of TIPCF was decreased by 10.7% when recommending new items on the Movielens dataset. TIPCF not only improves the accuracy of recommendation, but also increases the recommended probablity of new items.

To improve the accuracy and automation of extracting water body by using remote sensing image, a method was proposed for water body extraction based on Stacked AutoEncoder (SAE). A deep network model was built by stacking sparse autoencoders and each layer was trained in turn with the greedy layerwise approach. Features were learnt without supervision from the pixel level to avoid the problem that methods such as traditional neural network needed artificial feature analysis and selection. Softmax classifier was trained with supervision by using the learnt features and corresponding labels. Back Propagation (BP) algorithm was used to fine-tune and optimize the whole model. The accuracy of SAE-based method reaches 94.73% by using the Tarim River's ETM+ data to do the experiment, which is 3.28% and 4.04% higher than that of Support Vector Machine (SVM) and BP neural network separately. The experimental results show that the proposed method can effectively improve the accuracy of water body extraction.

An adaptive Virtual Machine (VM) dynamic migration strategy of soft energy-saving was put forward to optimize energy consumption and load balance in cloud computing. The energy-saving strategy adopted Dynamic Voltage Frequency Scaling (DVFS) as the static energy-aware technology to achieve the sub-optimized static energy saving, and used online VM migration to achieve an adaptive dynamic soft energy-saving in cloud platform. The two energy-saving strategies were simulated and compared with each other in CloudSim platform, and the data were tested on PlanetLab platform. The results show that: Firstly, the adaptive soft and hard combination strategy in energy-saving can significantly save 96% energy; secondly, DVFS+MAD_MMT strategy using Median Absolute Deviation (MAD) to determine whether the host is overload, and choosing VM to remove based on Minimum Migration Time (MMT), which can save energy about 87.15% with low-load in PlanetLab Cloudlets than that of experimental environment; finally, security threshold of 2.5 in MAD_MMT algorithm can consume the energy efficiently and achieve the adaptive load balancing of virtual machines migration dynamically.

The traditional User-based Collaborative Filtering (UCF) models do not consider the attributes of items fully in the process of measuring the similarity of users. In view of the drawback, this paper proposed two collaborative filtering recommendation models considering item attributes. Firstly, the models optimized the rating-based similarity between users, and then summed the rating numbers of different items by users according to item attributes, in order to obtain the optimized and attribute-based similarity between users. Finally, the models coordinated the two types of similarity measurements by a self-adaptive balance factor, to complete the item prediction and recommendation process. The experimental results demonstrate that the newly proposed models not only have reasonable time costs in different data sets, but also yield excellent improvements in prediction accuracy of ratings, involving an average improvement of 5%, which confirms that the models are efficient in improving the accuracy of user similarity measurements.