The complex nonlinear structure, large volume, and high redundancy of real high-dimensional data pose great challenges to data analysis and utilization. Currently, various nonlinear approaches have been used to reduce data dimensionality. However, current methods mainly face dual challenges, including the inefficiency in handling large-scale numerical data with implicit correlations, and the prohibitive computational costs of constructing complete neighborhood graphs for correlation preservation. In this paper, we propose a nonlinear Dimensionality Reduction (DR) framework to address these issues, which is based on embedding learning and designed to efficiently generate Vectors’ Embedding (VecE) of numerical matrices. First, an approximate algorithm is proposed to construct the neighborhood of numerical data rapidly by optimizing the top-k nearest neighbors (KNN). Second, a novel random sampling algorithm is proposed to extract the context of each vector from the constructed neighborhood, thus recognizing the intrinsic structural feature of the input data. Finally, an elaborate objective is designed to learn the embedding of the input numerical data from the extracted context, with the goal of maximizing the retention of data structural features in low-dimensional space. The computational complexity of VecE is also analyzed to illustrate its effectiveness. Extensive experiments on eight real-world datasets demonstrate that VecE outperforms most popular dimensionality reduction methods in terms of computational efficiency, while achieving acceptable accuracy and demonstrating excellent scalability to large-scale datasets.

Massive ocean data acquired by various observing platforms and sensors poses new challenges to data management and utilization. Typically, it is difficult to find the desired data from the large amount of datasets efficiently and effectively. Most of existing methods for data discovery are based on the keyword retrieval or direct semantic reasoning, and they are either limited in data access rate or do not take the time cost into account. In this paper, we creatively design and implement a novel system to alleviate the problem by introducing semantics with ontologies, which is referred to as Data Ontology and List-Based Publishing (DOLP). Specifically, we mainly improve the ocean data services in the following three aspects. First, we propose a unified semantic model called OEDO (Ocean Environmental Data Ontology) to represent heterogeneous ocean data by metadata and to be published as data services. Second, we propose an optimized quick service query list (QSQL) data structure for storing the pre-inferred semantically related services, and reducing the service querying time. Third, we propose two algorithms for optimizing QSQL hierarchically and horizontally, respectively, which aim to extend the semantics relationships of the data service and improve the data access rate. Experimental results prove that DOLP outperforms the benchmark methods. First, our QSQL-based data discovery methods obtain a higher recall rate than the keyword-based method, and are faster than the traditional semantic method based on direct reasoning. Second, DOLP can handle more complex semantic relationships than the existing methods.

Skyline group, also named as combinational skyline or group-based skyline, has attracted more attention recently. The concept of skyline groups is proposed to address the problem in the inadequacy of the traditional skyline to answer queries that need to analyze not only individual points but also groups of points. Skyline group algorithms aim at finding groups of points that are not dominated by any other same-size groups. Although two types of dominance relationship exist between the groups defined in existing works, they have not been compared systematically under the same experimental framework. Thus, practitioners face difficulty in selecting an appropriate definition. Furthermore, the experimental evaluation in most existing works features a weakness, that is, studies only experimented on small data sets or large data sets with small dimensions. For comprehensive comparisons of the two types of definition and existing algorithms, we evaluate each algorithm in terms of time and space on various synthetic and real data sets. We reveal the characteristics of existing algorithms and provide guidelines on selecting algorithms for different situations.