An efficient peer-to-peer indexing tree structure for multidimensional data

As one of the most important technologies for implementing large-scale distributed systems, peer-to-peer (P2P) computing has attracted much attention in both research and industrial communities, for its advantages such as high availability, high performance, and high flexibility to the dynamics of networks. However, multidimensional data indexing remains as a big challenge to P2P computing, because of the inefficiency in search and network maintenance caused by the complicated existing index structures, which greatly limits the scalability of applications and dimensionality of the data to be indexed.We propose SDI (Swift tree structure for multidimensional Data Indexing), a swift index scheme with a simple tree structure for multidimensional data indexing in large-scale distributed systems. While keeping the query efficiency in O(logN) in terms of routing hops, SDI has extremely low maintenance costs which is proved through theoretical analysis. Furthermore, SDI overcomes the root-bottleneck problem existing in most other tree-based distributed indexing systems. Extensive empirical study verifies the superiority of SDI in both query and maintenance performance.     

An efficient workload-based data layout scheme for multidimensional data

Physical data layout is a crucial factor in the performance of queries and updates in large data warehouses. Data layout enhances and complements other performance features such as materialized views and dynamic caching of aggregated results. Prior work has identified that the multidimensional nature of large data warehouses imposes natural restrictions on the query workload. A method based on a “uniform” query class approach has been proposed for data clustering and shown to be optimal. However, we believe that realistic query workloads will exhibit data access skew. For instance, if time is a dimension in the data model, then more queries are likely to focus on the most recent time interval. The query class approach does not adequately model the possibility of multidimensional data access skew. We propose the affinity graph model for capturing workload characteristics in the presence of access skew and describe an efficient algorithm for physical data layout. Our proposed algorithm considers declustering and load balancing issues which are inherent to the multidisk data layout problem. We demonstrate the validity of this approach experimentally.     

A new hierarchical triangle-based point-in-polygon data structure

The point-in-polygon or containment test is fundamental in computational geometry and is applied intensively in geographical information systems. When this test is repeated several times with the same polygon a data structure is necessary in order to reduce the linear time needed to obtain an inclusion result. In the literature different approaches, like grids or quadtrees, have been proposed for reducing the complexity of these algorithms. We propose a new data structure based on hierarchical subdivisions by means of tri-cones, which reduces the time necessary for this inclusion test. This data structure, named tri-tree, decomposes the whole space by means of tri-cones and classifies the edges of the polygon. For the inclusion test only the edges classified in one tri-cone are considered. The tri-tree has a set of properties which makes it specially suited to this aim, with a similar complexity to other data structures, but well suited to polygons which represent regions. We include a theoretical and practical study in which the new data structure is compared with classical ones, showing a significant improvement.     

G-tree: a new data structure for organizing multidimensional data

The author describes an efficient data structure called the G-tree (or grid tree) for organizing multidimensional data. The data structure combines the features of grids and B-trees in a novel manner. It also exploits an ordering property that numbers the partitions in such a way that partitions that are spatially close to one another in a multidimensional space are also close in terms of their partition numbers. This structure adapts well to dynamic data spaces with a high frequency of insertions and deletions, and to nonuniform distributions of data. We demonstrate that it is possible to perform insertion, retrieval, and deletion operations, and to run various range queries efficiently using this structure. A comparison with the BD tree, zkdb tree and the KDB tree is carried out, and the advantages of the G-tree over the other structures are discussed. The simulated bucket utilization rates for the G-tree are also reported     

A new data structure for efficient storing of images

The space efficiency of recent results on data structures for quadtrees [2,3,4] may be improved by defining a new data structure called Translation Invariant Data Structures (TID). This paper briefly describes the results of this new data structure for storing images.     

A robust and efficient spatial data structure

A new structure for organizing a set of multidimensional points called the nested interpolation-based grid file (NIBGF) is introduced. The structure represents a synthesis and an improvement over interpolation-based grid files (IBGF), BANG files, andK-D-B-trees. It decomposes the data search space into uniquely identifiable regions which may either be disjoint as in interpolation-based grid files or enclose each other as in the BANG files. In addition to possessing the symmetry of access and clustering properties characteristic of grid file structures, the performance of NIBGF is comparable to aB-tree performance as far as the index is concerned, even in the worst case scenario, and to the BANG file performance as far as the data regions are concerned. These properties make the new structure suitable for efficient implementation of relational database operations.Research supported by NSF IRI-9010365     

A compact data structure for representing a dynamic multiset

We develop a data structure for maintaining a dynamic multiset that uses bits and O(1) words, in addition to the space required by the n elements stored, supports searches in worst-case time and updates in amortized time. Compared to earlier data structures, we improve the space requirements from O(n) bits to bits, but the running time of updates is amortized, not worst-case.     

Identification of the hierarchical data structure

The task of identifying a hierarchical data structure is considered for the example of the problem of identifying personalizing reference characteristics. A model of a neural network based on radial basis functions is proposed as a possible solution of the task. The identification of the hierarchical dependence is practically aimed to create a classifier using a restricted set of input variables compared to the flat structured classifier. A multilayer perceptron is used as local classifiers. We also use self-organizing maps to visually show data structuredness.     

Exploring hierarchical multidimensional data with unified views of distribution and correlation

Data analysts explore data by inspecting features such as clustering, distribution and correlation. Much existing research has focused on different visualisations for different data exploration tasks. For example, a data analyst might inspect clustering and correlation with scatterplots, but use histograms to inspect a distribution. Such visualisations allow an analyst to confirm prior expectations. For example, a scatterplot may confirm an expected correlation or may show deviations from the expected correlation. In order to better facilitate discovery of unexpected features in data, however, a combination of different perspectives may be needed. In this paper, we combine distributional and correlational views of hierarchical multidimensional data. Our unified view supports the simultaneous exploration of data distribution and correlation. By presenting a unified view, we aim to increase the chances of discovery of unexpected data features, and to provide the means to explore such features in detail. Further, our unified view is equipped with a small number of primitive interaction operators which a user composes to facilitate smooth and flexible exploration.     

A dynamic balanced flow for filtering point-sampled geometry

3D point data acquisition has become a practical approach for generating complex 3D shapes. Subsequent smoothing or denoising operations on these raw data sets are required before performing sophisticated modeling operations. Based on covariance analysis and constructed directional curvature, a new approach of anisotropic curvature flow is developed for filtering the point data set. By introducing a forcing term, a balanced flow equation is constructed, which allows the anisotropic diffusion flow to be restricted in the flow diffusion band of the original surface. Thus, the common problem of shape shrinkage that puzzles most current denoising approaches for point-sampled geometry is avoided. Applying dynamic balance techniques, the equation converges to the solution quickly with appealing physical interpretations. The algorithms operate directly on the discrete sample points, requiring no vertex connectivity information. They are shown to be computationally efficient, robust and simple to implement.     

A shrinking-based clustering approach for multidimensional data

Existing data analysis techniques have difficulty in handling multidimensional data. Multidimensional data has been a challenge for data analysis because of the inherent sparsity of the points. In this paper, we first present a novel data preprocessing technique called shrinking which optimizes the inherent characteristic of distribution of data. This data reorganization concept can be applied in many fields such as pattern recognition, data clustering, and signal processing. Then, as an important application of the data shrinking preprocessing, we propose a shrinking-based approach for multidimensional data analysis which consists of three steps: data shrinking, cluster detection, and cluster evaluation and selection. The process of data shrinking moves data points along the direction of the density gradient, thus generating condensed, widely-separated clusters. Following data shrinking, clusters are detected by finding the connected components of dense cells (and evaluated by their compactness). The data-shrinking and cluster-detection steps are conducted on a sequence of grids with different cell sizes. The clusters detected at these scales are compared by a cluster-wise evaluation measurement, and the best clusters are selected as the final result. The experimental results show that this approach can effectively and efficiently detect clusters in both low and high-dimensional spaces.     

A hierarchical structure for surface approximation

Hierarchical triangulation is a method for point selection and surface representation where the surface is approximated at successively finer levels of detail by triangular patches whose projections in the horizontal plane are nested. A tree data structure for this representation can be constructed in O(n²) worst case and O(n log n) average case time, where n is the number of data points considered. Efficient algorithms for approximation of the elevation of an arbitrary point, contour extraction, and conversion of the hierarchical structure into an ordinary triangulated irregular network, are demonstrated. The convergence and the optimality of the approximation and the relationship of the hierarchical triangulation to a structured graph representation are examined.     

Efficient edit propagation using hierarchical data structure

This paper presents a novel unified hierarchical structure for scalable edit propagation. Our method is based on the key observation that in edit propagation, appearance varies very smoothly in those regions where the appearance is different from the user-specified pixels. Uniformly sampling in these regions leads to redundant computation. We propose to use a quadtree-based adaptive subdivision method such that more samples are selected in similar regions and less in those that are different from the user-specified regions. As a result, both the computation and the memory requirement are significantly reduced. In edit propagation, an edge-preserving propagation function is first built, and the full solution for all the pixels can be computed by interpolating from the solution obtained from the adaptively subdivided domain. Furthermore, our approach can be easily extended to accelerate video edit propagation using an adaptive octree structure. In order to improve user interaction, we introduce several new Gaussian Mixture Model (GMM) brushes to find pixels that are similar to the user-specified regions. Compared with previous methods, our approach requires significantly less time and memory, while achieving visually same results. Experimental results demonstrate the efficiency and effectiveness of our approach on high-resolution photographs and videos.     

A hierarchical model for structure learning based on the physiological characteristics of neurons

Almost all applications of Artificial Neural Networks (ANNs) depend mainly on their memory ability. The characteristics of typical ANN models are fixed connections, with evolved weights, globalized representations, and globalized optimizations, all based on a mathematical approach. This makes those models to be deficient in robustness, efficiency of learning, capacity, anti-jamming between training sets, and correlativity of samples, etc. In this paper, we attempt to address these problems by adopting the characteristics of biological neurons in morphology and signal processing. A hierarchical neural network was designed and realized to implement structure learning and representations based on connected structures. The basic characteristics of this model are localized and random connections, field limitations of neuron fan-in and fan-out, dynamic behavior of neurons, and samples represented through different sub-circuits of neurons specialized into different response patterns. At the end of this paper, some important aspects of error correction, capacity, learning efficiency, and soundness of structural representation are analyzed theoretically. This paper has demonstrated the feasibility and advantages of structure learning and representation. This model can serve as a fundamental element of cognitive systems such as perception and associative memory.     

A hierarchical organization scheme for video data

Video stream is based on bits of imagery and is thus difficult to be perceived (by machine) in the content level. To access video content, a suitable organization of video data is critical. This paper proposes a hierarchical structure and a process scheme for organizing video data to facilitate indexing, browsing and querying. Four layers can be distinguished, that is: video program, episode, shot and frame. This hierarchy provides an efficient and flexible structure as well as compact and meaningful abstraction of video program. To achieve such an organization, not only the boundary detection of shots and episodes, but also the extraction of key-frames for shots and the selection of representative shots and frames for episodes are important. Suitable criteria and methods for above tasks are proposed and these techniques have been integrated into a workable system. A number of organization experiments using real video data are performed and some results are presented, which show the effectiveness of the proposed organization scheme and techniques.     

A multidimensional and hierarchical model of mobile service quality

Using mobile brokerage service as an example, we propose and test a multidimensional and hierarchical model of mobile service (m-service) quality using a sample of 338 respondents from the two largest m-service providers in China: China Mobile and China Unicom. Through three-stage validation, we are able to confirm all three levels of the proposed hierarchical structure where a customer’s perceived m-service quality includes primary dimensions of interaction, outcome, and environment qualities. Each primary dimension further has its sub-dimensions. Our empirical results also show that corporate image moderates the effects of environment and outcome qualities on the service quality. Our proposed model provides implications for future research on mobile commerce.