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This paper presents a complexity analysis of 3D High Efficiency Video Coding (3D-HEVC) depth maps intra prediction. The 3D-HEVC inserts new coding tools in depth maps intra prediction such as Depth Intra Skip (DIS), Depth Modeling Modes (DMMs) and Segment-wise DC (SDC). Therefore, it is important to understand the complexity of each module to allow the design of new complexity reduction techniques...
Finding an optimal execution order of join operations is a crucial task in every cost-based query optimizer. Since there are many possible join trees for a given query, the overhead of the join (tree) enumeration algorithm per valid join tree should be minimal. In the case of a clique-shaped query graph, the best known top-down algorithm has a complexity of Θ(n2) per join tree, where n is the number...
The generalized 1+N protection, protects N link disjointly provisioned unicast connections by a single Steiner tree connecting all end points of the connections. By sending network coded packets on the protection Steiner tree in parallel with the working traffic, 1+N is able to recover from any single link failure without enduring the delay from switching to the backup path. This makes 1+N a better...
Similar Link Network (SiLN) is a semantic over layer on Web resources with similar relations instead of hyperlinks, which aims at providing plentiful semantics for intelligent Web activities. However, SiLN is difficult to be built based on cosine computation in large-scale Web resources due to its high building time complexity and weak connectivity. Herein, three strategies are proposed to address...
In this paper, we consider a novel problem referred to as term filtering with bounded error to reduce the term (feature) space by eliminating terms without (or with bounded) information loss. Different from existing works, the obtained term space provides a complete view of the original term space. More interestingly, several important questions can be answered such as: 1) how different terms interact...
We present fast adaptive parallel algorithms to compute the sum of N Gaussians at N points. Direct sequential computation of this sum would take O(N2) time. The parallel time complexity estimates for our algorithms are O (N/np) for uniform point distributions and O (N/np log N/+ np log np ) for nonuniform distributions using np CPUs. We incorporate a planewave representation of the Gaussian kernel...
We say an algorithm on n × n matrices with entries in [-M, M] (or n-node graphs with edge weights from [-M, M]) is truly subcubic if it runs in O(n3-δ - poly(log M)) time for some δ > 0. We define a notion of subcubic reducibility, and show that many important problems on graphs and matrices solvable in O(n3) time are equivalent under subcubic reductions. Namely, the following weighted problems...
We initiate the study of testing properties of images that correspond to sparse 0/1-valued matrices of size n × n. Our study is related to but different from the study initiated by Raskhodnikova (Proceedings of RANDOM, 2003), where the images correspond to dense 0/1-valued matrices. Specifically, while distance between images in the model studied by Raskhodnikova is the fraction of entries on which...
The k-means clustering algorithm is a widely used scheme to solve the clustering problem which classifies a given set of n data points in m-dimensional space into k clusters, whose centers are obtained by the centroids of the points in the same cluster. The problem with privacy consideration has been studied, when the data is distributed among different parties and the privacy of the distributed data...
This work focuses on the scalability of the Evidence Accumulation Clustering (EAC) method. We first address the space complexity of the co-association matrix. The sparseness of the matrix is related to the construction of the clustering ensemble. Using a split and merge strategy combined with a sparse matrix representation, we empirically show that a linear space complexity is achievable in this framework,...
This paper proposed a new point symmetry-based ant clustering algorithm which can defect the number of clusters and the proper partitions from data sets when data sets possess the property of symmetry. In the proposed algorithm, a revised ant clustering algorithm is presented which can reduce the running time of standard ant clustering algorithm. Each ant represents a data object. It will decide its...
In this paper, we give a new algorithm evolving from Atallah's algorithm proposed in 1984, and we make many improvements: First of all, combining replaced sewing in order to simplify the third step; Secondly, we delete the fourth step. And we simplify the construction of the auxiliary graph, avoiding the second time to find Euler tour and the introduction of a mass storage array. The improvements...
Currently, community detection has led to a huge interest in data analysis on real-world networks. However, the high computationally demanding of most community detection algorithms limits their applications. In this paper, we propose a heuristic algorithm to extract the community structure in large networks based on local community attractive force optimization whose time complexity is near linear...
K-dominant skyline query has been proposed as an important operator for multi-criteria decision making, data mining and so on, this technology can reduce the large result sets of skyline query in high dimensional space. In this paper, a new concept was firstly proposed: k-dominant Skyline cube, which consists of all the k-dominant skylines. Although existing algorithms can compute every k-dominant...
The clusters tend to have vague or imprecise boundaries in some fields such as web mining, since clustering has been widely used. Decision-theoretic rough set model (DTRSM) is a typical probabilistic rough set model, which has the ability to deal with imprecise, uncertain, and vague information. Therefore, a novel clustering algorithm based on the DTRSM is proposed in this paper, which can decide...
DBSCAN is a widely used technique for clustering in spatial databases. DBSCAN needs less knowledge of input parameters. Major advantage of DBSCAN is to identify arbitrary shape objects and removal of noise during the clustering process. Beside its familiarity, DBSCAN has problems with handling large databases and in worst case its complexity reaches to O(n2). Similarly, DBSCAN cannot produce correct...
We describe two algorithms for calculating the probability of m-symbol length-n patterns over k-element distributions, a partition-based algorithm with complexity roughly 2O(m log m) and a recursive algorithm with complexity roughly 2O(m+log n) with the precise bounds provided in the text. The problem is related to symmetric-polynomial evaluation, and the analysis reveals a connection to the number...
With the increasing of the context, the time complexity and the space complexity of structuring concept lattice will be dramatically increased accordingly. The new method of enhancing the structuring efficiency has been paid much attention because it is the premise for being used in a large and complicated data system. So far, there are mainly two structuring methods, collocation and overlay of context...
Clustering analysis method is one of the main analytical methods in data mining, the method of clustering algorithm will influence the clustering results directly. This paper discusses the standard k-means clustering algorithm and analyzes the shortcomings of standard k-means algorithm, such as the k-means clustering algorithm has to calculate the distance between each data object and all cluster...
Providing QoS and performance guarantees to arbitrarily divisible loads has become a significant problem for many cluster-based research computing facilities. While progress is being made in scheduling arbitrarily divisible loads, current approaches are not efficient and do not scale well. In this paper, we propose a linear algorithm for real-time divisible load scheduling. Unlike existing approaches,...
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