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Based on the distributed decision tree algorithm, this paper first proposes a method of vertically partitioning datasets and synchronously updating the hash table to establish an information-based mass data retrieval method in a heterogeneous distributed environment, as well as using interval segmentation and interval filtering technologies for improved algorithm of distributed decision tree. The distributed decision tree algorithm uses the attribute histogram data structure to merge the category list into each attribute list, reducing the amount of data that needs to reside in the memory. Second, we adopt the strategy of vertically dividing the dataset and synchronously updating the hash table, select the hash table entries that can be used to update according to the minimum Gini value, modify the corresponding entries and use the hash table to record and control each sub-site. In the case of node splitting, it has a high accuracy rate. In addition, for classification problems that meet monotonic constraints in a distributed environment, this paper will extend the idea of building a monotonic decision tree in a distributed environment, supplementing the distributed decision tree algorithm, adding a modification rule and modifying the generated nonmonotonic decision tree to monotonicity. In order to solve the high load problem of the privacy-protected data stream classification mining algorithm under a single node, a Storm platform for the parallel algorithm PPFDT_P based on the distributed decision tree algorithm is designed and implemented. At the same time, considering that the word vector model improves the deep representation of features and solves the problem of feature high-dimensional sparseness, and the iterative decision tree algorithm GBDT model is more suitable for non-high-dimensional dense features, the iterative decision tree algorithm will be integrated into the word vector model (GBDT) in the data retrieval application, using the distributed representation of words, namely word vectors, to classify short messages on the GBDT model. Experimental results show that the distributed decision tree algorithm has high efficiency, good speed-up and good scalability, so that there is no need to increase the number of datasets at each sub-site at any time. Only a small number of data items are inserted. By splitting some leaf nodes, a small amount is added by branching to achieve a monotonic decision tree. The proposed system achieves a massive data ratio of 54.1% while compared with other networks of massive data ratio. [ABSTRACT FROM AUTHOR]

Periodic high-utility sequential pattern mining (PHUSPM) is used to extract periodically occurring high-utility sequential patterns (HUSPs) from a quantitative sequence database according to a user-specified minimum utility threshold (minutil). A sequential pattern's periodicity is determined by measuring when the frequency of its periods (the time between two consecutive happenings of the sequential pattern) exceed a user-specified maximum periodicity threshold (maxPer). However, due to the strict judgment threshold, the traditional PHUSPM method has the problem that some useful sequential patterns are discarded and the periodic values of some sequential patterns fluctuate greatly (i.e., are unstable). In frequent itemset mining (FIM), some researchers put forward some strategies to solve these problems. Because of the symmetry of frequent itemset pattern (FIPs), these strategies cannot be directly applied to PHUSPM. In order to address these issues, this work proposes the stable periodic high-utility sequential pattern mining (SPHUSPM) algorithm. The contributions made by this paper are as follows. First, we introduce the concept of stability to overcome the abovementioned problems, mine sequential patterns with stable periodic behavior, and propose the concept of stable periodic high-utility sequential patterns (SPHUSPs) for the first time. Secondly, we design a new data structure named the PUL-list to record the periodic information of sequential patterns, thereby improving the mining efficiency. Thirdly, we propose the maximum lability pruning strategy in sequential pattern (MLPS), which can prune a large number of unstable sequential patterns in advance. To assess the algorithm's effectiveness, we perform many experiments. It turns out that the algorithm can not only mine patterns that are ignored by traditional algorithms, but also ensure that the discovered patterns have stable periodic behavior. In addition, after using the MLPS pruning strategy, the algorithm can prune 46.5% of candidates in advance on average in six datasets. Pruning a large number of candidates in advance not only speeds up the mining process, but also greatly reduces memory usage. [ABSTRACT FROM AUTHOR]

Approximate multi-pattern matching is an important issue that is widely and frequently utilized, when the pattern contains variable-length wildcards. In this paper, two suffix array-based algorithms have been proposed to solve this problem. Suffix array is an efficient data structure for exact string matching in existing studies, as well as for approximate pattern matching and multi-pattern matching. An algorithm called MMSA-S is for the short exact characters in a pattern by dynamic programming, while another algorithm called MMSA-L deals with the long exact characters by the edit distance method. Experimental results of Pizza & Chili corpus demonstrate that these two newly proposed algorithms, in most cases, are more time-efficient than the state-of-the-art comparison algorithms. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we consider the problems for covering multiple intervals on a line. Given a set B of m line segments (called "barriers") on a horizontal line L and another set S of n horizontal line segments of the same length in the plane, we want to move all segments of S to L so that their union covers all barriers and the maximum movement of all segments of S is minimized. Previously, an O (n 3 log ⁡ n) -time algorithm was given for the case m = 1. In this paper, we propose an O (n 2 log ⁡ n log ⁡ log ⁡ n + n m log ⁡ m) -time algorithm for a more general setting with any m ≥ 1 , which also improves the previous work when m = 1. We then consider a line-constrained version of the problem in which the segments of S are all initially on the line L. Previously, an O (n log ⁡ n) -time algorithm was known for the case m = 1. We present an algorithm of O (m log ⁡ m + n log ⁡ m log ⁡ n) time for any m ≥ 1. These problems may have applications in mobile sensor barrier coverage in wireless sensor networks. [ABSTRACT FROM AUTHOR]

Given two sequences A and B of lengths m and n , respectively, the consecutive suffix alignment (CSA) problem is to compute the longest common subsequence (LCS) between A and each suffix of B. The data structure of two-dimensional matrix for solving the CSA problem is named S-table. The S-table would be infeasible due to its quadratic-space requirement for long sequences. The linear-space S-table, proposed by Alves et al. (2005), consists of the first row of the S-table and the changes between every two consecutive rows. However, there is no further discussions and practical applications for the linear-space S-table. This paper proposes algorithms for improving three problems, related to LCS and S-table, by using the linear-space S-table. Suppose that A = A (1) A (2) (concatenation of two substrings), and we are given the S-table of A (2) and B , as well as the alignment result (LCS length) of A (1) to each prefix of B. The concatenated LCS (CoLCS) problem is to find the alignment result of A and B. For the CoLCS problem, this paper proposes an O(n)-time algorithm with the technique of set union-find to achieve the linear space. Next, for merging two linear S-tables, we propose an O (n log ⁡ n) -time algorithm with the technique of range query and update to improve the quadratic-time algorithm using quadratic-space S-tables. Then, we propose an O(n)-time algorithm to update the linear-space S-table of A and B to obtain the linear-space S-table of Aσ and B , where σ denotes a new character appended to the tail of A. For further applications to long sequences by using the linear-space S-table, the algorithms proposed in this paper are essential and necessary. [ABSTRACT FROM AUTHOR]

Subspace clustering can usually handle high-dimensional data well, but due to the influence of the noise of the data itself, the block diagonal structure of the coefficient matrix is often easily destroyed. To solve the above problems, this paper proposed a semi-supervised sparse subspace clustering with label discrimination and local linear reinforcement. On the one hand, it better captured the global structure of the data by constraining the coefficient between the labeled data to be 0. On the other hand, it strengthened the local correlation between the neighboring points of the data through the K-nearest neighbor relationship, and eliminated a large number of unrelated data points enhance the robustness of the algorithm. The experiments on a variety of data verify the effectiveness of the proposed semi-supervised clustering algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper continues the STACS contribution by Diekert, Ushakov, and the author as well as the IJAC publication by the same authors. We extend the results published there in two ways. First, we show that the data structure of power circuits can be generalized to work with arbitrary bases q≥2. This results in a data structure that can hold huge integers, arising by iteratively forming powers of q. We show that the properties of power circuits known for q=2 translate to the general case. This generalization is non-trivial and additional techniques are required to preserve the time bounds of arithmetic operations that were shown for the case q=2. The extended power circuit model permits us to conduct operations in the Baumslag-Solitar group BS(1, q) as efficiently as in BS(1,2). This allows us to solve the word problem in the generalization H(1, q) of Higman's group in polynomial time. The group H(1, q) is an amalgamated product of four copies of the Baumslag-Solitar group BS(1, q) rather than BS(1,2) in the original group H= H(1,2). As a second result, we allow arbitrary numbers f≥4 of copies of BS(1, q), leading to an even more generalized notion of Higman groups H(1, q). We prove that the word problem of the latter can still be solved within the ${\mathcal {O}}(n^{6})$ time bound that was shown for H(1,2). [ABSTRACT FROM AUTHOR]

In the mining algorithm for frequent weighted itemsets based on WN-list ( NFWI ), mining weighted frequent itemsets ( FWI) is inefficient. To solve the problem, this paper proposed a frequent weighted itemsets mining algorithm ( NegNFWI) based on WNegNodeset structure. Firstly, this algorithm used the data structure of WNegNodeset, an extension of NegNodeset. The data structure employed a novel encoding model for nodes in bitmap weighted-tree ( BMW-tree) based on the bitmap representation of sets, and used bitwise operators to extract WNegNodesets of itemsets quickly, avoiding a large quantity of intersection operations. Secondly, this algorithm used diffsets strategy to calculate the weighted support degree of itemsets quickly, thus decreasing computing time. Finally, results from simulation experiments show that the proposed algorithm is efficient and feasible. [ABSTRACT FROM AUTHOR]

We study probabilistic bit-probe schemes for the membership problem. Given a set A of at most n elements from the universe of size m we organize such a structure that queries of type ' x∈ A? ' can be answered very quickly. H. Buhrman, P.B. Miltersen, J. Radhakrishnan, and S. Venkatesh proposed a randomized bit-probe scheme that needs space of O( nlog m) bits. That scheme has a randomized algorithm processing queries; it needs to read only one randomly chosen bit from the memory to answer a query. For every x the answer is correct with high probability (with two-sided errors). In this paper we slightly modify the bit-probe model of Buhrman et al. and consider schemes with a small auxiliary information in 'cache' memory. In this model, we show that for the membership problem there exists a bit-probe scheme with one-sided error that needs space of O( nlog m+poly(log m)) bits, which cannot be achieved in the model without cache. We also obtain a slightly weaker result (space of size npoly(log m) bits and two bit probes for every query) for a scheme that is effectively encodable. [ABSTRACT FROM AUTHOR]

Copyright of Informatics in Education is the property of Informatics in Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Adaptive multiscale methods are among the many effective techniques for the numerical solution of partial differential equations. Efficient grid management is an important task in these solvers. In this paper we focus on this problem for discontinuous Galerkin discretization methods in 2 and 3 spatial dimensions and present a data structure for handling adaptive grids of different cell types in a unified approach, instead of tree-based techniques where connectivity is stored via pointers, we associate each cell that arises in the refinement hierarchy with a cell identifier and construct algorithms that establish hierarchical and spatial connectivity. By means of bitwise operations, the complexity of the connectivity algorithms can be bounded independent of the level. The grid is represented by a hash table which results in a low-memory data structure and ensures fast access to cell data. The spatial connectivity algorithm also supports the application of quadrature rules for face integrals that occur in discontinuous Galerkin discretizations. The concept allows us to implement discontinuous Galerkin methods largely independent of spatial dimension and cell type. We demonstrate this by outlining how typical algorithmic tasks that arise in these implementations can be performed with our data structure. In computation tests we compare our approach with that of a classical implementation using pointers. [ABSTRACT FROM AUTHOR]

Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions. In recent years, unsupervised feature selection algorithms have attracted considerable attention in the field of gene selection as they can find the most discriminating subsets of genes, namely the potential information in biological data. Recent research also shows that maintaining the important structure of data is necessary for gene selection. However, most current feature selection methods merely capture the local structure of the original data while ignoring the importance of the global structure of the original data. We believe that the global structure and local structure of the original data are equally important, and so the selected genes should maintain the essential structure of the original data as far as possible. In this paper, we propose a new, adaptive, unsupervised feature selection scheme which not only reconstructs high-dimensional data into a low-dimensional space with the constraint of feature distance invariance but also employs ℓ 2 , 1 -norm to enable a matrix with the ability to perform gene selection embedding into the local manifold structure-learning framework. Moreover, an effective algorithm is developed to solve the optimization problem based on the proposed scheme. Comparative experiments with some classical schemes on real tumor datasets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

For a set of colored points, a region is called color-spanning if it contains at least one point of each color. In this paper, we first consider the problem of maintaining the smallest color-spanning interval for a set of n points with k colors on the real line, such that the insertion and deletion of an arbitrary point takes O (log² n) the worst-case time. Then, we exploit the data structure to show that there is O(n log² n) time algorithm to compute the smallest color-spanning square for a set of n points with k colors in the plane. This is a new way to improve O (nk log n) time algorithm presented by Abellanas et al. [1] when k = w(logn). We also consider the problem of computing the smallest color-spanning square in a special case in which we have, at most, two points from each color. We present O(nlogn) time algorithm to solve the problem which improves the result presented by Arkin et al. [2] by a factor of log n. [ABSTRACT FROM AUTHOR]

Given a set $\mathcal{D}$ of q documents, the Longest Common Substring (LCS) problem asks, for any integer 2 ⩽ k ⩽ q, the longest substring that appears in k documents. LCS is a well-studied problem having a wide range of applications in Bioinformatics: from microarrays to DNA sequences alignments and analysis. This problem has been solved by Hui (2000International Journal of Computer Science and Engineering15 73–76) by using a famous constant-time solution to the Lowest Common Ancestor (LCA) problem in trees coupled with the use of suffix trees.In this article, we present a simple method for solving the LCS problem by using suffix trees (STs) and classical union-find data structures. In turn, we show how this simple algorithm can be adapted in order to work with other space efficient data structures such as the enhanced suffix arrays (ESA) and the compressed suffix tree. [ABSTRACT FROM AUTHOR]

High utility quantitative itemsets (HUQI) mining is a new research topic in the field of data mining. It not only provides high utility itemset (HUI), but also provides quantitative information of individual item in the itemset. HUQI can provide decision makers with information about items and their purchase quantities. However, the currently proposed HUQI mining algorithms assume that the datasets are static. In order to solve this problem, an incremental quantitative utility list (IQUL) data structure is proposed to store item information, including item name, item number, transaction weight utility of item, each entry in the list stores the transaction identifier, the utility of the original data, the remaining utility, the utility of the incremental data, the remaining utility, and the sum of the utility and the remaining utility. When data is inserted, the item information will be updated. Based on IQUL, an incrementally updating HUQI (IHUQI) mining algorithm is proposed to mine HUQI on incremental update data. A large number of experiments on real datasets show that the IHUQI algorithm can effectively mine HUQI Experimental results show better performance on sparse datasets. [ABSTRACT FROM AUTHOR]

We consider the problem of finding k centers for n weighted points on a real line. This (weighted) k -center problem was solved in O ( n log ⁡ n ) time previously by using Cole's parametric search and other complicated approaches. In this paper, we present an easier O ( n log ⁡ n ) time algorithm that avoids the parametric search, and in certain special cases our algorithm solves the problem in O ( n ) time. In addition, our techniques involve developing interesting data structures for processing queries that find a lowest point in the common intersection of a certain subset of half-planes. This subproblem is interesting in its own right and our solution for it may find other applications as well. [ABSTRACT FROM AUTHOR]

Given an input stream S of size N, a Φ-heavy hitter is an item that occurs at least ΦN times in S. The problem of finding heavy-hitters is extensively studied in the database literature. We study a real-time heavy-hitters variant in which an element must be reported shortly after we see its T = ΦN-th occurrence (and hence it becomes a heavy hitter). We call this the Timely Event Detection (TED) Problem. The TED problem models the needs of many real-world monitoring systems, which demand accurate (i.e., no false negatives) and timely reporting of all events from large, high-speed streams with a low reporting threshold (high sensitivity). Like the classic heavy-hitters problem, solving the TED problem without false-positives requires large space (Ω(N ) words). Thus in-RAM heavy-hitters algorithms typically sacrifice accuracy (i.e., allow false positives), sensitivity, or timeliness (i.e., use multiple passes). We show how to adapt heavy-hitters algorithms to external memory to solve the TED problem on large high-speed streams while guaranteeing accuracy, sensitivity, and timeliness. Our data structures are limited only by I/O-bandwidth (not latency) and support a tunable tradeoff between reporting delay and I/O overhead. With a small bounded reporting delay, our algorithms incur only a logarithmic I/O overhead. We implement and validate our data structures empirically using the Firehose streaming benchmark. Multithreaded versions of our structures can scale to process 11M observations per second before becoming CPU bound. In comparison, a naive adaptation of the standard heavy-hitters algorithm to external memory would be limited by the storage device's random I/O throughput, i.e., ≈100K observations per second. [ABSTRACT FROM AUTHOR]

Abstract: Clustering is an example of a class of optimization problems. In the classical clustering, an item must belong to any one cluster. But fuzzy clustering describes more accurately the ambiguous type of structure in data. The fuzzy ISODATA clustering exhibits the rapid convergence in finding the best classification program when the classification number is given. In this paper, we propose the algorithm to solve the choosing lands problem and show the result of the experiment. The result is satisfied to realtors in choosing lands. [Copyright &y& Elsevier]

Abstract: Factor and suffix oracles have been introduced in Allauzen et al. (1999) in order to provide an economic and efficient solution for storing all the factors and suffixes respectively of a given text. Whereas good estimations exist for the size of the factor/suffix oracle in the worst case, no average-case analysis has been done until now. In this paper, we give an estimation of the average size for the factor/suffix oracle of an n-length text when the alphabet size is 2 and under a Bernoulli distribution model with parameter 1/2. To reach this goal, a new oracle is defined, which shares many of the properties of a factor/suffix oracle but is easier to study and provides an upper bound of the average size we are interested in. Our study introduces tools that could be further used in other average-case analysis on factor/suffix oracles, for instance when the alphabet size is arbitrary. [Copyright &y& Elsevier]

We first design an (n2) solution for finding a maximum induced matching in permutation graphs given their permutation models, based on a dynamic programming algorithm with the aid of the sweep line technique. With the support of the disjoint-set data structure, we improve the complexity to (m+n). Consequently, we extend this result to give an (m+n) algorithm for the same problem in trapezoid graphs. By combining our algorithms with the current best graph identification algorithms, we can solve the MIM problem in permutation and trapezoid graphs in linear and (n2) time, respectively. Our results are far better than the best known (mn) algorithm for the maximum induced matching problem in both graph classes, which was proposed by Habib et al. [ABSTRACT FROM AUTHOR]

The maximum k-plex problem is an important, computationally complex graph based problem. In this study an effective k-plex local search (KLS) is presented for solving this problem on a wide range of graph types. KLS uses data structures suitable for the graph being analysed and has mechanisms for preventing search cycling and promoting search diversity. State of the art results were obtained on 121 dense graphs and 61 large real-life (sparse) graphs. Comparisons with three recent algorithms on the more difficult graphs show that KLS performed better or as well as in 93% of 332 significant k-plex problem instances investigated achieving either larger average k-plex sizes (including some new results) or, when these were equivalent, lower CPU requirements. [ABSTRACT FROM AUTHOR]

Due to advances in information technology, data collection is becoming much easier. Clustering is an important technique for exploring data structures used in many fields, such as customer segmentation, image recognition, social science, and so on. However, in real-world applications, there are a lot of noises or outliers which will seriously influence the clustering performance in the dataset. Besides, the clustering results are susceptible to the initial centroids and algorithm parameters. To overcome the influence of outliers on clustering results, this study combines the advantages of probability c-means and fuzzy c-ordered means to propose a fuzzy possibilistic c-ordered means (FPCOM) algorithm. In order to solve the problem of parameters and initial centroids determination, this study employs a sine cosine algorithm (SCA) combined with FPCOM to improve the clustering results. The proposed algorithm is named SCA-FPCOM algorithm. Ten benchmark datasets collected from the UCI machine repository were used to validate the proposed algorithm in terms of adjusted rand index and the Silhouette coefficient. According to the experimental results, the SCA-FPCOM algorithm can obtain better results than other algorithms. [ABSTRACT FROM AUTHOR]

A many-core parallel approach of the multilevel fast multipole algorithm (MLFMA) based on the Athread parallel programming model is presented on the homegrown many-core SW26010 CPU of China. In the proposed many-core implementation of MLFMA, the data access efficiency is improved by using data structures based on the structure of array. The adaptive workload distribution strategies are adopted on different MLFMA tree levels to ensure full utilization of computing capability and the scratchpad memory. A double buffering scheme is specially designed to make communication overlapped computation. The resulting Athread-based many-core implementation of the MLFMA is capable of solving real-life problems with over one million unknowns with a remarkable speedup. The capability and efficiency of the proposed method are analyzed through the examples of computing scattering by spheres and a practical aerocraft. Numerical results show that with the proposed parallel scheme, the total speedup ratios from 6.4 to 8.0 can be achieved, compared with the CPU master core. [ABSTRACT FROM AUTHOR]

The classical stochastic joint replenishment problem (SJRP) often assumes uncapacitated batch size, and identical items. However, real problems encountered relate to the truck , or the container which has limited capacity. When the shipping capacity is limited, and the items are dissimilar in size, the problem becomes more challenging. By defining a class of order‐up‐to policies, we formulate the SJRP of this kind as a combinatorial optimization problem. To solve the problem, a data structure and an algorithm are developed. Based on the algorithm, we also suggest several heuristics including the (U, S) policy—a generalization of the well‐known (Q, S) policy for dissimilar items. The numerical study shows the (U, S) policy is robust and efficient. It performs well, especially when the items become more dissimilar, in terms of shipping size and inventory velocity. [ABSTRACT FROM AUTHOR]

We investigate structural properties of ω-automatic presentations of infinite structures in order to sharpen our methods to determine whether a given structure is ω-automatic. We apply these methods to show that several classes of structures such as pairing functions and infinite integral domains do not have an ω-automatic model. [ABSTRACT FROM AUTHOR]

For a given collection $\mathcal{G}$ of directed graphs we define the join-reachability graph of $\mathcal{G}$, denoted by $\mathcal{J}(\mathcal{G})$, as the directed graph that, for any pair of vertices u and v, contains a path from u to v if and only if such a path exists in all graphs of $\mathcal{G}$. Our goal is to compute an efficient representation of $\mathcal{J}(\mathcal{G})$. In particular, we consider two versions of this problem. In the explicit version we wish to construct the smallest join-reachability graph for $\mathcal{G}$. In the implicit version we wish to build an efficient data structure, in terms of space and query time, such that we can report fast the set of vertices that reach a query vertex in all graphs of $\mathcal{G}$. This problem is related to the well-studied reachability problem and is motivated by emerging applications of graph-structured databases and graph algorithms. We consider the construction of join-reachability structures for two graphs and develop techniques that can be applied to both the explicit and the implicit problems. First we present optimal and near-optimal structures for paths and trees. Then, based on these results, we provide efficient structures for planar graphs and general directed graphs. [ABSTRACT FROM AUTHOR]