7582784
Hierarchical Clustering-Merging for Multidimensional Index Structures
The R-tree family index structures are among the most common index structures used in multidimensional databases. To improve the search performance it is very important to reduce the overlap between bounding regions in the R-tree. However the arbitrary insertion order in the tree construction procedure might result in tree structures inefficient in the search operations. In this paper we propose a new technique called Hierarchical Clustering-Merging (HCM) to improve the tree construction procedure of the R-tree family index structures. With this technique we can take advantage of the data distribution information in the data set to achieve an optimized tree structure and improve the search performance.
8030523
Video management in peer-to-peer systems
Providing scalable video services in a peer-to-peer (P2P) environment is challenging. Since videos are typically large and require high communication bandwidth for delivery, many peers may be unwilling to cache them in whole to serve others. In this paper, the authors addressed two fundamental research problems in providing scalable P2P video services, namely (1) how a host can find enough video pieces, which may scatter among the whole system, to assemble a complete video, and (2) given a limited buffer size, what part of a video a host should cache. A new distributed video management technique was proposed. This scheme organizes hosts into a number of cells, each of which is a distinct set of hosts which together can supply a video in its entirety. A client looking for a video can stop its search as soon as it finds a host that caches any part of the video. Caching operations can be coordinated within each cell to balance data redundancy in the system. The extensive study on a Gnutella-like simulation network shows convincingly the performance advantage of the new scheme.
140623
Optimal Algorithms for Finding User Access Sessions from Very Large Web Logs
Although efficient identification of user access sessions from very large web logs is an unavoidable data preparation task for the success of higher level web log mining, little attention has been paid to algorithmic study of this problem. In this paper we consider two types of user access sessions, interval sessions and gap sessions. We design two efficient algorithms for finding respectively those two types of sessions with the help of some proposed structures. We present theoretical analysis of the algorithms and prove that both algorithms have optimal time complexity and certain error-tolerant properties as well. We conduct empirical performance analysis of the algorithms with web logs ranging from 100 megabytes to 500 megabytes. The empirical analysis shows that the algorithms just take several seconds more than the baseline time, i.e., the time needed for reading the web log once sequentially from disk to RAM, testing whether each user access record is valid or not, and writing each valid user access record back to disk. The empirical analysis also shows that our algorithms are substantially faster than the sorting based session finding algorithms. Finally, optimal algorithms for finding user access sessions from distributed web logs are also presented.
2974790
The complexity of the matrix eigenproblem
The eigenproblem for an n-by-n matrix A is the problem of the approximation (within a relative error bound 2-‘) of all the eigenvalues of the matrix A and computing the associated eigenspaces of all these eigenvalues. We show that the arithmetic complexity of this problem is bounded by O(n3 + (nlog’n)log b). If the characteristic and minimum polynomials of the matrix A coincide with each other (which is the ca8e for generic matrices of all classes of general and special matrices that we consider), then the latter deterministic cost bound can be replaced by the randomized bound O(K.a(2n) + n* + (n log’n) log b) where Ka(2n) denotes the cost of the computation of the 2n 1 vectors A’v, i = 1,. ,2n 1, maximized over all n-dimensional vectors v; Ka(2n) = O(M(n)logn), for M(n) = o(n2.3’6) denoting the arithmetic complexity of n x n matrix multiplication. This bound on the complexity of the eigenproblem is optimal up to a logarithmic factor and implies much faster solution of the eigenprohlem for the important special classes of matrices. In particular, we prove the bound O(n’ log n + (n log’ n) log b) on the randomized arithmetic complexity of the eigenproblem for generic matrices of the classes of n x n Toeplitz, Hank& Toeplitzlike, Hank&like and Toeplits-likeplus-Hank&like matrices. Then again, this bound is optimal (up to a logarithmic factor) for each of the latter classes of input matrices. We also prove similar nearly optimal upper bounds for the generic Caucby-like, Vandermonde-like and sparse matrices. All our complexity estimates for the eigenproblem improve the known ones by order of magnitude.
4216648
Optimizing web search using web click-through data
The performance of web search engines may often deteriorate due to the diversity and noisy information contained within web pages. User click-through data can be used to introduce more accurate description (metadata) for web pages, and to improve the search performance. However, noise and incompleteness, sparseness, and the volatility of web pages and queries are three major challenges for research work on user click-through log mining. In this paper, we propose a novel iterative reinforced algorithm to utilize the user click-through data to improve search performance. The algorithm fully explores the interrelations between queries and web pages, and effectively finds "virtual queries" for web pages and overcomes the challenges discussed above. Experiment results on a large set of MSN click-through log data show a significant improvement on search performance over the naive query log mining algorithm as well as the baseline search engine.
16272457
SimFusion: measuring similarity using unified relationship matrix
In this paper we use a Unified Relationship Matrix (URM) to represent a set of heterogeneous data objects (e.g., web pages, queries) and their interrelationships (e.g., hyperlinks, user click-through sequences). We claim that iterative computations over the URM can help overcome the data sparseness problem and detect latent relationships among heterogeneous data objects, thus, can improve the quality of information applications that require com- bination of information from heterogeneous sources. To support our claim, we present a unified similarity-calculating algorithm, SimFusion. By iteratively computing over the URM, SimFusion can effectively integrate relationships from heterogeneous sources when measuring the similarity of two data objects. Experiments based on a web search engine query log and a web page collection demonstrate that SimFusion can improve similarity measurement of web objects over both traditional content based algorithms and the cutting edge SimRank algorithm.
15070183
A construction of interpolating wavelets on invariant sets
We introduce the concept of a refinable set relative to a family of contractive mappings on a metric space, and demonstrate how such sets are useful to recursively construct interpolants which have a multiscale structure. The notion of a refinable set parallels that of a refinable function, which is the basis of wavelet construction. The interpolation points we recursively generate from a refinable set by a set-theoretic multiresolution are analogous to multiresolution for functions used in wavelet construction. We then use this recursive structure for the points to construct multiscale interpolants. Several concrete examples of refinable sets which can be used for generating interpolatory wavelets are included.