1,721,102 research outputs found
Self Spatial Join Selectivity Estimation Using Fractal Concepts
No full textThe problem of selectivity estimation for queries of nontraditional databases is still an open issue. In this article, we examine the problem of selectivity estimation for some types of spatial queries in databases containing real data. We have shown earlier [Faloutsos and Kamel 1994] that real point sets typically have a nonuniform distribution, violating consistently the uniformity and independence assumptions. Moreover, we demonstrated that the theory of fractals can help to describe real point sets. In this article we show how the concept of fractal dimension, i.e., (noninteger) dimension, can lead to the solution for the selectivity estimation problem in spatial databases. Among the infinite family of fractal dimensions, we consider here the Hausdorff fractal dimension D0 and the “Correlation” fractal dimension D2. Specifically, we show that (a) the average number of neighbors for a given point set follows a power law, with D2 as exponent, and (b) the average number of nonempty range queries follows a power law with E − D0 as exponent (E is the dimension of the embedding space). We present the formulas to estimate the selectivity for “biased” range queries, for self-spatial joins, and for the average number of nonempty range queries. The result of some experiments on real and synthetic point sets are shown. Our formulas achieve very low relative errors, typically about 10%, versus 40%–100% of the formulas that are based on the uniformity and independence assumptions
Analysis of Range Queries and Self Spatial Join Queries on Real Region Datasets Stored Using an R-tree
No full textIn this paper, we study the node distribution of an R-tree storing region data, like, for instance, islands, lakes, or human-inhabited areas. We will show that real region datasets are packed in an R-tree into minimum bounding rectangles (MBRs) whose area distribution follows the same power law, named REGAL (REGion Area Law), as that for the regions themselves. Moreover, these MBRs are packed in their turn into MBRs following the same law, and so on iteratively, up to the root of the R-tree. Based on this observation, we are able to accurately estimate the search effort for range queries, using a small number of easy-to-retrieve parameters. Furthermore, since our analysis exploits, through a realistic mathematical model, the proximity relations existing among the regions in the dataset, we show how to use our model to predict the selectivity of a self-spatial join query posed on the dataset. Experiments on a variety of real datasets (islands, lakes, human-inhabited areas) show that our estimations are accurate, enjoying a geometric average relative error ranging from 22 percent to 32 percent for the search effort of a range query, and from 14 percent to 34 percent for the selectivity of a self-spatial join query. This is significantly better than using a naive model based on uniformity assumption, which gives rise to a geometric average relative error up to 270 percent and up to 85 percent for the two problems, respectively
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A Signature Technique for Similarity-Based Queries (Extended Abstract)
No full text) C. Faloutsos , H. V. Jagadish AT&T Bell Labs Murray Hill, NJ 07974 fchristos,[email protected] A. O. Mendelzon Dept. of Computer Science Univ. of Toronto [email protected] T. Milo Tel Aviv Univ. [email protected] 1 Introduction Sequences of real-valued data arise in many applications ranging from the stock market to electro-cardiograms. Often, it is of interest to locate sequences that are similar to a specified query sequence. The notion of similarity is application dependent, and even within a single application, may vary from one query to the next. Work in this area is usually specific to one particular domain and uses one specific notion of similarity. For example, Faloutsos et al [6, 1] studied the problem of searching a database of time sequences for sequences similar to one given. They reduced sequences to points in a low-dimensional space by using Fourier transforms and used the Euclidean distance in this space to measure similarity. Retrieval by similari..
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Estimating the selectivity of spatial queries using the `correlation' fractal dimension
No full textWe examine the estimation of selectivities for range and spatial join queries in real spatial databases. As we have shown earlier [FK94a], real point sets: (a) violate consistently the "uniformity " and "independence " assumptions, (b) can often be described as "fractals", with non-integer (fractal) dimension. In this paper we show that, among the innite family of fractal dimensions, the so called "Correlation Dimension " D 2 is the one that we need to predict the selectivity of spatial join. The main contribution is that, for all the real and synthetic point-sets we tried, the average number of neighbors for a given point of the point-set follows a power law, with D 2 as the expo-nent. This immediately solves the selectivity estimation for spatial joins, as well as for "biased" range queries (i.e., queries whose centers prefer areas of high point density). We present the formulas to estimate the selectivity for the biased queries, including an integra-tion constant (K `shape 0) for each query shape. Finally, we show results on real and synthetic point sets, where our formulas achieve very low relative errors (typically about 10%, versus 40%-100 % of the uniform assumption).
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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