1,721,072 research outputs found
A unifying framework for l0-sampling algorithms
No full textThe problem of building an l 0-sampler is to sample near-uniformly from the support set of a dynamic multiset. This problem has a variety of applications within data analysis, computational geometry and graph algorithms. In this paper, we abstract a set of steps for building an l 0-sampler, based on sampling, recovery and selection. We analyze the implementation of an l 0-sampler within this framework, and show how prior constructions of l 0-samplers can all be expressed in terms of these steps. Our experimental contribution is to provide a first detailed study of the accuracy and computational cost of l 0-samplers. © 2013 Springer Science+Business Media New York
On unifying the space of l0-sampling algorithms
No full textThe problem of building an l0-sampler is to sample nearuniformly from the support set of a dynamic multiset. This problem has a variety of applications within data analysis, computational geometry and graph algorithms. In this paper, we abstract a set of steps for building an l0-sampler, based on sampling, recovery and selection. We analyze the implementation of an l0-sampler within this framework, and show how prior constructions of l0-samplers can all be expressed in terms of these steps. Our experimental contribution is to provide a first detailed study of the accuracy and computational cost of l0-samplers
Streaming Algorithms for Estimating Quantiles with Novel Error Guarantees
No full textThis work deals with streaming algorithms for estimation of ranks and quantiles that perform a single pass through the input data stream using a small space. After reading a stream of N elements of a totally ordered universe, a streaming algorithm for rank (or quantile) estimation answers rank (or quantile) queries with additive error if the error is at most ±εN and with relative error if for item y with rank R(y), the error is at most ±ε R(y). The first problem is optimally solved by the KLL algorithm in space O(ε−1 ), and the best-known algorithm for the relative error is ReqSketch, which takes space O(ε−1 log1.5 N). Our algorithm called Jagged Sketch consists of two significant improvements to the ReqSketch algorithm. The first of the improvements reduces the error for high ranks by a factor of √︂ log(N), the second one improves the error by a factor up to log(N) for important ranks chosen by the user and for ranks close to them, all while maintaining the same space complexity. We support our theoretical analysis by experiments that demonstrate that Jagged Sketch can indeed reduce the error for selected ranks while maintaining the same space and similar error for other ranks compared to ReqSketch. For ε ∈ O(log−1.5 N) Jagged Sketch achieves additive error in the same space as KLL while..
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
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
Proudové algoritmy pro odhad kvantilů s novými garancemi chyby
No full textTato práce se zabývá proudovými (streaming) algoritmy pro odhad distribucí a kvantilů, které provedou jeden průchod přes vstupní proud dat za použití malé paměti. Po načtení proudu N prvků z lineárně uspořádaného univerza proudový algoritmus pro odhad kvantilů odpovídá s aditivní chybou, když je velikost chyby nejvýše ±εN a s re- lativní chybou, když je pro item y s rankem R(y) chyba nejvýše ±ε R(y). První z těchto problémů řeší optimálně algoritmus KLL v prostoru ε−1 , nejlepší známý algoritmus pro relativní chybu je ReqSketch, který potřebuje prostor ε−1 log1.5 (N). Náš algoritmus Jagged Sketch spočívá ve dvou vylepšeních algoritmu ReqSketch. První z vylepšení zmenšuje chybu √︂ log(N)-krát pro vysoké ranky, druhé zmenšuje chybu až log(N)-krát pro důležitý rank dle volby uživatele a ranky jemu blízké, to vše při zacho- vání stejného prostoru. Teoretickou analýzu jsme podpořili experimenty, které prokazují, že Jagged Sketch dokáže oproti ReqSketch skutečně snížit chybu pro vybrané ranky při zachování stejného prostoru a podobné chyby pro ostatní ranky. Pro ε ∈ O(log−1.5 N) odpovídá Jagged Sketch s aditivní chybou ve stejném prostoru jako KLL, přičemž si zároveň zachovává garanci téměř relativní chyby. V praxi je chyba Jagged Sketche pro velké ranky přibližně čtyřikrát větší, zatímco pro malé ranky...This work deals with streaming algorithms for estimation of ranks and quantiles that perform a single pass through the input data stream using a small space. After reading a stream of N elements of a totally ordered universe, a streaming algorithm for rank (or quantile) estimation answers rank (or quantile) queries with additive error if the error is at most ±εN and with relative error if for item y with rank R(y), the error is at most ±ε R(y). The first problem is optimally solved by the KLL algorithm in space O(ε−1 ), and the best-known algorithm for the relative error is ReqSketch, which takes space O(ε−1 log1.5 N). Our algorithm called Jagged Sketch consists of two significant improvements to the ReqSketch algorithm. The first of the improvements reduces the error for high ranks by a factor of √︂ log(N), the second one improves the error by a factor up to log(N) for important ranks chosen by the user and for ranks close to them, all while maintaining the same space complexity. We support our theoretical analysis by experiments that demonstrate that Jagged Sketch can indeed reduce the error for selected ranks while maintaining the same space and similar error for other ranks compared to ReqSketch. For ε ∈ O(log−1.5 N) Jagged Sketch achieves additive error in the same space as KLL while...Computer Science Institute of Charles UniversityInformatický ústav Univerzity KarlovyMatematicko-fyzikální fakultaFaculty of Mathematics and Physic
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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