Result Size Estimation Research Articles

A Correlated Network Scale-Up Model: Finding the Connection Between Subpopulations

Aggregated Relational Data (ARD), formed from “How many X’s do you know?” questions, is a powerful tool for learning important network characteristics with incomplete network data. Compared to traditional survey methods, ARD is attractive as it does not require a sample from the target population and does not ask respondents to self-reveal their own status. This is helpful for studying hard-to-reach populations like female sex workers who may be hesitant to reveal their status. From December 2008 to February 2009, the Kiev International Institute of Sociology (KIIS) collected ARD from 10,866 respondents to estimate the size of HIV-related groups in Ukraine. To analyze this data, we propose a new ARD model which incorporates respondent and group covariates in a regression framework and includes a bias term that is correlated between groups. We also introduce a new scaling procedure using the correlation structure to further reduce biases. The resulting size estimates of those most-at-risk of HIV infection can improve the HIV response efficiency in Ukraine. Additionally, the proposed model allows us to better understand two network features without the full network data: (a) What characteristics affect who respondents know, and (b) How is knowing someone from one group related to knowing people from other groups. These features can allow researchers to better recruit marginalized individuals into the prevention and treatment programs. Our proposed model and several existing NSUM models are implemented in the networkscaleup R package. Supplementary materials for this article are available online.

Particle motion artifacts in equilibrium magnetization measurements of large iron oxide nanoparticles

Iron oxide nanoparticles find many applications due to their response when subjected to externally applied magnetic fields. Equilibrium magnetization measurements (commonly known as magnetization curves) are an essential characterization tool to evaluate if particles display hysteresis, and to obtain magnetic properties such as the saturation magnetization. For superparamagnetic particles, one can obtain a magnetic size distribution by fitting the data to a theoretical model, such as the Langevin function, in what is called magnetogranulometric analysis. If one wishes to use the resulting size estimates as a predictor of particle performance in applications, magnetization data must be obtained under conditions that capture the response of the particles with minimal artifacts. In this paper, we used selected iron oxide nanoparticle batches with physical size ranging from 20 to 45 nm to demonstrate the influence of sample preparation methods on the magnetization data obtained. We show that measurements in powder form and in liquid solvents display varying degrees of particle interaction artifacts at low fields, depending strongly on particle size and on the thickness of the surface coating. In addition, measurements in ‘solid’ waxy hydrocarbon matrices are shown to be susceptible to particle rotation artifacts for large particle sizes. Hard crosslinked polymer matrices are shown to restrict particle motion completely, resulting in magnetization data that follows the Langevin function if the measurement is performed above the blocking temperature of the particles. We end with a discussion of how the presence of matrix-dependent measurement artifacts influence the magnetic diameter fits obtained using magnetogranulometry, and how measuring above and below the blocking temperature can affect fit results.

A mobile angular scattering microscope for organelle size estimation.

Angular light scattering measurements have been used to determine the size parameters of spherical particles. By measuring the angular scattering from biological specimen, the average size of the cellular organelles can be estimated, which can be used to determine information about the health of the biological sample. An angular scattering microscope with the ability to be easily moved was constructed from common inexpensive components, which has potential applications for clinical and low-resource settings. The stability and accuracy of the system were investigated by measuring the scattering from polystyrene beads with mean sizes of 5 and 1.75 μm with narrow size distributions. Resulting size estimates obtained from the scattering patterns were consistent with the manufacturer-specified range of diameters for each sample. Initial studies were also conducted on individual fixed HeLa cells. The results presented indicate that the system is capable of obtaining precise and accurate size estimates of beads and single cells' organelles.

Using multi-frequency acoustic attenuation to monitor grain size and concentration of suspended sediment in rivers

Multi-frequency acoustic backscatter profiles recorded with side-looking acoustic Doppler current profilers are used to monitor the concentration and size of sedimentary particles suspended in fluvial environments. Data at 300, 600, and 1200 kHz are presented from the Isère River in France where the dominant particles in suspension are silt and clay sizes. The contribution of suspended sediment to the through-water attenuation was determined for three high concentration (> 100 mg/L) events and compared to theoretical values for spherical particles having size distributions that were measured by laser diffraction in water samples. Agreement was good for the 300 kHz data, but it worsened with increasing frequency. A method for the determination of grain size using multi-frequency attenuation data is presented considering models for spherical and oblate spheroidal particles. When the resulting size estimates are used to convert sediment attenuation to concentration, the spheroidal model provides the best agreement with optical estimates of concentration, but the aspect ratio and grain size that provide the best fit differ between events. The acoustic estimates of size were one-third the values from laser grain sizing. This agreement is encouraging considering optical and acoustical instruments measure different parameters.

Dye shift: a neglected source of genotyping error in molecular ecology

Molecular ecologists must be vigilant in detecting and accounting for genotyping error, yet potential errors stemming from dye-induced mobility shift (dye shift) may be frequently neglected and largely unknown to researchers who employ 3-primer systems with automated genotyping. When left uncorrected, dye shift can lead to mis-scoring alleles and even to falsely calling new alleles if different dyes are used to genotype the same locus in subsequent reactions. When we used four different fluorophore labels from a standard dye set to genotype the same set of loci, differences in the resulting size estimates for a single allele ranged from 2.07 bp to 3.68 bp. The strongest effects were associated with the fluorophore PET, and relative degree of dye shift was inversely related to locus size. We found little evidence in the literature that dye shift is regularly accounted for in 3-primer studies, despite knowledge of this phenomenon existing for over a decade. However, we did find some references to erroneous standard correction factors for the same set of dyes that we tested. We thus reiterate the need for strict quality control when attempting to reduce possible sources of genotyping error, and in cases where different dyes are applied to a single locus, perhaps mistakenly, we strongly discourage researchers from assuming generic correction patterns.

A Result Size Estimation Algorithm for Value Predication in XML Query

Result size estimation of value predication in XML query is a multiple attributes dependent problem. It is different from the counterpart in relational database, for the multiple attributes in XML involve not only the value data, but also the structural information. To solve the problem, this paper proposes a wavelet-based histogram for the result size estimation of value predication in XML query. It also gives the way to identify the multi-dimensional dependent element set, to rewrite the value predication and value denotation of structural information. Experimental results show that the algorithm achieves on accurate result size estimation for value predication in XML query.

공간 질의 최적화를 위한 힐버트 공간 순서화에 따른 공간 분할

공간 질의 크기에 대한 근사치를 구하기 위해서는 입력 데이터 공간을 분할한 후 분할된 영역에 대하여 질의 결과 크기를 추정한다. 본 논문에서는 데이터 편재가 심한 공간 데이터에 대한 질의 크기 추정의 문제를 논의한다. 공간을 분할하는 기법으로 관계 데이터베이스에서 많이 사용되는 너비 균등, 높이 균등 히스토그램에 해당되는 면적 균등, 개수 균등 분할에 대한 방법을 검토하고 공간 인덱싱에 기초한 공간 분할방법에 대해서 알아본다. 본 논문에서는 공간 순서화 기법인 힐버트 공간 채움 곡선을 이용한 공간 분할을 제안한다. 제안한 방법과 기존의 방법을 실제 데이터와 인위 데이터를 사용하여 편재된 공간 데이터에 대한 질의 결과 크기의 추정에 대한 정확도를 비교한다. 본 실험에서 힐버트 채움 곡선에 의한 공간 분할이 공간 질의 크기 버켓 수의 변화, 데이터 위치 편재도의 변화, 데이터 크기의 변화에 대해서 기존의 분할 방법보다 질의 결과 크기 추정에 대해서 우수한 성능을 보였다. In order to approximate the spatial query result size we partition the input rectangles into subsets and estimate the query result size based on the partitioned spatial area. In this paper we examine query result size estimation in skewed data. We examine the existing spatial partitioning techniques such as equi-area and equi-count partitioning, which are analogous to the equi-width and equi-height histograms used in relational databases, and examine the other partitioning techniques based on spatial indexing. In this paper we propose a new spatial partitioning technique based on the Hilbert space filling curve. We present a detailed experimental evaluation comparing the proposed technique and the existing techniques using synthetic as well as real-life datasets. The experiments showed that the proposed partitioning technique based on the Hilbert space filling curve achieves better query result size estimation than the existing techniques for space query size, bucket numbers, skewed data, and spatial data size.

Benchmarking attribute cardinality maps for database systems using the tpc-d specifications

Benchmarking is an important phase in developing any new software technique because it helps to validate the underlying theory in the specific problem domain. But benchmarking of new software strategies is a very complex problem, because it is difficult (if not impossible) to test, validate and verify the results of the various schemes in completely different settings. This is even more true in the case of database systems because the benchmarking also depends on the types of queries presented to the databases used in the benchmarking experiments. Query optimization strategies in relational database systems rely on approximately estimating the query result sizes to minimize the response time for user-queries. Among the many query result size estimation techniques, the histogram-based techniques are by far the most commonly used ones in modern-day database systems. These techniques estimate the query result sizes by approximating the underlying data distributions, and, thus, are prone to estimation errors. In two recent works , we proposed (and thoroughly analyzed) two new forms of histogram-like techniques called the rectangular and trapezoidal attribute cardinality maps (ACM), respectively, that give much smaller estimation errors than the traditional equi-width and equi-depth histograms currently being used by many commercial database systems. This paper reports how the benchmarking of the Rectangular-ACM (R-ACM) and the Trapezoidal-ACM (T-ACM) for query optimization can be achieved. By conducting an extensive set of experiments using the acclaimed TPC-D benchmark queries and database , we demonstrate that these new ACM schemes are much more accurate than the traditional histograms for query result size estimation. Apart from demonstrating the power of the ACMs, this paper also shows how the TPC-D benchmarking can be achieved using a large synthetic database with many different patterns of synthetic queries, which are representative of a real-world business environment.