AlertInsight:Mining Multiple Correlation For Alert Reduction

Modern cloud services are monitored by numerous multidomain and multivendor monitoring tools,which generate massive numbers of alerts and events that are not actionable.These alerts usually carry isolated messages that are missing service contexts.Administrators become inundated with tickets caused by such alert events when they are routed directly to incident management systems.Noisy alerts increase the risk of crucial warnings going undetected and leading to service outages.One of the feasible ways to cope with the above problems involves revealing the correlations behind a large number of alerts and then aggregating the related alerts according to their correlations.Based on these guidelines,AlertInsight,a framework for alert event reduction,is proposed in this paper.In AlertInsight,the correlations among event sources are found by mining a sequence of historical events.Then,event correlation knowledge is employed to build an online detector targeting the correlated events that are hidden in the event stream.Finally,the correlated events are aggregated into a single high-level event for alert reduction.Because of theweaknesses of the commonly used pairwise correlation analysis methods in complex environments,an innovative approach for multiple correlation mining,which overcomes computational complexity challenges by scanning panoramic views of historical episodes from the perspective of holism,is proposed in this paper.In addition,a neural network-based correlated event detector that can learn the event correlation knowledge generated from correlation mining and then detect the correlated events in a sequence online is proposed.Experiments are conducted to test the effectiveness of AlertInsight.The experimental results(precision=0.92,recall=0.93,and F1-score=0.93)demonstrate the performance of AlertInsight for the recognition of multiple correlated alerts and its competence for alert reduction.

On Testing Equality of K Multiple Correlation Matrices

Coutsourides derived an ad hoc nuisance paratmeter removal test for testing equality of two multiple correlation matrices of two independent p variate normal populations under the assumption that a sample of size n is available from each population. This paper presents a likelihood ratio test criterion for testing equality of K multiple correlation matrices and extends the results to the testing of equality of K partial correlation matrices.

ON TESTING THE EQUALITY OF K MULTIPLE AND PARTIAL CORRELATION COEFFICIENTS

Coutsourides (1980) derives an ad hoc nuisance parameter removal test for testing the equality of two multiple correlation coefficients of two independent p variate normal populations, under the assumption that a sample of size n is available from each population. He also extends his ad hoc nuisance parameter removal test to the testing of the equality of two multiple correlation matrices. This paper presents likelihood ratio tests for testing the equality of k multiple correlation coefficients, and also k partial correlation coefficients.

Joint Analysis Method for Major Genes Controlling Multiple Correlated Quantitative Traits

Based on the major gene and polygene mixed inheritance model for multiple correlated quantitative traits, the authors proposed a new joint segregation analysis method of major gene controlling multiple correlated quantitative traits, which include major gene detection and its effect and variation estimation. The effect and variation of major gene are estimated by the maximum likelihood method implemented via expectation-maximization （EM） algorithm. Major gene is tested with the likelihood ratio （LR） test statistic. Extensive simulation studies showed that joint analysis not only increases the statistical power of major gene detection but also improves the precision and accuracy of major gene effect estimates. An example of the plant height and the number of tiller of F2 population in rice cross Duonieai x Zhonghua 11 was used in the illustration. The results indicated that the genetic difference of these two traits in this cross refers to only one pleiotropic major gene. The additive effect and dominance effect of the major gene are estimated as -21.3 and 40.6 cm on plant height, and 22.7 and -25.3 on number of tiller, respectively. The major gene shows overdominance for plant height and close to complete dominance for number of tillers.

Parton Rescattering Effect on the Charged Hadron Forward-Backward Multiplicity Correlation in pp Collisions at s～(1/2)=200 GeV

The parton rescattering effect on the charged hadron forward-backward multiplicity correlation in pp collisions at √s =200 GeV is studied by a parton and hadron cascade model, PACIAE, based on the PYTHIA model. The calculated multiplicity and pseudorapidity distribution of the final state charged hadrons are well compared with the experimental data. It is found that the final state charged hadron pseudorapidity distribution is different from the initial state charged partons. The parton rescattering effect on the charged hadron forward-backward multiplicity correlation increases with the increasing parton rescattering strength in the center pseudorapidity region （｜η｜ 〈 1）. However, this effect becomes weaker in the outer pseudorapidity region （｜η｜ 〉 1）.

Regression Analysis of the Number of Association Rules

The typical model, which involves the measures： support, confidence, and interest, is often adapted to mining association rules. In the model, the related parameters are usually chosen by experience; consequently, the number of useful rules is hard to estimate. If the number is too large, we cannot effectively extract the meaningful rules. This paper analyzes the meanings of the parameters and designs a variety of equations between the number of rules and the parameters by using regression method. Finally, we experimentally obtain a preferable regression equation. This paper uses multiple correlation coeficients to test the fitting efiects of the equations and uses significance test to verify whether the coeficients of parameters are significantly zero or not. The regression equation that has a larger multiple correlation coeficient will be chosen as the optimally fitted equation. With the selected optimal equation, we can predict the number of rules under the given parameters and further optimize the choice of the three parameters and determine their ranges of values.

3D image reconstruction with controllable spatial filtering based on correlation of multiple periodic functions in computational integral imaging

We propose a novel method of slice image reconstruction with controllable spatial filtering by using the correlation of periodic delta-function arrays （PDFAs） with elemental images in computational integral imaging. The multiple PDFAs, whose spatial periods correspond to object＇s depths with the elemental image array （EIA）, can generate a set of spatially filtered EIAs for multiple object depths compared with the conventional method for the depth of a single object. We analyze a controllable spatial filtering effect by the proposed method. To show the feasibility of the proposed method, we carry out preliminary experiments for multiple objects and present the results.

Slow Particle Production in Nucleus-Nucleus Collisions at Relativistic Energies

In this paper an effort has been made to study the general characteristics of slow particles produced in the interactions of 32S-Em at 200 AGeV to extract the information about the mechanism of particle production. The results have been compared with the experimental results obtained by other workers. The multiplicity distributions of the slow target associated particles (black, grey and heavy tracks) produced by 32S-beam with different targets have been studied. Also several types of correlations among them have been investigated. The variation of the produced particles with projectile mass number and target size has been studied. Also the multiplicity distributions of slow particles with NBD fits are presented and scaling multiplicity distributions of slow particles produced have been studied in order to check the validity of KNO-scaling.

Recent developments in two-dimensional (2D) correlation spectroscopy

Recent noteworthy developments in the field of two-dimensional（2D） correlation spectroscopy are reviewed.2D correlation spectroscopy has become a very popular tool due to its versatility and relative ease of use.The technique utilizes a spectroscopic or other analytical probe from a number of selections for a broad range of sample systems by employing different types of external perturbations to induce systematic variations in intensities of spectra.Such spectral intensity variations are then converted into2 D spectra by a form of correlation analysis for subsequent interpretation.Many different types of 2D correlation approaches have been proposed.In particular,2D hetero-correlation and multiple perturbation correlation analyses,including orthogonal sample design scheme,are discussed in this review.Additional references to other important developments in the field of 2D correlation spectroscopy,such as projection correlation and codistribution analysis,were also provided.

Correlation analysis of three influencing factors and the dust production rate for a free-falling particle stream

The effects of three factors （i.e., drop height h, hopper outlet diameter do, and material temperature T] on the dust generation rate derived from a free falling particle stream were investigated via filll factorial experiments. The correlation between the three factors and dust generation rate was also analysed. Results show that Tand h affect the first fugitive dust rate largely, whereas the second fugitive dust rate is mainly dominated by h and do. Through analysing the first fugitive dust percentage data, it is found that h and T should be considered first for higher temperatures and lower flow rates, whereas h and do can be considered under contrasting conditions, and h should be controlled in the remaining two sets of conditions. Relationships between the influencing factors and total and first fugitive dust rates were developed via multiple regression to quantify the dust emission rates for different contact surfaces （rigid or water）.

Neighboring azimuthal bin-bin multiplicity correlation as a direct measure for the shear viscosity in relativistic heavy ion collisions

Neighboring azimuthal bin-bin multiplicity correlation is suggested to be a good measure for internal layer-to-layer interactions of the formed matter in relativistic heavy ion collisions. It is shown to be directly related to the shear viscosity of the formed matter. As an application of this method, the shear viscosity in the samples generated by a multi-phase transport model （AMPT） is estimated. The results are in qualitative agreement with the theoretical calculation from microscopic interactions, i.e., the larger the scattering cross section, the smaller the shear viscosity.

Variable screening in multivariate linear regression with high-dimensional covariates

We propose two variable selection methods in multivariate linear regression with highdimensional covariates.The first method uses a multiple correlation coefficient to fast reduce the dimension of the relevant predictors to a moderate or low level.The second method extends the univariate forward regression of Wang[(2009).Forward regression for ultra-high dimensional variable screening.Journal of the American Statistical Association,104(488),1512–1524.https://doi.org/10.1198/jasa.2008.tm08516]in a unified way such that the variable selection and model estimation can be obtained simultaneously.We establish the sure screening property for both methods.Simulation and real data applications are presented to show the finite sample performance of the proposed methods in comparison with some naive method.