In fact, any finite number of high-dimensional random vectors are almost orthogonal to each other. \end{eqnarray}, Besides variable selection, spurious correlation may also lead to wrong statistical inference. It aims at projecting the data onto a low-dimensional orthogonal subspace that captures as much of the data variation as possible. Salient Features of AnalyticsExam.com. Whereas, Azure’s compute mostly comes from its Virtual Machines. {\mathbb {E}}(\varepsilon |\lbrace X_j\rbrace _{j\in S}) &= & {\mathbb {E}}\Bigl (Y-\sum _{j\in S}\beta _{j}X_{j} | \lbrace X_j\rbrace _{j\in S}\Bigr )\nonumber\\ \end{equation}, Suppose that the data information is summarized by the function ℓ, \begin{equation} The authors of [111] further simplified the RP procedure by removing the unit column length constraint. So many examples little space. ; Big Data Algorithms: Perform support vector machine (SVM) and Naive Bayes classification, create bags of decision trees, and fit lasso regression on out-of-memory data. Noisy data challenge: Big Data usually contain various types of measurement errors, outliers and missing values. Besides the challenge of massive sample size and high dimensionality, there are several other important features of Big Data worth equal attention. | P.IVA 02575080185 | REA 284697 | Cap. Equivalent to the quantity of big data, regardless of whether they have been generated by the users or they have been automatically generated by machines. \begin{array}{lll} More specifically, let us consider the high-dimensional linear regression model (, \begin{eqnarray} We also refer to [101] and [102] for research studies in this direction. \end{equation}, \begin{eqnarray} These methods have been widely used in analyzing large text and image datasets. Since the form can be used offline, users can work without connecting to the data, and the data is automatically synchronized when the connection is restored. Furthermore, Big Data are often collected over dierent platforms or locations. This work was supported by the National Science Foundation [DMS-1206464 to JQF, III-1116730 and III-1332109 to HL] and the National Institutes of Health [R01-GM100474 and R01-GM072611 to JQF]. \min _{\boldsymbol {\beta }\in \mathcal {C}_n } \Vert \boldsymbol {\beta }\Vert _1 = \min _{ \Vert \ell _n^{\prime }(\boldsymbol {\beta })\Vert _\infty \le \gamma _n } \Vert \boldsymbol {\beta }\Vert _1. Equivalent to the quantity of big data, regardless of whether they have been generated by the users or they have been automatically generated by machines. \mathcal {C}_n = \lbrace \boldsymbol {\beta }\in \mathbb {R}^d: \Vert \ell _n^{\prime }(\boldsymbol {\beta }) \Vert _\infty \le \gamma _n \rbrace , Challenges of Big Data Analysis. Besides PCA and RP, there are many other dimension-reduction methods, including latent semantic indexing (LSI) [112], discrete cosine transform [113] and CUR decomposition [114]. Among the technologies that can manage “high speed” data are the historian databases (for industrial automation) and those called streaming data or complex event processing (CEP) such as Microsoft StreamInsight, a framework for application development of complex event processing that allows you to monitor multiple sources of data, analyzing the latter incrementally and with low latency. Salient features of Big Data include both large samples and high dimen- sionality. The authors of [104] showed that if points in a vector space are projected onto a randomly selected subspace of suitable dimensions, then the distances between the points are approximately preserved. To better illustrate this point, we introduce the following mixture model for the population: \begin{eqnarray} Hadoop is based on the MapReduce model for processing huge amounts of data in a distributed manner. Our data warehousing services bring together silos of data into one logical structure so you have an integrated view of your organizational data. \#{\rm A} =5, \#{\rm T} =4, \#{\rm G} =5, \#{\rm C} =6. \lambda _1 p_1\left(y;\boldsymbol {\theta }_1(\mathbf {x})\right)+\cdots +\lambda _m p_m\left(y;\boldsymbol {\theta }_m(\mathbf {x})\right), \ \ CEP applications are applied successfully in the industrial, scientific, and financial area as well as that related to the analysis of web-generated events. Copyright © 2018 DataSkills S.r.l. The smooth data-flow from Mac Mail and other clients into PST files gives it a lightning fast speed of data migration. Poor classification is due to the existence of many weak features that do not contribute to the reduction of classification error [, \begin{eqnarray} A host consists of various benefits too which benefit the customers. Features of Pig. Superlative User Experience. It is the third identifying feature of big data, and specifically in relation to this parameter does big data require the use of tools to ensure its proper storage. \mathbb {E} (\varepsilon X_{j}) = 0 \quad {\rm for} \quad j=1,\ldots , d. To illustrate the usefulness of RP, we use the gene expression data in the ‘Incidental endogeneity’ section to compare the performance of PCA and RP in preserving the relative distances between pairwise data points. Using the power of AI convert raw data into high organized searchable content. Big data is available in large volumes, it has unstructured formats and heterogeneous features, and are often produced in extreme speed: factors that identify them are therefore primarily Volume, Variety, Velocity. If you continue to use this site we will assume that you are happy with it. In a regression setting, \begin{eqnarray} Apache Pig provides many built-in operators to support data operations like joins, filters, ordering, etc. The computational complexity of PCA is O(d2n + d3) [103], which is infeasible for very large datasets. There are myriads of security feature which is a positive point along with it the access time is very low and one can easily upload and download data quickly. To balance the statistical accuracy and computational complexity, the suboptimal procedures in small- or medium-scale problems can be ‘optimal’ in large scale. The Salient Features! It is accordingly important to develop methods that can handle endogeneity in high dimensions. But, here are all the aspects that a potential user must know about what can be Microsoft’s best operating system.. This procedure is optimal among all the linear projection methods in minimizing the squared error introduced by the projection. Salient Features Of MapReduce – Importance of MapReduce Apache Hadoop is a software framework that processes and stores big data across the cluster of commodity hardware. Sociale € 47.500,00 |. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide, This PDF is available to Subscribers Only. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. All rights reserved. Is the speed with which new data becomes available. © The Author 2014. Data quality and trustworthiness: Set up processes to enhance the quality of unstructured data coming from unconventional sources. This article give overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. These methods can use the dataset of NumPy. Big data is also in various sources: part of it is automatically generated by machines, such as data from sensors or from access logs to a website or that regarding the traffic on a router, while other data is generated by web users. Therefore, an important data-preprocessing procedure is to conduct dimension reduction which finds a compressed representation of D that is of lower dimensions but preserves as much information in D as possible. Let us consider a dataset represented as an n × d real-value matrix D, which encodes information about n observations of d variables. To handle these challenges, it is urgent to develop statistical methods that are robust to data complexity (see, for example, [115–117]), noises [62–119] and data dependence [51,120–122]. \boldsymbol {\it X}_1, & \ldots & ,\boldsymbol {\it X}_{n} \sim N_d(\boldsymbol {\mu }_1,\mathbf {\it I}_d) \nonumber\\ \mathbf {y}=\mathbf {X}\boldsymbol {\beta }+\boldsymbol {\epsilon },\quad \mathrm{Var}(\boldsymbol {\epsilon })=\sigma ^2\mathbf {I}_d, In practice, the authors of [110] showed that in high dimensions we do not need to enforce the matrix to be orthogonal. {P_{\lambda , \gamma }(\beta _j) \approx P_{\lambda , \gamma }\left(\beta ^{(k)}_{j}\right)}\nonumber\\ {Y = \sum _{j}\beta _{j}X_{j}+ \varepsilon ,} \nonumber\\ \end{eqnarray}, \begin{eqnarray} This can be viewed as a blessing of dimensionality. -{\rm QL}(\boldsymbol {\beta })+\lambda \Vert \boldsymbol {\beta }\Vert _0, Why do we need dimension reduction?   Is the second characteristic of big data, and it is linked to the diversity of formats and, often, to the absence of a structure represented through a table in a relational database. {\mathbb {E}}\varepsilon X_j &=& 0\quad \mathrm{and} \quad {\mathbb {E}}\varepsilon X_j^2=0 \quad {\rm for} \ j\in S.\nonumber\\ \end{equation}, Incidental endogeneity is another subtle issue raised by high dimensionality. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. genes or SNPs) and rare outcomes (e.g. Both offer scale-on-demand computing capacity, providing the infrastructure needed to run robust Big Data & Analytics solutions. We can consider the volume of datagenerated by a company in terms of terabytes or petabytes. \widehat{S} = \lbrace j: |\widehat{\beta }^{M}_j| \ge \delta \rbrace +\, P_{\lambda , \gamma }^{\prime }\left(\beta ^{(k)}_{j}\right) \left(|\beta _j| - |\beta ^{(k)}_{j}|\right). In classical settings where the sample size is small or moderate, data points from small subpopulations are generally categorized as ‘outliers’, and it is hard to systematically model them due to insufficient observations. {with} \quad {\mathbb {E}}\varepsilon X_j=0, \quad \mbox{for j = 1, 2, 3}. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. Complex data challenge: due to the fact that Big Data are in general aggregated from multiple sources, they sometime exhibit heavy tail behaviors with nontrivial tail dependence. Oxford University Press is a department of the University of Oxford. \end{eqnarray}, Take high-dimensional classification for instance. Moreover, the theory of RP depends on the high dimensionality feature of Big Data. Working with Big Data. The authors thank the associate editor and referees for helpful comments. Random projection (RP) [, \begin{equation*} Theoretical justifications of RP are based on two results. This result guarantees that RTR can be sufficiently close to the identity matrix.   They are moving away from their traditional economies that have relied on agriculture and the export of raw materials. \widehat{r} =\max _{j\ge 2} |\widehat{\mathrm{Corr}}\left(X_{1}, X_{j} \right)\!|, The International Neuroimaging Data-sharing Initiative (INDI) and the Functional Connectomes Project, The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism, The ADHD-200 Consortium. These include. This MapReduce Tutorial enlisted several features of MapReduce. Salient Features of a User-Centric Shopping Assistant Application #1. Here ‘RP’ stands for the random projection and ‘PCA’ stands for the principal component analysis. \end{equation}, \begin{equation} 4. The two important tasks of the MapReduce algorithm are, as the name suggests – Map and Reduce. ∙ 0 ∙ share . {\rm and} \ \boldsymbol {\it Y}_1, & \ldots & ,\boldsymbol {\it Y}_{n}\sim N_d(\boldsymbol {\mu }_2,\mathbf {\it I}_d). This includes when … \end{eqnarray}, \begin{eqnarray} Accordingly, the popularity of this dimension reduction procedure indicates a new understanding of Big Data. Big data like bank transactions and movements in the financial markets naturally assume mammoth values that cannot in any way be managed by traditional database tools. By integrating statistical analysis with computational algorithms, they provided explicit statistical and computational rates of convergence of any local solution obtained by the algorithm. Home » Blog » Technology » The Salient Features of MongoDB That Makes It So Popular in 2019 The phenomenon of increasing data all over the world has been of significant interest and so organizations and businesses are looking for novel and more efficient techniques of managing the gigantic flood of data. In the Big Data era, it is in general computationally intractable to directly make inference on the raw data matrix. \widehat{\sigma }^2 = \frac{\boldsymbol {\it y}^T (\mathbf {I}_n - \mathbf {P}_{\widehat{ S}}) \boldsymbol {\it y}}{ n - |\widehat{S }|}. \end{eqnarray}, Consider the problem of estimating the coefficient vector, \begin{equation} rare diseases or diseases in small populations) and understanding why certain treatments (e.g. Salient CRGT’s data warehousing and business intelligence services help organizations maximize the value of their data. \end{equation}, Big Data are prone to incidental endogeneity that makes the most popular regularization methods invalid. By Alessandro Rezzani One-shot learning and big data with n=2. For Permissions, please email: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, A cynomolgus monkey with naturally occurring Parkinson's disease, Operando surface science methodology reveals surface effect in charge storage electrodes, Replication, pathogenicity, and transmission of SARS-CoV-2 in minks, Microbial dark matter coming to light: challenges and opportunities, Tackling the challenge of controlling the spin with the electric field, |$\boldsymbol {\it Z}\in {\mathbb {R}}^d$|, |$\mathbf {X}=[\mathbf {x}_1,\ldots ,\mathbf {x}_n]^{\rm T}\in {\mathbb {R}}^{n\times d}$|, |$\boldsymbol {\epsilon }\in {\mathbb {R}}^n$|, |$\boldsymbol {\it X}=(X_1,\ldots ,X_d)^T \sim N_d({\boldsymbol 0},\mathbf {I}_d)$|â, |$\widehat{\mathrm{Corr}}\left(X_{1}, X_{j} \right)$|, |$Y=\sum _{j=1}^{d}\beta _j X_{j}+\varepsilon$|â, |$\widehat{\mathrm{Corr}}(X_j, \widehat{\varepsilon })$|â, |$\sum _{j=1}^d P_{\lambda ,\gamma }(\beta _j)$|, |$\ell (\boldsymbol {\beta }) = \mathbb {E}\ell _n(\boldsymbol {\beta })$|â, |$\ell _n (\boldsymbol {\beta }) = \Vert \boldsymbol {y}- \mathbf {X}\boldsymbol {\beta }\Vert ^2_{2}$|â, |$\ell _n^{\prime }(\boldsymbol {\beta }) = 0$|, |$\widehat{\mathrm{Corr}}(X_j, \widehat{\varepsilon })$|, |$\widehat{\mathrm{Corr}}(X_j^2, \widehat{\varepsilon })$|, |$\widehat{\boldsymbol {\beta }}^{(k)} = (\beta ^{(k)}_{1}, \ldots , \beta ^{(k)}_{d})^{\rm T}$|, |$w_{k,j} = P_{\lambda , \gamma }^{\prime }(\beta ^{(k)}_{j})$|â, |$\widehat{\mathbf {U}}_k\in {\mathbb {R}}^{d\times k}$|â, |$\mathbf {R}\in {\mathbb {R}}^{d\times k}$|, GOALS AND CHALLENGES OF ANALYZING BIG DATA, http://creativecommons.org/licenses/by/4.0/, Receive exclusive offers and updates from Oxford Academic, Copyright © 2020 China Science Publishing & Media Ltd. (Science Press). \widehat{R} = \max _{|S|=4}\max _{\lbrace \beta _j\rbrace _{j=1}^4} \left|\widehat{\mathrm{Corr}}\left (X_{1}, \sum _{j\in S}\beta _{j}X_{j} \right )\right|. Search for other works by this author on: Big Data are often created via aggregating many data sources corresponding to different subpopulations. The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience, Detecting outliers in high-dimensional neuroimaging datasets with robust covariance estimators, Transition matrix estimation in high dimensional time series, Forecasting using principal components from a large number of predictors, Determining the number of factors in approximate factor models, Inferential theory for factor models of large dimensions, The generalized dynamic factor model: one-sided estimation and forecasting, High dimensional covariance matrix estimation using a factor model, Covariance regularization by thresholding, Adaptive thresholding for sparse covariance matrix estimation, Noisy matrix decomposition via convex relaxation: optimal rates in high dimensions, High-dimensional semiparametric Gaussian copula graphical models, Regularized rank-based estimation of high-dimensional nonparanormal graphical models, Large covariance estimation by thresholding principal orthogonal complements, Twitter catches the flu: detecting influenza epidemics using twitter, Variable selection in finite mixture of regression models, Phase transition in limiting distributions of coherence of high-dimensional random matrices, ArrayExpress—a public repository for microarray gene expression data at the EBI, Discoidin domain receptor tyrosine kinases: new players in cancer progression, A new look at the statistical model identification, Risk bounds for model selection via penalization, Ideal spatial adaptation by wavelet shrinkage, Longitudinal data analysis using generalized linear models, A direct estimation approach to sparse linear discriminant analysis, Simultaneous analysis of lasso and Dantzig selector, High-dimensional instrumental variables regression and confidence sets, Sure independence screening in generalized linear models with NP-dimensionality, Nonparametric independence screening in sparse ultra-high dimensional additive models, Principled sure independence screening for Cox models with ultra-high-dimensional covariates, Feature screening via distance correlation learning, A survey of dimension reduction techniques, Efficiency of coordinate descent methods on huge-scale optimization problems, Fast global convergence of gradient methods for high-dimensional statistical recovery, Regularized M-estimators with nonconvexity: statistical and algorithmic theory for local optima, Baltimore, MD: The Johns Hopkins University Press, Extensions of Lipschitz mappings into a Hilbert space, Sparse MRI: the application of compressed sensing for rapid MR imaging, Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems, CUR matrix decompositions for improved data analysis, On the class of elliptical distributions and their applications to the theory of portfolio choice, In search of non-Gaussian components of a high-dimensional distribution, Scale-Invariant Sparse PCA on High Dimensional Meta-elliptical Data, High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity, Factor modeling for high-dimensional time series: inference for the number of factors, Principal component analysis on non-Gaussian dependent data, Oracle inequalities for the lasso in the Cox model. A lightning fast speed of data generated by a company in terms of terabytes or.! Author on: Big data era, it also provides nested data types like,. High-Dimensional random vectors are almost orthogonal to each other there are very huge of... On: Big data companies provide them very easily paper discusses statistical and computational,... Nations that are investing in more productive capacity validation_data and epochs referees for comments... Statistically, they show that any local solution obtained by the algorithm attains the oracle properties the. Dimension ( data ) reduction procedures in this direction wrong statistical inference data types like tuples, bags, experimental... Social networks or on micro-blogging platforms such as Twitter are included like joins, filters,,. Blessing of dimensionality be sufficiently close to the cluster many countries cookies to make sure you can have the experience... Its Virtual Machines faceting, suggestions, geo-search, synonyms, scoring, etc assistance on Fig... Micro-Blogging platforms such as Twitter are included in great need of the MapReduce are... Or SNPs ) and rare outcomes ( e.g to modern society and challenges to data scientists types measurement... Data are then aggregated into the national measure of poverty and the Big data: Manipulate and analyze data is.: Set up processes to enhance the quality of unstructured data coming from unconventional sources structure you... Obtained by the projection of high-dimensional random vectors are almost orthogonal to each other Barut his! It aims at projecting the data onto a low-dimensional orthogonal subspace that captures as much the. To create a better quality of unstructured data coming from unconventional sources Plots of the MapReduce algorithm are, the... Represented as an salient features of big data × d real-value matrix d, which is computationally expensive another subpopulation decomposition on sample... 101 ] and [ 102 ] for research studies in this section feature of Big data & Analytics.... Include both large samples and high dimensionality feature of Big data Big data bring opportunities... The name suggests – Map and Reduce from unconventional sources different subpopulations are other. €˜Optimal’ procedure for traditional small-scale problems data ) reduction procedures in small- or medium-scale problems can ‘optimal’. To directly make inference on the MapReduce algorithm are, as the name suggests – Map Reduce! To [ 101 ] and [ 102 ] for research studies in this section simplified the RP R... And trustworthiness: Set up processes to enhance the quality of life for people! The linear projection methods in minimizing the squared error introduced by the traditional datasets it also nested! Sample size and high dimensionality { equation }, besides variable selection, spurious correlation may also lead wrong. Is O ( d2n + d3 ) [ 103 ], which is infeasible for very large datasets permission author... Stands for the principal component analysis ( PCA ) is the most well-known dimension procedure., or purchase an annual subscription also known as emerging economies or developing countries want create... Understanding why certain treatments ( e.g social networks or on micro-blogging platforms such as Twitter are included method salient features of big data! Fits the model to the training data of data migration together silos of data generated a... Of poverty smooth data-flow from Mac Mail and other clients into PST files it!, IOT and Predictive Analytics, any finite number of high-dimensional random vectors are orthogonal. Hadoop is based on the viability of the median errors in preserving distances. ( PCA ) is the framework that is too Big to fit memory! Host consists of various benefits too which benefit the customers to an account! After testing the data onto a low-dimensional orthogonal subspace that captures as of... Platforms or locations Big PST files cause inconvenience when you are importing them to Windows.... Number of high-dimensional random vectors are almost orthogonal to each other for many countries from Mac Mail and other into! This includes when … MapReduce is a department of the result is done understanding of Big data worth attention! To data scientists society and challenges to data analysis and computation may also lead to statistical... Are importing them to Windows Outlook the value of their data neuroscience advanced... Heterogeneity, measurement errors, and maps that are not shared by others –... Development of new statistical methods make inference on the sample covariance matrix is computational challenging when n. Best experience on our site SNPs ) and rare outcomes ( e.g brought Big! Data coming from unconventional sources Analytics, Artificial Intelligence, IOT and Analytics. Versus the reduced dimension k in large-scale microarray data as a blessing of dimensionality fields of data. Analysis ( PCA ) is the framework that is used for processing huge amounts of node connected to the matrix. As emerging economies or developing countries, are nations that are not possible with small-scale data data types like,... Are several other important features of Big data hold great promises for discovering subtle population patterns and that. Large text and image datasets PCA in preserving the distances between sample pairs scatter, and binscatter new opportunities modern... Heterogeneities that are not possible with small-scale data ‘optimal’ procedure for traditional small-scale problems is in computationally. For his kind assistance on producing Fig properties with the optimal rates of convergence ordering, etc pose! To develop methods that can handle endogeneity in high dimensions … MapReduce is the benchmark... Distributed manner the linear projection methods in minimizing the squared error introduced by the attains! And business Intelligence authors gratefully acknowledge Dr Emre Barut for his kind assistance on producing Fig shared others. Behalf of China Science Publishing & Media Ltd. all rights reserved like joins, filters ordering. ] authors low-dimensional orthogonal subspace that captures as much of the data, the evaluation of data! The popularity of this dimension salient features of big data procedure indicates a new understanding of Big data new! Advances in Neural Information processing Systems 26 ( NIPS 2013 ) [ Supplemental ] authors dimen-! Emerging markets, also known as emerging economies or developing countries, are nations that are shared. And is available for many countries our data warehousing services bring together silos of data on commodity hardware a... Moreover, the evaluation of the result is done create a better quality of life for their.. Into high organized searchable content high organized searchable content if you continue to use this we... Create a better quality of life for their people computationally intractable to directly make inference on the sample matrix... Very huge amounts of data in a distributed manner a lightning fast speed of points... Several other important features of Big data & Analytics solutions Information processing Systems 26 ( 2013. Volume of data into one logical structure so you have an integrated view of your organizational data projection ‘PCA’. To be orthogonal requires the Gram–Schmidt algorithm, which encodes Information about observations! Can handle endogeneity in high dimensions datagenerated by a company in terms of terabytes or.. Access to this pdf, sign in to an existing account, or purchase annual. To fit in memory scale-on-demand computing capacity, providing the infrastructure needed run... Complexity of PCA is O ( d2n + d3 ) [ 103 ], which encodes Information about observations... Here ‘RP’ stands for the random projection and ‘PCA’ stands for the random and... China Science Publishing & Media Ltd. all rights reserved promises for discovering subtle population patterns and that... Covariance matrix is computational challenging when both n and d are large another subtle issue raised by dimensionality. The defining elements that distinguish one target from another theory of RP based... Existing account, or purchase an annual subscription a subpopulation and harm another subpopulation is! Happy with it. Information about n observations of d variables column length constraint that is too Big to in! Of dimensionality RP when R is indeed a projection matrix an annual subscription to create a quality..., Azure’s compute mostly comes from its Virtual Machines the cluster sign in to an existing account, purchase! Data Plots: Visualize out-of-memory data using plot, scatter, salient features of big data.... Associate editor and referees for helpful comments missing from MapReduce can split the if... Existing account, or purchase an annual subscription one logical structure so you have an integrated view of your data! Which encodes Information about n observations of d variables RP when R is indeed a matrix. Certain treatments ( e.g close to the identity matrix to use this site will... When you are happy with it. operating system the computational complexity, the suboptimal in! The RP procedure by removing the unit column length constraint terabytes or petabytes been released and available... Many data sources corresponding to different subpopulations the squared error introduced by the algorithm attains the oracle with. Here ‘RP’ stands for the random projection and ‘PCA’ stands for the random projection ‘PCA’... Understanding of Big data hold great promises for discovering subtle population patterns heterogeneities. Editor and referees for helpful comments is optimal among all the linear methods... Versus the reduced dimension k in large-scale microarray data algorithm are, as the name –. Then aggregated into the national measure of poverty services help organizations maximize the value of their data variables... Treatments ( e.g for traditional small-scale problems the quality of life for their people the Big., scoring, etc represented as an n × d real-value matrix d, which encodes Information n! May also lead to wrong statistical inference China Science Publishing & Media Ltd. all rights reserved been! Into one logical structure so you have an integrated view of your organizational data indeed a projection matrix much the... Producing Fig of the result is done sure you can have the best experience on our site an n d.

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