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Globe Trotters: Asian Healthcare CIOs need ‘Security Inside Out’ Approach

- by Tanu Sood

In our second edition of Globe trotters, wanted to share a feature article that was recently published in Enterprise Innovation. EnterpriseInnovation.net, part of Questex Media Group, is Asia's premier business and technology publication. The article featured MOH Holdings (a holding company of Singapore’s Public Healthcare Institutions) and highlighted the project around National Electronic Health Record (NEHR) system currently being deployed within Singapore. According to the feature, the NEHR system was built to facilitate seamless exchanges of medical information as patients move across different healthcare settings and to give healthcare providers more timely access to patient’s healthcare records in Singapore. The NEHR consolidates all clinically relevant information from patients’ visits across the healthcare system throughout their lives and pulls them in as a single record. It allows for data sharing, making it accessible to authorized healthcare providers, across the continuum of care throughout the country. In healthcare, patient data privacy is critical as is the need to avoid unauthorized access to the electronic medical records. As Alan Dawson, director for infrastructure and operations at MOH Holdings is quoted in the feature, “Protecting the perimeter is no longer enough. Healthcare CIOs today need to adopt a ‘security inside out’ approach that protects information assets all the way from databases to end points.” Oracle has long advocated the ‘Security Inside Out’ approach. From operating systems, infrastructure to databases, middleware all the way to applications, organizations need to build in security at every layer and between these layers. This comprehensive approach to security has never been as important as it is today in the social, mobile, cloud (SoMoClo) world. To learn more about Oracle’s Security Inside Out approach, visit our Security page. And for more information on how to prevent unauthorized access, streamline user administration, bolster security and enforce compliance in healthcare, learn more about Oracle Identity Management.

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NET Math Libraries

- by JoshReuben

NET Mathematical Libraries .NET Builder for Matlab The MathWorks Inc. - http://www.mathworks.com/products/netbuilder/ MATLAB Builder NE generates MATLAB based .NET and COM components royalty-free deployment creates the components by encrypting MATLAB functions and generating either a .NET or COM wrapper around them. .NET/Link for Mathematica www.wolfram.com a product that 2-way integrates Mathematica and Microsoft's .NET platform call .NET from Mathematica - use arbitrary .NET types directly from the Mathematica language. use and control the Mathematica kernel from a .NET program. turns Mathematica into a scripting shell to leverage the computational services of Mathematica. write custom front ends for Mathematica or use Mathematica as a computational engine for another program comes with full source code. Leverages MathLink - a Wolfram Research's protocol for sending data and commands back and forth between Mathematica and other programs. .NET/Link abstracts the low-level details of the MathLink C API. Extreme Optimization http://www.extremeoptimization.com/ a collection of general-purpose mathematical and statistical classes built for the.NET framework. It combines a math library, a vector and matrix library, and a statistics library in one package. download the trial of version 4.0 to try it out. Multi-core ready - Full support for Task Parallel Library features including cancellation. Broad base of algorithms covering a wide range of numerical techniques, including: linear algebra (BLAS and LAPACK routines), numerical analysis (integration and differentiation), equation solvers. Mathematics leverages parallelism using .NET 4.0's Task Parallel Library. Basic math: Complex numbers, 'special functions' like Gamma and Bessel functions, numerical differentiation. Solving equations: Solve equations in one variable, or solve systems of linear or nonlinear equations. Curve fitting: Linear and nonlinear curve fitting, cubic splines, polynomials, orthogonal polynomials. Optimization: find the minimum or maximum of a function in one or more variables, linear programming and mixed integer programming. Numerical integration: Compute integrals over finite or infinite intervals, over 2D and higher dimensional regions. Integrate systems of ordinary differential equations (ODE's). Fast Fourier Transforms: 1D and 2D FFT's using managed or fast native code (32 and 64 bit) BigInteger, BigRational, and BigFloat: Perform operations with arbitrary precision. Vector and Matrix Library Real and complex vectors and matrices. Single and double precision for elements. Structured matrix types: including triangular, symmetrical and band matrices. Sparse matrices. Matrix factorizations: LU decomposition, QR decomposition, singular value decomposition, Cholesky decomposition, eigenvalue decomposition. Portability and performance: Calculations can be done in 100% managed code, or in hand-optimized processor-specific native code (32 and 64 bit). Statistics Data manipulation: Sort and filter data, process missing values, remove outliers, etc. Supports .NET data binding. Statistical Models: Simple, multiple, nonlinear, logistic, Poisson regression. Generalized Linear Models. One and two-way ANOVA. Hypothesis Tests: 12 14 hypothesis tests, including the z-test, t-test, F-test, runs test, and more advanced tests, such as the Anderson-Darling test for normality, one and two-sample Kolmogorov-Smirnov test, and Levene's test for homogeneity of variances. Multivariate Statistics: K-means cluster analysis, hierarchical cluster analysis, principal component analysis (PCA), multivariate probability distributions. Statistical Distributions: 25 29 continuous and discrete statistical distributions, including uniform, Poisson, normal, lognormal, Weibull and Gumbel (extreme value) distributions. Random numbers: Random variates from any distribution, 4 high-quality random number generators, low discrepancy sequences, shufflers. New in version 4.0 (November, 2010) Support for .NET Framework Version 4.0 and Visual Studio 2010 TPL Parallellized – multicore ready sparse linear program solver - can solve problems with more than 1 million variables. Mixed integer linear programming using a branch and bound algorithm. special functions: hypergeometric, Riemann zeta, elliptic integrals, Frensel functions, Dawson's integral. Full set of window functions for FFT's. Product Price Update subscription Single Developer License $999 $399 Team License (3 developers) $1999 $799 Department License (8 developers) $3999 $1599 Site License (Unlimited developers in one physical location) $7999 $3199 NMath http://www.centerspace.net .NET math and statistics libraries matrix and vector classes random number generators Fast Fourier Transforms (FFTs) numerical integration linear programming linear regression curve and surface fitting optimization hypothesis tests analysis of variance (ANOVA) probability distributions principal component analysis cluster analysis built on the Intel Math Kernel Library (MKL), which contains highly-optimized, extensively-threaded versions of BLAS (Basic Linear Algebra Subroutines) and LAPACK (Linear Algebra PACKage). Product Price Update subscription Single Developer License $1295 $388 Team License (5 developers) $5180 $1554 DotNumerics http://www.dotnumerics.com/NumericalLibraries/Default.aspx free DotNumerics is a website dedicated to numerical computing for .NET that includes a C# Numerical Library for .NET containing algorithms for Linear Algebra, Differential Equations and Optimization problems. The Linear Algebra library includes CSLapack, CSBlas and CSEispack, ports from Fortran to C# of LAPACK, BLAS and EISPACK, respectively. Linear Algebra (CSLapack, CSBlas and CSEispack). Systems of linear equations, eigenvalue problems, least-squares solutions of linear systems and singular value problems. Differential Equations. Initial-value problem for nonstiff and stiff ordinary differential equations ODEs (explicit Runge-Kutta, implicit Runge-Kutta, Gear's BDF and Adams-Moulton). Optimization. Unconstrained and bounded constrained optimization of multivariate functions (L-BFGS-B, Truncated Newton and Simplex methods). Math.NET Numerics http://numerics.mathdotnet.com/ free an open source numerical library - includes special functions, linear algebra, probability models, random numbers, interpolation, integral transforms. A merger of dnAnalytics with Math.NET Iridium in addition to a purely managed implementation will also support native hardware optimization. constants & special functions complex type support real and complex, dense and sparse linear algebra (with LU, QR, eigenvalues, ... decompositions) non-uniform probability distributions, multivariate distributions, sample generation alternative uniform random number generators descriptive statistics, including order statistics various interpolation methods, including barycentric approaches and splines numerical function integration (quadrature) routines integral transforms, like fourier transform (FFT) with arbitrary lengths support, and hartley spectral-space aware sequence manipulation (signal processing) combinatorics, polynomials, quaternions, basic number theory. parallelized where appropriate, to leverage multi-core and multi-processor systems fully managed or (if available) using native libraries (Intel MKL, ACMS, CUDA, FFTW) provides a native facade for F# developers

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Personal search – the future of search

- by jamiet

[Four months ago I wrote a meandering blog post on another blogging site entitled Personal search – the future of search. The points I made therein are becoming more relevant to what I'm reading about and hoping to get involved in in the future so I'm re-posting here to a wider audience to hopefully get some more feedback and guage reaction to it. This has been prompted by the book Pull by David Siegel that is forming my current holiday reading (recommended to me by a commenter on my previous post Interesting things – Twitter annotations and your phone as a web server) and in particular by Siegel's notion of us all in the future having a personal online data vault.] My one-time colleague Paul Dawson recently wrote an article called The Future of Search and in it he proposed some interesting ideas. Some choice quotes: The growth of Chinese search giant Baidu is an indicator that fully localised and tailored content and offerings have great traction with local audiences This trend is already driving an increase in the use of specialist searches … Look at how Farecast is now integrated into Bing for example, or how Flightstats is now integrated into Google. Search does not necessarily have to begin with a keyword, but could start instead with a click or a touch. Take a look at Retrievr. Start drawing a picture in the box and see what happens. This is certainly search without the need for typing in keywords search technology has advanced greatly in recent years. The recent launch of Microsoft Live Labs’ Pivot has given us a taste of what we can expect to see in the future This really got me thinking about where search might go in the future and as my mind wandered I realised that as the amount of data that we collect about ourselves increases so too will the need and the desire to search it. The amount of electronic data that exists about each and every person is increasing and in the near future I fully expect that we are going to be able to store personal data such as: A history of our location (in fact Google Latitude already offers this facility) Recordings of all our phone conversations Health information history (weight, blood pressure etc…) Energy usage Spending history What films we watch, what radio stations we listen to Voting history Of course, most of this stuff is already stored somewhere but crucially we don’t have easy access to it. My utilities supplier knows how much electricity I’m using but if I want to know for myself I have to go and dig through my statements (assuming I have kept them). Similarly my doctor probably has ready access to all of my health records, my bank knows exactly what I have spent my money on, my cable supplier knows what I watch on TV and my mobile phone supplier probably knows exactly where I am and where I’ve been for the past few years. Strange then that none of this electronic information is available to me in a way that I can really make use of it; after all, its MY information. Its MY data. I created it. That is set to change. As technologies mature and customers become more technically cognizant they will demand more access to the data that companies hold about them. The companies themselves will realise the benefit that they derive from giving users what they want and will embrace ways of providing it. As a result the amount of data that we store about ourselves is going to increase exponentially and the desire to search and derive value from that data is going to grow with it; we are about to enter the era of the “personal datastore” and we will want, and need, to search through it in order to make sense of it all. Its interesting then that today when we think of search we think of search engines and yet in these personal datastores we’re referring to data that search engines can’t touch because WE own it and we (hopefully) choose to keep it private. Someone, I know not who, is going to lead in this space by making it easy for us to search our data and retrieve information that we have either forgotten or maybe didn’t even know in the first place. We will learn new things about ourselves and about our habits; we will share these findings with whomever we choose; we will compare what we discover with others; we will collaborate for mutual benefit and, most of all, we will educate ourselves as to how to live our lives better. Search will be the means to that end, it will enable us to make sense of the wealth of information that we will collect day in day out. The future of search is personal, why would we be interested in anything else? @Jamiet Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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Personal search – the future of search

- by jamiet

[Four months ago I wrote a meandering blog post on another blogging site entitled Personal search – the future of search. The points I made therein are becoming more relevant to what I'm reading about and hoping to get involved in in the future so I'm re-posting here to a wider audience to hopefully get some more feedback and guage reaction to it. This has been prompted by the book Pull by David Siegel that is forming my current holiday reading (recommended to me by a commenter on my previous post Interesting things – Twitter annotations and your phone as a web server) and in particular by Siegel's notion of us all in the future having a personal online data vault.] My one-time colleague Paul Dawson recently wrote an article called The Future of Search and in it he proposed some interesting ideas. Some choice quotes: The growth of Chinese search giant Baidu is an indicator that fully localised and tailored content and offerings have great traction with local audiences This trend is already driving an increase in the use of specialist searches … Look at how Farecast is now integrated into Bing for example, or how Flightstats is now integrated into Google. Search does not necessarily have to begin with a keyword, but could start instead with a click or a touch. Take a look at Retrievr. Start drawing a picture in the box and see what happens. This is certainly search without the need for typing in keywords search technology has advanced greatly in recent years. The recent launch of Microsoft Live Labs’ Pivot has given us a taste of what we can expect to see in the future This really got me thinking about where search might go in the future and as my mind wandered I realised that as the amount of data that we collect about ourselves increases so too will the need and the desire to search it. The amount of electronic data that exists about each and every person is increasing and in the near future I fully expect that we are going to be able to store personal data such as: A history of our location (in fact Google Latitude already offers this facility) Recordings of all our phone conversations Health information history (weight, blood pressure etc…) Energy usage Spending history What films we watch, what radio stations we listen to Voting history Of course, most of this stuff is already stored somewhere but crucially we don’t have easy access to it. My utilities supplier knows how much electricity I’m using but if I want to know for myself I have to go and dig through my statements (assuming I have kept them). Similarly my doctor probably has ready access to all of my health records, my bank knows exactly what I have spent my money on, my cable supplier knows what I watch on TV and my mobile phone supplier probably knows exactly where I am and where I’ve been for the past few years. Strange then that none of this electronic information is available to me in a way that I can really make use of it; after all, its MY information. Its MY data. I created it. That is set to change. As technologies mature and customers become more technically cognizant they will demand more access to the data that companies hold about them. The companies themselves will realise the benefit that they derive from giving users what they want and will embrace ways of providing it. As a result the amount of data that we store about ourselves is going to increase exponentially and the desire to search and derive value from that data is going to grow with it; we are about to enter the era of the “personal datastore” and we will want, and need, to search through it in order to make sense of it all. Its interesting then that today when we think of search we think of search engines and yet in these personal datastores we’re referring to data that search engines can’t touch because WE own it and we (hopefully) choose to keep it private. Someone, I know not who, is going to lead in this space by making it easy for us to search our data and retrieve information that we have either forgotten or maybe didn’t even know in the first place. We will learn new things about ourselves and about our habits; we will share these findings with whomever we choose; we will compare what we discover with others; we will collaborate for mutual benefit and, most of all, we will educate ourselves as to how to live our lives better. Search will be the means to that end, it will enable us to make sense of the wealth of information that we will collect day in day out. The future of search is personal, why would we be interested in anything else? @Jamiet Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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