BI, EPM & Analytics Services
Using sophisticated analytical processing technologies, robust relational databases, and data mining techniques, Analysis Express can help you effectively tap into your organization’s digital storehouse to discover previously hidden relationships. We can instantly generate truly meaningful reports on every aspect of your operations.
Analysis Express is intimately familiar with relational and multidimensional database technologies, data mining algorithms and tools, and data analysis products from industry leaders. We offer the technical expertise you need in order to generate business intelligence of extraordinary quality and relevance—available the instant you require it.
The certified experts at Analysis Express developed their broad business knowledge and honed their technical skills during the design and implementation of analytical systems in some of the world’s largest, most demanding corporations. Our team members pride themselves in their ability to create fast, innovative, and cost-effective solutions to fit each client’s specific performance management and intelligence needs.
Our skilled consultants also have experience in working with most major relational databases, programming languages and ETL softwares as related to BI/BPM projects, including (but not limited to):
Extraction Transformation Loading (ETL):
- Ab Initio
- SQL Server
Statistical & Data Analytics:
BI, OLAP & EPM :
- Business Objects
- Business Intelligence
A Multilayer Perceptron
A Multilayer Perceptron (MLP) is a feed-forward artificial neural network model that maps sets of input data onto a set of appropriate output. An MLP consists of multiple layers of nodes in a directed graph, with each layer fully connected to the next one. Except for the input nodes, each node is a neuron (or processing element) with a nonlinear activation function. MLP utilizes a supervised learning technique called back propagation for training the network. MLP is a modification of the standard linear perceptron and can distinguish data that is not linearly separable
Radial Basis Function Network
A radial basis function network is an artificial neural network that uses radial basis functions as activation functions. It is a linear combination of radial basis functions. They are used in function approximation, time series prediction, and control.
The term neural network was traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus the term has two distinct usages.
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. A learner can take advantage of examples (data) to capture characteristics of interest of their unknown underlying probability distribution. Data can be seen as examples that illustrate relations between observed variables.
Signal Processing is the mathematical manipulation of an information signal to modify or improve it in some way. It is characterized by the representation of discrete time, discrete frequency, or other discrete domain signals by a sequence of numbers or symbols and the processing of these signals. Digital Signal Processing and analog signal processing are subfields of signal processing. DSP includes subfields like: audio and speech signal processing, sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, digital image processing, signal processing for communications, control of systems, biomedical signal processing, seismic data processing, etc.
A Bayesian network, Bayes network, belief network, Bayes(ian) model, or probabilistic directed acyclic graphical model is a probabilistic graphical model (a type of statistical model) that represents a set of random variables and their conditional dependencies via a Directed Acyclic Graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal. If in practice decisions have to be taken online with no recall under incomplete knowledge, a decision tree should be paralleled by a probability model as a best choice model or online selection model algorithm. Another use of decision trees is as a descriptive means for calculating conditional probabilities.
In statistics, ensemble methods use multiple models to obtain better predictive performance than could be obtained from any of the constituent models.Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble refers only to a concrete finite set of alternative models.
Support Vector Machines
A Support Vector Machine (SVM) is a concept in statistics and computer science for a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis. The standard SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the input, making the SVM a non-probabilistic binary linear classifier. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.
A mathematical optimization model consists of an objective function and a set of constraints expressed in the form of a system of equations or inequalities. Optimization models are used extensively in almost all areas of decision-making such as engineering design, and financial portfolio selection. This site presents a focused and structured process for optimization analysis, design of optimal strategy, and controlled process that includes validation, verification, and post-solution activities.
A Markov chain is a mathematical system that undergoes transitions from one state to another, between a finite or countable number of possible states. It is a random process characterized as memory-less: the next state depends only on the current state and not on the sequence of events that preceded it. This specific kind of “memorylessness” is called the Markov property. Markov chains have many applications as statistical models of real-world processes.
Queuing Theory is the mathematical study of waiting lines, or queues. The theory enables mathematical analysis of several related processes, including arriving at the (back of the) queue, waiting in the queue (essentially a storage process), and being served at the front of the queue. The theory permits the derivation and calculation of several performance measure including the average waiting time in the queue or the system, the expected number waiting or receiving service, and the probability of encountering the system in certain states such as empty, full, having an available server, or having to wait a certain time to be served.
Regression is a statistical measure that attempts to determine the strength of the relationship between one dependent variable (usually denoted by Y) and a series of other changing variables (known as independent variables). The two basic types of regression are linear regression and multiple regression. Linear regression uses one independent variable to explain and/or predict the outcome of Y, while multiple regression uses two or more independent variables to predict the outcome.
Univariate Statistics are statistical techniques appropriate for analysis in which there is a single measurement on each of n sample objects or there are several measurements on each of the n observations, but each variable is to be analyzed in isolation.
The statistical principle of multivariate statistics involves observation and analysis of more than one statistical variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest.
Forecasting is the establishment of future expectations by the analysis of past data, or the formations of opinions. Forecasting is usually categorized in tow groups namely quantitative and qualitative. Quantitative techniques include simple and multiple regressions, time trends, and moving averages while qualitative techniques include the Delphi Method, Scenario Projection and Nominal Group technique.
is a statistical technique used in market research to determine how people value different features that make up an individual product or service. Techniques used in conjoint analysis include linear regression for full profile tasks and maximum likelihood estimation and logistic regression for choice based tasks.
Factor Analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. In other words, it is possible that variations in three or four observed variables mainly reflect the variations in fewer such unobserved variables. Factor analysis searches for such joint variations in response to unobserved latent variables.
In statistics, semi-parametric regression includes regression models that combine parametric and non-parametric models. They are often used in situations where the fully non-parametric model may not perform well or when the researcher wants to use a parametric model but the functional form with respect to a subset of the regressors or the density of the errors is not known. Techniques used in semi-parametric regression include partially linear models and index models.