IFORS Developing Countries OR Resources Website

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Artigos e software relacionado com Investigação Operacional

Click below on required topic headings to access papers or click here to access International Abstracts in OR

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Data Analysis Method: Mathematics Optimization to Build Decision Making

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Uma pequena introdução à utilização de otimização na análise de dados

Optimization is a problem associated with the best decision that is effective and efficient decisions whether it is worth maximum or minimum by way of determining a satisfactory solution.

Optimization is not a new science. It has grown even since Newton in the 17th century discovered how to count roots. Currently the science of optimization is still evolving in terms of techniques and applications. Many cases or problems in everyday life that involve optimization to solve them. Lately much developed especially in the emergence of new techniques to solve the problem of optimization. To mention some, among others, conic programming, semi definite programming, semi infinite programming and some meta heuristic techniques.

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Top Excel Tips For Data Analysts

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Excelentes concelhos para utilização das últimas ferramentas implementadas no Excel.

TIPS FOR DATA CLEANING
1) Change format of numbers from text to numeric
2) Unpivot columns in a data set (Multiple consolidation ranges and Power Query)
3) Merge data from several csv files into a single folder (RDBMerge Add-in and Power Query)
4) Fill empty spaces from content above (Ctrl + Enter trick and Power Query)
DATA ANALYSIS
5) Create auto expandable ranges with Excel Tables (Source for pivots, dropdown lists and formulas)
6) How to do two way lookup with INDEX and MATCH
7) Creating OR criteria within SUMIF/COUNTIF (Combination of SUMPRODUCT and SUMIF/COUNTIF)
8) Counting unique items within PivotTables (Using the Excel Data Model)
DATA VISUALIZATION
9) Quickly visualize trends with Sparklines
10) Create dynamic titles in charts (Use of cell references within chart objects)
11) Dealing with empty cells in charts and sparklines [use NA()]
12) Save time with Quick Analysis

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Deconstructing Recommender Systems

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uma boa descrição dos sistemas de recomendação usados em sites web

Deconstructing Recommender Systems

How Amazon and Netflix predict your preferences and prod you to purchase

By Joseph A. Konstan, John Riedl

Posted
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Photo-illustration: Christina Beard

One morning in April, we each directed our browsers to Amazon.com’s website. Not only did the site greet us by name, the home page opened with a host of suggested purchases. It directed Joe to Barry Greenstein’s Ace on the River: An Advanced Poker Guide, Jonah Lehrer’s Imagine: How Creativity Works, and Michael Lewis’s Boomerang: Travels in the New Third World. For John it selected Dave Barry’s Only Travel Guide You’ll Ever Need, the spy novel Mission to Paris, by Alan Furst, and the banking exposé The Big Short: Inside the Doomsday Machine, also by Michael Lewis.

Guardar

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Data Visualization with JavaScript

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Um bom e-book sobre como construir visualizações com JavaScript

It’s getting hard to ignore the importance of data in our lives. Data is critical to the largest social organizations in human history. It can affect even the least consequential of our everyday decisions. And its collection has widespread geopolitical implications. Yet it also seems to be getting easier to ignore the data itself. One estimate suggests that 99.5% of the data our systems collect goes to waste. No one ever analyzes it effectively.

Data visualization is a tool that addresses this gap.

Effective visualizations clarify; they transform collections of abstract artifacts (otherwise known as numbers) into shapes and forms that viewers quickly grasp and understand. The best visualizations, in fact, impart this understanding subconsciously. Viewers comprehend the data immediately—without thinking. Such presentations free the viewer to more fully consider the implications of the data: the stories it tells, the insights it reveals, or even the warnings it offers. That, of course, defines the best kind of communication.

If you’re developing web sites or web applications today, there’s a good chance you have data to communicate, and that data may be begging for a good visualization. But how do you know what kind of visualization is appropriate? And, even more importantly, how do you actually create one? Answers to those very questions are the core of this book. In the chapters that follow, we explore dozens of different visualizations and visualization techniques and tool kits. Each example discusses the appropriateness of the visualization (and suggests possible alternatives) and provides step-by-step instructions for including the visualization in your own web pages.

To give you a better idea of what to expect from the book, here’s a quick description of what the book is, and what it is not.

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ontologies and data models

Já se perguntaram qual a diferença entre ontologias e modelos de dados?

Já se perguntaram qual a diferença entre ontologias e modelos de dados?

Ontologies versus Data Models

By Malcolm Chisholm
AUG 12, 2014 5:00am ET

Data models have been with us since Ted Codd described normalization in 1970 and Peter Chen published his paper on entity relationship diagrams in 1976. Ontology as a discipline in philosophy can trace its roots to ancient Greece. As applied to data management, it is much more recent than data modeling and has only appeared in the past few years. But just what is the difference between ontologies and data models? If they are both about data, do they not boil down to the same thing?

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IFORS Education Resources Project

portal com materal sobre Investigação Operacional, otimização e SADs

portal com materal sobre Investigação Operacional, otimização e SADs

Welcome to the International Federation of Operational Research Societies (IFORS) Education Resources Project

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Analytic Hierarchy Process (AHP)

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Uma página no facebook sobre esta técnica de multicritério

The Analytic Hierarchy Process (AHP) is structured technique for dealing with complex decisions. Prof. Thomas L. Saaty is the architect of the decision theory.
Missão

• To disseminate knowledge and resources on Analytic Hierarchy Process (AHP) based Multi Criteria Decision Making (MCDM) technique • To create a forum for AHP users • To disseminate AHP related activities taking place globally • Helping people and institutions in making complex decisions •

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Data Warehousing Review

Bom site com muitos conselhos úteis sobre DW e BI

Bom site com muitos conselhos úteis sobre DW e BI

Data Warehouses are increasingly used by enterprises to increase efficiency and competitiveness. Using Scorecarding, Data Mining and OLAP analysis, business value can be extracted from Data Warehouses.

Data Cleansing for Data Warehousing:  How important is Extract, Transform, Load (ETL) to data Warehousing?

Introduction to OLAP :  Slice, Dice and Drill!

Selecting an OLAP Application:  Minimizing risks in the product selection process

Planning for a Data Warehouse:  Starting a Data Warehousing Project? Three words – Plan, Plan and Plan!

Designing OLAP Solutions:  MOLAP, ROLAP, HOLAP and other acronyms!

Introduction to Metadata:  Case study of an implementation in the insurance industry

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WEKA Cost Benefit Analysis

Análise de custo benefício para avaliação de modelos

Análise de custo benefício para avaliação de modelos

The Cost/Benefit analysis component is a new visualization tool that was released in Weka versions 3.6.2 and 3.7.1. The tool is particularly useful for the analysis of predictive analytic outcomes for direct mail campaigns (or any ranking application where costs are involved). It allows the user to explore various cost/benefit tradeoffs by interactively selecting different population sizes from the ranked list of prospects or by varying the threshold on the predicted probability of the positive class.

The Cost/Benefit analysis tool is available from both the Explorer and Knowledge Flow user interfaces. In the figure below, the Knowledge Flow is being used to build a predictive model for a real-world direct mail application. The data is historical campaign data from a mail out to solicit donations to a charitable organization. The data set contains 47,706 records with 476 variables (summary variables for donor lifetime giving history, overlay demographics etc.). The percentage of donors in the data is approximately 5%. A 10-fold cross-validation is used to generate predictions from a naive Bayes classifier, and these are then passed to the Cost/Benefit analysis tool.

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