Last summer in Solver International, we explored quantitative models to deal with uncertainty and risk using Monte Carlo simulation. And last fall, we explained the basics of mathematical optimization: finding the best way to allocate scarce resources, such as money, people and equipment. In many cases, what we really want is the best, or optimal decision under conditions where there is uncertainty and risk. That’s the topic of this article, where we’ll combine ideas from simulation and optimization to build and solve a simulation optimization model.
While the mantra of real estate has long been “Location, Location, Location,” that focus is just becoming more obvious in data analysis. Location analytics has the potential to render the complex data landscape more orderly. Location is a common link between seemingly disconnected dumps of Big Data analytics. Data sets that are disconnected and seem to have no relevance to each other can suddenly make sense once the dimension of location is added. Relationships between data sets with no obvious connection can and will emerge once you geo-enrich them, giving you a better view of customer behavior.
Text mining is the practice of automated analysis of one document or a collection of documents (corpus) to extract non-trivial information. Text mining usually involves the process of transforming unstructured textual data into a structured representation by analyzing the patterns derived from text. The results can be analyzed to discover interesting information, some of which would only be found by a human carefully reading and analyzing the text. Typical, text mining includes but is not limited to Automatic Text Classification/Categorization, Topic Extraction, Concept Extraction, Documents/Terms Clustering, Sentiment Analysis, Frequency-based Analysis and many more.
Miguel Martinez is a Senior Product Marketing Manager for Microsoft Power BI. He oversees the digital strategy for the cloud business intelligence solution. His background includes formal training and practical experience in Marketing and Engineering.