Get Them to the Geek Fest: Incorporating Analytics Into Everyday Software
(Page 2 of 2)
that story to a wider audience,” says Clancy, who sits on the board of the conference sponsor, the San Diego Software Industry Council. As part of that effort, Clancy and other organizers have worked to bring prominent analytics leaders to San Diego as a way to both present their recent work to the San Diego audience and to highlight projects in San Diego for the analytics community at large. For example:
—LinkedIn search architect John Wang is set to demonstrate a beta version of the Mountain View, CA-based company’s new social media product, Signal, which incorporates analytics that enables users to consume the news and information that is most relevant to them. Supermath attendees also will get access to the private beta program to experience how Signal’s social search and news search engine capabilities can be used with their personal LinkedIn profiles.
—Mountain View, CA-based Intuit, which maintains its TurboTax software development in San Diego, plans to demonstrate how it applies text analytics to large amounts of unstructured customer feedback—and how that leads to improvements in Intuit’s products and customer support.
—SANDAG, the San Diego Association of Governments, is scheduled to explain how traffic simulation works in conjunction with an $8 million grant the agency received to analyze the Interstate-15 corridor as a potential freeway and light-rail “multi-modal” transporation system. The presentation is intended to demonstrate how computer-based simulation can affect regional decisions about transportation infrastructure, and how information can be leveraged to optimize roadway operations.
—LISA, the Laboratory for Intelligent and Safe Automobiles at UC San Diego, has been exploring innovated ways to incorporate real-time sensor data into driving a car to make automobiles of the future safer and more “intelligent.” Researchers have been working with Volkswagen and Nissan to develop a “driver assistance system” that can predict—and mitigate—how a driver will make lane changes, turns, and react to various emergencies, based on their prior behavior.