It’s all a numbers game for Purdue student Jeremy Frank from his major in data science to his passion for baseball statistics, as captured in the popular @MLBRandomStats, which he runs on Twitter. Frank hopes to continue his passion beyond college by working in baseball, possibly for a major league team or as a sports statistician.
In the insideBIGDATA Research Highlights column we take a look at new and upcoming results from the research community for data science, machine learning, AI and deep learning. Our readers need to get a glimpse for technology coming down the pipeline that will make their efforts more strategic and competitive. In this installment we review MIDAS – Real-time Anomaly/Fake News/Intrusion Detection developed by Ph.D. candidate Siddharth Bhatia and his team at the National University of Singapore.
Lucidworks, leader in AI-powered search, announced the Advanced Linguistics Package for Lucidworks Fusion to power personalized search for users in Asian, European, and Middle Eastern markets. Lucidworks now embeds text analytics from Basis Technology, a leading provider of AI for natural language processing. With the Advanced Linguistics Package, global organizations that support multiple languages can make the information and insights they manage more accessible, more relevant and more personalized for their global audience.
Matillion, a leading provider of data transformation software for cloud data warehouses (CDWs), announced the availability of Matillion ETL for Azure Synapse to enable data transformations in complex IT environments, at scale. Empowering enterprises to achieve faster time to insights by loading, transforming, and joining together data, the release extends Matillion’s product portfolio to further serve Microsoft Azure customers.
In this contributed article, Marta V. Lopata, Chief Growth Officer at Kgbase, discusses the use of knowledge graphs. With a no-code approach, they bring the best of the data science world to medicine, finance, business, education and the arts enabling anyone to generate and visualize unique insights from siloed data sources.
In this special guest feature, Vladimir Kuchkanov, Pricing Solution Architect at Competera, examines how data scientists often forget about classics while good old algorithms are still relevant and efficient. In particular, Monte Carlo method (MCM) pops up in mind. Among many fields of its application, MCM has established itself as a solid solution in price prediction and automation of pricing rules in retail. Like any analytical approach, MCM has limitations and inaccuracies. Despite this, many fields, including retail, still utilize it.