Ramakrishnan's contributions span foundational technical innovation on algorithmic and systems aspects of data mining.
His work on scalable data mining algorithms started with BIRCH, the first truly scalable clustering algorithm. BIRCH introduced the groundbreaking idea of a cluster feature, a concise summary of a cluster, which was then used in many subsequent clustering algorithms as an integral component.
Because of its novelty and importance, this is one of the highest cited data mining papers in the last decade. Ramakrishnan later extended this work into a clustering framework for arbitrary metric spaces. He also worked on scalable algorithms for decision tree construction that are still considered state-of-the-art today. BIRCH is also the first true data stream mining algorithm: it constructs a clustering model in a single scan over the data with limited memory. Such algorithms for mining data streams have become a very important area of research in the data mining community over the last decade.
Further, Ramakrishnan developed a general framework for incrementally mining evolving data and created a framework for measuring change in data streams, again, visionary research topics that have generated much follow-up work since then. His work also introduced a new construct for analysis of ordered data, reflected in the inclusion of WINDOW functions in the SQL language.
Ramakrishnan’s work includes important contributions to data anonymization, and applying the multi-dimensional model from OLAP to develop a framework for exploratory data mining.
In addition to his academic research at the University of Wisconsin-Madison, Ramakrishnan has been active in applying data mining in industry. From 2000 to 2003, he was CTO and chairman of QUIQ, a company that developed technology for mass collaboration, a visionary concept that now with the arrival of Web 2.0 has gained widespread acceptance; the QUIQ-powered Ask Jeeves AnswerPoint question-answering portal was the forerunner of similar portals from Amazon, Linked-In and Yahoo!.
As Chief Scientist for Audience at Yahoo! he has led the research on content optimization, i.e., the task of algorithmically selecting the right content to display on a page when a user visits a web portal. This technology is already having a significant impact in practice. At Yahoo!, Ramakrishnan is also leading the research in cloud computing to develop a family of data hosting and analysis services, which, among other applications, will make it much easier to do data mining on the massive datasets seen at web-scale.
Ramakrishnan was Program Co-Chair of the Sixth International Conference on Knowledge Discovery and Data Mining (KDD 2000), and served as an Editor-in-Chief of the primary technical journal in the field, Data Mining and Knowledge Discovery.
He is Chair of ACM SIGMOD, on the Board of Directors of ACM SIGKDD, and on the Board of Trustees of the VLDB Endowment. He is also a Fellow of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE).
He has received several awards, including the ACM SIGMOD Contributions Award, a Distinguished Alumnus Award from IIT Madras, a Packard Foundation Fellowship in Science and Engineering, and an NSF Presidential Young Investigator Award.