Research: Kalev H. Leetaru

My work is strongly interdisciplinary, drawing from across a myriad fields of study and unified by the central theme of applying vast computational resources and novel methods to "big data" grand challenges.


  Big Data Analysis
Big Data Analysis

Analyzing truly large-scale data collections ranging into the hundreds of trillions of data points across large numbers of dimensions requires highly specialized approaches. Many factors must be taken into account: computational (exploiting algorithmic optimization and data architecture techniques to make problems computationally tractable), data processing (sifting through the noise and making results robust against conflicting information), and interface (how does one visually present a network analysis of a network with 500 trillion connections?). My work makes use of a wide array of approaches including GIS, intelligence, network analysis, relationship extraction, sentiment analysis, and translation.

  Humanities, Arts, and Social Sciences (HASS)
Humanities, Arts, and Social Sciences (HASS)

Some of the largest datasets and most complex questions relate to the social world and to studing human society and human behavior. From mass digitization to exploring themes in the humanities, arts, and social sciences, a lot of my work revolves around disciplinary approaches and methods from the so-called HASS fields.

  Knowledge Management / Measurements and Metrics / Records Management
Knowledge Management / Measurements and Metrics / Records Management

I have extensive experience architecting and implementing enterprise-class knowledge management environments and the underlying human processes that make them successful. I have worked on all levels of records management, including preservation, versioning, and provenance, and a study published in governmental document management in 2008 was published in the New York Times and covered in radio, television, and on thousands of news and blog websites around the world. A landmark 2006 study based on my graduate research explored the use of non-traditional data sources as a model for how to understand institutions of higher education through a variety of data-driven dimensions including mining all university web sites to generate topical networks relating departments and using human resoures systems to measure interdisciplinary collaboration.

  Other Projects
Other Projects

I receive a lot of requests for some of my selected undergraduate and graduate coruse papers and other working and draft papers from various topics that do not fit into my core research themes, which are made available on an as-is basis.