Information & Data Management and Analytics (IDEA) Laboratory


Our understanding of the world is based on our available data. The myriad amounts of digital data, i.e., Big Data, can bring about great advancements in various areas of science, medicine, business, and technology and better our lives. These potentials, however, can be realized only if people can manage , explore, and obtain actionable knowledge from various large data sets naturally, easily, efficiently, and effectively. In IDEA Lab, we set forth the foundations of natural, easy-to-use, efficient, and effective data management and analytics. We apply these principles to build systems by which people can easily manage, explore, and extract interesting and useful knowledge from their data. We are part of the School of EECS and affiliated with the Center of for Genome Research and Biocomputing at OSU.
Cape Perpetua

Email: termehca [at] oregonstate.edu
Address: 3053 Kelley Engineering Center, Corvallis, OR 97330-5501

News

  • Our paper: A Signaling Game Approach to Database Querying and Interaction will appear at the SIGIR International Conference on the Theory of Information Retrieval in September 2015. It considers querying as a collaboration between two potentially rational agents: the user and the database system, to establish a mutual language for representing information and intents. We formalize this collaboration as a signaling game, where each mutual language is an equilibrium for the game.
  • We have two new manuscripts on representation independent analytics project:
    • Our first manuscript shows that relational learning algorithms tend to vary quite substantially over the choice of the database schema, both in terms of learning accuracy and efficiency, which complicates their off-the-shelf application. Hence, it proposes a schema independent, efficient, and effective learning algorithm to solve this problem.
    • Our second manuscript sets forth a novel framework to explore the representation independence of similarly and proximity search in graph data. It further proposes novel effective similarity and proximity search algorithms that are robust over widely popular representational changes.
  • We will demonstrate Universal DB, our representation independent graph analytic system, in VLDB 2015.
  • We have a new manuscript out on automatically finding the necessary amount of specificity in database design.
  • Our work on Automatic Data Organization will appear in the June issue of ACM Transactions on Database Systems (TODS), 2015.
  • Check out our vision paper on representation independent data analytics. Because the results of current data mining and machine learning algorithms depend how their input databases are represented, developers have to spend great deal of time and resources to transform the databases to their desired representations for these algorithms. The paper argues for representation independent methods for data analytics.
  • Thanks to NSF for supporting our research through award "III-Generalizable Similarity and Proximity Metrics For Data Exploration". The grant will fund our work on representation independent graph analytics.

Template by BlackTie.co