Information & Data Management and Analytics (IDEA) Laboratory


The myriad amounts of digital data, i.e., Big Data, can bring about exciting advancements in various areas of science and technology. We set forth the foundations of and build systems for easy, effective, and efficient data management and analytics. Our research lies primarily in the areas of databases and data management.
  • Email: termehca [at] oregonstate.edu
  • Address: 3053 Kelley Engineering Center, Corvallis, OR 97330-5501
Cape Perpetua

Current projects

  • RIDE: Representation Independent Data Exploration

    The output of database exploration and analytics algorithms highly depend on the structure and representation of their input data. To use current database analytics algorithms, users have to find the desired representation for these algorithms and transform (wrangle) their data to these representations. These tasks are hard and time-consuming and major obstacles for unlocking the value of data. RIDE aims at developing algorithms that return the desired results no matter how their input data is represented. more information
  • CHAIN: Strategic Communication of Humans And Information Systems

    Because humans and information systems express information in different forms, they do not usually communicate effectively: users cannot precisely express their information needs and database systems do not understand users' intents. CHAIN aims at designing interaction strategies and interfaces that enable users and database systems to establish an effective mutual understanding and common language fast. It leverages concepts from game theory to analyze the interaction between users and database systems and find its eventual stable states. more information

News

  • We present an analysis of learning strategies in game-theoretic data interaction at the SIGKDD IDEA workshop. Our results indicate that because users learn and modify their strategies of looking for information, popular methods used in data management systems to understand users' intents are not generally effective. We propose following new types of algorithms to understand users' intents behind theiry queries.
  • We present our work on managing and maintainng variational data at DBPL 2017.
  • Ben presents an overview on our work of modeling users and database systems as rational agents and interesting equilibria that appear in their interactions in HILDA 2017.
  • Jose will present his paper Schema Independent Relational Learning in SIGMOD 2017. His paper measures the robustness of learning algorithms to data representation and proposes a representationally robust, accurate, and efficient learning over relational data. Here is the one-slide teaser.
  • Jose presents his paper on autoamtically setting the language bias of learning systems over relational data in DEEM 2017
  • Yodsawalai will present our paper Cost-effective Concept Annotation Using Taxonomies, which is on the tradeoff between the usability and overhead of organizing data sets in WebDB 2017.
  • We will give an overview of our work on representation independent relational learning in ILP 2016.
  • Yodsawalai will present her work on representation independent similarity and proximity search at CIKM 2016.
  • We give an invited talk on representation independent graph analytics on the Eighth Linked Data Benchmark Council (LDBC), Technical User Community Meeting at the Oracle Conference Center in the Redwood Shores.
  • We have a manuscript on game theoretic and language game modeling of database querying and interaction.
  • We will demo Castor , our schema independent and scalable relational learning system, at VLDB'16 .

Selected awards

  • Distinguished PC member of SIGMOD 2017.
  • Best Student Paper Award, ICDE, 2011.
  • Yahoo! Key Scientific Challenges Award, 2011.
  • ICDE Best Papers Selection, 2011.

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