My Research Papers, now more accessible

Many readers have complained that this blog is always full of artsy and time-wasting material… “what about all the technical stuff? Aren’t you a computer person?!” they ask. To pacify these masses, I have just converted three of my recent papers to HTML format. For the first two, I used the HEVEA LaTeX to HTML converter, which I found slightly better than LaTeX2HTML. For the 3rd paper, I have inexplicably misplaced the source files, and hence the HTMLization was done via Gmail’s PDF Viewer.

The picture above is from the 2007 SIGMOD demo paper. I’ll post videos of the demo in a later post. Here’s a quick preview of each paper:

  • Qunits: queried units in database search CIDR, 2009
    Keyword search against structured databases has become a popular topic of investigation, since many users find structured queries too hard to express, and enjoy the freedom of a “Google-like” query box into which search terms can be entered. Attempts to address this problem face a fundamental dilemma. Database querying is based on the logic of predicate evaluation, with a precisely defined answer set for a given query. On the other hand, in an information retrieval approach, ranked query results have long been accepted as far superior to results based on boolean query evaluation. As a consequence, when keyword queries are attempted against databases, relatively ad-hoc ranking mechanisms are invented (if ranking is used at all), and there is little leverage from the large body of IR literature regarding how to rank query results.
  • Effective Phrase Prediction VLDB, 2007
    Autocompletion is a widely deployed facility in systems that require user input. Having the system complete a partially typed “word” can save user time and effort. In this paper, we study the problem of autocompletion not just at the level of a single “word”, but at the level of a multi-word “phrase”. There are two main challenges: one is that the number of phrases (both the number possible and the number actually observed in a corpus) is combinatorially larger than the
    number of words; the second is that a “phrase”, unlike a “word”, does not have a well-defined boundary, so that the autocompletion system has to decide not just what to predict, but also how far. We introduce a FussyTree structure to address the first challenge and the concept of a significant hrase to address the second. We develop a probabilistically driven multiple completion choice model, and exploit features such as frequency distributions to improve the quality of our suffix completions. We experimentally demonstrate the practicability and value of our technique for an email composition application and show that we can save approximately a fifth of the keystrokes typed
  • Assisted querying using instant-response interfaces SIGMOD 2007
    We demonstrate a novel query interface that enables users to construct a rich search query without any prior knowledge of the underlying schema or data. The interface, which is in the form of a single text input box, interacts in real-time with the users as they type, guiding them through the query construction. We discuss the issues of schema and data complexity, result size estimation, and query validity; and provide novel approaches to solving these problems. We demonstrate our query interface on two popular applications; an enterprise-wide personnel search, and a biological information database.
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About the author:

Arnab Nandi is an Assistant Professor in the Department of Computer Science and Engineering at The Ohio State University. You can read more about him here.


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