Efficient interactive fuzzy keyword search

Traditional information systems return answers after a user submits a complete query. Users often feel “left in the dark” when they have limited knowledge about the underlying data, and have to use a try-and-see approach for finding information. A recent trend of supporting autocomplete in these systems is a first step towards solving this problem. In this paper, we study a new information-access paradigm, called “interactive, fuzzy search,” in which the system searches the underlying data “on the fly” as the user types in query keywords. It extends autocomplete interfaces by (1) allow- ing keywords to appear in multiple attributes (in an arbi- trary order) of the underlying data; and (2) finding relevant records that have keywords matching query keywords ap- proximately. This framework allows users to explore data as they type, even in the presence of minor errors. We study research challenges in this framework for large amounts of data. Since each keystroke of the user could invoke a query on the backend, we need efficient algorithms to process each query within milliseconds. We develop various incremental- search algorithms using previously computed and cached re- sults in order to achieve an interactive speed. We have deployed several real prototypes using these techniques. One of them has been deployed to support interactive search on the UC Irvine people directory, which has been used regularly and well received by users due to its friendly interface and high efficiency

A Work-Pattern Centric Approach to Building a Personal Knowledge Advantage Machine

A work pattern, also known as a usage pattern, can be broadly defined as the methods by which a user typically utilizes a particular system. Data mining has been applied to web usage patterns for a variety of purposes. This thesis presents a framework by which data mining techniques could be used to extract patterns from an individual\u27s work flow data in order facilitate a new type of architecture known as a knowledge advantage machine. This knowledge advantage machine is a type of semantic desktop and semantic web application that would assist people in constructing their own personal knowledge networks, as well as sharing that information in an efficient manner with colleagues using the same system. A knowledge advantage machine would be capable of automatically discovering new knowledge which is relevant to the user\u27s personal ontology.;Through experimentation, we demonstrate that a user\u27s file usage patterns can be utilized by software in order to automatically and seamlessly learn what is important as defined by the user. Further research is necessary to apply this principle to a more realized knowledge advantage machine such that decisions can be fueled by work patterns as well as semantic or contextual information

Machine Learning Techniques to Make Computers Easier to Use

Identifying user-dependent information that can be automatically collected helps build a user model by which 1) to predict what the user wants to do next and 2) to do relevant preprocessing. Such information is often relational and is best represented by a set of directed graphs. A machine learning technique called graph-based induction (GBI) efficiently extracts regularities from such data, based on which a user-adaptive interface is built that can predict next command, generate scripts and prefetch les in a multi task environment. The heart of GBI is pairwise chunking. The paper shows how this simple mechanism applies to the top down induction of decision trees for nested attribute representation as well as nding frequently occurring patterns in a graph. The results clearly shows that the dependency analysis of computational processes activated by the user commands which is made possible by GBI is indeed useful to build a behavior model and increase prediction accuracy