Querying Complex Structured Databases
Correctly generating a structured query (e.g., an XQuery or a SQL query) requires the user to have a full understanding of the database schema, which can be a daunting task. Alternative query models have been proposed to give users the ability to query the database without schema knowledge. Those models, including simple keyword search and labeled keyword search, aim to extract meaningful data fragments that match the structure-free query conditions (e.g., keywords) based on various matching semantics. Typically, the matching semantics are content-based: they are defined on data node inter-relationships and incur significant query evaluation cost. Our first contribution is a novel matching semantics based on analyzing the database schema. We show that query models employing a schema-based matching semantics can reduce query evaluation cost significantly while maintaining or even improving result quality. 
 
The adoption of schema-based matching semantics does not change the nature of those query models: they are still schema-ignorant, i.e., users express no schema knowledge (except the labels in labeled keyword search) in the query. While those models work well for some queries on some databases, they often encounter problems when applied to complex queries on databases with complex schemas. Our second contribution is a novel query model that incorporates partial schema knowledge through the use of schema summary. This new summary-aware query model, called Meaningful Summary Query (MSQ), seamlessly integrates summary-based structural conditions and structure-free conditions, and enables ordinary users to query complex databases. We design algorithms for evaluating MSQ queries, and demonstrate that MSQ queries can produce better results against complex databases when compared with previous approaches, and that they can be efficiently evaluated.

title: Querying structured text in an XML database
abstract: XML databases often contain documents comprising structured text. Therefore, it is important to integrate "information retrieval style" query evaluation, which is well-suited for natural language text, with standard "database style" query evaluation, which handles structured queries efficiently. Relevance scoring is central to information retrieval. In the case of XML, this operation becomes more complex because the data required for scoring could reside not directly in an element itself but also in its descendant elements.In this paper, we propose a bulk-algebra, TIX, and describe how it can be used as a basis for integrating information retrieval techniques into a standard pipelined database query evaluation engine. We develop new evaluation strategies essential to obtaining good performance, including a stack-based TermJoin algorithm for efficiently scoring composite elements. We report results from an extensive experimental evaluation, which show, among other things, that the new TermJoin access method outperforms a direct implementation of the same functionality using standard operators by a large factor.

title: Schema-Free XQuery
abstract: The widespread adoption of XML holds out the promise that document structure can be exploited to specify precise database queries. However, the user may have only a limited knowledge of the XML structure, and hence may be unable to produce a correct XQuery, especially in the context of a heterogeneous information collection. The default is to use keyword-based search and we are all too familiar with how difficult it is to obtain precise answers by these means. We seek to address these problems by introducing the notion of Meaningful Lowest Common Ancestor Structure (MLCAS) for finding related nodes within an XML document. By automatically computing MLCAS and expanding ambiguous tag names, we add new functionality to XQuery and enable users to take full advantage of XQuery in querying XML data precisely and efficiently without requiring (perfect) knowledge of the document structure. Such a Schema-Free XQuery is potentially of value not just to casual users with partial knowledge of schema, but also to experts working in a data integration or data evolution context. In such a context, a schema-free query, once written, can be applied universally to multiple data sources that supply similar content under different schemas, and applied "forever" as these schemas evolve. Our experimental evaluation found that it was possible to express a wide variety of queries in a schema-free manner and have them return correct results over a broad diversity of schemas. Furthermore, the evaluation of a schema-free query is not expensive using a novel stack-based algorithm we develop for computing MLCAS: from 1 to 4 times the execution time of an equivalent schema-aware query.

title: Making database systems usable
abstract: Database researchers have striven to improve the capability of a database in terms of both performance and functionality. We assert that the usability of a database is as important as its capability. In this paper, we study why database systems today are so difficult to use. We identify a set of five pain points and propose a research agenda to address these. In particular, we introduce a presentation data model and recommend direct data manipulation with a schema later approach. We also stress the importance of provenance and of consistency across presentation models.

title: Effective Integration of Protein Data through Better Data Modeling
abstract: Protein data, from sequence and structure to interaction, is being generated through many diverse methodologies; it is stored and reported in numerous forms and multiple places. The magnitude of the data limits researchers abilities to utilize all information generated. Effective integration of protein data can be accomplished through better data modeling. We demonstrate this through the MIPD project.

title: Schema summarization
abstract: Real database systems can often be very complex. A person wishing to access data from an unfamiliar database has the daunting task of understanding its schema before being able to pose a correct query against it. A schema summary can be of great help, providing a succinct overview of the entire schema, and making it possible to explore in depth only the relevant schema components.In this paper we formally define a schema summary and two desirable properties (in addition to minimizing size) of a summary: presenting important schema elements and achieving broad information coverage. We develop algorithms that allow us to automatically generate schema summaries based on these two goals. We further develop an objective metric for assessing the quality of a schema summary using query information. Experimental evaluation using this metric demonstrates that the summaries produced by our algorithms can significantly reduce the amount of user effort required to formulate a query through schema exploration.

title: Attentional object spotting by integrating multimodal input
abstract: An intelligent human-computer interface is expected to allow computers to work with users in a cooperative manner. To achieve this goal, computers need to be aware of user attention and provide assistance without explicit user requests. Cognitive studies of eye movements suggest that in accomplishing well-learned tasks, the performer's focus of attention is locked onto ongoing work and more than 90% of eye movements are closely related to the objects being manipulated in the tasks. In light of this, we have developed an attentional object spotting system that integrates multimodal data consisting of eye position, head position and video from the "first-person" perspective. To detect the user's focus of attention, we modeled eye gaze and head movements using a hidden Markov model (HMM) representation. For each attentional point in time, the object of user interest is automatically extracted and recognized. We report the results of experiments on finding attentional objects in the natural task of "making a peanut-butter sandwich".

title: Personalized Web search for improving retrieval effectiveness
abstract: Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.

title: An effective approach to document retrieval via utilizing WordNet and recognizing phrases
abstract: Noun phrases in queries are identified and classified into four types: proper names, dictionary phrases, simple phrases and complex phrases. A document has a phrase if all content words in the phrase are within a window of a certain size. The window sizes for different types of phrases are different and are determined using a decision tree. Phrases are more important than individual terms. Consequently, documents in response to a query are ranked with matching phrases given a higher priority. We utilize WordNet to disambiguate word senses of query terms. Whenever the sense of a query term is determined, its synonyms, hyponyms, words from its definition and its compound words are considered for possible additions to the query. Experimental results show that our approach yields between 23% and 31% improvements over the best-known results on the TREC 9, 10 and 12 collections for short (title only) queries, without using Web data.

title: The Role of Embodied Intention in Early Lexical Acquisition
abstract: We examine the influence of inferring interlocutors' referential intentions from their body movements at the early stage of lexical acquisition. By testing human participants and comparing their performances in different learning conditions, we find that those embodied intentions facilitate both word discovery and word-meaning association. In light of empirical findings, the main part of this article presents a computational model that can identify the sound patterns of individual words from continuous speech, using nonlinguistic contextual information, and employ body movements as deictic references to discover word-meaning associations. To our knowledge, this work is the first model of word learning that not only learns lexical items from raw multisensory signals to closely resemble infant language development from natural environments, but also explores the computational role of social cognitive skills in lexical acquisition.

title: A Methodology to Retrieve Text Documents from Multiple Databases
abstract: This paper presents a methodology for finding the n most similar documents across multiple text databases for any given query and for any positive integer n. This methodology consists of two steps. First, the contents of databases are indicated approximately by database representatives. Databases are ranked using their representatives with respect to the given query. We provide a necessary and sufficient condition to rank the databases optimally. In order to satisfy this condition, we provide three estimation methods. One estimation method is intended for short queries; the other two are for all queries. Second, we provide an algorithm, OptDocRetrv, to retrieve documents from the databases according to their rank and in a particular way. We show that if the databases containing the n most similar documents for a given query are ranked ahead of other databases, our methodology will guarantee the retrieval of the n most similar documents for the query. When the number of databases is large, we propose to organize database representatives into a hierarchy and employ a best-search algorithm to search the hierarchy. It is shown that the effectiveness of the best-search algorithm is the same as that of evaluating the user query against all database representatives.