Constraint-based XML query rewriting for data integration
We study the problem of answering queries through a target schema, given a set of mappings between one or more source schemas and this target schema, and given that the data is at the sources. The schemas can be any combination of relational or XML schemas, and can be independently designed. In addition to the source-to-target mappings, we consider as part of the mapping scenario a set of target constraints specifying additional properties on the target schema. This becomes particularly important when integrating data from multiple data sources with overlapping data and when such constraints can express data merging rules at the target. We define the semantics of query answering in such an integration scenario, and design two novel algorithms, basic query rewrite and query resolution, to implement the semantics. The basic query rewrite algorithm reformulates target queries in terms of the source schemas, based on the mappings. The query resolution algorithm generates additional rewritings that merge related information from multiple sources and assemble a coherent view of the data, by incorporating target constraints. The algorithms are implemented and then evaluated using a comprehensive set of experiments based on both synthetic and real-life data integration scenarios.

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: Michigan Molecular Interactions (MiMI): putting the jigsaw puzzle together
abstract: Protein interaction data exists in a number of repositories. Each repository has its own data format, molecule identifier and supplementary information. Michigan Molecular Interactions (MiMI) assists scientists searching through this overwhelming amount of protein interaction data. MiMI gathers data from well-known protein interaction databases and deep-merges the information. Utilizing an identity function, molecules that may have different identifiers but represent the same real-world object are merged. Thus, MiMI allows the users to retrieve information from many different databases at once, highlighting complementary and contradictory information. To help scientists judge the usefulness of a piece of data, MiMI tracks the provenance of all data. Finally, a simple yet powerful user interface aids users in their queries, and frees them from the onerous task of knowing the data format or learning a query language. MiMI allows scientists to query all data, whether corroborative or contradictory, and specify which sources to utilize. MiMI is part of the National Center for Integrative Biomedical Informatics (NCIBI) and is publicly available at: .

title: Querying Complex Structured Databases
abstract: 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: Semantic Adaptation of Schema Mappings when Schemas Evolve
abstract: Schemas evolve over time to accommodate the changes in the information they represent. Such evolution causes invalidation of various artifacts depending on the schemas, such as schema mappings. In a heterogenous environment, where cooperation among data sources depends essentially upon them, schema mappings must be adapted to reflect schema evolution. In this study, we explore the mapping composition approach for addressing this mapping adaptation problem. We study the semantics of mapping composition in the context of mapping adaptation and compare our approach with the incremental approach of Velegrakis et al [21]. We show that our method is superior in terms of capturing the semantics of both the original mappings and the evolution. We design and implement a mapping adaptation system based on mapping composition as well as additional mapping pruning techniques that significantly speed up the adaptation. We conduct comprehensive experimental analysis and show that the composition approach is practical in various evolution scenarios. The mapping language that we consider is a nested relational extension of the second-order dependencies of Fagin et al . Our work can also be seen as an implementation of the mapping composition operator of the model management framework.

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: Distributed Top-N Query Processing with Possibly Uncooperative Local Systems
abstract: We consider the problem of processing top-N queries in a distributed environment with possibly uncooperative local database systems. For a given top-N query, the problem is to find the N tuples that satisfy the query the best but not necessarily completely in an efficient manner. Top-N queries are gaining popularity in relational databases and are expected to be very useful for e-commerce applications. Many companies provide the same type of goods and services to the public on the Web, and relational databases may be employed to manage the data. It is not feasible for a user to query a large number of databases. It is therefore desirable to provide a facility where a user query is accepted at some site, suitable tuples from appropriate sites are retrieved and the results are merged and then presented to the user. In this paper, we present a method for constructing the desired facility. Our method consists of two steps. The first step determines which databases are likely to contain the desired tuples for a given query so that the databases can be ranked based on their desirability with respect to the query. Four different techniques are introduced for this step with one requiring no cooperation from local systems. The second step determines how the ranked databases should be searched and what tuples from the searched databases should be returned. A new algorithm is proposed for this purpose. Experimental results are presented to compare different methods and very promising results are obtained using the method that requires no cooperation from local databases.

title: On the Integration of Grounding Language and Learning Objects
abstract: This paper presents a multimodal learning system that can ground spoken names of objects in their physical referents and learn to recognize those objects simultaneously from naturally co-occurring multisensory input. There are two technical problems involved: (1) the correspondence problem in symbol grounding - how to associate words (symbols) with their perceptually grounded meanings from multiple cooccurrences between words and objects in the physical environment. (2) object learning - how to recognize and categorize visual objects. We argue that those two problems can be fundamentally simplified by considering them in a general system and incorporating the spatio-temporal and cross-modal constraints of multimodal data. The system collects egocentric data including image sequences as well as speech while users perform natural tasks. It is able to automatically infer the meanings of object names from vision, and categorize objects based on teaching signals potentially encoded in speech. The experimental results reported in this paper reveal the effectiveness of using multimodal data and integrating heterogeneous techniques in machine learning, natural language processing and computer vision.

title: Understanding Human Behaviors Based on Eye-Head-Hand Coordination
abstract: Action recognition has traditionally focused on processing fixed camera observations while ignoring non-visual information. In this paper, we explore the dynamic properties of the movements of different body parts in natural tasks: eye, head and hand movements are quite tightly coupled with the ongoing task. In light of this, our method takes an agent-centered view and incorporates an extensive description of eye-head-hand coordination. With the ability to track the course of gaze and head movements, our approach uses gaze and head cues to detect agent-centered attention switches that can then be utilized to segment an action sequence into action units. Based on recognizing those action primitives, parallel hidden Markov models are applied to model and integrate the probabilistic sequences of the action units of different body parts. An experimental system is built for recognizing human behaviors in three natural tasks: "unscrewing a jar", "stapling a letter" and "pouring water", which demonstrates the effectiveness of the approach.

title: A multimodal learning interface for grounding spoken language in sensory perceptions
abstract: Most speech interfaces are based on natural language processing techniques that use pre-defined symbolic representations of word meanings and process only linguistic information. To understand and use language like their human counterparts in multimodal human-computer interaction, computers need to acquire spoken language and map it to other sensory perceptions. This paper presents a multimodal interface that learns to associate spoken language with perceptual features by being situated in users' everyday environments and sharing user-centric multisensory information. The learning interface is trained in unsupervised mode in which users perform everyday tasks while providing natural language descriptions of their behaviors. We collect acoustic signals in concert with multisensory information from non-speech modalities, such as user's perspective video, gaze positions, head directions and hand movements. The system firstly estimates users' focus of attention from eye and head cues. Attention, as represented by gaze fixation, is used for spotting the target object of user interest. Attention switches are calculated and used to segment an action sequence into action units which are then categorized by mixture hidden Markov models. A multimodal learning algorithm is developed to spot words from continuous speech and then associate them with perceptually grounded meanings extracted from visual perception and action. Successful learning has been demonstrated in the experiments of three natural tasks: "unscrewing a jar", "stapling a letter" and "pouring water".