3879547
Determining Text Databases to Search in the Internet
Text data in the Internet can be partitioned into many databases naturally. Efficient retrieval of desired data can be achieved if we can accurately predict the usefulness of each database, because with such information, we only need to retrieve potentially useful documents from useful databases. In this paper, we propose two new methods for estimating the usefulness of text databases. For a given query, the usefulness of a text database in this paper is defined to be the number of documents in the database that are sufficiently similar to the query. Such a usefulness measure enables naive-users to make informed decision about which databases to search. We also consider the collection fusion problem. Because local databases may employ similarity functions that are different from that used by the global database, the threshold used by a local database to determine whether a document is potentially useful may be different from that used by the global database. We provide techniques that determine the best threshold for a given local database.
16546019
Mining officially unrecognized side effects of drugs by combining web search and machine learning
We consider the problem of finding officially unrecognized side effects of drugs. By submitting queries to the Web involving a given drug name, it is possible to retrieve pages concerning the drug. However, many retrieved pages are irrelevant and some relevant pages are not retrieved. More relevant pages can be obtained by adding the active ingredient of the drug to the query. In order to eliminate irrelevant pages, we propose a machine learning process to filter out the undesirable pages. The process is shown experimentally to be very effective. Since obtaining training data for the machine learning process can be time consuming and expensive, we provide an automatic method to generate the training data. The method is also shown to be very accurate. The side effects of three drugs which are not recognized by FDA are validated by an expert. We believe that the same approach can be applied to many real life problems and will yield high precision. Thus, this could lead a new way to perform retrieval with high accuracy.
12835059
Distributed Top-N Query Processing with Possibly Uncooperative Local Systems
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.
6106112
WISE-Integrator: An Automatic Integrator of Web Search Interfaces for E-Commerce
More and more databases are becoming Web accessible through form-based search interfaces, and many of these sources are E-commerce sites. Providing a unified access to multiple E-commerce search engines selling similar products is of great importance in allowing users to search and compare products from multiple sites with ease. One key task for providing such a capability is to integrate the Web interfaces of these E-commerce search engines so that user queries can be submitted against the integrated interface. Currently, integrating such search interfaces is carried out either manually or semi-automatically, which is inefficient and difficult to maintain. In this paper, we present WISE-Integrator - a tool that performs automatic integration of Web Interfaces of Search Engines. WISE-Integrator employs sophisticated techniques to identify matching attributes from different search interfaces for integration. It also resolves domain differences of matching attributes. Our experimental results based on 20 and 50 interfaces in two different domains indicate that WISE-Integrator can achieve high attribute matching accuracy and can produce high-quality integrated search interfaces without human interactions.
6149544
Fully automatic wrapper generation for search engines
When a query is submitted to a search engine, the search engine returns a dynamically generated result page containing the result records, each of which usually consists of a link to and/or snippet of a retrieved Web page. In addition, such a result page often also contains information irrelevant to the query, such as information related to the hosting site of the search engine and advertisements. In this paper, we present a technique for automatically producing wrappers that can be used to extract search result records from dynamically generated result pages returned by search engines. Automatic search result record extraction is very important for many applications that need to interact with search engines such as automatic construction and maintenance of metasearch engines and deep Web crawling. The novel aspect of the proposed technique is that it utilizes both the visual content features on the result page as displayed on a browser and the HTML tag structures of the HTML source file of the result page. Experimental results indicate that this technique can achieve very high extraction accuracy.
8794785
Understanding Human Behaviors Based on Eye-Head-Hand Coordination
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.
8301379
TIMBER: A native XML database
Abstract. This paper describes the overall design and architecture of the Timber XML database system currently being implemented at the University of Michigan. The system is based upon a bulk algebra for manipulating trees, and natively stores XML. New access methods have been developed to evaluate queries in the XML context, and new cost estimation and query optimization techniques have also been developed. We present performance numbers to support some of our design decisions. We believe that the key intellectual contribution of this system is a comprehensive set-at-a-time query processing ability in a native XML store, with all the standard components of relational query processing, including algebraic rewriting and a cost-based optimizer.
5853440
Michigan Molecular Interactions (MiMI): putting the jigsaw puzzle together
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: .
53235167
A multimodal learning interface for grounding spoken language in sensory perceptions
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".
32686
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.