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: .
3398727
Semantic Adaptation of Schema Mappings when Schemas Evolve
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 [7]. Our work can also be seen as an implementation of the mapping composition operator of the model management framework.
5887673
Making database systems usable
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.
6132969
Efficient discovery of XML data redundancies
As XML becomes widely used, dealing with redundancies in XML data has become an increasingly important issue. Redundantly stored information can lead not just to a higher data storage cost, but also to increased costs for data transfer and data manipulation. Furthermore, such data redundancies can lead to potential update anomalies, rendering the database inconsistent.One way to avoid data redundancies is to employ good schema design based on known functional dependencies. In fact, several recent studies have focused on defining the notion of XML Functional Dependencies (XML FDs) to capture XML data redundancies. We observe further that XML databases are often "casually designed" and XML FDs may not be determined in advance. Under such circumstances, discovering XML data redundancies (in terms of FDs) from the data itself becomes necessary and is an integral part of the schema refinement process.In this paper, we present the design and implementation of the first system, DiscoverXFD, for effcient discovery of XML data redundancies. It employs a novel XML data structure and introduces a new class of partition based algorithms. DiscoverXFD can not only be used for the previous definitions of XML functional dependencies, but also for a more comprehensive notion we develop in this paper, capable of detecting redundancies involving set elements while maintaining clear semantics. Experimental evaluations using real life and benchmark datasets demonstrate that our system is practical and scales well with increasing data size.
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.
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.
27420151
Recent Advances in PC-Linux Systems for Electronic Structure Computations by Optimized Compilers and Numerical Libraries
One of the most frequently used packages for electronic structure research, GAUSSIAN 98, is compiled on Linux systems with various hardware configurations, including AMD Athlon (with the "Thunderbird" core), AthlonMP, and AthlonXP (with the "Palomino" core) systems as well as the Intel Pentium 4 (with the "Willamette" core) machines. The default PGI FORTRAN compiler (pgf77) and the Intel FORTRAN compiler (ifc) are respectively employed with different architectural optimization options to compile GAUSSIAN 98 and test the performance improvement. In addition to the BLAS library included in revision A.11 of this package, the Automatically Tuned Linear Algebra Software (ATLAS) library is linked against the binary executables to improve the performance. Various Hartree-Fock, density-functional theories, and the MP2 calculations are done for benchmarking purposes. It is found that the combination of ifc with ATLAS library gives the best performance for GAUSSIAN 98 on all of these PC-Linux computers, including AMD and Intel CPUs. Even on AMD systems, the Intel FORTRAN compiler invariably produces binaries with better performance than pgf77. The enhancement provided by the ATLAS library is more significant for post-Hartree-Fock calculations. The performance on one single CPU is potentially as good as that on an Alpha 21264A workstation or an SGI supercomputer. The floating-point marks by SpecFP2000 have similar trends to the results of GAUSSIAN 98 package.
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.
11444648
On the Integration of Grounding Language and Learning Objects
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.
848788
An effective approach to document retrieval via utilizing WordNet and recognizing phrases
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.