12806654
Effective Integration of Protein Data through Better Data Modeling
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.
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: .
2809118
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.
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.
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.
1959999
Schema summarization
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.
482012
Detecting user engagement in everyday conversations
This paper presents a novel application of speech emotion recognition: estimation of the level of conversational engagement between users of a voice communication system. We begin by using machine learning techniques, such as the support vector machine (SVM), to classify users’ emotions as expressed in individual utterances. However, this alone fails to model the temporal and interactive aspects of conversational engagement. We therefore propose the use of a multilevel structure based on coupled hidden Markov models (HMM) to estimate engagement levels in continuous natural speech. The first level is comprised of SVM-based classifiers that recognize emotional states, which could be (e.g.) discrete emotion types or arousal/valence levels. A high-level HMM then uses these emotional states as input, estimating users’ engagement in conversation by decoding the internal states of the HMM. We report experimental results obtained by applying our algorithms to the LDC EMOTIONAL PROSODY and CALLFRIEND speech corpora.
5675375
Attentional object spotting by integrating multimodal input
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".
7418443
Efficient and effective metasearch for text databases incorporating linkages among documents
Linkages among documents have a significant impact on the importance of documents, as it can be argued that important documents are pointed to by many documents or by other important documents. Metasearch engines can be used to facilitate ordinary users for retrieving information from multiple local sources (text databases). There is a search engine associated with each database. In a large-scale metasearch engine, the contents of each local database is represented by a representative. Each user query is evaluated against he set of representatives of all databases in order to determine the appropriate databases (search engines) to search (invoke) In previous word, the linkage information between documents has not been utilized in determining the appropriate databases to search. In this paper, such information is employed to determine the degree of relevance of a document with respect to a given query. Specifically, the importance (rank) of each document as determined by the linkages is integrated in each database representative to facilitate the selection of databases for each given query. We establish a necessary and sufficient condition to rank databases optimally, while incorporating the linkage information. A method is provided to estimate the desired quantities stated in the necessary and sufficient condition. The estimation method runs in time linearly proportional to the number of query terms. Experimental results are provided to demonstrate the high retrieval effectiveness of the method.
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.
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.