Support For Queries Research Articles

Supporting various top-k queries over uncertain datasets

There have been many researches and semantics in answering top-k queries on uncertain data in various applications. However, most of these semantics must consume much of their time in computing position probability. Our approach to support various top-k queries is based on position probability distribution (PPD) sharing. In this paper, a PPD-tree structure and several basic operations on it are proposed to support various top-k queries. In addition, we proposed an approximation method to improve the efficiency of PPD generation. We also verify the effectiveness and efficiency of our approach by both theoretical analysis and experiments.

Information Integration of Drug Discovery and Clinical Studies to Support Complex Queries Using an Information Supply Chain Framework

Query support of frameworks which are capable of linking and integrating trusted information in disciplines such as clinical studies face many challenges, as usually data are neither semantically consistent nor in a form which can be easily extracted and shared.. Drawing conclusions from clinical studies usually requires integrating data from various sources at various time points and inferring facts from an integrated information set. However, relevant information may not be present at all or not in sufficient detail or quality to support a particular query. In this paper, we present an approach for query support in integrating diverse information sources and how trust can be strengthened in using subject oriented specific Information Supply Chains (ISC). We assess feasibility of query support in an ISC framework using an underlying Information Dependency Relation Model for relevant information sources and how such a framework may facilitate collaboration in supplying information at the right time and with required quality.

From Knowledge Organization to Knowledge Representation

So far, within the Library and Information Science (LIS) community, Knowledge Organization (KO) has developed its own very successful solutions to document search, allowing for the classification and search of millions of books. However, current KO solutions are limited in expressivity as they only support queries by document properties, e.g., by title, author and subject. In parallel, within the Artificial Intelligence and Semantic Web communities, Knowledge Representation (KR), has developed very powerful end expressive techniques which, via the use of ontologies, support queries by any entity property (e.g., the properties of the entities described in a document). However, KR has not scaled yet to the level of KO, mainly because of the lack of a precise and scalable entity specification methodology. In this paper we present DERA, a new methodology, inspired by the faceted approach, as introduced in KO, that retains all the advantages of KR and compensates for the limitations of KO. DERA guarantees at the same time quality, extensibility, scalability and effectiveness in search.

Trusted Information Supply Chain Framework in Clinical Studies

Query support of frameworks which are capable of integrating trusted information in disciplines such as clinical studies face many challenges, as usually data are neither semantically consistent nor in a form which can be easily extracted and shared. Drawing conclusions from clinical studies usually requires integrating data from various sources and inferring facts from an integrated information set. However, relevant information may not be present in sufficient detail to support a particular query. In this paper, we present an approach for query support in integrating diverse information sources and how trust can be strengthened in using subject oriented specific Information Supply Chains (ISC). We assess feasibility of query support in an ISC framework using an underlying Information Dependency Relation Model for relevant information sources.

Accessing biomedical literature in the current information landscape.

Biomedical and life sciences literature is unique because of its exponentially increasing volume and interdisciplinary nature. Biomedical literature access is essential for several types of users including biomedical researchers, clinicians, database curators, and bibliometricians. In the past few decades, several online search tools and literature archives, generic as well as biomedicine specific, have been developed. We present this chapter in the light of three consecutive steps of literature access: searching for citations, retrieving full text, and viewing the article. The first section presents the current state of practice of biomedical literature access, including an analysis of the search tools most frequently used by the users, including PubMed, Google Scholar, Web of Science, Scopus, and Embase, and a study on biomedical literature archives such as PubMed Central. The next section describes current research and the state-of-the-art systems motivated by the challenges a user faces during query formulation and interpretation of search results. The research solutions are classified into five key areas related to text and data mining, text similarity search, semantic search, query support, relevance ranking, and clustering results. Finally, the last section describes some predicted future trends for improving biomedical literature access, such as searching and reading articles on portable devices, and adoption of the open access policy.

Local Semantic Indexing for Resource Discovery on Overlay Network Using Mobile Agents

AbstractOne of the most crucial problems in a peer-to-peer system is locating of resources that are shared by various nodes. Various techniques suggested in literature suffer from drawbacks viz. saturation of network, inability to locate multi-keyword based resource or locate resource based on semantics. We present the solution that is more efficient and effective for discovering shared resources on a network that is influenced by content shared by nodes. To reduce the search load on nodes that have uncorrelated content, an efficient migration route is proposed for mobile agent that is based on cosine similarity of content shared by nodes and user query and minimum support. Results show reduction in search load and traffic due to communication, and increase in locating of resources defined by multiple keys using mobile agent that are logically similar to user query. Furthermore, the results indicate that by use of our technique the relevance of search results is higher; that is obtained by minimal traffic...

EnContRA: a generic multimedia information retrieval meta-framework

Over the last years, multimedia collections have largely increased as new items are produced every day, such as pictures, audio/music or video. In Multimedia Information Retrieval, this exponential growth leads content-based approaches to gain advantage over other solutions, not only because they take advantage of the intrinsic information contained in the objects, but also because they automatically process and extract it, reducing the burden taken by developers. Several domain specific frameworks have been developed to efficiently retrieve multimedia items empowering the creation of new content-based applications. However, these frameworks are attached to a specific media type, are too complex to be used in a fast prototyping environment, and are not very flexible nor extensible. To solve these issues, we developed EnContRA, an architectural meta-framework that provides generic building blocks for creating domain specific frameworks. Our meta-framework aims at being ready to be used for fast prototyping, with support for rich and multimodal queries, allowing validation of new descriptors, indexing structures or searching algorithms, while creating domain specific frameworks. In this paper we present the meta-framework architecture and describe in detail its modules and features. To validate the meta-framework, we created an image retrieval framework and a demo application that combines image descriptors with textual information, showing how the hierarchical design of EnContRA could be applied to a searching system and to empower the creation of queries.

Demonstration of Hadoop-GIS: A Spatial Data Warehousing System Over MapReduce.

The proliferation of GPS-enabled devices, and the rapid improvement of scientific instruments have resulted in massive amounts of spatial data in the last decade. Support of high performance spatial queries on large volumes data has become increasingly important in numerous fields, which requires a scalable and efficient spatial data warehousing solution as existing approaches exhibit scalability limitations and efficiency bottlenecks for large scale spatial applications. In this demonstration, we present Hadoop-GIS - a scalable and high performance spatial query system over MapReduce. Hadoop-GIS provides an efficient spatial query engine to process spatial queries, data and space based partitioning, and query pipelines that parallelize queries implicitly on MapReduce. Hadoop-GIS also provides an expressive, SQL-like spatial query language for workload specification. We will demonstrate how spatial queries are expressed in spatially extended SQL queries, and submitted through a command line/web interface for execution. Parallel to our system demonstration, we explain the system architecture and details on how queries are translated to MapReduce operators, optimized, and executed on Hadoop. In addition, we will showcase how the system can be used to support two representative real world use cases: large scale pathology analytical imaging, and geo-spatial data warehousing.

JI.FI: Visual test and debug queries for hard real‐time

SUMMARYHard real‐time systems have stringent timing and resource requirements. As such, debugging and tracing such systems often requires low‐level hardware support, and online debugging is usually precluded entirely. In other areas, however, visual debugging has greatly improved program understanding and late cycle development times for nonreal‐time applications. In this paper, we introduce a visual test and debug framework for hard real‐time Java applications built around the JIVE platform and realized in the Fiji virtual machine.Our framework, called JI.FI [" dZIfi], provides high‐level debugging support over low‐level execution traces. JI.FI provides both powerful visualizations and real‐time centric temporal query support. To ensure preservation of the real‐time characteristics of the application being tested and debugged, JI.FI leverages a real‐time event log infrastructure that logs only relevant application and virtual machine level events, such as synchronization and modifications to priorities or thread state. Our performance results indicate that our logging infrastructure is suitable for hard real‐time systems, as the performance impact is both uniform and quantifiable. Copyright © 2013 John Wiley & Sons, Ltd.

Hadoop GIS

Support of high performance queries on large volumes of spatial data becomes increasingly important in many application domains, including geospatial problems in numerous fields, location based services, and emerging scientific applications that are increasingly data- and compute-intensive. The emergence of massive scale spatial data is due to the proliferation of cost effective and ubiquitous positioning technologies, development of high resolution imaging technologies, and contribution from a large number of community users. There are two major challenges for managing and querying massive spatial data to support spatial queries: the explosion of spatial data, and the high computational complexity of spatial queries. In this paper, we present Hadoop-GIS - a scalable and high performance spatial data warehousing system for running large scale spatial queries on Hadoop. Hadoop-GIS supports multiple types of spatial queries on MapReduce through spatial partitioning, customizable spatial query engine RESQUE, implicit parallel spatial query execution on MapReduce, and effective methods for amending query results through handling boundary objects. Hadoop-GIS utilizes global partition indexing and customizable on demand local spatial indexing to achieve efficient query processing. Hadoop-GIS is integrated into Hive to support declarative spatial queries with an integrated architecture. Our experiments have demonstrated the high efficiency of Hadoop-GIS on query response and high scalability to run on commodity clusters. Our comparative experiments have showed that performance of Hadoop-GIS is on par with parallel SDBMS and outperforms SDBMS for compute-intensive queries. Hadoop-GIS is available as a set of library for processing spatial queries, and as an integrated software package in Hive.

Tight and simple Web graph compression for forward and reverse neighbor queries

Analyzing Web graphs has applications in determining page ranks, fighting Web spam, detecting communities and mirror sites, and more. This study is however hampered by the necessity of storing a major part of huge graphs in the external memory which prevents efficient random access to edge (hyperlink) lists. A number of algorithms involving compression techniques have thus been presented, to represent Web graphs succinctly, but also providing random access. Those techniques are usually based on differential encodings of the adjacency lists, finding repeating nodes or node regions in the successive lists, more general grammar-based transformations or 2-dimensional representations of the binary matrix of the graph. In this paper we present three Web graph compression algorithms. The first can be seen as engineering of the Boldi and Vigna (2004) [8] method. We extend the notion of similarity between link lists and use a more compact encoding of residuals. The algorithm works on blocks of varying size (in the number of input lists) and sacrifices access time for better compression ratio, achieving more succinct graph representation than other algorithms reported in the literature. The second algorithm works on blocks of the same size in the number of input lists. Its key mechanism is merging the block into a single ordered list. This method achieves much more attractive space–time tradeoffs. Finally, we present an algorithm for bidirectional neighbor query support, which offers compression ratios better than those known from the literature.

DrugMap Central: an on-line query and visualization tool to facilitate drug repositioning studies

Systematic studies of drug repositioning require the integration of multi-level drug data, including basic chemical information (such as SMILES), drug targets, target-related signaling pathways, clinical trial information and Food and Drug Administration (FDA)-approval information, to predict new potential indications of existing drugs. Currently available databases, however, lack query support for multi-level drug information and thus are not designed to support drug repositioning studies. DrugMap Central (DMC), an online tool, is developed to help fill the gap. DMC enables the users to integrate, query, visualize, interrogate, and download multi-level data of known drugs or compounds quickly for drug repositioning studies all within one system. DMC is accessible at http://r2d2drug.org/DMC.aspx. STWong@tmhs.org.

Efficient processing of substring match queries with inverted variable-length gram indexes

With the widespread use of the internet, text-based data sources have become ubiquitous and the demand for effective support of string matching queries continues to increase. The relational query language SQL supports LIKE clauses for string data to handle substring matching queries. Due to the popularity of such substring matching queries, there have been many studies on designing efficient indexes to support the LIKE clause in SQL. Among them, the indexes based on q-grams with fixed or variable sizes have been studied extensively.In this paper, we show that the optimal processing of substring matching queries with inverted variable-length gram indexes should be decided judiciously. However, the search space of finding the optimal variable-length gram set among those available is exponential with the number of grams. To reduce the search space, we present effective pruning strategies which do not sacrifice optimality. Based on the cost estimation, we propose the optimal algorithms to find the best plan with the minimum cost for substring matching queries using these pruning strategies. We also provide the approximate algorithms to overcome the exponential nature of search space to find an optimal query plan. Our performance study confirms that the proposed techniques improve query execution time for substring matching significantly compared to the running time of the traditional algorithms.

An Envisioned Approach for Modeling and Supporting User-Centric Query Activities on Data Warehouses

In this vision paper, the authors discuss models and techniques for integrating, processing and querying data, information and knowledge within data warehouses in a user-centric manner. The user-centric emphasis allows us to achieve a number of clear advantages with respect to classical data warehouse architectures, whose most relevant ones are the following: (i) a unified and meaningful representation of multidimensional data and knowledge patterns throughout the data warehouse layers (i.e., loading, storage, metadata, etc); (ii) advanced query mechanisms and guidance that are capable of extracting targeted information and knowledge by means of innovative information retrieval and data mining techniques. Following this main framework, the authors first outline the importance of knowledge representation and management in data warehouses, where knowledge is expressed by existing ontology or patterns discovered from data. Then, the authors propose a user-centric architecture for OLAP query processing, which is the typical applicative interface to data warehouse systems. Finally, the authors propose insights towards cooperative query answering that make use of knowledge management principles and exploit the peculiarities of data warehouses (e.g., multidimensionality, multi-resolution, and so forth).

A high-performance spatial database based approach for pathology imaging algorithm evaluation

Background: Algorithm evaluation provides a means to characterize variability across image analysis algorithms, validate algorithms by comparison with human annotations, combine results from multiple algorithms for performance improvement, and facilitate algorithm sensitivity studies. The sizes of images and image analysis results in pathology image analysis pose significant challenges in algorithm evaluation. We present an efficient parallel spatial database approach to model, normalize, manage, and query large volumes of analytical image result data. This provides an efficient platform for algorithm evaluation. Our experiments with a set of brain tumor images demonstrate the application, scalability, and effectiveness of the platform. Context: The paper describes an approach and platform for evaluation of pathology image analysis algorithms. The platform facilitates algorithm evaluation through a high-performance database built on the Pathology Analytic Imaging Standards (PAIS) data model. Aims: (1) Develop a framework to support algorithm evaluation by modeling and managing analytical results and human annotations from pathology images; (2) Create a robust data normalization tool for converting, validating, and fixing spatial data from algorithm or human annotations; (3) Develop a set of queries to support data sampling and result comparisons; (4) Achieve high performance computation capacity via a parallel data management infrastructure, parallel data loading and spatial indexing optimizations in this infrastructure. Materials and Methods: We have considered two scenarios for algorithm evaluation: (1) algorithm comparison where multiple result sets from different methods are compared and consolidated; and (2) algorithm validation where algorithm results are compared with human annotations. We have developed a spatial normalization toolkit to validate and normalize spatial boundaries produced by image analysis algorithms or human annotations. The validated data were formatted based on the PAIS data model and loaded into a spatial database. To support efficient data loading, we have implemented a parallel data loading tool that takes advantage of multi-core CPUs to accelerate data injection. The spatial database manages both geometric shapes and image features or classifications, and enables spatial sampling, result comparison, and result aggregation through expressive structured query language (SQL) queries with spatial extensions. To provide scalable and efficient query support, we have employed a shared nothing parallel database architecture, which distributes data homogenously across multiple database partitions to take advantage of parallel computation power and implements spatial indexing to achieve high I/O throughput. Results: Our work proposes a high performance, parallel spatial database platform for algorithm validation and comparison. This platform was evaluated by storing, managing, and comparing analysis results from a set of brain tumor whole slide images. The tools we develop are open source and available to download. Conclusions: Pathology image algorithm validation and comparison are essential to iterative algorithm development and refinement. One critical component is the support for queries involving spatial predicates and comparisons. In our work, we develop an efficient data model and parallel database approach to model, normalize, manage and query large volumes of analytical image result data. Our experiments demonstrate that the data partitioning strategy and the grid-based indexing result in good data distribution across database nodes and reduce I/O overhead in spatial join queries through parallel retrieval of relevant data and quick subsetting of datasets. The set of tools in the framework provide a full pipeline to normalize, load, manage and query analytical results for algorithm evaluation.

Development of a text search engine for medicinal chemistry patents

Division of Medical Information Sciences (SIMED), University Hospitals of Geneva and University of Geneva, Geneva, Switzerland Bibliomics and Text-Mining Group (BiTeM), Information Science Department, University of Applied Sciences, Geneva, Switzerland Novartis Institute for BioMedical Research – Text Mining Services (NIBR-IT/TMS), Novartis Pharma AG, Basel, Switzerland e-mail: emilie.pasche@hcuge.ch (corresponding), julien.gobeill@hesge.ch, fatma.oezdemirzaech@novartis.com, therese.vachon@novartis.com, christian.lovis@hcuge.ch, patrick.ruch@hesge.ch

Genomic and proteomic data integration for comprehensive biodata search

Genomic and proteomic data integration for comprehensive biodata search

Towards Building a High Performance Spatial Query System for Large Scale Medical Imaging Data.

Support of high performance queries on large volumes of scientific spatial data is becoming increasingly important in many applications. This growth is driven by not only geospatial problems in numerous fields, but also emerging scientific applications that are increasingly data- and compute-intensive. For example, digital pathology imaging has become an emerging field during the past decade, where examination of high resolution images of human tissue specimens enables more effective diagnosis, prediction and treatment of diseases. Systematic analysis of large-scale pathology images generates tremendous amounts of spatially derived quantifications of micro-anatomic objects, such as nuclei, blood vessels, and tissue regions. Analytical pathology imaging provides high potential to support image based computer aided diagnosis. One major requirement for this is effective querying of such enormous amount of data with fast response, which is faced with two major challenges: the "big data" challenge and the high computation complexity. In this paper, we present our work towards building a high performance spatial query system for querying massive spatial data on MapReduce. Our framework takes an on demand index building approach for processing spatial queries and a partition-merge approach for building parallel spatial query pipelines, which fits nicely with the computing model of MapReduce. We demonstrate our framework on supporting multi-way spatial joins for algorithm evaluation and nearest neighbor queries for microanatomic objects. To reduce query response time, we propose cost based query optimization to mitigate the effect of data skew. Our experiments show that the framework can efficiently support complex analytical spatial queries on MapReduce.

Enabling semantic querying in mobile and location-aware DSS for mass gatherings

Location information is one of key pieces of information for real-time decision support systems that are used in emergency management. In the context of mass gatherings, mobile and location-aware Decision Support Systems (DSS) can significantly improve split-second decision making, facilitate coordination of team members and reduce medical response time. While existing location-aware DSS enable tracking patients, medical emergency staff and resources, they have limited or no support for semantic querying and reasoning. Semantic search provides the ability to produce more precise and accurate results compared to the syntax-based search. The use of ontologies in DSS not only enables capturing and sharing the domain knowledge but provides the capability to perform semantic querying and reasoning. In this paper, we introduce a mobile and location-aware DSS architecture for medical emergency management in mass gatherings that integrates a domain ontology and provides reasoning and semantic research capabilities. The implementation and evaluation of the proposed architecture using an Android phone are described.

Effective support of databases with ontological dependencies: Relational languages instead of description logics

The paper provides a brief survey of the languages for efficient support of access to the databases satisfying the ontological dependencies. The first part of the paper retraces the development of description logics intended for application in the database and information systems context, as well as experimental results of building of the "ontologically based" data access systems. The main part of the paper is devoted to the survey of the development of relational database languages providing broader and more efficient support of queries over databases with the ontological dependencies compared to the description logics.