Querying Data Sources Research Articles

Examining and Fine-tuning the Selection of Glycan Compositions with GlyConnect Compozitor

A key point in achieving accurate intact glycopeptide identification is the definition of the glycan composition file that is used to match experimental with theoretical masses by a glycoproteomics search engine. At present, these files are mainly built from searching the literature and/or querying data sources focused on posttranslational modifications. Most glycoproteomics search engines include a default composition file that is readily used when processing MS data. We introduce here a glycan composition visualizing and comparative tool associated with the GlyConnect database and called GlyConnect Compozitor. It offers a web interface through which the database can be queried to bring out contextual information relative to a set of glycan compositions. The tool takes advantage of compositions being related to one another through shared monosaccharide counts and outputs interactive graphs summarizing information searched in the database. These results provide a guide for selecting or deselecting compositions in a file in order to reflect the context of a study as closely as possible. They also confirm the consistency of a set of compositions based on the content of the GlyConnect database. As part of the tool collection of the Glycomics@ExPASy initiative, Compozitor is hosted at https://glyconnect.expasy.org/compozitor/ where it can be run as a web application. It is also directly accessible from the GlyConnect database.

A Framework for Executing Complex Querying for Relational and NoSQL Databases (CQNS)

The increasing of data on the web poses major confrontations. The amount of stored data and query data sources have become needful features for huge data systems. There are a large number of platforms used to handle the NoSQL database model such as: Spark, H2O and Hadoop HDFS / MapReduce, which are suitable for controlling and managing the amount of big data. Developers of different applications impose data stores on difficult tasks by interacting with mixed data models through different APIs and queries. In this paper, a complex SQL Query and NoSQL (CQNS) framework that acts as an interpreter sends complex queries received from any data store to its corresponding executable engine called CQNS. The proposed framework supports application queries and database transformation at the same time, which in turn speeds up the process. Moreover, CQNS handles many NoSQL databases like MongoDB and Cassandra. This paper provides a spark framework that can handle SQL and NoSQL databases. This work also examines the importance of MongoDB block sharding and composition. Cassandra database deals with two types of sections vertex and edge Portioning. The four scenarios criteria datasets are used to evaluate the proposed CQNS to query the various NOSQL databases in terms of optimization performance and timing of query execution. The results show that among the comparative system, CQNS achieves optimum latency and productivity in less time.

Type-theoretic Means for Querying Heterogeneous Data Sources Available via Cloud APIs

We propose a unified approach for querying data sources which are accessible in form of heterogeneous APIs rather than some standards compliant databases. Different APIs use different query structures and support different capabilities. Type-theoretic approach is used for query structure representation. Because of the fact that not any API will support the full expressibility of the query language we propose techniques for “widening” the query to make it compatible with the given API possibilities. After the query is executed additional filters are applied to the acquired data. In order to achieve this goal a special set of query expansion rules was developed. It consists of the rules that generate special conversion functions which transform more specific queries into less specific ones.

Data mining from web search queries: A comparison of google trends and baidu index

Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.

Web Data Extraction and Generating Mashup

Web contains data that are assorted and are present in abundance. Various kinds of data can be easily extracted from the web, although not all of the data are relevant to the users. Maximum number of the web pages are in unstructured HTML format due to which problems arise in querying data sources making web data extraction process extremely time consuming and expensive. Therefore there arises a necessity to convert the unstructured HTML format into a new structured format such as XML or XHTML. To address this issue, we propose an approach for implementing web data extraction by extracting targeted data from various data sources and make Mashup by generating Extractor system. The Extractor system collaborates and integrates various stages of building a Mashup. Algorithms are used so that the Extractor system can specifically analyze the HTML tags and extract the data into a new format; however the core algorithm used, extracts data using recursive technique while rendering the DOM tree model automatically. Furthermore, the Mashup being created will help in the decision making process, which is the primary requirement for success in corporate world.

A comprehensive study of view maintenance approaches in data warehousing evolution

A data warehouse mainly stores integrated information over data from many different remote data sources for query and analysis. The integrated information at the data warehouse is stored in the form of materialized views. Using these materialized views, user queries may be answered quickly and efficiently as the information may be directly available. These materialized views must be maintained in answer to actual relation updates in the different remote sources. One of the issues related to materialized views is that whether they should be recomputed or they should be adapted incrementally after every change in the base relations. View maintenance is the process of updating a materialized view in response to changes to the underlying data is called view maintenance. There are several algorithms developed by different authors to ease the problem of view maintenance for data warehouse systems. In this paper, we have provided a comprehensive study on research works of different authors related to DW view maintenance considering various parameters and presented the same in tabular way.

Querying Data Sources that Export Infinite Sets of Views

We study the problem of querying data sources that accept only a limited set of queries, such as sources accessible by Web services which can implement very large (potentially infinite) families of queries. We revisit a classical setting in which the application queries are conjunctive queries and the source accepts families of conjunctive queries specified as the expansions of a (potentially recursive) Datalog program with parameters.We say that query Q is expressible by the program \(\ensuremath{\mathcal{P}}\) if it is equivalent to some expansion of \(\ensuremath{\mathcal{P}}\). Q is supported by \(\ensuremath{\mathcal{P}}\) if it has an equivalent rewriting using some finite set of \(\ensuremath{\mathcal{P}}\)’s expansions. We present the first study of expressibility and support for sources that satisfy integrity constraints, which is generally the case in practice.

BioExtract Server—An Integrated Workflow-Enabling System to Access and Analyze Heterogeneous, Distributed Biomolecular Data

Many in silico investigations in bioinformatics require access to multiple, distributed data sources and analytic tools. The requisite data sources may include large public data repositories, community databases, and project databases for use in domain-specific research. Different data sources frequently utilize distinct query languages and return results in unique formats, and therefore researchers must either rely upon a small number of primary data sources or become familiar with multiple query languages and formats. Similarly, the associated analytic tools often require specific input formats and produce unique outputs which make it difficult to utilize the output from one tool as input to another. The BioExtract Server (http://bioextract.org) is a Web-based data integration application designed to consolidate, analyze, and serve data from heterogeneous biomolecular databases in the form of a mash-up. The basic operations of the BioExtract Server allow researchers, via their Web browsers, to specify data sources, flexibly query data sources, apply analytic tools, download result sets, and store query results for later reuse. As a researcher works with the system, their "steps" are saved in the background. At any time, these steps can be preserved long-term as a workflow simply by providing a workflow name and description.

Virtual Observatories in Geosciences

Motivated by the Electronic Geophysical Year (eGY; www. egy.org), which is one of four international celebrations of the 50th anniversary of the International Geophysical Year which occurred in 1957–1958, the first ever conference devoted to Virtual Observatories in Geosciences (VOiG) was held in Denver, Colorado, USA between June 11 and 14, 2007. The conference was sponsored by National Aeronautics and Space Administration (NASA), United States Geological Survey (USGS), eGY, National Center for Atmospheric Research (NCAR), and National Science Foundation (NSF), and was attended by 74 people from nine countries. Of the many contributed presentations from the conference, initially six were reviewed and selected to appear in this volume of Earth Science Informatics. These contributions are clustered under the broad discipline of space physics and largely sponsored by the innovative Virtual Observatories for Heliophysics Data program from the U.S. National Aeronautical and Space Administration. As is noted in all the contributed papers, the virtual observatory paradigm which was originated in the astronomy and astrophysics community, was quickly adopted into solar physics and space physics. More recently, other fields such as environmental science, aeronomy, oceanography, seismology, volcanology, hydrology, and ecology are embracing the paradigm to serve their diverse community needs. The papers represent an excellent cross-section of the developments that have taken place for solar and space physics virtual observatories, and the needs that have emerged and the developed solutions. Modern data frameworks involve multi-tiered architecture (King et al.) and the new virtual observatories are no exception. Deciding on topologies for how best to update loose federations of VOs so that accuracy, currency, relevance and scalability are satisfied is one of the present challenges. How do virtual observatories communicate their holdings to each other? For space physics, the Developing a Space Physics Archive Search and Extract (SPASE) Query Language contribution (Narock et al.) indicates how a community vocabulary, and existing standards developed in the astronomy community, are being brought together to solve the exchange problem in three space physics virtual observatories. In contrasting the differences between astronomical VO needs and those of solar and space physics (Harvey et al.), another contribution lays out the categories of VO participants and their characteristics which drive the interoperability needs of VOs and in particular, the development of the aforementioned, community-based SPASE effort. The practical implementation of the SPASE vocabulary (Merka et al.) in a modern data framework setting, so that users can query and access interdisciplinary data sources via distributed searches from both web portals and web services, demonstrates the substantial and science-relevant application of virtual observatories to space science. In assessing the relevance of searches returned from the “Brave New World” of VOs being implemented (King et al.), it is clear that while some of the techniques for ranking used in general internet searches apply, many do not, and new scoring methods are required, in addition to the importance of faceted search, in order to lead the user to the accurate source of information. The paper on the Virtual Earth–Sun Observatory describes Earth Sci Inform (2008) 1:3–4 DOI 10.1007/s12145-008-0001-8

GIS application in mineral resource analysis—A case study of offshore marine placer gold at Nome, Alaska

Geographic information system (GIS) technology has been applied to analyze the offshore marine placer gold deposits at Nome, Alaska. Two geodatabases, namely Integrated Geodatabase (IG) and Regularized 2.5D Geodatabase (R2.5DG), were created to store and integrate digital data sets in heterogeneous formats. The IG served as a data warehouse and used to manage various geological data, such as borehole, bedrock geology, surficial geology, and geochemical data. The R2.5DG was generated based on the IG and could be used for gold resource estimate at any given spatial domain. Information on placer gold deposits can be updated, queried, visualized, and analyzed by making use of these geodatabases. Ore body boundaries, gold distribution, and the resource estimation at various cutoff grades can be calculated in a timely manner. Based on the enhanced GIS architecture, a web-based GIS ( http://uaf-db.uaf.edu/website/) was developed to facilitate remote users to access the offshore marine placer gold data. Users can integrate local data sources with remote data sources for query, visualization and analysis via a web browser. The GIS architecture developed in this project can be readily adapted to mineral resource management in other areas of the state.

An XML query engine for network-bound data

XML has become the lingua franca for data exchange and integration across administrative and enterprise boundaries. Nearly all data providers are adding XML import or export capabilities, and standard XML Schemas and DTDs are being promoted for all types of data sharing. The ubiquity of XML has removed one of the major obstacles to integrating data from widely disparate sources - namely, the heterogeneity of data formats. However, general-purpose integration of data across the wide are a also requires a query processor that can query data sources on demand, receive streamed XML data from them, and combine and restructure the data into new XML output - while providing good performance for both batch-oriented and ad hoc, interactive queries. This is the goal of the Tukwila data integration system, the first system that focuses on network-bound, dynamic XML data sources. In contrast to previous approaches, which must read, parse, and often store entire XML objects before querying them, Tukwila can return query results even as the data is streaming into the system. Tukwila is built with a new system architecture that extends adaptive query processing and relational-engine techniques into the XML realm, as facilitated by a pair of operators that incrementally evaluate a query's input path expressions as data is read. In this paper, we describe the Tukwila architecture and its novel aspects, and we experimentally demonstrate that Tukwila provides better overall query performance and faster initial answers than existing systems, and has excellent scalability.

ObjectGlobe: Ubiquitous query processing on the Internet

We present the design of ObjectGlobe, a distributed and open query processor for Internet data sources. Today, data is published on the Internet via Web servers which have, if at all, very localized query processing capabilities. The goal of the ObjectGlobe project is to establish an open marketplace in which data and query processing capabilities can be distributed and used by any kind of Internet application. Furthermore, ObjectGlobe integrates cycle providers (i.e., machines) which carry out query processing operators. The overall picture is to make it possible to execute a query with – in principle – unrelated query operators, cycle providers, and data sources. Such an infrastructure can serve as enabling technology for scalable e-commerce applications, e.g., B2B and B2C market places, to be able to integrate data and data processing operations of a large number of participants. One of the main challenges in the design of such an open system is to ensure privacy and security. We discuss the ObjectGlobe security requirements, show how basic components such as the optimizer and runtime system need to be extended, and present the results of performance experiments that assess the additional cost for secure distributed query processing. Another challenge is quality of service management so that users can constrain the costs and running times of their queries.