Conversão de bases de dados MicroISIS para Internet

Researchers frequently find that archival data that they need to test their theoretical models must undergo a variety of preparatory steps before the data are ready to be loaded into their favorite statistical analysis package. Although some powerful software tools and techniques to handle such data preparation do exist and are relatively easy to learn, many researchers remain unaware of them. The purpose of this paper is to introduce some of these data preparation tools and techniques by use of a hands-on demonstration. The demonstration begins with data embedded in a Web page and shows how a spreadsheet, a word processor, and the SQL language from a relational database can be used to make the extracted “ready for prime-time.” The original Web page data for the demonstration can be downloaded from the authors’ web page; the software packages used to prepare the data are MS Excel, MS Word, and MS Access. Although SPSS is used for the statistical analysis described in the final phase of the process, readers do not have to have this software to benefit from the demonstration. Any statistical analysis package could be used.

The Internet presents large amount of useful information which is usually formatted for its users, which makes it hard to extract relevant data from diverse sources. Therefore, there is a significant need of robust, flexible Information Extraction (IE) systems that transform the web pages into program friendly structures such as a relational database will become essential. IE produces structured data ready for post processing. Roadrunner will be used to extract information from template web pages. In this paper, we present novel algorithm for extracting templates from a large number of web documents which are generated from heterogeneous templates. The proposed system focuses on information extraction from heterogeneous web pages. We cluster the web documents based on the common template structures so that the template for each cluster is extracted simultaneously. The resultant clusters will be given as input to the Roadrunner system.

There exist many interrelated information sources on the Internet that can be categorized into structured (database) and semistructured (documents). A key challenge is to integrate, query and analyze such heterogeneous collections of information. In this paper, we defend the idea of building web metadata repositories using relational databases as the main source and central data management technology of structured data, enriched by the semistructured data surrounding it. Our proposal rests on the assumption that heterogeneous relational databases can be integrated (i.e. entity resolution is assumed to work well) and thus can serve as references for external data. That is, we tackle the problem of integrating information in the deep web, departing from databases. We discuss a prototype system that can integrate and query metadata and related documents, based on relational database technology. Metadata includes database ER model elements like database name, table, and column (entity, attribute). Web document data include files, documents and web pages. Links between metadata and external documents are built with SQL queries. Once databases and documents are linked, they are managed and queried with SQL. We discuss an interesting scientific application of our solution with a water pollution database.

OntoGRator is a system for integration and organization of access to various information and knowledge stored in independent computerized sources that stay external to it. The sources can be of various structuralization: data in relational data bases, documents, XML files, web pages etc. It is assumed that the sources gathered knowledge of some domain like casting technology or metallurgy. Integration of such sources is realized by defining two kinds of ontologies: “domain otologies” representing concepts of the domain together with dependences between these concepts, and “data source ontologies” describing each data source. Use of the knowledge gathered in the system consists in navigating through notions of the domain ontology and - after identifying the interesting concept – in retrieval of the information about chosen this way topic (coming from different data sources). The paper discusses some architectural and implementation issues of OntoGrator as well as its distinctive application in the field of casting technology.

A huge amount of data is being generated every day from different sources. Access to these data can be very valuable for decision-making. Nevertheless, the extraction of information of interest remains a major challenge given a large number of heterogeneous databases. Building shareable and (re)usable data access mechanisms including automated verification and inference mechanisms for knowledge discovery needs to use a common knowledge model with a secure, coherent, and efficient database. For this purpose, an ontology provides an interesting knowledge model and a relational database provides an interesting storage solution. Many papers propose methods for converting ontology to a relational database. This paper describes issues, challenges, and trends derived from the evaluation of 10 methods using 23 criteria. Following this study, this paper shows that none of the methods are complete as well as the conversion process does not use the full expressivity of ontology to derive a complete relational schema including advanced constraints and modification procedures. Thus, more work must be done to decrease the gap between ontologies, a relation database.

the massive volume of data in databases, web pages, and document files usually causes information to be disorganized and unclear for the user. Therefore, information in such an environment can be classified into three forms: structured, semistructured, or unstructured. Structured information is the best form of information because it facilitates the acquisition and comprehension of knowledge. Relational Database Management System (RDBMS) has a robust structure that manages, organizes and retrieves data. There are many attempts have been made in order to deal with such data. These attempts can be categorized into three groups: within a database schema, by a developed data model within the database, or by query-based techniques in database. Nonetheless, RDBMS contain massive amount of unstructured data such as textual data.. This paper proposed Textual Virtual Schema Model (TVSM). TVSM is conducted to perform semantic textual data linking and clustering and is embedded in the relational database structure (schema). In addition, linking and converting the unstructured information to structured data. Quality improvement of textual data clusters. Achievement of high query processing efficiency in retrieving data clusters. TVSM initially developed to assist researchers, developers, and database administrators who are concerned on unstructured information management, information extraction, multi-document clustering, information retrieval, query processing efficiency, personal information management, question answering, information integration, news tracking, and news summarization

Although Power Pivot provides many types of connections you can use to query data, there are times when you need to manipulate the data before loading it into the model. This process is commonly known as tttthe transform part of the ETL (extract, transform, and load) process. This is where Power Query really shines and is a very useful part of your BI arsenal. Power Query provides an easy-to-use interface for discovering and transforming data. It contains tools to clean and shape data such as removing duplicates, replacing values, and grouping data. In addition, it supports a vast array of data sources both structured and unstructured, such as relational databases, web pages, and Hadoop, just to name a few. Once the data is extracted and transformed, you can then easily load it into a Power Pivot model.

Efficient and secure collection and management of information is essential in any modern biomedical study. Data management and coordination of multisite studies is a complex process. It involves development of systems for data collection, data cleaning with quality assurance checks, and specimen tracking, as well as development of procedures for conducting the study, training clinical sites, and communicating with sites to answer study questions and resolve and track data inquiries and resolutions. We developed a secure web-based system that is designed to automate evaluation of eligibility criteria and data collection, track specimens, serve as a resource for study-specific information, facilitate communication across sites in multisite studies, track data queries and resolutions, and allow administrative management of studies. The system combines a common framework across studies that defines the internal structure for all the web pages, with a study-specific one that defines the content of each page via a relational database. This combination creates a flexible and efficient environment enabling several multisite studies to be simultaneously or consecutively implemented and managed in a timely manner. We describe the development process, the system and its evaluation, current status, lessons learned, and future development plans.

The uptake of semantic technology depends on the availability of useful tools that enable Web developers to generate linked course data automaticly. RDF triple allows web page to contain machine-readable content that is easier to find and mashable with other content. This paper describes a framework that turns this idea around, using RDF as a template language for the generation of machine-readable triple from human-readable data on Web page. Most existing methords generate RDF triple by combining the template with query results from a relational database. In the Linked Course Data Generating framework, the raw course data is turned into RDF triple, then is turned into linked data, finally is turned into ontology. This paper evaluates the performance of framework.

With the maturity of network technology, the deterministic factor of a 'successful' web site does not as much depend on its connectivity to the Internet, rather the content of web pages plays a more important role. The inclusion of dynamic data, for example, has become an increasingly desirable feature of a web page. Web servers that support dynamic web pages are capable of retrieving data at the time of user requests. Rather than being statically included in the web page, these dynamically retrieved data may physically reside in various file systems or database servers across the Internet.JAVA, a new object-oriented programming language, has become a buzzword since its introduction by Sun Microsystems in 1994 [1, 2, 3]. It was claimed that JAVA, via its ability to embed applets in a web page, would make the content of a web page alive and dynamic. Data and information on a web page can now be updated dynamically when users request them.In this paper, we discuss our experience of developing JAVA applets that use various types of JDBC (JAVA Database Connectivity) drivers to connect to and then manipulate data stored in SQL (Structured Query Language) database servers. We first start with the discussion of general client/server model involving JDBC, and then introducing the procedure of setting up programming projects employing this approach in an undergraduate database course.

Despite continual improvements in the performance and reliability of large scale file systems, the management of user-defined file system metadata has changed little in the past decade. The mismatch between the size and complexity of large scale data stores and their ability to organize and query their metadata has led to a de facto standard in which raw data is stored in traditional file systems, while related, application-specific metadata is stored in relational databases. This separation of data and semantic metadata requires considerable effort to maintain consistency and can result in complex, slow, and inflexible system operation. To address these problems, we have developed the Quasar File System (QFS), a metadata-rich file system in which files, user-defined attributes, and file relationships are all first class objects. In contrast to hierarchical file systems and relational databases, QFS defines a graph data model composed of files and their relationships. QFS incorporates Quasar, an XPATH-extended query language for searching the file system. Results from our QFS prototype show the effectiveness of this approach. Compared to the de facto standard, the QFS prototype shows superior ingest performance and comparable query performance on user metadata-intensive operations and superior performance on normal file metadata operations.

Despite continual improvements in the performance and reliability of large scale file systems, the management of file system metadata has changed little in the past decade. The mismatch between the size and complexity of large scale data stores and their ability to organize and query their metadata has led to a de facto standard in which raw data is stored in traditional file systems, while related, application-specific metadata is stored in relational databases. This separation of data and metadata requires considerable effort to maintain consistency and can result in complex, slow, and inflexible system operation. To address these problems, we have developed the Quasar File System (QFS), a metadata-rich file system in which files, metadata, and file relationships are all first class objects. In contrast to hierarchical file systems and relational databases, QFS defines a graph data model composed of files and their relationships. QFS includes Quasar, an XPATH-extended query language for searching the file system. Results from our QFS prototype show the effectiveness of this approach. Compared to the defacto standard, the QFS prototype shows superior ingest performance and comparable query performance on user metadata-intensive operations and superior performance on normal file metadata operations.

In order to ensure the interoperability between semantic web and relational databases, several approaches have been developed to ensure SQL-to-SPARQL query transformation direction, but all these approaches have the same weakness. In fact, they convert directly the input SQL query to its equivalent SPARQL one without any pre-processing phase enabling the optimization of this input query filled by users before starting the conversion process. This weakness has motivated us to add a pretreatment phase aiming to optimize the most important SQL statements which seem to have the biggest impact on the effectiveness of the transformed queries. Our main contribution is to enrich these rewriting systems by adding an optimization layer that integrate a set of simplification rules of Left, Right and Full Outer Join in order to avoid, firstly unnecessary operations during the conversion process, and secondly SPARQL queries with a high complexity due to Optional patterns obtained from outer join in this conversion context.

The Extensible Markup Language (XML) is emerging as the dominant data format for data exchange between applications. Many translation techniques have been devised to publish large amounts of existing conventional relational data in XML format. There also exists a need to be able to represent imprecise data in both relational databases and XML. This paper describes a fuzzy XML schema model for representing a fuzzy relational database in XML format. It also outlines a translation algorithm to include fuzzy relations and similarity matrices with their associated conventional relations. We also describe how the proposed fuzzy XML schema and the conversion process from fuzzy relational data into fuzzy XML have been integrated into VIREX, which is a prototype with powerful visual tool for transforming relational data into XML.KeywordsFuzzy XMLFuzzy Relational DatabaseSchema ConversionVisual Interface

Web search engines such as Google and Bing provide an easy and convenient way to find web pages that contain input keywords. This provides a user-friendly interface for non-technical users to explore the Web and find relevant data among thousands of Web pages. While numerous advancement has been made to store e-commerce data in the cloud, we have not seen great advancement in terms of search over such data. E-commerce data is usually stored as structured data in relational and graph databases. Thus, an answer to a query keyword is composed of different pieces of data stitched together. As of now, the main method to find answers over this structured data is through predefined search forms. However, these search forms are limited, and developing a new search form is time consuming and expensive. In this work, we present an easy way to explore structured e-commerce data for business users that eliminate the dependency to predefined forms. The new search system is similar to Google, in which the interface is essentially a text box, and non-technical business users enter input keywords into the system. The output is a portion of the data, that covers the input keywords. We propose a new ranking strategy based on machine learning to rank more relevant answers ahead of less relevant ones. Our experiments show this ranking strategy is successful in returning relevant answers.