Semi-structured data

Résumé

Semi-structured data is a form of structured data that does not obey the tabular structure of data models associated with relational databases or other forms of data tables, but nonetheless contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data. Therefore, it is also known as self-describing structure. In semi-structured data, the entities belonging to the same class may have different attributes even though they are grouped together, and the attributes' order is not important. Semi-structured data are increasingly occurring since the advent of the Internet where full-text documents and databases are not the only forms of data anymore, and different applications need a medium for exchanging information. In object-oriented databases, one often finds semi-structured data. Types XML XML, other markup languages, email, and EDI are all forms of semi-structured data. OEM (Object Exchange Model) was crea

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https://en.wikipedia.org/wiki/Semi-structured_data

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Graph integration of structured, semistructured and unstructured data for data journalism

Angelos Christos Anadiotis, Jingmao You

Digital data is a gold mine for modern journalism. However, datasets which interest journalists are extremely heterogeneous, ranging from highly structured (relational databases), semi-structured (JSON, XML, HTML), graphs (e.g., RDF), and text. Journalists (and other classes of users lacking advanced IT expertise, such as most non-governmental-organizations, or small public administrations) need to be able to make sense of such heterogeneous corpora, even if they lack the ability to define and deploy custom extract-transform-load workflows, especially for dynamically varying sets of data sources. We describe a complete approach for integrating dynamic sets of heterogeneous datasets along the lines described above: the challenges we faced to make such graphs useful, allow their integration to scale, and the solutions we proposed for these problems. Our approach is implemented within the ConnectionLens system; we validate it through a set of experiments. (C) 2021 Elsevier Ltd. All rights reserved.

PERGAMON-ELSEVIER SCIENCE LTD2022

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An Extensible Query Execution Engine for Supporting New Query Execution Models

Query processing in traditional Database Management Systems (DBMS) has been extensively studied in the literature and adopted in industry. Such success is, in part, due to the performance of their Query Execution En-gines (QEE) for supporting the execution of traditional queries. With the advent of the web and its semi-structured data model, new query scenarios were cre-ated, suggesting new execution models such as: adaptive, continuous, and stream based. To support these models, the traditional QEE must be extended, resulting in a great development effort as the one recently seen to support the XML data model. This paper proposes the design and construction of an exten-sible QEE adapted to new execution models and our approach is to implement each execution model as a combination of execution modules. Thus, adding new modules to this QEE, new execution models will be supported. To achieve this goal, we use a software framework technique to produce a framework, named QEEF (Query Execution Engine Framework).

2005

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