scholarly journals Ariadne's thread

2021 ◽  
Author(s):  
C. M. Sperberg-McQueen
Keyword(s):  
Data Model ◽  
Query Language ◽  
Document Structure ◽  
Domain Experts ◽  
Partially Ordered

Ariadne is a query language intended to be powerful enough to allow domain experts to find interesting passages in their documents, but simple enough for them to learn even if XPath and other expression languages are too complex. Its assumptions about document structure (elements have parents and are at least partially ordered) are compatible with XML and the XPath Data Model but are also compatible with many non-XML models of text; Ariadne could thus serve as a query language for documents with overlapping structures.

GuessXQ

2013 ◽  
pp. 57-76
Author(s):  
Daniela Morais Fonte ◽  
Daniela da Cruz ◽  
Pedro Rangel Henriques ◽  
Alda Lopes Gancarski
Keyword(s):  
Information Retrieval ◽  
Learning Process ◽  
Web Application ◽  
Relational Databases ◽  
Query Language ◽  
General Purpose ◽  
Considerable Effort ◽  
Document Structure ◽  
Query By Example ◽  
Xml Documents

XML is a widely used general-purpose annotation formalism for creating custom markup languages. XML annotations give structure to plain documents to interpret their content. To extract information from XML documents XPath and XQuery languages can be used. However, the learning of these dialects requires a considerable effort. In this context, the traditional Query-By-Example methodology (for Relational Databases) can be an important contribution to leverage this learning process, freeing the user from knowing the specific query language details or even the document structure. This chapter describes how to apply the Query-By-Example concept in a Web-application for information retrieval from XML documents, the GuessXQ system. This engine is capable of deducing, from an example, the respective XQuery statement. The example consists of marking the desired components directly on a sample document, picked-up from a collection. After inferring the corresponding query, GuessXQ applies it to the collection to obtain the desired result.

Towards a Normal Form and a Query Language for Extended Relations Defined by Regular Expressions

2016 ◽  
Vol 27 (2) ◽  
pp. 27-48
Author(s):  
András Benczúr ◽  
Gyula I. Szabó
Keyword(s):  
Normal Form ◽  
Data Base ◽  
Data Model ◽  
Query Language ◽  
Xml Schema ◽  
Relational Model ◽  
Regular Expressions ◽  
Functional Dependencies ◽  
Decision Algorithm ◽  
Implication Problem

This paper introduces a generalized data base concept that unites relational and semi structured data models. As an important theoretical result we could find a quadratic decision algorithm for the implication problem of functional and join dependencies defined on the united data model. As practical contribution we presented a normal form for the new data model as a tool for data base design. With our novel representations of regular expressions, a more effective searching method could be developed. XML elements are described by XML schema languages such as a DTD or an XML Schema definition. The instances of these elements are semi-structured tuples. A semi-structured tuple is an ordered list of (attribute: value) pairs. We may think of a semi-structured tuple as a sentence of a formal language, where the values are the terminal symbols and the attribute names are the non-terminal symbols. In the authors' former work (Szabó and Benczúr, 2015) they introduced the notion of the extended tuple as a sentence from a regular language generated by a grammar where the non-terminal symbols of the grammar are the attribute names of the tuple. Sets of extended tuples are the extended relations. The authors then introduced the dual language, which generates the tuple types allowed to occur in extended relations. They defined functional dependencies (regular FD - RFD) over extended relations. In this paper they rephrase the RFD concept by directly using regular expressions over attribute names to define extended tuples. By the help of a special vertex labeled graph associated to regular expressions the specification of substring selection for the projection operation can be defined. The normalization for regular schemas is more complex than it is in the relational model, because the schema of an extended relation can contain an infinite number of tuple types. However, the authors can define selection, projection and join operations on extended relations too, so a lossless-join decomposition can be performed. They extended their previous model to deal with XML schema indicators too, e.g., with numerical constraints. They added line and set constructors too, in order to extend their model with more general projection and selection operators. This model establishes a query language with table join functionality for collected XML element data.

scholarly journals CaosDB—Research Data Management for Complex, Changing, and Automated Research Workflows

Data ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 83 ◽  
Author(s):  
Timm Fitschen ◽  
Alexander Schlemmer ◽  
Daniel Hornung ◽  
Henrik tom Wörden ◽  
Ulrich Parlitz ◽  
...  
Keyword(s):  
Data Management ◽  
Data Model ◽  
Query Language ◽  
Research Data ◽  
Data Management System ◽  
Biomedical Sciences ◽  
Seamless Integration ◽  
Research Data Management ◽  
The Status ◽  
Data Acquisition And Processing

We present CaosDB, a Research Data Management System (RDMS) designed to ensure seamless integration of inhomogeneous data sources and repositories of legacy data in a FAIR way. Its primary purpose is the management of data from biomedical sciences, both from simulations and experiments during the complete research data lifecycle. An RDMS for this domain faces particular challenges: research data arise in huge amounts, from a wide variety of sources, and traverse a highly branched path of further processing. To be accepted by its users, an RDMS must be built around workflows of the scientists and practices and thus support changes in workflow and data structure. Nevertheless, it should encourage and support the development and observation of standards and furthermore facilitate the automation of data acquisition and processing with specialized software. The storage data model of an RDMS must reflect these complexities with appropriate semantics and ontologies while offering simple methods for finding, retrieving, and understanding relevant data. We show how CaosDB responds to these challenges and give an overview of its data model, the CaosDB Server and its easy-to-learn CaosDB Query Language. We briefly discuss the status of the implementation, how we currently use CaosDB, and how we plan to use and extend it.

Spatiotemporal Data Model and Query Language for Tracking Land Use Change

2005 ◽  
Vol 1902 (1) ◽  
pp. 107-113 ◽  
Author(s):  
Bo Huang ◽  
Christophe Claramunt
Keyword(s):  
Land Use ◽  
Data Model ◽  
Query Language ◽  
Spatiotemporal Data ◽  
Spatiotemporal Information ◽  
Management Group ◽  
Object Databases ◽  
Temporal Dimensions ◽  
Spatiotemporal Queries ◽  
Spatiotemporal Data Model

Management of spatiotemporal information requires a more generic and consolidated data model to facilitate applications such as tracking land use parcel changes. This paper presents such a spatiotemporal data model in the context of object databases by extending the Object Data Management Group (ODMG) standard and examines its feasibility in a land use application. This model extends the ODMG object model with a parameterized type, TimeSeries<T>, which allows the shifting of spatial types into spatiotemporal types to support the representation of a series of states (i.e., the history) of an object. An object query language (OQL), spatiotemporal OQL (STOQL), which adds spatial and temporal dimensions to ODMG's OQL, is also designed. A case study demonstrates that STOQL supports the formulation of various spatiotemporal queries pertaining to historical states of spatial objects as well as spatial changes, including spatial type substitution. The model and query language have been implemented by using an object-oriented language in a geographic information system environment.

scholarly journals A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

2021 ◽  
Author(s):  
Waqas Ali ◽  
Muhammad Saleem ◽  
Yao Bin ◽  
Aidan Hogan ◽  
A.-C. Ngonga Ngomo
Keyword(s):  
Data Model ◽  
Incomplete Data ◽  
Large Scale ◽  
Query Language ◽  
Real World Data ◽  
Diverse Range ◽  
Survey Paper ◽  
Rdf Graph ◽  
Knowledge Graphs ◽  
Rdf Graphs

Recent years have seen the growing adoption of non-relational data models for representing diverse, incomplete data. Among these, the RDF graph-based data model has seen ever-broadening adoption, particularly on the Web. This adoption has prompted the standardization of the SPARQL query language for RDF, as well as the development of a variety of local and distributed engines for processing queries over RDF graphs. These engines implement a diverse range of specialized techniques for storage, indexing, and query processing. A number of benchmarks, based on both synthetic and real-world data, have also emerged to allow for contrasting the performance of different query engines, often at large scale. This survey paper draws together these developments, providing a comprehensive review of the techniques, engines and benchmarks for querying RDF knowledge graphs.

Knowledge discovery using SPARQL property path: The case of disease data set

2019 ◽  
pp. 016555151986549
Author(s):  
Enayat Rajabi ◽  
Salvador Sanchez-Alonso
Keyword(s):  
Semantic Web ◽  
Knowledge Discovery ◽  
Query Language ◽  
Data Sets ◽  
Web Portal ◽  
Semantic Web Technologies ◽  
Data Set ◽  
Domain Experts ◽  
Manual Exploration ◽  
Reasoning Engine

The Semantic Web allows knowledge discovery on graph-based data sets and facilitates answering complex queries that are extremely difficult to achieve using traditional database approaches. Intuitively, the Semantic Web query language (SPARQL) has a ‘property path’ feature that enables knowledge discovery in a knowledgebase using its reasoning engine. In this article, we utilise the property path of SPARQL and the other Semantic Web technologies to answer sophisticated queries posed over a disease data set. To this aim, we transform data from a disease web portal to a graph-based data set by designing an ontology, present a template to define the queries and provide a set of conjunctive queries on the data set. We illustrate how the reasoning engine of ‘property path’ feature of SPARQL can retrieve the results from the designed knowledgebase. The results of this study were verified by two domain experts as well as authors’ manual exploration on the disease web portal.

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