A graph query language and its query processing

Lei, Sheng; Özsoyoğlu, Z. Meral; Özsoyoǧlu, Gültekin

doi:10.1109/icde.1999.754973

Cited by 53 publications

(46 citation statements)

References 12 publications

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“…Our work was greatly inspired by interesting work on efficient graph querying (Buneman et al 2000;Sheng et al 1999). Unlike trees, graphs involve subtle issues on their representation and equivalence.…”

Section: Related Workmentioning

confidence: 99%

Bidirectionalizing graph transformations

Hidaka

Inaba

et al. 2010

SIGPLAN Not.

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Bidirectional transformations provide a novel mechanism for synchronizing and maintaining the consistency of information between input and output. Despite many promising results on bidirectional transformations, these have been limited to the context of relational or XML (tree-like) databases. We challenge the problem of bidirectional transformations within the context of graphs, by proposing a formal definition of a well-behaved bidirectional semantics for UnCAL, i.e., a graph algebra for the known UnQL graph query language. The key to our successful formalization is full utilization of both the recursive and bulk semantics of structural recursion on graphs. We carefully refine the existing forward evaluation of structural recursion so that it can produce sufficient trace information for later backward evaluation. We use the trace information for backward evaluation to reflect in-place updates and deletions on the view to the source, and adopt the universal resolving algorithm for inverse computation and the narrowing technique to tackle the difficult problem with insertion. We prove our bidirectional evaluation is well-behaved. Our current implementation is available online and confirms the usefulness of our approach with nontrivial applications.

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Section: Related Workmentioning

confidence: 99%

Bidirectionalizing graph transformations

Hidaka

Inaba

et al. 2010

SIGPLAN Not.

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“…GOQL GOQL (Sheng et al 1999) is an extension of OQL enriched with constructs to create, manipulate and query objects of type graph, path and edge. GOQL is applied to graph database that use an object oriented data model.…”

Section: B Graphdbmentioning

confidence: 99%

Graph Database for Collaborative Communities

Soussi

Aufaure²,

Baazaoui³

2011

Community-Built Databases

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Data manipulated in an enterprise context are structured data as well as unstructured data such as emails, documents, social networks, etc. Graphs are a natural way of representing and modeling such data in a unified manner (Structured, semi-structured and unstructured ones). The main advantage of such a structure relies in the dynamic aspect and the capability to represent relations, even multiple ones, between objects. Recent database research work shows a growing interest in the definition of graph models and languages to allow a natural way of handling data appearing. In this chapter, we give a survey of the main graph database models and the associated graph query languages. We then present an application using a graph database to extract social networks.

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“…Typical interaction methods include focus+context methods (i.e. zoom and filter), graph queries using language-based methods [35], and filtering databases of graphs using graph similarity metrics, typically based on non-trivial graph theoretic algorithms [29].…”

Section: The Interactive Approachmentioning

confidence: 99%

Dynamic Visualization of Coexpression in Systems Genetics Data

Kendall

Huang

et al. 2008

IEEE Trans. Visual. Comput. Graphics

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Abstract-Biologists hope to address grand scientific challenges by exploring the abundance of data made available through microarray analysis and other high-throughput techniques. However, the impact of this large volume of data is limited unless researchers can effectively assimilate the entirety of this complex information and integrate it into their daily research; interactive visualization tools are called for to support the effort. Specifically, typical studies of gene co-expression can make use of novel visualization tools that enable the dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters and achieving data reduction. These tools should allow biologists to develop an intuitive understanding of the structure of biological networks and discover genes which reside in critical positions in networks and pathways. By using a graph as a universal data representation of correlation in gene expression data, our novel visualization tool employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool for interacting with gene co-expression data integrates techniques such as: graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by querying an optimized b-tree, dynamic level-of-detail graph abstraction, and template-based fuzzy classification using neural networks. We demonstrate our system using a real-world workflow from a large-scale, systems genetics study of mammalian gene co-expression.

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A graph query language and its query processing

Cited by 53 publications

References 12 publications

Bidirectionalizing graph transformations

Bidirectionalizing graph transformations

Graph Database for Collaborative Communities

Dynamic Visualization of Coexpression in Systems Genetics Data

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