AURORA: Data-driven Construction of Visual Graph Query Interfaces for Graph Databases

2020 ◽  
Author(s):  
Sourav S. Bhowmick ◽  
Kai Huang ◽  
Huey Eng Chua ◽  
Zifeng Yuan ◽  
Byron Choi ◽  
...  
Keyword(s):  
Data Driven ◽  
Graph Databases ◽  
Graph Query ◽  
Query Interfaces ◽  
Visual Graph

scholarly journals Towards plug-and-play visual graph query interfaces

2021 ◽  
Vol 14 (11) ◽  
pp. 1979-1991
Author(s):  
Zifeng Yuan ◽  
Huey Eng Chua ◽  
Sourav S Bhowmick ◽  
Zekun Ye ◽  
Wook-Shin Han ◽  
...  
Keyword(s):  
Real World ◽  
Domain Knowledge ◽  
Experimental Studies ◽  
Plug And Play ◽  
Large Networks ◽  
Graph Query ◽  
Query Interfaces ◽  
Wide Range ◽  
Real World Datasets ◽  
Visual Graph

Canned patterns ( i.e. , small subgraph patterns) in visual graph query interfaces (a.k.a GUI) facilitate efficient query formulation by enabling pattern-at-a-time construction mode. However, existing GUIS for querying large networks either do not expose any canned patterns or if they do then they are typically selected manually based on domain knowledge. Unfortunately, manual generation of canned patterns is not only labor intensive but may also lack diversity for supporting efficient visual formulation of a wide range of subgraph queries. In this paper, we present a novel, generic, and extensible framework called TATTOO that takes a data-driven approach to automatically select canned patterns for a GUI from large networks. Specifically, it first decomposes the underlying network into truss-infested and truss-oblivious regions. Then candidate canned patterns capturing different real-world query topologies are generated from these regions. Canned patterns based on a user-specified plug are then selected for the GUI from these candidates by maximizing coverage and diversity , and by minimizing the cognitive load of the pattern set. Experimental studies with real-world datasets demonstrate the benefits of TATTOO. Importantly, this work takes a concrete step towards realizing plug-and-play visual graph query interfaces for large networks.

scholarly journals Data-driven visual graph query interface construction and maintenance

2016 ◽  
Vol 9 (12) ◽  
pp. 984-992 ◽  
Author(s):  
Sourav S. Bhowmick ◽  
Byron Choi ◽  
Curtis Dyreson
Keyword(s):  
Data Driven ◽  
Query Interface ◽  
Graph Query ◽  
Visual Graph

scholarly journals Visual Graph Query Construction and Refinement

2017 ◽  
Author(s):  
Robert Pienta ◽  
Fred Hohman ◽  
Acar Tamersoy ◽  
Alex Endert ◽  
Shamkant Navathe ◽  
...  
Keyword(s):  
Graph Query ◽  
Visual Graph

scholarly journals Similarity Measure of Graphs

2017 ◽  
Vol 5 (2) ◽  
pp. 42
Author(s):  
Amine Labriji ◽  
Salma Charkaoui ◽  
Issam Abdelbaki ◽  
Abdelouhaed Namir ◽  
El Houssine Labriji
Keyword(s):  
Artificial Intelligence ◽  
Similarity Measure ◽  
Shape Recognition ◽  
Research Field ◽  
Graph Databases ◽  
Graph Query ◽  
Information Research ◽  
Measure Of Similarity ◽  
The One ◽  
The Web

<p class="0abstract">The topic of identifying the similarity of graphs was considered as highly recommended research field in the Web semantic, artificial intelligence, the shape recognition and information research. One of the fundamental problems of graph databases is finding similar graphs to a graph query. Existing approaches dealing with this problem are usually based on the nodes and arcs of the two graphs, regardless of parental semantic links. For instance, a common connection is not identified as being part of the similarity of two graphs in cases like two graphs without common concepts, the measure of similarity based on the union of two graphs, or the one based on the notion of maximum common sub-graph (SCM), or the distance of edition of graphs. This leads to an inadequate situation in the context of information research. To overcome this problem, we suggest a new measure of similarity between graphs, based on the similarity measure of Wu and Palmer. We have shown that this new measure satisfies the properties of a measure of similarities and we applied this new measure on examples. The results show that our measure provides a run time with a gain of time compared to existing approaches. In addition, we compared the relevance of the similarity values obtained, it appears that this new graphs measure is advantageous and  offers a contribution to solving the problem mentioned above.</p>

Querying Graph Databases

2011 ◽  
pp. 304-322
Author(s):  
Sherif Sakr ◽  
Ghazi Al-Naymat
Keyword(s):  
Social Networks ◽  
Query Language ◽  
Future Research ◽  
Graph Databases ◽  
Research Directions ◽  
Query Graph ◽  
Graph Indexing ◽  
Graph Query ◽  
Graph Query Language ◽  
Future Research Directions

Recently, there has been a lot of interest in the application of graphs in different domains. Graphs have been widely used for data modeling in different application domains such as: chemical compounds, protein networks, social networks, and Semantic Web. Given a query graph, the task of retrieving related graphs as a result of the query from a large graph database is a key issue in any graph-based application. This has raised a crucial need for efficient graph indexing and querying techniques. This chapter provides an overview of different techniques for indexing and querying graph databases. An overview of several proposals of graph query language is also given. Finally, the chapter provides a set of guidelines for future research directions.

Visual graph query formulation and exploration: a new perspective on information retrieval at the edge

2016 ◽  
Author(s):  
Sue E. Kase ◽  
Michelle Vanni ◽  
Joanne A. Knight ◽  
Yu Su ◽  
Xifeng Yan
Keyword(s):  
Information Retrieval ◽  
Query Formulation ◽  
Graph Query ◽  
New Perspective ◽  
Visual Graph

scholarly journals Considering User Intention in Differential Graph Queries

2015 ◽  
Vol 26 (3) ◽  
pp. 21-40 ◽  
Author(s):  
Elena Vasilyeva ◽  
Maik Thiele ◽  
Christof Bornhövd ◽  
Wolfgang Lehner
Keyword(s):  
Pattern Matching ◽  
Search Space ◽  
Management Systems ◽  
Graph Databases ◽  
User Intention ◽  
Graph Query ◽  
Graph Queries ◽  
Data Graph ◽  
Empty Answers ◽  
Selection Of

Empty answers are a major problem by processing pattern matching queries in graph databases. Especially, there can be multiple reasons why a query failed. To support users in such situations, differential queries can be used that deliver missing parts of a graph query. Multiple heuristics are proposed for differential queries, which reduce the search space. Although they are successful in increasing the performance, they can discard query subgraphs relevant to a user. To address this issue, the authors extend the concept of differential queries and introduce top-k differential queries that calculate the ranking based on users' preferences and significantly support the users' understanding of query database management systems. A user assigns relevance weights to elements of a graph query that steer the search and are used for the ranking. In this paper the authors propose different strategies for selection of relevance weights and their propagation. As a result, the search is modelled along the most relevant paths. The authors evaluate their solution and both strategies on the DBpedia data graph.

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