Visual Query System Research Articles

KGNav: A Knowledge Graph Navigational Visual Query System

Visual query is a vital technique for comprehending and analyzing knowledge graphs, which provides an effective method to lower the barrier of querying knowledge graphs for non-professional users. Nevertheless, visual query techniques for knowledge graphs and ontologies that have emerged in recent years cannot bridge the gap between global information provided by the knowledge graph schema and underlying data of knowledge graph. Thus it cannot fully exploit the global information to navigate users for querying knowledge graphs. This demonstration showcases KGNav, a Knowledge Graph Navigational visual query system. KGNav (1) redefines the minimal unit of operation to abstract the conceptual hierarchy, i.e., Knowledge Graph Schema, in the domain from the original knowledge graph in an offline semi-automatic way through the equivalence relations between these units; it also (2) provides a series of operators and an interactive GUI to capture user query intentions, guiding users to explore the Knowledge Graph Schema to achieve in-depth analysis of knowledge graphs. We will demonstrate the capability of KGNav in reducing tedious queries, enabling users to swiftly grasp the structure of the knowledge graph, and performing queries through several fundamental scenarios.

OVI-3: A NoSQL visual query system supporting efficient anti-joins

The aim of this work was to develop a technique to speed up complex joins in an incremental visual query system. When designing a visual, highly interactive interface for ad-hoc (read-only) queries, fast response times are of paramount importance. While a column-oriented DBMS reduces the inherent latency found in relational DBMS, there is still the question of how to index the data, especially so as to support complex joins. Equi-joins that involve a many-to-many relationship are an example of complex joins that arise frequently and whose efficient processing is essential for fast query processing. We present OVI-3, a NoSQL visual query system based on incremental querying that uses a simple directory-based indexing scheme for faster processing of such complex joins. The system has been piloted using real data from a student database at Aalto University. The results demonstrated that for certain complex joins the presented indexing scheme outperforms SQL queries from a data server, especially for queries involving anti-joins (negation), where OVI-3 provided an orders of magnitude speed improvement.

Correlation between the Dissemination of Classic English Literary Works and Cultural Cognition in the New Media Era

With the continuous development of new media technology, the spiritual needs of the masses have been greatly satisfied and the aesthetic ability has also been significantly improved compared with the past. From the current point of view, “literary works,” as the spiritual food of contemporary people, are promoting social spirit. The use of natural language processing and knowledge graph technology can improve cultural cognition to promote the dissemination and development of classic English literature, which has become a necessary means of dissemination of classic English literature. Most of the existing classic English literary works are appreciated based on modern literature datasets. Nowadays, with the continuous development of new media technology, there are fewer studies on the dissemination and cultural cognition of classic English literary works. This makes it impossible for readers to obtain cultural cognition from classic English literary works, making it difficult for the dissemination and development of classic English literary works. In view of the above problems, using natural language processing and knowledge graph technology, taking Shakespeare's play “Hamlet” represented by classic English literary works as an example, the research on the construction method of knowledge graph is carried out and the cultural characteristics in literary works are extracted and analyzed. In parsing, a bidirectional gated recurrent unit network model based on hybrid character embedding is proposed. Based on n-gram embedding, by combining pretraining embedding and radical embedding, it can fully consider the rich semantic information in English literature works to extract. Feature: in terms of named entity recognition, based on the existing iterative atrous convolutional network model, an iterative atrous convolutional network model is proposed. To get the best sequence label and get the last labeled entity information, in terms of knowledge graph construction and visual query, a workflow method for building knowledge graph from unstructured text is proposed and a flask-based knowledge graph visual query system is designed, which applies the best model of the above two tasks. We decode the complete “Hamlet” text, extract entities and their semantic links as nodes and relationships in the knowledge graph, store knowledge through the graph database, and finally form a visual query system that combines the front and back end.

KGVQL: A knowledge graph visual query language with bidirectional transformations

With the rapid development of artificial intelligence, knowledge graphs have been widely recognized as a critical component in many AI techniques and systems. A complex knowledge graph may contain hundreds of millions of nodes and edges, thus is challenging for end-users to understand and query. In this paper, we present a knowledge graph interactive visual query language, KGVQL, to improve the efficiency of end-users’ understanding and querying of knowledge graphs. Furthermore, KGVQL realizes the novel capability of flexible bidirectional transformations between query graphs and query results, therefore significantly assisting end-users in constructing queries over large and unfamiliar knowledge graphs in an incremental way. We present the visual syntax of KGVQL, discuss our design rationale behind this interactive visual query language, and illustrate a number of case studies. We empirically evaluate the effectiveness of a visual query system based on KGVQL against a number of textual and visual query environments over a large knowledge graph, DBpedia. Our evaluation demonstrates the superiority of KGVQL in effectiveness and accuracy.

DDLVis: Real-time Visual Query of Spatiotemporal Data Distribution via Density Dictionary Learning.

Visual query of spatiotemporal data is becoming an increasingly important function in visual analytics applications. Various works have been presented for querying large spatiotemporal data in real time. However, the real-time query of spatiotemporal data distribution is still an open challenge. As spatiotemporal data become larger, methods of aggregation, storage and querying become critical. We propose a new visual query system that creates a low-memory storage component and provides real-time visual interactions of spatiotemporal data. We first present a peak-based kernel density estimation method to produce the data distribution for the spatiotemporal data. Then a novel density dictionary learning approach is proposed to compress temporal density maps and accelerate the query calculation. Moreover, various intuitive query interactions are presented to interactively gain patterns. The experimental results obtained on three datasets demonstrate that the presented system offers an effective query for visual analytics of spatiotemporal data.

A Semantic Approach for Big Data Exploration in Industry 4.0

The growing trends in automation, Internet of Things, big data and cloud computing technologies have led to the fourth industrial revolution (Industry 4.0), where it is possible to visualize and identify patterns and insights, which results in a better understanding of the data and can improve the manufacturing process. However, many times, the task of data exploration results difficult for manufacturing experts because they might be interested in analyzing also data that does not appear in pre-designed visualizations and therefore they must be assisted by Information Technology experts. In this paper, we present a proposal materialized in a semantic-based visual query system developed for a real Industry 4.0 scenario that allows domain experts to explore and visualize data in a friendly way. The main novelty of the system is the combined use that it makes of captured data that are semantically annotated first, and a 2D customized digital representation of a machine that is also linked with semantic descriptions. Those descriptions are expressed using terms of an ontology, where, among others, the sensors that are used to capture indicators about the performance of a machine that belongs to a Industry 4.0 scenario have been modeled. Moreover, this semantic description allows to: formulate queries at a higher level of abstraction, provide customized graphical visualizations of the results based on the format and nature of the data, and download enriched data enabling further types of analysis.

Peax: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning.

We present Peax, a novel feature-based technique for interactive visual pattern search in sequential data, like time series or data mapped to a genome sequence. Visually searching for patterns by similarity is often challenging because of the large search space, the visual complexity of patterns, and the user’s perception of similarity. For example, in genomics, researchers try to link patterns in multivariate sequential data to cellular or pathogenic processes, but a lack of ground truth and high variance makes automatic pattern detection unreliable. We have developed a convolutional autoencoder for unsupervised representation learning of regions in sequential data that can capture more visual details of complex patterns compared to existing similarity measures. Using this learned representation as features of the sequential data, our accompanying visual query system enables interactive feedback-driven adjustments of the pattern search to adapt to the users’ perceived similarity. Using an active learning sampling strategy, Peax collects user-generated binary relevance feedback. This feedback is used to train a model for binary classification, to ultimately find other regions that exhibit patterns similar to the search target. We demonstrate Peax’s features through a case study in genomics and report on a user study with eight domain experts to assess the usability and usefulness of Peax. Moreover, we evaluate the effectiveness of the learned feature representation for visual similarity search in two additional user studies. We find that our models retrieve significantly more similar patterns than other commonly used techniques.

Big Data Analysis of the Ecological Environment in the Dianchi Watershed

To analyze the massive data on the ecological environment in Dianchi Watershed scientifically, understand its situation and dynamic trend over time, we plan to establish a feature-based four-dimensional space-time data model and build an integrated space-time database. The SuperMap map database is used to establish a space-time visual query and analysis system of the ecological environment information based on WebGIS with the B/S structure. The ecological environment information of the Dianchi Watershed is taken as the research object for scientific and comprehensive analysis and management of the ecological environment situation, thereby providing intuitive and reliable data information support for the decision-making of the ecological environment construction.

Query Extension Suggestions for Visual Query Systems Through Ontology Projection and Indexing

Ontology-based visual query formulation is a viable alternative to textual query editors in the Semantic Web domain for extracting data from structured data sources in terms of the skills and knowledge required. A visual query system is at any moment responsible for providing the user with query extension suggestions; however, suggestions leading to empty results are often not useful. To this end, in this article, we first present an approach for projecting OWL 2 ontologies into navigation graphs to be used for query formulation and then a solution where an efficient finite index is used to calculate non-ranked approximated extension suggestions for ontology-based visual query systems using navigation graphs. The results of our experiments suggest that one can efficiently project an ontology into a navigation graph, query it for running an interactive user interface, and suggest query extensions that do not lead to dead-ends.

RSATree: Distribution-Aware Data Representation of Large-Scale Tabular Datasets for Flexible Visual Query.

Analysts commonly investigate the data distributions derived from statistical aggregations of data that are represented by charts, such as histograms and binned scatterplots, to visualize and analyze a large-scale dataset. Aggregate queries are implicitly executed through such a process. Datasets are constantly extremely large; thus, the response time should be accelerated by calculating predefined data cubes. However, the queries are limited to the predefined binning schema of preprocessed data cubes. Such limitation hinders analysts' flexible adjustment of visual specifications to investigate the implicit patterns in the data effectively. Particularly, RSATree enables arbitrary queries and flexible binning strategies by leveraging three schemes, namely, an R-tree-based space partitioning scheme to catch the data distribution, a locality-sensitive hashing technique to achieve locality-preserving random access to data items, and a summed area table scheme to support interactive query of aggregated values with a linear computational complexity. This study presents and implements a web-based visual query system that supports visual specification, query, and exploration of large-scale tabular data with user-adjustable granularities. We demonstrate the efficiency and utility of our approach by performing various experiments on real-world datasets and analyzing time and space complexity.

GraphTQL: A visual query system for graph databases

Abstract End-users who are experts in an application domain need to retrieve data from databases in order to answer particular inquiries. Visual Query Systems (VQSs) facilitate this task; but, formulating complex queries is still challenging for end-users. In particular, VQSs for graph databases can be broadly classified into two groups: One group is based on data exploration and allow for direct manipulation of data graphs, but with limited query expressiveness. The other group, oriented to build visual sentences that represent the query, provide greater expressiveness, but they transfer complex language concepts and operations to the visual language. Thus, this research developed GraphTQL a visual query system that provides greater expressiveness and ease in the query formulation. GraphTQL uses a graph data model to depict the domain of interest at the conceptual level. A set of operators provides the means to transform schema and instance graphs, reducing them progressively to obtain the data of interest. The operators include implicit management of incomplete data; as well as clarification dialogues to guide users in order to express complex filter conditions and to select the paths which connect entities in the graph. The development of GraphTQL followed a user-centered design approach which focused on the medical domain. Thus, potential users’ contribution and feedback played a significant role in this work. A comparison with a graphical query interface for SPARQL showed better results for GraphTQL regarding the number of queries correctly formulated by users, the number of errors and the time spent for query formulation.

An intelligent query approach by integrating visual queries with full-text search

This research focuses on a combined query approach for handling structured and unstructured data. A Visual Query System (VQS) is usually developed for handling structured data. It enables end users to formulate queries through visual representations without the need of having knowledge of database and query languages. As an important branch of today's information retrieval technology, Full-Text Search (FTS) is an important approach for dealing with unstructured data. Hence, a hybrid system can be developed, which integrates a Visual Query System with a Full-Text Search engine and which acts as an intelligent search interface. A customized ranking algorithm can rank the results according to their relevance. This hybrid approach converts the natural language text queries and visual queries into a database query language, such as SQL. It can be adopted for different examples of data with a combination of structured and unstructured types to provide an efficient query interface solution. In particular, it can be integrated in web search engines to integrate structured data with unstructured data in the search interface.

Querying industrial stream-temporal data: An ontology-based visual approach1

An increasing number of sensors are being deployed in business-critical environments, systems, and equipment; and stream a vast amount of data. The operational efficiency and effectiveness of business processes rely on domain experts’ agility in interpreting data into actionable business information. A domain expert has extensive domain knowledge but not necessarily skills and knowledge on databases and formal query languages. Therefore, centralised approaches are often preferred. These require IT experts to translate the information needs of domain experts into extract-transform-load (ETL) processes in order to extract and integrate data and then let domain experts apply predefined analytics. Since such a workflow is too time intensive, heavy-weight and inflexible given the high volume and velocity of data, domain experts need to extract and analyse the data of interest directly. Ontologies, i.e., semantically rich conceptual domain models, present an intelligible solution by describing the domain of interest on a higher level of abstraction closer to the reality. Moreover, recent ontology-based data access (OBDA) technologies enable end users to formulate their information needs into queries using a set of terms defined in an ontology. Ontological queries could then be translated into SQL or some other database query languages, and executed over the data in its original place and format automatically. To this end, this article reports an ontology-based visual query system (VQS), namely OptiqueVQS, how it is extended for a stream-temporal query language called STARQL, a user experiment with the domain experts at Siemens AG, and STARQL’s query answering performance over a proof of concept implementation for PostgreSQL.

Experiencing OptiqueVQS: a multi-paradigm and ontology-based visual query system for end users

Data access in an enterprise setting is a determining factor for value creation processes, such as sense-making, decision-making, and intelligence analysis. Particularly, in an enterprise setting, intuitive data access tools that directly engage domain experts with data could substantially increase competitiveness and profitability. In this respect, the use of ontologies as a natural communication medium between end users and computers has emerged as a prominent approach. To this end, this article introduces a novel ontology-based visual query system, named OptiqueVQS, for end users. OptiqueVQS is built on a powerful and scalable data access platform and has a user-centric design supported by a widget-based flexible and extensible architecture allowing multiple coordinated representation and interaction paradigms to be employed. The results of a usability experiment performed with non-expert users suggest that OptiqueVQS provides a decent level of expressivity and high usability and hence is quite promising.

Design, realization and user evaluation of the SmartVortex Visual Query System for accessing data streams in industrial engineering applications

Objective: In this paper we present a Visual Query System, which allows users to graphically build queries over data streams and traditional relational data. The Visual Query System runs on top of a DSMS (Data Stream Management System).Methods: The system has been designed and implemented following the user-centered design approach. Two different releases of the system have been incrementally and iteratively designed and evaluated. The first release has been evaluated using heuristic evaluation. The second release, whose design was a refinement based on the results of the foregoing heuristic evaluation, was evaluated by several users. Moreover, a comparative evaluation involving users has been conducted on the second release.Results: The paper presents the visual query system, and the results ofthe various evaluations.Conclusion: Visual Query Systems for data streams are an effective tool for industrial engineers in querying and analyzing data streams from equipments.Practice and implications: The proposed visual query system has been employed in real industrial scenarios in order to visually query and analyze data streams produced by industrial machines.

Visual spatio-temporal function-based querying

Visual interfaces are very important for human interactions in cyberworlds. Visual spatio-temporal querying should be one of the basic tools for data mining and retrieval in cyberworlds. In this paper, we propose a novel function-based query model for arbitrary shape spatio-temporal querying. The queries are defined as geometric shapes changing over time. In our model, data are interpreted geometrically as multidimensional points with time dimension or as moving points. The queries are formulated with geometric objects and operations over them to form the query solid changing over time. The proposed query model allows us to pose arbitrary shape spatio-temporal range queries. With the uniform geometric model we integrate visual mining and querying of time-dependent data employing 3D visualization tools. It allows for creating an intuitive visual interface using 2D projections of 3D query shapes. Our approach combines visualization of spatio-temporal data with visualization of the range query formulation employing very compact function-based query model. The implemented visual query system and its visual interface are proposed and described. An example of application of the system in analysis of simulation results in molecular dynamics is considered.

HVQS: The Hierarchical Visual Query System for Databases

This work describes the design and implementation of hierarchical visual query system (HVQS). It is a user interface for querying and browsing databases in a hierarchical database schema. HVQS enables users to use ahierarchical visual query , i.e. they can visually formulate a query and watch the query results on a few higher layers of database schema. This simplifies the query, reduces the failure rate, conserves query time, allows users to work on a database schema with wider scope, and helps users understand database content and focus on meaningful data using a top-down method. This work first gives an overview of the hierarchical visual query, and then describes the prototype of HVQS, defines a hierarchical graph for representing hierarchical database schema, and discusses the Query Manager and Visual Interface Manager of HVQS. Also described herein is the expressive power and semantics of HVQS query. Finally, a evaluation report of an experiment is given to demonstrate that HVQS improves the query success rate and facilitates users in query formulation.

MitBASE pilot: a database on nuclear genes involved in mitochondrial biogenesis and its regulation in Saccharomyces cerevisiae.

In the framework of the EU BIOTECH PROGRAM and within the 'MITBASE: a comprehensive and integrated database on mtDNA' project, we have prepared a pilot database (MitBASE Pilot) on nuclear genes involved in mitochondrial biogenesis and its regulation in Saccharomyces cerevisiae. MitBASE Pilot includes nuclear genes encoding mitochondrial proteins as well as nuclear genes encoding products which are localised in other sub-cellular compartments but nevertheless interact with mitochondrial functions. Genes have been classified on the basis of the mitochondrial process in which they participate and the mitochondrial phenotype of the gene knockout. The structure of the MitBASE Pilot database has been conceived for a flexible organisation of the information. An intuitive visual query system has been developed which allows users to select information in different combinations, both in the query and the output format, according to their needs. MitBASE Pilot is a relational database, is maintained at the EMBL-European Bioinformatics Institute (EBI) and is available at the World Wide Web site http://www3.ebi.ac. uk/Research/Mitbase/mitbiog.pl

DataSplash

Database visualization is an area of growing importance as database systems become larger and more accessible. DataSplash is an easy-to-use, integrated environment for navigating, creating, and querying visual representations of data. We will demonstrate the three main components which make up the DataSplash environment: a navigation system, a direct-manipulation interface for creating and modifying visualizations, and a direct-manipulation visual query system.

Query by diagram: A fully visual query system

The need of a friendly man-machine interaction is becoming crucial for a large variety of applications, in particular, those requiring frequent extraction of information from the database. Experience suggests that traditional query languages are not friendly enough for the causal user: s/he is requested to formulate queries in a textural language, without any iconic or spatial clues to help the querying process. A new generation of languages (visual languages) has been recently investigated, that attempts to make extensive use of the person's instincts and senses. In this paper we propose a fully visual system, called Query by Diagram* (QBD*), which is based on a conceptual data model, a query language defined on this model and a graphical user interface. The main characteristics of the interface are the ease of use, and the availability of a rich set of primitives for both schema selection and query formulation. Unlike many present proposals of graphical query systems, graphical operations are formally defined from both a syntactic and a semantic point of view.