Interactive Query Research Articles

An efficient ontology model with query execution for accurate document content extraction

<span lang="EN-US">The technique of extracting important documents from massive data collections is known as information retrieval (IR). The dataset provider coupled with the increasing demand for high-quality retrieval results, has resulted in traditional information retrieval approaches being increasingly insufficient to meet the challenge of providing high-quality search results. Research has concentrated on information retrieval and interactive query formation through ontologies in order to overcome these challenges, with a specific emphasis on enhancing the functionality between information and search queries in order to bring the outcome sets closer to the research requirements of users. In the context of document retrieval technologies, it is a process that assists researchers in extracting documents from data collections. It is discussed in this research how to use ontology-based information retrieval approaches and techniques, taking into account the issues of ontology modelling, processing, and the transformation of ontological knowledge into database search queries. In this research work, an efficient optimized ontology model with query execution for content extraction from documents (OOM-QE-CE) is proposed. The existing ontology-to-database transformation and mapping methodologies are also thoroughly examined in terms of data and semantic loss, structural mapping and domain knowledge applicability.</span>

Design of Internet of Things Information Interactive Perception System Based on 5G Mobile Communication Technology in a Hospital Network

Abstract In the communication process of 5G big data Internet of Things, because of the difference of communication data, the data interaction process needs information mutual recognition scheduling, which greatly reduces the effectiveness of the interactive perception system. In order to improve the real-time performance of platform interaction in the Internet of Things environment, an Internet of Things information interaction perception system based on 5G mobile communication technology is designed. This is mainly important in a hospital network. The system perception layer completes the accurate acquisition of Internet of Things information through data acquisition module and transmits it to the application layer through 5G mobile communication module in the network layer. The intelligent interaction module based on context awareness in the application layer will be heterogeneous and unstructured. The Internet of Things perceived data is encapsulated in context as a unified data processing object of the Internet of Things. The context rule-matching algorithm based on Storm is used to achieve accurate matching between heterogeneous and unstructured perceptual data and rules, and an interactive perceptual parallel query scheduling strategy is used to achieve the optimal scheduling of information mutual perception in the Internet of Things. The results show that there is a positive correlation between the validity of sensing temperature information and the amount of sensing data. The average data processing delay of the system is stable at 50 ms, which meets the real-time requirement of data processing.

S4: Self-Supervised Learning of Spatiotemporal Similarity.

We introduce an ML-driven approach that enables interactive example-based queries for similar behavior in ensembles of spatiotemporal scientific data. This addresses an important use case in the visual exploration of simulation and experimental data, where data is often large, unlabeled and has no meaningful similarity measures available. We exploit the fact that nearby locations often exhibit similar behavior and train a Siamese Neural Network in a self-supervised fashion, learning an expressive latent space for spatiotemporal behavior. This space can be used to find similar behavior with just a few user-provided examples. We evaluate this approach on several ensemble datasets and compare with multiple existing methods, showing both qualitative and quantitative results.

Retraction Note: Design of interactive digital media course teaching information query system

Retraction Note: Design of interactive digital media course teaching information query system

HTCA: a database with an in-depth characterization of the single-cell human transcriptome.

Single-cell RNA-sequencing (scRNA-seq) is one of the most used single-cell omics in recent decades. The exponential growth of single-cell data has immense potential for large-scale integration and in-depth explorations that are more representative of the study population. Efforts have been made to consolidate published data, yet extensive characterization is still lacking. Many focused on raw-data database constructions while others concentrate mainly on gene expression queries. Hereby, we present HTCA (www.htcatlas.org), an interactive database constructed based on ∼2.3 million high-quality cells from ∼3000 scRNA-seq samples and comprised in-depth phenotype profiles of 19 healthy adult and matching fetal tissues. HTCA provides a one-stop interactive query to gene signatures, transcription factor (TF) activities, TF motifs, receptor-ligand interactions, enriched gene ontology (GO) terms, etc. across cell types in adult and fetal tissues. At the same time, HTCA encompasses single-cell splicing variant profiles of 16 adult and fetal tissues, spatial transcriptomics profiles of 11 adult and fetal tissues, and single-cell ATAC-sequencing (scATAC-seq) profiles of 27 adult and fetal tissues. Besides, HTCA provides online analysis tools to perform major steps in a typical scRNA-seq analysis. Altogether, HTCA allows real-time explorations of multi-omics adult and fetal phenotypic profiles and provides tools for a flexible scRNA-seq analysis.

High-Dimensional Data Cubes

This paper introduces an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. The approach is based on binary(-domain) data cubes that are judiciously partially materialized; the missing information can be quickly reconstructed using statistical or linear programming techniques. This enables new applications such as exploratory data analysis for feature engineering and other fields of data science. Moreover, it removes the need to compromise when building a data cube - all columns that we might ever wish to use can be included as dimensions. Our approach also speeds up certain dice, roll-up, and drill-down operations on data cubes with hierarchical dimensions compared to traditional data cubes.

A systematic overview on methods to protect sensitive data provided for various analyses

In view of the various methodological developments regarding the protection of sensitive data, especially with respect to privacy-preserving computation and federated learning, a conceptual categorization and comparison between various methods stemming from different fields is often desired. More concretely, it is important to provide guidance for the practice, which lacks an overview over suitable approaches for certain scenarios, whether it is differential privacy for interactive queries, k-anonymity methods and synthetic data generation for data publishing, or secure federated analysis for multiparty computation without sharing the data itself. Here, we provide an overview based on central criteria describing a context for privacy-preserving data handling, which allows informed decisions in view of the many alternatives. Besides guiding the practice, this categorization of concepts and methods is destined as a step towards a comprehensive ontology for anonymization. We emphasize throughout the paper that there is no panacea and that context matters.

Identifying the landscape of developmental toxicity new approach methodologies.

The dynamics and complexities of in utero fetal development create significant challenges in transitioning from lab animal-centric developmental toxicity testing methods to assessment strategies based on new approach methodologies (NAMs). Nevertheless, considerable progress is being made, stimulated by increased research investments and scientific advances, such as induced pluripotent stem cell-derived models. To help identify developmental toxicity NAMs for toxicity screening and potential funding through the American Chemistry Council's Long-Range Research Initiative, a systematic literature review was conducted to better understand the current landscape of developmental toxicity NAMs. Scoping review tools were used to systematically survey the literature (2010-2021; ~18,000 references identified), results and metadata were then extracted, and a user-friendly interactive dashboard was created. The data visualization dashboard, developed using Tableau® software, is provided as a free, open-access web tool. This dashboard enables straightforward interactive queries and visualizations to identify trends and to distinguish and understand areas or NAMs where research has been most, or least focused. Herein, we describe the approach and methods used, summarize the benefits and challenges of applying the systematic-review techniques, and highlight the types of questions and answers for which the dashboard can be used to explore the many different facets of developmental toxicity NAMs.

Discerning appropriate reviews based on hierarchical deep neural network for answering product-related queries

In recent years, E-Commerce is globally increasing among online purchaser, in which customer post product related queries for finding the best product in online shopping. Manually answering the product related queries in real-time, cause online traffic and practically not possible. So, automatic answering system is helpful for answering product related queries. But, the product queries are always in product-explicit, so discovering related product queries and recovering its responds is distinctly be impractical. Accordingly, we propose Hierarchical Deep Neural Network (HiDeNN) model using MOQA framework to discern the appropriate reviews based on Mixtures of Opinions Question Answering (MOQA). The Hierarchical Deep Neural Network provides discerning the most relevant review for queries and it also provides the relevant answer for specific product category queries. The proposed method is executed on Python and it provides 9.594% and 7.574% higher accuracy value for Discerning Appropriate Reviews compared with the existing method like Relevant Reviews for Answering Product-related Queries (MOQA-BERTQA+FLTR+PT) and IQA: Interactive Query Construction on Semantic Question Answering Systems (IQC-SQA). The experimental result indicates that the proposed MOQA- HiDeNN method can efficiently and accurately get the optimal global solutions for recognizing the appropriate discerning of most relevant review for queries and it also provides the relevant answer for specific product category queries.

GraceOutZip: Graceful labeled Graph summarization approach with Percolated anomalous discovery

Voluminous graph data management is a daunting problem in every real world application of a kind, besides the advancements in computation and storage technology. Efficient graph summarization techniques were contributed to achieve the substantial need for preserving novelty in social graphs. A GraceOutZip compression friendly graph reordering technique using graceful labeling strategy is adopted. User defined probabilistic selection method that provides unique labels for every identified outlier for potential use. Proposed method exploits unicycle-star based community representation rendering assignment of both node and edge labels based on graceful property. A novel mathematical programming model GraceOutZip is proposed to perform lossless compression with graph decomposition and unique label arrangement with intention for futuristic graph reconstruction. The experimental study on different real world network datasets demonstrates that GraceOutZip shows better scalable performance in perspective of interactive large-scale visual analytics and query optimization with 4 times better compression than the state-of-the-art representative method.

Optimizing the computation of overriding in [formula omitted

One of the factors that is hindering the adoption of nonmonotonic description logics in applications is performance. Even when monotonic and nonmonotonic inferences have the same asymptotic complexity, the implementation of nonmonotonic reasoning may be significantly slower. This happens also with the family of nonmonotonic logics DLN.In this work we address this issue by introducing two optimizations for reasoning in DLN. The first optimization, called optimistic evaluation, aims at exploiting incremental reasoning in a better way. The second is a module extractor forDLN, that has the purpose of focusing reasoning on a relevant subset of the knowledge base. The proposed optimization iterates the module extractor that, unlike classical module extractors, is not idempotent, in general.We prove that the proposed optimizations are correct and complete, and assess them through extensive experiments. Our results prove that optimized DLN reasoning is often compatible with interactive query answering, which brings nonmonotonic description logics closer to practical applications.

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.

ConfusionVis: Comparative evaluation and selection of multi-class classifiers based on confusion matrices

In machine learning, the presumably best model is selected from a variety of model candidates generated by testing different model types, hyperparameters, or feature subsets. The advent of deep learning has made model selection even more challenging due to the huge parameter search space. Relying on a single metric to select the best model does not consider class imbalances or the different costs of misclassifications. We argue that incorporating human knowledge to interactively analyse the per-class errors and class confusions over all model candidates enables a more efficient training process and yields better models for given applications. This paper proposes the model-agnostic approach ConfusionVis which allows to comparatively evaluate and select multi-class classifiers based on their confusion matrices. This contributes to making the models’ results understandable, while treating the models as black boxes. Therefore, we propose a novel method to measure and visualise distances between confusion matrices and an interactive query interface to incorporate all composition levels of class errors. The approach is evaluated in a user study and the applicability is shown by a case study where marine biologists investigate the conservation efforts of baleen whales by classifying whale species in acoustic recordings. ConfusionVis is available online: https://www.ml-and-vis.org/confusionvis.

A gaze‐based interaction method for large‐scale and large‐space disaster scenes within mobile virtual reality

AbstractA three‐dimensional (3D) visualization of disaster scenes based on mobile virtual reality (VR) can improve the application scenarios and emergency service capabilities of traditional 3D visualization of disaster scenes. Because a smartphone needs to be placed into a mobile head‐mounted display, conventional touch scene interaction cannot be used by mobile VR, and the user's gaze usually serves as the default scene interaction method. However, the existing gaze‐based interaction methods for mobile VR scenes are passive scene interaction methods and cannot meet the basic interaction requirement for actively roaming through and exploring large‐scale and large‐space disaster scenes. Therefore, this study focuses on gaze‐based mobile VR interactions to satisfy the various interaction requirements of large‐scale and large‐space disaster scenes. First, a dynamic user interface (UI) generation method for gaze interaction in large‐scale and large‐space disaster scenes is proposed to solve the problem of the active exploration of mobile VR disaster scenes. Second, disaster scene exploration and disaster information query methods based on a dynamic UI and gaze are proposed. Finally, using a flood disaster as an example, a prototype system and associated experiments are discussed. As indicated by the experimental results, the gaze‐based mobile VR interaction methods addressed in this study can effectively support users in actively roaming through and exploring large‐scale and large‐space disaster scenes, disaster simulation analysis, and the interactive querying of disaster information within mobile VR, making the effective interaction of mobile VR disaster scenes possible.

MUSE: Multimodal Separators for Efficient Route Planning in Transportation Networks

Many algorithms compute shortest-path queries in mere microseconds on continental-scale networks. Most solutions are, however, tailored to either road or public transit networks in isolation. To fully exploit the transportation infrastructure, multimodal algorithms are sought to compute shortest paths combining various modes of transportation. Nonetheless, current solutions still lack performance to efficiently handle interactive queries under realistic network conditions where traffic jams, public transit cancelations, or delays often occur. We present a multimodal separators–based algorithm (MUSE), a new multimodal algorithm based on graph separators to compute shortest travel time paths. It partitions the network into independent, smaller regions, enabling fast and scalable preprocessing. The partition is common to all modes and independent of traffic conditions so that the preprocessing is only executed once. MUSE relies on a state automaton that describes the sequence of modes to constrain the shortest path during the preprocessing and the online phase. The support of new sequences of mobility modes only requires the preprocessing of the cliques, independently for each partition. We also augment our algorithm with heuristics during the query phase to achieve further speedups with minimal effect on correctness. We provide experimental results on France’s multimodal network containing the pedestrian, road, bicycle, and public transit networks.

Estimate the Performance of Cloudera Decision Support Queries

Hive and Impala queries are used to process a big amount of data. The overwriting amount of information requires an efficient data processing system. When we deal with a long-term batch query and analysis Hive will be more suitable for this query. Impala is the most powerful system suitable for real-time interactive Structured Query Language (SQL) query which are added a massive parallel processing to Hadoop distributed cluster. The data growth makes a problem with SQL Cluster because the execution processing time is increased. In this paper, a comparison is demonstrated between the performance time of Hive, Impala and SQL on two different data models with different queries chosen to test the performance. The results demonstrate that Impala outperforms Hive and SQL cluster when it comes to analyze data and processing tasks. Using two benchmark datasets, TPC-H and statistical computing, we compare the performance of Hive, Impala, and SQL clusters 2009 Statistical Graphics Data Expo.

Copyright Transaction Mode and Copyright Protection Risk Analysis of Green Industry from the Perspective of Information Environment

At present, the requirements of the current environment and background require us to have higher requirements for the data mode of copyright transaction. According to the previous way to conduct copyright transactions, it will cause a lot of unnecessary trouble, and according to the establishment of the copyright transaction database, we analyse the data types and patterns of copyright transactions and related copyright transaction protection measures and risk prevention. According to the copyright transaction database, we adopt BS network model, CIF copyright transaction data set model, CI data set model, data interactive query technology, and block matching technology to deal with the copyright transaction data model and copyright transaction risk prevention under the background of Informa ionization as follows: (1) we use BS network model, CIF copyright transaction data set model, and CI data set model to analyse and detect the copyright transaction data before transaction, which better reflects the network model effect of self‐enhancing operation rate of copyright transaction data and effectively avoids overfitting problem. (2) By using interactive data query technology, the data presented in the preparation work before the transaction is more detailed, the samples are normalized, and the collected transaction data are tested for accuracy, which can eliminate potential dangers. (3) The method of similarity feature reconstruction of copyright transaction data set completes the extraction of association matching features of copyright transaction data system, which provides strong support for us to prevent risks and effectively match data in copyright transaction.

ProRes: Proactive re-selection of materialized views

Materialized View Selection is one of the most studied problems in the database field, covering SQL and NoSQL technologies as well as different deployment infrastructures (centralized, parallel, cloud). This problem has become more complex with the arrival of data warehouses, being coupled with the physical design phase that aims at optimizing query performance. Selecting the best set of materialized views to optimize query performance is a challenging task. Given their importance and the complexity of their selection, several research efforts both from academia and industry have been conducted. Results are promising ? some solutions are being implemented by commercial and open-source DBMSs ?, but they do not factor in the following properties of nowadays analytical queries: (i) largescale queries, (ii) their dynamicity, and (iii) their high interaction. Studies to date fail to consider that complete set of properties. Considering the three properties simultaneously is crucial regarding today?s analytical requirements, which involve dynamic and interactive queries. In this paper, we first present a concise state of the art of the materialized view selection problem (VSP) by analyzing its ecosystem. Secondly, we propose a proactive re-selection approach that considers the three properties concurrently. It features two main phases: offline and online. In the offline phase, we manage a set of the first queries based on a given threshold ? by selecting materialized views through a hypergraph structure. The second phase manages the addition of new queries by scheduling them, updates the structure of the hypergraph, and selects new views by eliminating the least beneficial ones. Finally, extensive experiments are conducted using the Star Schema Benchmark data set to evaluate the effectiveness and efficiency of our approach.

Interactive Visual Pattern Search on Graph Data via Graph Representation Learning.

Graphs are a ubiquitous data structure to model processes and relations in a wide range of domains. Examples include control-flow graphs in programs and semantic scene graphs in images. Identifying subgraph patterns in graphs is an important approach to understand their structural properties. We propose a visual analytics system GraphQ to support human-in-the-loop, example-based, subgraph pattern search in a database containing many individual graphs. To support fast, interactive queries, we use graph neural networks (GNNs) to encode a graph as fixed-length latent vector representation, and perform subgraph matching in the latent space. Due to the complexity of the problem, it is still difficult to obtain accurate one-to-one node correspondences in the matching results that are crucial for visualization and interpretation. We, therefore, propose a novel GNN for node-alignment called NeuroAlign, to facilitate easy validation and interpretation of the query results. GraphQ provides a visual query interface with a query editor and a multi-scale visualization of the results, as well as a user feedback mechanism for refining the results with additional constraints. We demonstrate GraphQ through two example usage scenarios: analyzing reusable subroutines in program workflows and semantic scene graph search in images. Quantitative experiments show that NeuroAlign achieves 19%-29% improvement in node-alignment accuracy compared to baseline GNN and provides up to 100× speedup compared to combinatorial algorithms. Our qualitative study with domain experts confirms the effectiveness for both usage scenarios.

Applying NoSQL Data Adapter with the Learning Paths mechanism for better knowledge transfer in the age of distance learning

Database technology applies to many engineering problems. Hence it is the subject of study at many university majors. Since teaching practical usage of theory at a distance is difficult or even impossible, we present novel mechanisms that significantly enrich e-learning platform functionalities in individualized learning of databases installed on the teacher-managed server. The crucial component of our proposal, Data Adapter, allows for interactive querying and manipulating of the NoSQL databases through an e-learning platform. The Adapter is complemented by the Learning Paths component, which enables teachers the creation of individualized didactic paths for students with special educational needs. The e-learning platform load tests confirm that new activities do not substantially affect the comfort of students’ work. Its impact on learning effectiveness was verified by rating the competence test for the group of 104 course participants. Half of them were the participants of the e-learning NoSQL course and used the developed mechanisms, while the remaining, control, group was taught with the conventional methods. The research shows that the proposal successfully improves teaching modern NoSQL languages. The scores improvement in the e-learning group of students compared to results in the control group is noticeable, and observed differences are statistically significant.