Approach For Queries Research Articles

Accurate Sampling-Based Cardinality Estimation for Complex Graph Queries

Accurately estimating the cardinality (i.e., the number of answers) of complex queries plays a central role in database systems. This problem is particularly difficult in graph databases, where queries often involve a large number of joins and self-joins. Recently, Park et al. [ 55 ] surveyed seven state-of-the-art cardinality estimation approaches for graph queries. The results of their extensive empirical evaluation show that a sampling method based on the WanderJoin online aggregation algorithm [ 47 ] consistently offers superior accuracy. We extended the framework by Park et al. [ 55 ] with three additional datasets and repeated their experiments. Our results showed that WanderJoin is indeed very accurate, but it can often take a large number of samples and thus be very slow. Moreover, when queries are complex and data distributions are skewed, it often fails to find valid samples and estimates the cardinality as zero. Finally, complex graph queries often go beyond simple graph matching and involve arbitrary nesting of relational operators such as disjunction, difference, and duplicate elimination. Neither of the methods considered by Park et al. [ 55 ] is applicable to such queries. In this article, we present a novel approach for estimating the cardinality of complex graph queries. Our approach is inspired by WanderJoin, but, unlike all approaches known to us, it can process complex queries with arbitrary operator nesting. Our estimator is strongly consistent, meaning that the average of repeated estimates converges with probability one to the actual cardinality. We present optimisations of the basic algorithm that aim to reduce the chance of producing zero estimates and improve accuracy. We show empirically that our approach is both accurate and quick on complex queries and large datasets. Finally, we discuss how to integrate our approach into a simple dynamic programming query planner, and we confirm empirically that our planner produces high-quality plans that can significantly reduce end-to-end query evaluation times.

Shift-Reduce Task-Oriented Semantic Parsing with Stack-Transformers

Intelligent voice assistants, such as Apple Siri and Amazon Alexa, are widely used nowadays. These task-oriented dialogue systems require a semantic parsing module in order to process user utterances and understand the action to be performed. This semantic parsing component was initially implemented by rule-based or statistical slot-filling approaches for processing simple queries; however, the appearance of more complex utterances demanded the application of shift-reduce parsers or sequence-to-sequence models. Although shift-reduce approaches were initially considered the most promising option, the emergence of sequence-to-sequence neural systems has propelled them to the forefront as the highest-performing method for this particular task. In this article, we advance the research on shift-reduce semantic parsing for task-oriented dialogue. We implement novel shift-reduce parsers that rely on Stack-Transformers. This framework allows to adequately model transition systems on the transformer neural architecture, notably boosting shift-reduce parsing performance. Furthermore, our approach goes beyond the conventional top-down algorithm: we incorporate alternative bottom-up and in-order transition systems derived from constituency parsing into the realm of task-oriented parsing. We extensively test our approach on multiple domains from the Facebook TOP benchmark, improving over existing shift-reduce parsers and state-of-the-art sequence-to-sequence models in both high-resource and low-resource settings. We also empirically prove that the in-order algorithm substantially outperforms the commonly used top-down strategy. Through the creation of innovative transition systems and harnessing the capabilities of a robust neural architecture, our study showcases the superiority of shift-reduce parsers over leading sequence-to-sequence methods on the main benchmark.

VQFT: A Visual Query Approach Based on Full-Text Search for Knowledge Graphs

Existing knowledge graph query approaches, whether traditional textual query languages or visual query languages, have steep learning curves that are unfriendly for non-expert users. This demonstration presents a Visual Query approach based on Full-Text search for knowledge graphs, called VQFT, which simplifies the process of querying knowledge graphs for users. Inspired by full-text search techniques, VQFT aims to combine the user-friendliness of visual query with the intuitiveness of full-text search , enabling users to query knowledge graphs as straightforward as using a search engine. Faceted full-text indexes, visual query constructor , and an interactive user interface are designed to achieve this goal. User tests and surveys have demonstrated that VQFT is more user-friendly and easier to learn than existing methods, which simplifies the construction of knowledge graph queries for non-expert users.

An Adaptive Query Approach for Extracting Medical Images for Disease Detection Applications

An Adaptive Query Approach for Extracting Medical Images for Disease Detection Applications

A national evaluation analysis and expert interview study of real-world data sources for research and healthcare decision-making

Real-world data (RWD) can provide intel (real-world evidence, RWE) for research and development, as well as policy and regulatory decision-making along the full spectrum of health care. Despite calls from global regulators for international collaborations to integrate RWE into regulatory decision-making and to bridge knowledge gaps, some challenges remain. In this work, we performed an evaluation of Austrian RWD sources using a multilateral query approach, crosschecked against previously published RWD criteria and conducted direct interviews with representative RWD source samples. This article provides an overview of 73 out of 104 RWD sources in a national legislative setting where major attempts are made to enable secondary use of RWD (e.g. law on the organisation of research, "Forschungsorganisationsgesetz"). We were able to detect omnipresent challenges associated with data silos, variable standardisation efforts and governance issues. Our findings suggest a strong need for a national health data strategy and data governance framework, which should inform researchers, as well as policy- and decision-makers, to improve RWD-based research in the healthcare sector to ultimately support actual regulatory decision-making and provide strategic information for governmental health data policies.

Streamlining Industrial Robot Maintenance: An Intelligent Voice Query Approach for Enhanced Efficiency

Streamlining Industrial Robot Maintenance: An Intelligent Voice Query Approach for Enhanced Efficiency

Logical–Mathematical Foundations of a Graph Query Framework for Relational Learning

Relational learning has attracted much attention from the machine learning community in recent years, and many real-world applications have been successfully formulated as relational learning problems. In recent years, several relational learning algorithms have been introduced that follow a pattern-based approach. However, this type of learning model suffers from two fundamental problems: the computational complexity arising from relational queries and the lack of a robust and general framework to serve as the basis for relational learning methods. In this paper, we propose an efficient graph query framework that allows for cyclic queries in polynomial time and is ready to be used in pattern-based learning methods. This solution uses logical predicates instead of graph isomorphisms for query evaluation, reducing complexity and allowing for query refinement through atomic operations. The main differences between our method and other previous pattern-based graph query approaches are the ability to evaluate arbitrary subgraphs instead of nodes or complete graphs, the fact that it is based on mathematical formalization that allows the study of refinements and their complementarity, and the ability to detect cyclic patterns in polynomial time. Application examples show that the proposed framework allows learning relational classifiers to be efficient in generating data with high expressiveness capacities. Specifically, relational decision trees are learned from sets of tagged subnetworks that provide both classifiers and characteristic patterns for the identified classes.

Context based diversification on keyword search by conceptualization of typed terms of the query

The human era is evolved and dominated through the ultimate intention to know about the Universe and its assets more and more. This in turn persuaded him to gather the immense information of need in the form of theory, tools, intuitions, visuals, and ultimately as the form of abstract objects. In modern Information practice, the art of Conceptualization and the induction of the data driven paradigm identifies the user query intention over a Conceptual network to mold a confident search segment of user choice. The proposed Conceptualization with Typed Terms of Query (CTTQ) approach derives a confident segment of the user query which is contextually measured by means of novel randomized Machine learning algorithms and its surrogates. The Normalized Discounted Cumulative Gain (nDCG) measures of the CTTQ show the usefulness of the query suggestions. It's efficiency improvements are represented with varying impact lexical feature indicator (θ) values.

An ontology-based approach for harmonization and cross-cohort query of Alzheimer’s disease data resources

BackgroundIn the United States, the National Alzheimer’s Coordinating Center (NACC) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) are two major data sharing resources for Alzheimer’s Disease (AD) research. NACC and ADNI strive to make their data more FAIR (findable, interoperable, accessible and reusable) for the broader research community. However, there is limited work harmonizing and supporting cross-cohort interoperability of the two resources.MethodIn this paper, we leverage an ontology-based approach to harmonize data elements in the two resources and develop a web-based query system to search patient cohorts across the two resources. We first mapped data elements across NACC and ADNI, and performed value harmonization for the mapped data elements with inconsistent permissible values. Then we built an Alzheimer’s Disease Data Element Ontology (ADEO) to model the mapped data elements in NACC and ADNI. We further developed a prototype cross-cohort query system to search patient cohorts across NACC and ADNI.ResultsAfter manual review, we found 172 mappings between NACC and ADNI. These 172 mappings were further used to construct common concepts in ADEO. Our data element mapping and harmonization resulted in five files storing common concepts, variables in NACC and ADNI, mappings between variables and common concepts, permissible values of categorical type data elements, and coding inconsistency harmonization, respectively. Our cross-cohort query system consists of three core architectural elements: a web-based interface, an advanced query engine, and a backend MongoDB database.ConclusionsIn this work, ADEO has been specifically designed to facilitate data harmonization and cross-cohort query of NACC and ADNI data resources. Although our prototype cross-cohort query system was developed for exploring NACC and ADNI, its backend and frontend framework has been designed and implemented to be generally applicable to other domains for querying patient cohorts from multiple heterogeneous data sources.

Pseudo-Mono for Monocular 3D Object Detection in Autonomous Driving

Current monocular 3D object detection algorithms generally suffer from inaccurate depth estimation, which leads to reduction of detection accuracy. The depth error from image-to-image generation for the stereo view is insignificant compared with the gap in single-image generation. Therefore, a novel pseudo-monocular 3D object detection framework is proposed, which is called Pseudo-Mono. Particularly, stereo images are brought into monocular 3D detection. Firstly, stereo images are taken as input, then a lightweight depth predictor is used to generate the depth map of input images. Secondly, the left input images obtained from stereo camera are used as subjects, which generate enhanced visual feature and multi-scale depth feature by depth indexing and feature matching probabilities, respectively. Finally, sparse anchors set by the foreground probability maps and the multi-scale feature maps are used as reference points to find the suitable initialization approach of object query. The encoded visual feature is adopted to enhance object query for enabling deep interaction between visual feature and depth feature. Compared with popular monocular 3D object detection methods, Pseudo-Mono is able to achieve richer fine-grained information without additional data input. Extensive experimental results on the datasets of KITTI, NuScenes, and MS-COCO demonstrate the generalizability and portability of the proposed method. The effectiveness and efficiency of Pseudo-Mono have been demonstrated by extensive ablation experiments. Experiments on a real vehicle platform have shown that the proposed method maintains high performance in complex real-world environments.

Empowering Authenticated and Efficient Queries for STK Transaction-Based Blockchains

Owing to the attractive properties of decentralization, unforgeability, transparency, and traceability, blockchain is increasingly being used in various scenarios such as supply chain and public services, where massive Spatial-Temporal-Keywords (STK) transactions need to be packaged to the blockchain systems. However, due to the multi-dimensionality of STK transactions and transaction randomness among different blocks, existing solutions fail to enable queries in a verifiable and efficient way for blockchains storing multidimensional transactions. To this end, this paper takes the first step to innovatively propose an authenticated and efficient query approach in hybrid blockchain systems consisting of on-chain and off-chain parts. We first design a new data structure named MRK-Tree in the block body, which organizes STK transactions for efficient nodes pruning of both kNN and range queries. Then we propose an improved block header, which improves the efficient pruning of blocks on the basis of ensuring the authentication of query results. Also, we design a cross-block searching algorithm named Efficient Block Pruning (EBP) and intra-block searching algorithms named Authenticated kNN/Range Query (AKQ/ARQ) to accelerate authenticated queries for multiple MRK-Trees in the hybrid blockchain systems. Authentication mechanisms are proposed to ensure the soundness and completeness of query results. Rigorous security analysis validates the practicability of the proposed approach. We build a blockchain prototype to comprehensively evaluate the performance of proposed query schemes. Extensive evaluation results with real datasets reveal that our approach can ensure authenticated queries, meanwhile improving the time efficiency by up to 36.45x and space efficiency by up to 4 orders of magnitude compared with the well-known benchmark query schemes.

A privacy-aware visual query approach for location-based data

Visual querying of location-based data assists users in expressing query requirements, investigating query results and making inferences. However, directly accessing data records exposes individual location information and may cause privacy issues. Conventional aggregation-based methods can preserve location-relevant privacy but may lead to the loss of detailed information and failure of analysis. Visualization aids users in gaining a deeper comprehension of the query process and the variation of information concerning privacy-preservation. In this paper, we present a privacy-aware visual query approach for location-based data. We propose a graph-based privacy-preserving scheme to protect location privacy in the visualization, and two visual metaphors to enhance understandings of information-variation in the privacy-preserving process. We design and implement a visual interface that supports a progressive process of query conditions specification and query results exploration. Experiments on real-world urban datasets demonstrate that our approach is capable of making a fair balance between location privacy and data analysis.

Active querying approach to epidemic source detection on contact networks

The problem of identifying the source of an epidemic (also called patient zero) given a network of contacts and a set of infected individuals has attracted interest from a broad range of research communities. The successful and timely identification of the source can prevent a lot of harm as the number of possible infection routes can be narrowed down and potentially infected individuals can be isolated. Previous research on this topic often assumes that it is possible to observe the state of a substantial fraction of individuals in the network before attempting to identify the source. We, on the contrary, assume that observing the state of individuals in the network is costly or difficult and, hence, only the state of one or few individuals is initially observed. Moreover, we presume that not only the source is unknown, but also the duration for which the epidemic has evolved. From this more general problem setting a need to query the state of other (so far unobserved) individuals arises. In analogy with active learning, this leads us to formulate the active querying problem. In the active querying problem, we alternate between a source inference step and a querying step. For the source inference step, we rely on existing work but take a Bayesian perspective by putting a prior on the duration of the epidemic. In the querying step, we aim to query the states of individuals that provide the most information about the source of the epidemic, and to this end, we propose strategies inspired by the active learning literature. Our results are strongly in favor of a querying strategy that selects individuals for whom the disagreement between individual predictions, made by all possible sources separately, and a consensus prediction is maximal. Our approach is flexible and, in particular, can be applied to static as well as temporal networks. To demonstrate our approach’s practical importance, we experiment with three empirical (temporal) contact networks: a network of pig movements, a network of sexual contacts, and a network of face-to-face contacts between residents of a village in Malawi. The results show that active querying strategies can lead to substantially improved source inference results as compared to baseline heuristics. In fact, querying only a small fraction of nodes in a network is often enough to achieve a source inference performance comparable to a situation where the infection states of all nodes are known.

A smart clinical cohort selection system for clinical oncology research.

e13613 Background: Cohort selection for specialized clinical trials is a cardinal pillar of the evidence-based medicine; however, it is the most difficult, complex, time consuming, and expensive step. Determining the efficacy of a new treatment (or intervention) requires finding eligible patients meeting the inclusion and exclusion criteria. In specialized scenarios, the complex criteria may even require researchers to do time consuming manual reviews and analyses of electronic health records (EHRs) to shortlist qualified patients. The major contribution of this research is a set of novel semantic models to build a cohort for clinical research by enabling semantic search over electronic health records represented as Knowledge graphs (KGs). Methods: We present the design of a novel cohort retrieval system, satisfying inclusion and exclusion criteria of an oncology clinical research study. Knowledge graphs and semantic models: We construct knowledge graphs (KGs) to interconnect different data sources, stored in a data-lake, and develop semantic models that enable semantic search over the processed data. We designed and constructed an oncology knowledge graph that enables semantics driven cohort selection. In addition, we have built a novel cohort retrieval system, satisfying the inclusion and exclusion criteria of an oncology clinical research study, that utilizes a semantics driven dynamic query engine to generate and execute cohort selection queries on heterogeneous EHR data. Results: We obtained real world oncology data of 21,000 oncology patients and then constructed knowledge graphs for five cancer types -- Colon (C18), Lung (C34), Breast (C50), prostate (C61), and Multiple Myeloma (C90). The cohort building scenarios are designed to represent a mix of criterion types and combinations including both inclusion and exclusion criteria, involving conjunctions, disjunctions and numeric ranges. Our cohort builder is evaluated against ten well known key competency scenarios. A team of experts validated the results of our cohort builder obtained against these competency scenarios by directly querying the graph. Our extensive evaluations demonstrate that the cohort builder searches with 100% accuracy the patients that match the criteria specified in all ten competencies. The average time to build the cohort on all graphs for these competencies is less than 10 seconds compared with that of days when patients are manually searched in EHR systems. Conclusions: Our query engine is not tightly coupled with the architecture of our data-lake; rather its architecture is flexible and can be easily integrated with other enterprise data-lakes or EMR systems. In future, we plan to scale the extent of inclusion and exclusion criteria to provide interoperability with existing clinical trial knowledge. We also aim to empirically evaluate the efficiency of cohort selection queries using a knowledge graph with classical database query approaches.

Conserved transcriptional connectivity of regulatory T cells in the tumor microenvironment informs new combination cancer therapy strategies

While regulatory T (Treg) cells are traditionally viewed as professional suppressors of antigen presenting cells and effector T cells in both autoimmunity and cancer, recent findings of distinct Treg cell functions in tissue maintenance suggest that their regulatory purview extends to a wider range of cells and is broader than previously assumed. To elucidate tumoral Treg cell ‘connectivity’ to diverse tumor-supporting accessory cell types, we explored immediate early changes in their single-cell transcriptomes upon punctual Treg cell depletion in experimental lung cancer and injury-induced inflammation. Before any notable T cell activation and inflammation, fibroblasts, endothelial and myeloid cells exhibited pronounced changes in their gene expression in both cancer and injury settings. Factor analysis revealed shared Treg cell-dependent gene programs, foremost, prominent upregulation of VEGF and CCR2 signaling-related genes upon Treg cell deprivation in either setting, as well as in Treg cell-poor versus Treg cell-rich human lung adenocarcinomas. Accordingly, punctual Treg cell depletion combined with short-term VEGF blockade showed markedly improved control of PD-1 blockade-resistant lung adenocarcinoma progression in mice compared to the corresponding monotherapies, highlighting a promising factor-based querying approach to elucidating new rational combination treatments of solid organ cancers.

Abstract 3167: The need for structured pathology data in matching human subjects to cancer clinical trial criteria: A methodological approach

Abstract Introduction: Identification of human subjects who qualify for a cancer trial can be challenging, particularly given narrow time frames for enrollment and highly specific criteria for eligibility. We sought to identify patients on a timely basis by matching trial criteria to data present in both the Pathology and clinical information systems. In a separate abstract submitted to the AACR meeting, we described the enrollment outcomes of our effort for two clinical trials. In this companion abstract, we describe the query methods that we employed. Methods: Method #1 involved querying structured data fields in the EMR, using dashboards or SQL queries of relational databases. Method #2 utilized natural language processing (NLP) queries of narrative text in pathology reports. We assessed the useability of each query approach, on a near-real-time basis, against eligibility criteria for open clinical trials at our institution. Results: We examined the first 25 open clinical trials for treatment of solid tumors listed publicly on our institutional website. For each trial, eligibility and query criteria were compiled by two reviewers (DC,YZ); discrepancies between reviewers were resolved by a third reviewer (NY). We found that of the 33 trials examined: 26 trials (79%) could be successfully analyzed by NLP in the pathology report. 2 trials (6%) required SQL query of discrete data fields in clinical databases. 2 trials (6%) required both methods to be used together. 3 trials (9%) required data elements outside the scope of either method. The data fields provided by NLP were: tumor morphology; tumor stage; specimen margin; and tumor receptor status. The data fields provided by SQL were: ICD 10 codes, CPT codes, Chemotherapy, and quantitative lab results. The data fields not accessible by either approach were: tumor resectability and risk calculation. Conclusion: There is no single method that is best for all trials, and availability of a broad set of tools is needed to craft a fitting query for each trial. However, 26 of 33 trials (79%) required NLP to parse eligibility criteria from narrative pathology reports. Cancer clinical trials enrollment may be substantially enhanced by NLP extraction of structured data from pathology reports as part of a near-real-time workflow for identifying eligible Human Subjects. Citation Format: Nalan Yurtsever, Yonah Ziemba, Daniel A. King, Dylan J. Cooper, Sergio Garza, Cheryl B. Schleicher, Sharon S. Fox, James M. Crawford, Joseph Herman. The need for structured pathology data in matching human subjects to cancer clinical trial criteria: A methodological approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3167.

PSA-Det3D: Pillar set abstraction for 3D object detection

Small object detection for 3D point cloud is a challenging problem because of two limitations: (1) The sparsity of point clouds significantly increases the difficulty of perceiving small objects. (2) The occlusion of small objects can easily break the shape of their point clouds. To alleviate these problems, we design a point-based detection network PSA-Det3D which mainly consists of a pillar set abstraction (PSA) and a foreground point compensation (FPC). The PSA improves the query approach of set abstraction, which benefits the point-wise feature aggregation for small objects. The FPC fuses the foreground points and the estimated centers to select the candidate points, which effectively improves the detection performance for occluded objects. Extensive experiments show that our proposed PSA-Det3D achieves higher performance on all categories. For small object detection, our method outperforms existing point based algorithms on the KITTI 3D detection benchmark.

DCGNN: a single-stage 3D object detection network based on density clustering and graph neural network

Currently, single-stage point-based 3D object detection network remains underexplored. Many approaches worked on point cloud space without optimization and failed to capture the relationships among neighboring point sets. In this paper, we propose DCGNN, a novel single-stage 3D object detection network based on density clustering and graph neural networks. DCGNN utilizes density clustering ball query to partition the point cloud space and exploits local and global relationships by graph neural networks. Density clustering ball query optimizes the point cloud space partitioned by the original ball query approach to ensure the key point sets containing more detailed features of objects. Graph neural networks are very suitable for exploiting relationships among points and point sets. Additionally, as a single-stage 3D object detection network, DCGNN achieved fast inference speed. We evaluate our DCGNN on the KITTI dataset. Compared with the state-of-the-art approaches, the proposed DCGNN achieved better balance between detection performance and inference time.

GeodesicEmbedding (GE): A High-Dimensional Embedding Approach for Fast Geodesic Distance Queries.

In this article, we develop a novel method for fast geodesic distance queries. The key idea is to embed the mesh into a high-dimensional space, such that the euclidean distance in the high-dimensional space can induce the geodesic distance in the original manifold surface. However, directly solving the high-dimensional embedding problem is not feasible due to the large number of variables and the fact that the embedding problem is highly nonlinear. We overcome the challenges with two novel ideas. First, instead of taking all vertices as variables, we embed only the saddle vertices, which greatly reduces the problem complexity. We then compute a local embedding for each non-saddle vertex. Second, to reduce the large approximation error resulting from the purely euclidean embedding, we propose a cascaded optimization approach that repeatedly introduces additional embedding coordinates with a non-euclidean function to reduce the approximation residual. Using the precomputation data, our approach can determine the geodesic distance between any two vertices in near-constant time. Computational testing results show that our method is more desirable than previous geodesic distance queries methods.

An approach for cross-data querying and spatial reasoning of tunnel alignments

In mechanized tunneling projects, finding a low-risk and cost-effective alignment is an important task. Several alignment variants are usually created and each one is intensely scrutinized. Variants often have individual advantages and disadvantages and can lead to different constructive designs of a tunnel. In order to find the best alignment possible the variants have to be analyzed and evaluated based on requirements and evaluation criteria, such as safety, cost, built environment and operational requirements. To perform this evaluation and to enable comprehensive decision making, a holistic planning environment is examined that includes documents and models of different domains. In general, these domain specific data differ schematically and semantically, which consequently makes it challenging to combine and compare such diverse data. For this purpose, information from different sources must be linked and evaluated in a structured way. In particular, spatial relationships have to be investigated. Therefore, in this paper, ontology databases are utilized to merge BIM and GIS at data level to create an integrated model of the entire tunneling project. Relevant information for decision-making can then be derived, such as the location of private and public buildings that are in a certain vicinity of the planned alignment. On the one hand, the implementation of queries is a popular and frequently used approach to check for semantic properties. On the other hand, using a query language to derive information from geometric data can be challenging, due to the necessity of processing geometric data prior to and during query execution. Additionally, geometric definitions can differ in the considered coordinate reference system, the dimension or the structure. To handle geometry information by employing query languages, representations are methodically transformed to well-known text literals. A simplified and uniform geometric representation can be utilized for spatial reasoning, for example by adopting GeoSPARQL methods.