Query Capabilities Research Articles

Construction and Application of Place Name and Address Management System Based on Elasticsearch

Abstract. Place names and addresses serve as essential references for locating information about people's living, production, and various activity sites. They are a crucial component of public geographic framework data and act as tools and bridges for social communication in daily production and life. This paper presents a comprehensive system for managing place names and addresses by integrating a multi-level address model, data synchronization between PostgreSQL and Elasticsearch, and advanced query capabilities to ensure efficient and accurate data management. The system implements full lifecycle management of place names and addresses, fully leveraging the advantages of relational databases and distributed search engines. It provides essential functions such as address standardization, real-time updates, maintenance management, conditional queries, and statistical analysis. It has been successfully implemented in the National platform for common geospatial information service, laying the foundation for the next steps in refining social governance based on place names and addresses, and further advancing the digital transformation of government operations.

RefXAS: an open access database of X-ray absorption spectra.

Under DAPHNE4NFDI, the X-ray absorption spectroscopy (XAS) reference database, RefXAS, has been set up. For this purpose, we developed a method to enable users to submit a raw dataset, with its associated metadata, via a dedicated website for inclusion in the database. Implementation of the database includes an upload of metadata to the scientific catalogue and an upload of files via object storage, with automated query capabilities through a web server and visualization of the data and files. Based on the mode of measurements, quality criteria have been formulated for the automated check of any uploaded data. In the present work, the significant metadata fields for reusability, as well as reproducibility of results (FAIR data principles), are discussed. Quality criteria for the data uploaded to the database have been formulated and assessed. Moreover, the usability and interoperability of available XAS data/file formats have been explored. The first version of the RefXAS database prototype is presented, which features a human verification procedure, currently being tested with a new user interface designed specifically for curators; a user-friendly landing page; a full list of datasets; advanced search capabilities; a streamlined upload process; and, finally, a server-side automatic authentication and (meta-) data storage via MongoDB, PostgreSQL and (data-) files via relevant APIs.

RoBERTa-GCN: A New Method for Relation Extraction in Automobile Accessory Domain

The automotive industry's rapid expansion has sparked increasing interest in the realm of automotive accessories. Navigating vast information landscapes to find accurate matches has become paramount. Leveraging cutting-edge information technologies, such as knowledge graphs and graph database-based question-answering systems, offers a crucial avenue for enhancing search efficiency. Addressing challenges posed by the domain's specialized terminology and intricate relationships, this paper introduces an innovative approach that combines a pre-trained model (RoBERTa) with graph convolutional networks (GCN). Initially, the text undergoes processing through the pre-trained model, yielding semantic feature vectors that enhance comprehension of industry-specific terminology. Subsequently, a graph convolutional network (GCN) is employed to process these semantic vectors, capturing a broader scope of neighboring vector node information. This approach not only strengthens the relationships between semantic information but also captures the intricate interconnections among entities. Ultimately, an automotive accessory query knowledge graph question-answering system is constructed using extracted entity relationship triplets. Experimental results demonstrate that the proposed RoBERTa-GCN model outperforms other baseline models, achieving an impressive F1 score of 83.93%. This research significantly enhances query capabilities and exhibits versatility in handling natural language inputs from diverse users.

Improving voice of the customer analysis with generative AI

This paper explores the integration of generative artificial intelligence (GenAI) in voice of the customer (VoC) analysis to provide deeper understanding of prospects and customers. GenAI has enormous potential to enhance customer satisfaction, refine products and services and improve the customer experience. This speculative paper illustrates how GenAI can keep pace with increasing customer expectations and the volume of feedback by uncovering nuanced sentiments, trends and customer needs through context comprehension and its conversational query capabilities. The paper explores the power of GenAI in VoC analysis for improving customer satisfaction, accelerating troubleshooting and resolution and upgrading products and services. Additionally, this paper addresses the role of GenAI in advanced communication routing, agent support, multilingual support and sentiment analysis, showcasing its ability to provide comprehensive and context-aware insights.

Ascle-A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study.

Medical texts present significant domain-specific challenges, and manually curating these texts is a time-consuming and labor-intensive process. To address this, natural language processing (NLP) algorithms have been developed to automate text processing. In the biomedical field, various toolkits for text processing exist, which have greatly improved the efficiency of handling unstructured text. However, these existing toolkits tend to emphasize different perspectives, and none of them offer generation capabilities, leaving a significant gap in the current offerings. This study aims to describe the development and preliminary evaluation of Ascle. Ascle is tailored for biomedical researchers and clinical staff with an easy-to-use, all-in-one solution that requires minimal programming expertise. For the first time, Ascle provides 4 advanced and challenging generative functions: question-answering, text summarization, text simplification, and machine translation. In addition, Ascle integrates 12 essential NLP functions, along with query and search capabilities for clinical databases. We fine-tuned 32 domain-specific language models and evaluated them thoroughly on 27 established benchmarks. In addition, for the question-answering task, we developed a retrieval-augmented generation (RAG) framework for large language models that incorporated a medical knowledge graph with ranking techniques to enhance the reliability of generated answers. Additionally, we conducted a physician validation to assess the quality of generated content beyond automated metrics. The fine-tuned models and RAG framework consistently enhanced text generation tasks. For example, the fine-tuned models improved the machine translation task by 20.27 in terms of BLEU score. In the question-answering task, the RAG framework raised the ROUGE-L score by 18% over the vanilla models. Physician validation of generated answers showed high scores for readability (4.95/5) and relevancy (4.43/5), with a lower score for accuracy (3.90/5) and completeness (3.31/5). This study introduces the development and evaluation of Ascle, a user-friendly NLP toolkit designed for medical text generation. All code is publicly available through the Ascle GitHub repository. All fine-tuned language models can be accessed through Hugging Face.

Semi-supervised Open-World Object Detection

Conventional open-world object detection (OWOD) problem setting first distinguishes known and unknown classes and then later incrementally learns the unknown objects when introduced with labels in the subsequent tasks. However, the current OWOD formulation heavily relies on the external human oracle for knowledge input during the incremental learning stages. Such reliance on run-time makes this formulation less realistic in a real-world deployment. To address this, we introduce a more realistic formulation, named semi-supervised open-world detection (SS-OWOD), that reduces the annotation cost by casting the incremental learning stages of OWOD in a semi-supervised manner. We demonstrate that the performance of the state-of-the-art OWOD detector dramatically deteriorates in the proposed SS-OWOD setting. Therefore, we introduce a novel SS-OWOD detector, named SS-OWFormer, that utilizes a feature-alignment scheme to better align the object query representations between the original and augmented images to leverage the large unlabeled and few labeled data. We further introduce a pseudo-labeling scheme for unknown detection that exploits the inherent capability of decoder object queries to capture object-specific information. On the COCO dataset, our SS-OWFormer using only 50% of the labeled data achieves detection performance that is on par with the state-of-the-art (SOTA) OWOD detector using all the 100% of labeled data. Further, our SS-OWFormer achieves an absolute gain of 4.8% in unknown recall over the SOTA OWOD detector. Lastly, we demonstrate the effectiveness of our SS-OWOD problem setting and approach for remote sensing object detection, proposing carefully curated splits and baseline performance evaluations. Our experiments on 4 datasets including MS COCO, PASCAL, Objects365 and DOTA demonstrate the effectiveness of our approach. Our source code, models and splits are available here https://github.com/sahalshajim/SS-OWFormer

Stronger compact representations of object trajectories

ABSTRACT GraCT and ContaCT were the first compressed data structures to represent object trajectories, demonstrating that it was possible to use orders of magnitude less space than classical indexes while staying competitive in query times. In this paper we considerably enhance their space, query capabilities, and time performance with three contributions. (1) We design and evaluate algorithms for more sophisticated nearest neighbor queries, finding the trajectories closest to a given trajectory or to a given point during a time interval. (2) We modify the data structure used to sample the spatial positions of the objects along time. This improves the performance on the classic spatio-temporal and the nearest neighbor queries, by orders of magnitude in some cases. (3) We introduce RelaCT, a tradeoff between the faster and larger ContaCT and the smaller and slower GraCT, offering a new relevant space-time tradeoff for large repetitive datasets of trajectories.

Applicances of Different Kind of Storage Systems for Network Traffic Analysis Results

Network Traffic Analysis (NTA) helps identify security threats, monitor network performance, and plan for future capacity. While real-time analysis is ideal, it can be difficult due to high data volume and complexity. Large amounts of traffic require parsing, and real-time data may miss hidden threats. Post-analysis can address these challenges. It hardly depends on choosing an effective and appropriate storage solutions. A variety of storage systems exist, each employing different approaches and formats to retain data. This article explores the applications of various storage systems for NTA results. Three different types of storage systems considered, including Greenplum, Nebula graph and OpenSearch. A comparative approach is employed, analyzing the same dataset across various storage systems.This allows to examine how different database structures and query capabilities influence the efficiency and accuracy of NTA. The resulting insights will not only provide valuable guidance for selecting the optimal storage solution for specific NTA tasks, but also serve as a foundation for future research in this area.

Optimal utilization of multicore processors with PLINQ in .NET applications

This article explores the utilization of Parallel Language Integrated Query (PLINQ) as a powerful tool for enhancing the processing of large datasets through parallelism in .NET applications. PLINQ leverages the capabilities of modern multicore processors to accelerate data operations, thereby enabling developers to significantly reduce processing time while efficiently managing computational resources. The discussion begins with an overview of PLINQ’s integration within the .NET framework, emphasizing its ability to parallelize standard LINQ queries seamlessly. The article then delves into practical applications of PLINQ, illustrating through examples how it can optimize tasks such as financial data analysis and image processing. The core concepts and architecture of PLINQ, including its support for complex query capabilities and advanced aggregation functions, are examined to highlight how PLINQ manages data partitioning, load balancing, and thread safety. Further, the article addresses the strategic design considerations necessary for maximizing the efficiency of PLINQ, focusing on the importance of thoughtful system design to overcome potential limitations. Best practices for employing PLINQ are discussed to ensure optimal performance and effective use of parallel programming constructs.Finally, the conclusion underscores the significance of PLINQ in modern software development, particularly for applications that demand high-performance data processing capabilities. The article advocates for the strategic integration of PLINQ in developing applications that not only perform faster but are also scalable and robust, thereby meeting the challenges of processing large volumes of data in today’s computing environments.

OPTION: OPTImization Algorithm Benchmarking ONtology

Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research. However, different platforms use different data models and formats, which drastically complicates the identification of relevant datasets, their interpretation, and their interoperability. Therefore, a semantically rich, ontology-based, machine-readable data model that can be used by different platforms is highly desirable. In this paper, we report on the development of such an ontology, which we call OPTION (OPTImization algorithm benchmarking ONtology). Our ontology provides the vocabulary needed for semantic annotation of the core entities involved in the benchmarking process, such as algorithms, problems, and evaluation measures. It also provides means for automatic data integration, improved interoperability, and powerful querying capabilities, thereby increasing the value of the benchmarking data. We demonstrate the utility of OPTION, by annotating and querying a corpus of benchmark performance data from the BBOB collection of the COCO framework and from the Yet Another Black-Box Optimization Benchmark (YABBOB) family of the Nevergrad environment. In addition, we integrate features of the BBOB functional performance landscape into the OPTION knowledge base using publicly available datasets with exploratory landscape analysis. Finally, we integrate the OPTION knowledge base into the IOHprofiler environment and provide users with the ability to perform meta-analysis of performance data.

Spatial Blockchain: Enhancing Spatial Queries and Applications through Integrating Blockchain and Spatial Database Technologies

The fusion of spatial data with blockchain technologies presents an innovative approach towards a decentralized, secure, and trustworthy framework for spatial information management. This integration brings spatial representation to the forefront of blockchain, opening avenues for various sectorial applications. However, challenges like slow processing times, restricted query capabilities, and consistency issues have been identified within the blockchain system. Addressing these challenges, we propose an optimized method for spatial queries by leveraging the high-performance capabilities of spatial databases. Unlike conventional off-chain query techniques, our approach synergistically combines hyperledger fabric with a specialized spatial database. This fusion facilitates distributed spatial queries within blockchain framework, incorporating spatial operation functionalities into smart contracts while preserving the distributed nature of nodes and spatial databases. Enhancing system security, we incorporate a dual-stage verification mechanism alongside a salt-hash storage strategy to counteract potential unauthorized alterations. Initial results validate the efficacy of our methodology in terms of performance and security. Building on this foundation, we introduce a rental transaction system that effectively merges spatial data with blockchain, demonstrating the feasibility and potential of integrating spatial information into the blockchain, especially in the realm of real estate.

A Comparative Study of Relasional and NoSQL database for Big Data Analytics

The Database technology suite is rapidly evolving and consistently answering solutions user needs, one of which is the ability of the database to accommodate one piece of information quite a lot, the ability of the Database to accommodate data must be balanced together speed in loading the information needed by the user. In these fast-paced times today’s sophisticated information technology forces us to be able to serve data requests as well as process data quickly and efficiently, but if a system has accommodated too much a lot of data will certainly experience a time constraint in processing the data. The research method used is the type of library research (Library Research). The data obtained is sourced from the literature. NoSQL database discussion literature searched using keywords: SQL, NoSQL, databases from various publication databases. The results of the study are comparisons between relational databases and NoSQL involving factors
 Such as data models, scalability, data flexibility, and query and analytics capabilities. Databases Relational is more suitable for structured data and strict consistency, whereas database NoSQL is better suited for unstructured data and prioritizes scalability and flexibility.

Automated database design for document stores with multicriteria optimization

Document stores have gained popularity among NoSQL systems mainly due to the semi-structured data storage structure and the enhanced query capabilities. The database design in document stores expands beyond the first normal form by encouraging de-normalization through nesting. This hinders the process, as the number of alternatives grows exponentially with multiple choices in nesting (including different levels) and referencing (including the direction of the reference). Due to this complexity, document store data design is mostly carried out in trial-and-error or ad-hoc rule-based approaches. However, the choices affect multiple, often conflicting, aspects such as query performance, storage space, and complexity of the documents. To overcome these issues, in this paper, we apply multicriteria optimization. Our approach is driven by a query workload and a set of optimization objectives. First, we formalize a canonical model to represent alternative designs and introduce an algebra of transformations that can systematically modify a design. Then, using these transformations, we implement a local search algorithm driven by a loss function that can propose near-optimal designs with high probability. Finally, we compare our prototype against an existing document store data design solution purely driven by query cost, where our proposed designs have better performance and are more compact with less redundancy.

АНДРОЦЕНТРИЗМ В УКРАЇНСЬКІЙ МОВІ ТА СЛОВНИКАХ: ПОЧАТОК ХХ СТ. І СУЧАСНІСТЬ

The article considers peculiarities of the vocabulary presentation related to the masculine form in particular based on the three well-known Russian-Ukrainian dictionaries of the beginning of the 20th century. A holistic analysis of different groups of vocabulary in the selected dictionaries can be performed due to digital lexicographic systems created considering paper dictionaries and a noun database with extensive querying capabilities. This study demonstrates the presence of a significant number of units to refer to individuals in translated sources of the early twentieth century. The major part is represented by masculine nouns. The most comprehensive section was the lexical-semantic group for designating individuals determined by occupation, profession, specialization, job title, etc. Even though the group includes nouns of both genders, masculine nouns prevail. Consistently, without feminine counterparts, only professional names, which are borrowed words with the components -log, -graph, -meter, are given in common language dictionaries (both of the previous era and modern ones). Only in the form of the masculine gender, translated lexicographic works also record the names of persons by military positions, ranks, place of service, etc., and the majority of names used to indicate religious and church titles, ranks, etc. A similar situation can be observed when it comes to modern sources analysis. In contrast to lexicographic practice, the modern Ukrainian language (media mainly) shows a significant increase in usage of neo-feminine gendered words, including the ones with previously unpopular affixes. In the perspective, it is planned to cover the vocabulary provided in lexicographical sources of the early twentieth century, which belongs to different lexical and semantic groups and compare it with its modern equivalents.

Query Operators for Transactional Data: Detecting Similar and Periodic Transactions

Abstract Pattern detection for revealing the patterns of users’ behavior is an important analysis-assisting tool toward the understanding and prediction of their attitudes, manners, activities and habits. In this paper, two novel query operators applied to transactional data are introduced to ease the query processing, strengthening query capabilities and revealing valuable patterns for data analysis and mining. The operators are named as PeriodicTransactions and SimilarTransactions, and as their names imply, they measure periodicity and similarity, respectively, in a set of transactions. The operators are formally defined and the corresponding algorithms are also provided. To show the expediency of the operators, the proposed algorithms are implemented and a set of experiments were conducted with real data from the Ethereum blockchain. The results show the feasibility and usefulness of the proposal for identifying these patterns that help to understand user behavior and reveal a rich interaction between senders and recipients, where periodic and similar transactions occur.

Benchmarking JSON Document Stores in Practice

The increasing dissemination of JSON as exchange and storage format through its popularity in business and analytical applications requires efficient storage and processing of JSON documents. Consequently, this led to the development of specialized JSON document stores and the extension of existing relational stores, while no JSON-specific benchmarks were available to assess these systems. In this work, we assess currently available JSON document store benchmarks and select the recently developed DeepBench benchmark to experimentally study important dimensions like analytical querying capabilities, object nesting and array unnesting. To make the computational complexity of array unnesting more tractable, we introduce an improvement that we evaluate within a commercial system as part of the common, performance-oriented development process in practice. We conclude our evaluation of well-known document stores with DeepBench and give new insights into strengths and potential weaknesses of those systems that were not found by existing, non-JSON benchmarking practices. In particular the algebraic optimization of JSON query processing is still limited despite prior work on hierarchical data models in the XML context.

Spatial-Keyword Skyline Publish/Subscribe Query Processing Over Distributed Sliding Window Streaming Data

Current spatial-keyword publish/subscribe systems need to handle spatial-keyword skyline queries over geo-textual streams to continuously obtain good results. The skyline queries in such systems face two main problems: (1) query problems, because the powerful query capability is required for the strict limit of the response time and the large number of items concerned by the users, and (2) scalability issue, because millions of active users are maintained simultaneously with many network-connected machines. Unfortunately, the current approach is towards static data. Thus, this paper first proposes a distributed skyline query processing framework. Then, we optimize the skyline computing by introducing MF-R <inline-formula><tex-math notation="LaTeX">$^t$</tex-math></inline-formula> -tree, which is an update-efficient and space-saving indexing structure and a fast approach for processing a continuous spatial-keyword skyline query called <inline-formula><tex-math notation="LaTeX">$eager^*$</tex-math></inline-formula> . Finally, a spatial and textual signature-based communication optimization method is proposed to support scalability. The experimental results indicate that (1) MF-R <inline-formula><tex-math notation="LaTeX">$^t$</tex-math></inline-formula> -tree can significantly reduce update costs, while maintaining a low storage cost, and a query performance comparable to IL-Quadtree, (2) <inline-formula><tex-math notation="LaTeX">$eager^*$</tex-math></inline-formula> can averagely accelerate 79.72 × faster than the method based on BNL, (3) the communication optimization method significantly reduces the communication cost, and (4) the distributed framework can efficiently support large-scale skyline queries.

Making Sense of Language Signals for Monitoring Radicalization

Understanding radicalization pathways, drivers, and factors is essential for the effective design of prevention and counter-radicalization programs. Traditionally, the primary methods used by social scientists to detect these drivers and factors include literature reviews, qualitative interviews, focus groups, and quantitative methods based on surveys. This article proposes to complement social science approaches with computational methods to detect these factors automatically by analyzing the language signals expressed in social networks. To this end, the article categorizes radicalization drivers and factors following the micro, meso, and macro levels used in the social sciences. It identifies the corresponding language signals and available language resources. Then, a computational system is developed to monitor these language signals. In addition, this article proposes semantic technologies since they offer unique exploration, query, and discovery capabilities. The system was evaluated based on a set of competency questions that show the benefits of this approach.

Securely Sharing and Visualizing Connected Vehicle Analytics: THEA CV Pilot Performance Evaluation Dashboard

Dashboards are increasingly being used by organizations to process and visualize complex information and to reduce data complexity for planning and reporting. Their main function is to synthesize information for rapid and smarter decision making. This paper details the development of an innovative interactive dashboard to process connected vehicle (CV) data securely, report CV application evaluation analytics, and monitor CV infrastructure system performance. We detail the methods used to process complex, high-frequency (up to 10 Hz) information generated by more than 1,000 participants’ vehicles in the Tampa Hillsborough Expressway Authority (THEA) CV Pilot deployment over the course of two and half years of operation. The dashboard provides advanced query capabilities and custom visualization via interactive maps, graphics, and dynamic reporting to help inform decision making and securely share information. Further, individual CV application warnings can be analyzed using the warning profile feature that is equipped with visual animation replay. This novel approach not only compensated for the lack of in-vehicle dashboard cameras integrated into the CV Pilot infrastructure to assess behavioral responses to the human–machine interface, but also sped up manual validation time. The dashboard allows different levels of user access with customized views to meet a variety of stakeholder types and needs. This tool has been successfully used by the THEA CV Pilot evaluation team and U.S. Department of Transportation independent evaluators to perform precursory evaluations and to automate and perform false positive assessments of vehicle-to-vehicle and vehicle-to-infrastructure safety and mobility applications.

Evaluation and Application of Environmental Value of Rural Land Resources Based on Improved GIS System

As the basis and material source of human production and life, land is an important part of natural resources. With the deepening of the concept of sustainable development, more and more attention has been paid to the sustainable development of land use. Reasonable assessment of land resources and environment is the premise of accelerating agricultural infrastructure construction, optimizing ecological environment construction, and rationally disposing land use structure. This paper proposes an evaluation method of rural land resources and environment based on the improved GIS system and takes Lingshan County as an example, based on the powerful spatial processing and rich spatial query and analysis capabilities of GIS, to analyze the current situation and sustainable development of its land use. It has established a representative and operable evaluation index system, which has certain reference significance for rural land and environmental evaluation.