Data Query System Research Articles

Anonymization: The imperfect science of using data while preserving privacy.

Information about us, our actions, and our preferences is created at scale through surveys or scientific studies or as a result of our interaction with digital devices such as smartphones and fitness trackers. The ability to safely share and analyze such data is key for scientific and societal progress. Anonymization is considered by scientists and policy-makers as one of the main ways to share data while minimizing privacy risks. In this review, we offer a pragmatic perspective on the modern literature on privacy attacks and anonymization techniques. We discuss traditional de-identification techniques and their strong limitations in the age of big data. We then turn our attention to modern approaches to share anonymous aggregate data, such as data query systems, synthetic data, and differential privacy. We find that, although no perfect solution exists, applying modern techniques while auditing their guarantees against attacks is the best approach to safely use and share data today.

The economic effect of financial compensation in China’s healthcare system: comprehensive insights regarding supply and demand factors

ObjectivesWe aim to analyse the effects of government subsidies on residents’ health and healthcare expenditure from the perspectives of supply and demand.Data and methodsAccording to the regional division adopted in the data query system of the National Bureau of Statistics, this study divides 31 provinces and cities into three regions: eastern, central, and western. The data used are from public databases, such as the “China Statistical Yearbook,” “China Health Statistical Yearbook,” and “Government Final Account Report”. In this study, mathematical model derivation is used to construct a fixed effects model, and an empirical study based on cross-sectional data and general linear regression is conducted. To prevent endogeneity issues, this study introduces instrumental variables and uses 2SLS regression to further analyse the output results.ResultsFor every 1% increase in supplementary funding on the supply side, the perinatal mortality rate decreases by 1.765%, while for every 1% increase in financial compensation on the demand side, per capita outpatient expenses increase by 0.225% and per capita hospitalization expenses increase by 0.196%. Regarding medical resources, for every 1% increase in the number of beds per 1,000 people, per capita hospitalization expenses decrease by 0.099%. In the central and eastern regions, where economic levels are higher, supply-side government funding is more effective than demand-side funding. In contrast, demand-side funding is more effective in the western region.ConclusionsThe roles of multiple influencing factors and significant regional heterogeneity are clarified. Increasing financial compensation to providers positively impacts perinatal mortality but leads to higher per capita outpatient and hospital expenditures. Finally, this study provides targeted policy recommendations and solid theoretical support for policymakers.

The IBdata Web System for Biological Collections: Design Focused on Usability

The software design process must put users at the core of the process to enable them to meet their specific objectives effectively, efficiently, and successfully. Thus, a software design for a computing system to consult biological collections guided by the concept of usability will result in an effective and efficient biodiversity informatics tool. Here, we introduce IBdata, a web system to consult biological collections, developed using a design approach based on the architecture of three layers: database, business rules, and user interface. The user interface design was guided by the concept of usability focused on four core concepts: simplicity, adaptability, guide the user through the journey, and feedback. The IBdata web system that we developed is composed of three modules (query, capture and editing, and administration), permitting it to query a database with about 1.7 million specimen records. Biodiversity data query systems must be effective and efficient and should meet the user’s expectations. Software design methodologies play a central role in achieving these goals, and, in this context, interface design techniques that put the user at the core of development are valuable, as in the development of the IBdata web system.

Using the power query system for processing and data mining SAP ERP

The article discusses the use of an intelligent Power Query system for processing data from SAP ERP. When used with SAP ERP data, Power Query can greatly simplify the data analysis process, as well as improve the accuracy and reliability of results. The article provides an overview of the main features of Power Query and how they can be used for data processing in conjunction with the SAP ERP system. It highlights the benefits of using Power Query, including speeding up data processing time, improving data accuracy, and the ability to easily exchange and reuse data processing procedures. Using Power Query to process and analyze bank statements via SAP ERP provides many advantages. Power Query's powerful data processing and transformation capabilities enable organizations to analyze their financial data quickly and accurately, making it easier to identify trends, improve decision-making and stimulate business growth. In addition, the integration of Power Query with SAP ERP provides organizations with a centralized representation of financial data in real time, which allows them to make informed decisions and optimize their activities. Using Power Query to analyze bank statements can also help organizations reduce manual data processing and minimize the risk of errors, increasing the accuracy and reliability of financial information. In general, Power Query offers a powerful solution for organizations seeking to optimize financial analysis processes and ensure business success.

Diphenhydramine-involved Fatal and Nonfatal Drug Overdoses in Tennessee, 2019–2022

Background Diphenhydramine (DPH), known as the brand name Benadryl, is an over-the-counter medication associated with accidental ingestion leading to nonfatal overdoses. Additionally, DPH has been used in tandem with illicit substances leading to fatal drug overdoses. Objective In response to DPH being seized with illicit drugs as an adulterant, as well as its growing intentional misuse, we sought to explore its recent involvement in fatal and nonfatal drug overdoses in the state of Tennessee. Methods We conducted a statewide cross-sectional study to determine the characteristics of DPH-involved fatal and nonfatal overdoses in Tennessee during 2019–2022 using data from the State Unintentional Drug Overdose Reporting System, the Electronic Surveillance System for the Early Notification of Community-based Epidemics, and the National Forensic Laboratory Information System Public Data Query System. Frequencies were generated to compare demographic characteristics, circumstances, and toxicology between fatal and nonfatal DPH-involved overdoses. Results We identified 143 suspected nonfatal DPH and 409 fatal DPH-involved overdoses in Tennessee from 2019 to 2022. Nonfatal overdoses remained consistent while fatal overdoses peaked in 2021. Most nonfatal overdoses were under 18 (63.4%), while most fatal overdoses were between 18 and 64 years of age (95.7%). For fatal overdoses, fentanyl was the most prevalent substance on toxicology followed by prescription opioids. Conclusion Nonfatal overdoses remained consistent while fatal overdoses peaked in 2021 in Tennessee. Use of DPH among other illicit substances lends to evidence suggesting its use as an adulterant. Monitoring of DPH-involved fatal and nonfatal overdoses is critical to inform harm reduction initiatives.

Big Data and precision agriculture: a novel spatio-temporal semantic IoT data management framework for improved interoperability

Precision agriculture in the realm of the Internet of Things is characterized by the collection of data from multiple sensors deployed on the farm. These data present a spatial, temporal, and semantic characterization, which further complicates the performance in the management and implementation of models and repositories. In turn, the lack of standards is reflected in insufficient interoperability between management solutions and other non-native services in the framework. In this paper, an innovative system for spatio-temporal semantic data management is proposed. It includes a data query system that allows farmers and users to solve queries daily, as well as feed decision-making, monitoring, and task automation solutions. In the proposal, a solution is provided to ensure service interoperability and is validated against two European smart farming platforms, namely AFarCloud and DEMETER. For the evaluation and validation of the proposed framework, a neural network is implemented, fed through STSDaMaS for training and validation, to provide accurate forecasts for the harvest and baling of forage legume crops for livestock feeding. As a result of the evaluation for the training and execution of neural networks, high performance on complex spatio-temporal semantic queries is exposed. The paper concludes with a distributed framework for managing complex spatio-temporal semantic data by offering service interoperability through data integration to external agricultural data models.Graphical

Modernizing data management at the US Bureau of Labor Statistics

The US Bureau of Labor Statistics (BLS) is undertaking initiatives to improve its data and metadata systems. Planning for the replacement of the public facing LABSTAT data query system and efforts within the Office of Productivity and Technology to combine multiple production systems within a single cross-divisional database platform are examples. BLS views time-series data as a combination of three elemental components found in every time-series. A measure element; a person, places, and things element; and a time element are the components. The authors turned this basic approach into a formal conceptual model represented in UML (Unified Modeling Language). The UML model describes a flexible multi-dimensional data structure, of which time-series are a kind, and supports any kind of query into the data. The Office of Productivity and Technology has adopted the model, and it is guiding their approach moving forward. The model was also adopted by the Financial Industry Business Ontology project under the Object Management Group and by the Data Documentation Initiative Cross-Domain Integration (DDI-CDI) development project. There are other similarities between the OPT effort and DDI-CDI as well. In this way, the OPT project demonstrates the feasibility and usefulness of many of the ideas in DDI-CDI. In this paper we describe the time-series formulation and the UML conceptual model. Then, the design of the OPT system and some of its features are described, relating those that are like DDI-CDI where appropriate. In doing so, we provide a thorough understanding of the structure of time-series.

Clinicopathologic features, treatment, and prognosis of pregnancy-associated breast cancer.

To identify the clinicopathological features, treatment, and prognosis of patients with breast cancer, who were diagnosed during and after pregnancy. We searched for patients with pregnancy-associated breast cancer (PABC) using the big data query and analysis system of Peking Union Medical College Hospital from between January 1, 2013, and December 31, 2021, and matched each patient with two non-PABC patients by age at diagnosis, year at diagnosis, and tumor stage. The clinicopathologic features, treatment, and outcomes of breast cancer during pregnancy (BC-P) and breast cancer during the first-year post-partum (BC-PP) were examined retrospectively in two case-control studies. Eighteen BC-P cases, 36 controls for BC-P cases, 62 BC-PP cases, and 124 controls for BC-PP cases were enrolled in our study. The expression of HER-2 and Ki-67 was higher in BC-PP cases than in its controls (P=0.01, 0.018, respectively). Patients with BC-PP were more likely to choose mastectomy than breast-conserving surgery (P=0.001). There were no significant differences in event-free survival (EFS) between patients with BC-P and BC-PP and their controls. BC-P and BC-PP patients displayed adverse clinicopathological features in our population. However, when matched by age at diagnosis, year of diagnosis, and tumor stage, BC-P and BC-PP patients did not show inferior outcomes to controls, probably due to aggressive multimodality therapy.

Network Architecture for Intelligent Identification of Faults in Rabbit Farm Environment Monitoring Based on a Biological Neural Network Model.

Currently, livestock and poultry farming is gradually developing towards modernization and scale, and closed livestock and poultry farms are widely used for poultry feeding management, but at the same time, the farming risks of large-scale farms are increasing. In this paper, based on the study of wireless sensor networks and biological neural network models, the environmental factors that mainly affect the growth of domestic rabbits are analyzed as an example, and the technology is used to design and implement an environmental monitoring system for modern farms. The design of the system is divided into three main parts: hardware design of each node, software design, and upper computer monitoring software design. The hardware part of the system uses coordinator nodes, router nodes, sensor nodes, and control nodes to form a wireless sensor network in the farm, carries out the hardware circuit design of each node, and based on the protocol stack, designs the software program of each node to realize the collection, transmission, and regulation of environmental information in the farm. In the upper computer part, the design and development of the upper computer monitoring software interface are used to complete the real-time display of environmental data, historical query, database storage, and curve drawing, and to design a remote client data query system based on the architecture to realize the query of environmental data of the farm by remote users and to carry out monitoring fault intelligent identification alarm. At the same time, the paper investigates the optimal deployment of wireless sensor network nodes and searches for the optimal location of sensor nodes through an improved biological neural network algorithm to maximize the network coverage and reduce the coverage of blind areas, and conducts simulation experiments with the coverage rate of a rabbit farm as the optimization target.

SmartAuction: A blockchain-based secure implementation of private data queries

A data marketplace provides a platform for data trading by bringing together data owners and data consumers with a data broker. Recent advancements in mechanism design of data marketplaces have introduced a number of private data query mechanisms that facilitate the trading of private data. A critical assumption to ensuring that these mechanisms function as intended in a real-world online platform, which is often overlooked in the mechanism design literature, is that they are implemented in a network infrastructure that guarantees secure and fair trade. Such a network infrastructure should allow data storage, communications and transactions to satisfy confidentiality, data integrity, fair brokerage, and privacy requirements. In this paper, we propose SmartAuction, the first blockchain-based secure implementation of a private data query system. SmartAuction utilises a public-key encryption scheme, a digital signature scheme and two smart contracts to facilitate the four tasks of a data trade: data submission, data request, auction and query, and payment and delivery. Using universal composability (UC) framework, we verify that SmartAuction satisfies the desirable properties mentioned above. We also conduct experiment to show that SmartAuction can be implemented in real Ethereum platforms and it has a broad applicability.

OBD-Data-Assisted Cost-Based Map-Matching Algorithm for Low-Sampled Telematics Data in Urban Environments

A myriad of connected vehicles collects large-scale telematics data throughout cities, enabling data-based infrastructure planning. To truly benefit from this emerging technology, it is important to integrate pervasive telematics data with map data to produce more tractable and readable information for traffic flows and safety. Map-matching algorithms enable the projection of noisy trajectory data onto map data as a means of integrating telematics data. However, map-matching poses challenges due to higher levels of positioning errors and complex road networks. The authors propose a novel map-matching algorithm that can fuse in-vehicle data with trajectory data to improve the efficiency and accuracy of the algorithm. The proposed algorithm combines the probabilistic and weight-based map-matching frameworks. The novelty of the proposed algorithm includes (i) an adaptive segment candidate search mechanism based on in-vehicle speed information, (ii) adaptive matching parameters to reflect the variations in the Global Positioning System (GPS) noise levels, (iii) a novel transition probability that uses in-vehicle speed data, and (iv) a backend data query system for the shortest routes. Map-matching results were validated based on ground-truth data collected using an in-vehicle sensing device developed by the authors, as well as comparing with a commonly-used off-the-shelf map-matching platform. The proposed algorithm is proven to be robust, with an accuracy of 97.45%, particularly where map data are denser and GPS noise is high.

Privacy Protection Algorithm Model of We-Media Network from the Perspective of Big Data

In recent years, with the in-depth research on the privacy protection methods of We-media network, various types of big data analysis technologies gradually protect the network privacy data, but there are still problems of low intelligence and poor protection in the existing research. Based on this, this paper first uses big data technology and artificial intelligence deep learning algorithm to complete the construction of different types of self-media control databases. Then, it analyzes the common privacy data types of We-media network, constructs an optimization protection model based on secondary identification and verification strategy, and forms a data query system. Finally, simulation experiments are conducted to verify whether the constructed network privacy protection model can realize the intelligent protection of network privacy algorithms from different dimensions. In the process of privacy protection of experimental data at different stages, the internal correlation differences of different types of protection algorithm strategies are obvious in the multidimensional analysis of specific databases. For different types of factor data in different types of We-media networks, the protection rate of the We-media network privacy protection model designed in this study has reached more than 95%. The research results show that the self-media network privacy protection model based on big data and artificial intelligence deep learning technology can realize protection from the aspects of gateway verification and data encryption and has high accuracy and reliability.

Language database construction method based on big data and deep learning

This paper first realizes the construction of multi-level language database by using big data technology and deep learning algorithm. Then, through the analysis of neural network algorithm, the extended Kalman filter (EKF) model is established to form a data query system. Finally, simulation experiments verify whether the language database can correctly retrieve language bytes. Experiments show that the database based on big data and deep learning algorithm has high result adaptability. The debugging result of the simulation experiment is the average fitness of each classification after the simulation program runs 20 times continuously. When the number of analysis is more than 15, it is significantly higher than the other two methods, and it is also the smoothest on the convergence curve. The simulation results show that the language database construction method based on the combination of deep learning algorithm and C-type traveling wave has a good effect on the processing of branch point feature data. In addition, it has a good fitting effect on large-scale data, which is conducive to improve the recognition ability of different language systems in the database, and greatly improve the accuracy and efficiency of retrieval.

Progress on clinical trials of common gastrointestinal cancer drugs in China from 2012 to 2021

Objective: Systematically summarize the research progress of clinical trials of gastric cancer oncology drugs and the overview of marketed drugs in China from 2012 to 2021, providing data and decision-making evidence for relevant departments. Methods: Based on the registration database of the drug clinical trial registration and information disclosure platform of Food and Drug Administration of China and the data query system of domestic and imported drugs, the information on gastric cancer drug clinical trials, investigational drugs and marketed drugs from January 1, 2012 to December 31, 2021 was analyzed, and the differences between Chinese and foreign enterprises in terms of trial scope, trial phase, treatment lines and drug type, effect and mechanism studies were compared. Results: A total of 114 drug clinical trials related to gastric tumor were registered in China from 2012 to 2021, accounting for 3.7% (114/3 041) of all anticancer drug clinical trials in the same period, the registration number showed a significant growth rate after 2016 and reached its peak with 32 trials in 2020. Among them, 85 (74.6%, 85/114) trials were initiated by Chinese pharmaceutical enterprise. Compared with foreign pharmaceutical enterprise, Chinese pharmaceutical enterprise had higher rates of phase I trials (35.3% vs 6.9%, P=0.001), but the rate of international multicenter trials (11.9% vs 67.9%, P<0.001) was relatively low. There were 76 different drugs involved in relevant clinical trials, of which 65 (85.5%) were targeted drugs. For targeted drugs, HER2 is the most common one (14 types), followed by PD-1 and multi-target VEGER. In the past ten years, 3 of 4 marketed drugs for gastric cancer treatment were domestic and included in the national medical insurance directory. Conclusions: From 2012 to 2021, China has made some progress in drug research and development for gastric carcinoma. However, compared with the serious disease burden, it is still insufficient. Targeted strengthening of research and development of investment in many aspects of gastric cancer drugs, such as new target discovery, matured target excavating, combination drug development and early line therapy promotion, is the key work in the future, especially for domestic companies.

Application of Garden Plant Data Query and Analysis System Based on Big Data

With the popularity of the Internet, big data technology has gradually attracted attention and has been widely used in various fields. Therefore, the proportion of garden plant data query system in the domestic market is gradually increasing, and garden plant research has also become a new scientific research direction. Garden plants play a huge role in garden construction. For this reason, this article has done research on the application of garden plant data system based on big data technology. This article mainly analyzes the garden plant data through investigation method, system analysis method, experimental investigation method, etc., designs the corresponding query system and conducts experiments. The survey results show that 89.7% of the citizens are in favor of the construction of gardens. Therefore, based on big data, improving the functional structure of the garden data query system is conducive to the development of garden design.

Retracted] Construction and Application of Music Audio Database Based on Collaborative Filtering Algorithm

In recent years, with the development of Big Data and dynamic modeling technology, people have gradually deepened the research on the efficient construction of music audio database. Based on this background, this paper first realizes the construction of multilevel music audio database using Big Data technology and collaborative filtering algorithm. Then, through the Big Data analysis technology based on collaborative filtering algorithm, the QRE (quick reaction estimate) model is constructed and the data query system is formed. Finally, experiments are designed to verify whether the music audio database based on collaborative filtering algorithm can correctly retrieve the target music audio data. The experimental results show that compared with the traditional single ∗input stacked database, the music and audio database constructed by the discrete Big Data dynamic modeling technology based on collaborative filtering algorithm has faster search rate and higher accuracy and can accurately locate the data nodes in the database. This research uses Big Data and collaborative filtering algorithm technology to establish a new multilevel music audio database with discrete dynamic modeling characteristics of complex systems, which can greatly improve the data structure of each level of the database, so as to improve the efficiency and accuracy of retrieving various types of music and audio files in the database.

Key issues for a manufacturing data query system based on graph

Manufacturing industry data are distributed, heterogeneous and numerous, resulting in different challenges including fast, exhaustive and relevant querying of data. In order to provide an innovative answer to this challenge, the authors consider an information retrieval system based on a graph database. In this paper, the authors focus on determining the key issues to consider in this context. The authors define a three-step methodology using root causes analysis. This methodology is then applied to a data set and queries representative of an industrial use case. As a result, the authors list four main issues: (i) semantic extension of keyword search, (ii) the treatment of syntactic heterogeneity contained in unstructured data, (iii) the results treatment by relevance order and (iv) the detection of relationships between a priori unrelated data. The authors conclude by discussing potential resolutions of these four issues, suggest adapting the methodology used in the paper to evaluate a future proposal, and finally open the possibility of using the results beyond the manufacturing domain.

Barriers to Dissemination of Local Health Data Faced by US State Agencies: Survey Study of Behavioral Risk Factor Surveillance System Coordinators.

BackgroundAdvances in information technology have paved the way to facilitate accessibility to population-level health data through web-based data query systems (WDQSs). Despite these advances in technology, US state agencies face many challenges related to the dissemination of their local health data. It is essential for the public to have access to high-quality data that are easy to interpret, reliable, and trusted. These challenges have been at the forefront throughout the COVID-19 pandemic.ObjectiveThe purpose of this study is to identify the most significant challenges faced by state agencies, from the perspective of the Behavioral Risk Factor Surveillance System (BRFSS) coordinator from each state, and to assess if the coordinators from states with a WDQS perceive these challenges differently.MethodsWe surveyed BRFSS coordinators (N=43) across all 50 US states and the District of Columbia. We surveyed the participants about contextual factors and asked them to rate system aspects and challenges they faced with their health data system on a Likert scale. We used two-sample t tests to compare the means of the ratings by participants from states with and without a WDQS.ResultsOverall, 41/43 states (95%) make health data available over the internet, while 65% (28/43) employ a WDQS. States with a WDQS reported greater challenges (P=.01) related to the cost of hardware and software (mean score 3.44/4, 95% CI 3.09-3.78) than states without a WDQS (mean score 2.63/4, 95% CI 2.25-3.00). The system aspect of standardization of vocabulary scored more favorably (P=.01) in states with a WDQS (mean score 3.32/5, 95% CI 2.94-3.69) than in states without a WDQS (mean score 2.85/5, 95% CI 2.47-3.22).ConclusionsSecuring of adequate resources and commitment to standardization are vital in the dissemination of local-level health data. Factors such as receiving data in a timely manner, privacy, and political opposition are less significant barriers than anticipated.

Design and optimization of integrated energy management network system based on internet of things technology

In recent years, China's economy continues to develop at a high speed, but also faces the enormous pressure of energy shortage and environmental deterioration. As an energy-saving discipline, we should actively adopt advanced energy-saving management methods and technologies for energy utilization management. The purpose of this paper is to study an integrated energy pipeline system based on Internet of things technology. In this paper, the actual data of a university is used to establish a prediction model to predict energy consumption, so as to better carry out energy-saving planning. Based on the theoretical basis of Internet of things technology, combined with the current mainstream technology, windows + Apache + MySQL + PHP as the development environment, ThinkPHP framework as the development framework, and a set of integrated energy data query and energy trend analysis management system. The platform integrates system login, energy consumption data query, historical energy consumption and energy consumption trend analysis, Energy saving report, and provide security and scalable features. The system test shows that the performance of the system designed in this paper is stable and can meet the actual needs. In this paper, four groups of different number of users are simulated. The average response time is less than 5S, and the CPU utilization is less than 70 %.

Epiviz File Server: Query, transform and interactively explore data from indexed genomic files.

MotivationGenomic data repositories like The Cancer Genome Atlas, Encyclopedia of DNA Elements, Bioconductor’s AnnotationHub and ExperimentHub etc., provide public access to large amounts of genomic data as flat files. Researchers often download a subset of data files from these repositories to perform exploratory data analysis. We developed Epiviz File Server, a Python library that implements an in situ data query system for local or remotely hosted indexed genomic files, not only for visualization but also data transformation. The File Server library decouples data retrieval and transformation from specific visualization and analysis tools and provides an abstract interface to define computations independent of the location, format or structure of the file. We demonstrate the File Server in two use cases: (i) integration with Galaxy workflows and (ii) using Epiviz to create a custom genome browser from the Epigenome Roadmap dataset.Availability and implementationEpiviz File Server is open source and is available on GitHub at http://github.com/epiviz/epivizFileServer. The documentation for the File Server library is available at http://epivizfileserver.rtfd.io.