Data Warehouse Applications Research Articles

Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults.

The clinical narrative in electronic health records (EHRs) carries valuable information for predictive analytics; however, its free-text form is difficult to mine and analyze for clinical decision support (CDS). Large-scale clinical natural language processing (NLP) pipelines have focused on data warehouse applications for retrospective research efforts. There remains a paucity of evidence for implementing NLP pipelines at the bedside for health care delivery. We aimed to detail a hospital-wide, operational pipeline to implement a real-time NLP-driven CDS tool and describe a protocol for an implementation framework with a user-centered design of the CDS tool. The pipeline integrated a previously trained open-source convolutional neural network model for screening opioid misuse that leveraged EHR notes mapped to standardized medical vocabularies in the Unified Medical Language System. A sample of 100 adult encounters were reviewed by a physician informaticist for silent testing of the deep learning algorithm before deployment. An end user interview survey was developed to examine the user acceptability of a best practice alert (BPA) to provide the screening results with recommendations. The planned implementation also included a human-centered design with user feedback on the BPA, an implementation framework with cost-effectiveness, and a noninferiority patient outcome analysis plan. The pipeline was a reproducible workflow with a shared pseudocode for a cloud service to ingest, process, and store clinical notes as Health Level 7 messages from a major EHR vendor in an elastic cloud computing environment. Feature engineering of the notes used an open-source NLP engine, and the features were fed into the deep learning algorithm, with the results returned as a BPA in the EHR. On-site silent testing of the deep learning algorithm demonstrated a sensitivity of 93% (95% CI 66%-99%) and specificity of 92% (95% CI 84%-96%), similar to published validation studies. Before deployment, approvals were received across hospital committees for inpatient operations. Five interviews were conducted; they informed the development of an educational flyer and further modified the BPA to exclude certain patients and allow the refusal of recommendations. The longest delay in pipeline development was because of cybersecurity approvals, especially because of the exchange of protected health information between the Microsoft (Microsoft Corp) and Epic (Epic Systems Corp) cloud vendors. In silent testing, the resultant pipeline provided a BPA to the bedside within minutes of a provider entering a note in the EHR. The components of the real-time NLP pipeline were detailed with open-source tools and pseudocode for other health systems to benchmark. The deployment of medical artificial intelligence systems in routine clinical care presents an important yet unfulfilled opportunity, and our protocol aimed to close the gap in the implementation of artificial intelligence-driven CDS. ClinicalTrials.gov NCT05745480; https://www.clinicaltrials.gov/ct2/show/NCT05745480.

Marketing Decision Support System Based on Data Mining Technology

With the continuous development of business intelligence technology, the application research of decision support systems (DSSs) is deepening. In China, the work in this area started relatively late, and there are few DSS research cases to assist in marketing decision-making. Currently, marketing decision support systems have shortcomings in data integration, historical data, query functions, and data analysis. This article analyzes the characteristics of marketing decision-making, discusses the application of data warehouse, OLAP, and data mining technology in marketing decision support systems, and designs a marketing decision support system based on data mining technology. The system uses a BP neural network to conduct data mining marketing forecasting. A three-layer network model for marketing prediction is established, with sales time, product price, and customer purchasing power as network inputs and output as the sales volume of a certain type of product in different locations. The test results show that the average absolute percentage error of this method is 15.13%, and the prediction accuracy is high. Research shows that with the continuous development of data mining technology, the system cannot only help users conduct scientific and reasonable marketing decision-making analyses, making the marketing decision-making process more scientific and reasonable, but also can bring new ideas to enterprise decision-makers, promoting the continuous improvement and progress of the system.

Analysis and Identification of International Tourist Visits to Indonesia Based on Data Warehouse Implementation

Foreign tourist visits to Indonesia play an important role in the economic growth of the tourism sector. In increasing the interest of visitors, development resources are needed as a tourism sector. The development carried out must be carried out optimally with the growth trend of tourist visits so that development is right on target, effective and efficient. This study aims to visit foreign tourists to Indonesia using the implementation of a data warehouse. The dataset used is data on foreign tourist arrivals by nationality in 2021 sourced from the Central Statistics Agency (BPS). This is done to determine the interest and potential of tourism to support the country's economy. The steps taken in this analysis and identification are Choosing the Process, Choosing the Grain, Identifying and confirming the dimension, Choosing The Facts, Rounding out the dimension table, Choosing the duration of database, Tracking Slowly changing Dimensions, Deciding the query priorities and the query mode, and the last Storing pre-calculation in the fact information. The result can be seen Foreign Tourism Visits in Indonesia using the Application of OLAP Data Warehouse for analysis. So that in 2021 it can be analyzed that monthly tourist visits tend to be stable with the periods of July-August and January-December increasing. These results in the future can be used as a policy reference in increasing tourist visits to foreign tourist targets in certain countries. This is done to determine the interest and potential of tourism to support the country's economy through the tourism sector

Optimization of Printing and Dyeing Energy Consumption Based on Multimedia Machine Learning Algorithm

In order to solve the problem with the expansion of the industrial scale, the contradiction between energy resources and development of printing and dyeing enterprises must implement refined management and optimize the allocation of resources such as production technology, energy consumption types, and metering instruments. As a printing and dyeing textile industry with high energy consumption and high pollution, energy conservation and emission reduction have become difficult problem to be solved in this industry. The premise of optimizing resource allocation is to have an objective and scientific evaluation of the current energy resource allocation of printing and dyeing enterprises. In view of this, through the investigation of printing and dyeing enterprises, this paper puts forward the index system of enterprise energy consumption optimization evaluation. Based on the application of data warehouse and combined with historical data, a new energy consumption optimization evaluation method is proposed. This survey has basically understood the current situation of energy and water resources management of a printing and dyeing enterprise and pointed out the direction for the development of enterprise energy optimization project in the next step. By means of multimedia informatization, with the help of Internet of Things sensing technology, building a new generation of energy management system, improving the refined management of energy measurement, and improving the energy assessment system, enterprises can achieve significant economic and social benefits in terms of energy conservation and emission reduction. On this basis, a low-power scheduling strategy for typical data center applications is designed and implemented. The algorithm uses the input data of different calculation parts, performs matching to determine the redundant part of the calculation process, and schedules the algorithm. Experimental data show that the mean square error of the limit tree is 0.0004248 and the mean square error of the decision tree is 0.01581; through calculation, the algorithm can achieve 23% and 17% energy saving.

The Application of Data Warehouse in Teaching Management in Colleges and Universities

Data warehousing refers to the process of organizing data from different sources to support the process of decision making in organizations as well as support data analytics. Data warehousing assimilates and systematizes data from across different departments in an organization to have a single analysis of the full information. This data is then used to make the decisions in an organization. However, data warehousing has been underestimated in education institutions. The reason is that they are non-profits organizations. With the increase in the number of educational institutions coming up, colleges and universities should consider the integration of such data-driven support systems to make better decisions and for more organization in the academic processes especially for those institutions with different branches. The intricacy in managing these institutions requires development on how consistent information can be relayed to the decision-makers of an institution. This paper explains the need for data warehouses in education institutions and how the teaching institutions can implement data warehousing to help in the management of the institutions. The paper also explores and reviews various methods that institutions can use to mine data. Various stages for promoting and actualizing the system have been shown.

An Improved Framework for Modelling Data Warehouse Systems Using UML Profile

Data Warehouse (DW) applications provide past detail for judgment process for the companies. It is acknowledged that these systems depend on Multidimensional (MD) modelling different from traditional database modelling. MD modelling keeps data in the form of facts and dimensions. Some proposals have been presented to achieve the modelling of these systems, but none of them covers the MD modelling completely. There is no any approach which considers all the major components of MD systems. Some proposals provide their proprietary visual notations, which force the architects to gain knowledge of new precise model. This paper describes a framework which is in the form of an extension to Unified Modelling Language (UML). UML is worldwide known to design a variety of perspectives of software systems. Therefore, any method using the UML reduces the endeavour of designers in understanding the novel notations. Another exceptional characteristic of the UML is that it can be extended to bring in novel elements for different domains. In addition, the proposed UML profile focuses on the accurate representations of the properties of the MD systems based on domain specific information. The proposed framework is validated using a specific case study. Moreover, an evaluation and comparative analysis of the proposed framework is also provided to show the efficiency of the proposed work

PHYSICAL CONDITIONS ASSOCIATED WITH SUICIDE ATTEMPTS: DO RISKS DIFFER AMONG OUR OLDER VETERANS?

Physical illness confers risk for late-life suicide, yet few studies report whether risks differ with older age and among Veterans. We examined age-stratified associations between physical illness and suicide attempt among Veterans 65+ years (n=8452, 97% male) from a larger retrospective case-control study that utilized secondary data from the Veterans Affairs Corporate Data Warehouse and Suicide Prevention Applications Network. Controls were matched by age, sex, and mental health treatment utilization. Risk estimates for 15 conditions and a combined comorbidity score were stratified by young-old (65-74), middle-old (75-84), and oldest-old (85+), adjusting for age and sex within strata. Neurodegenerative disorder (ORs=4.5, 6.0, 6.5) or dementia (ORs=5.0, 5.7, 4.4) diagnosis within 180 days conferred the highest risks across young-, middle-, and oldest-old. Cerebrovascular disorder was associated with higher risk among the oldest-old versus young-old (ORs=6.1 vs 2.2). Findings differ from reported risks for suicide death. Illness may be experienced differently across later-life.

Dashboarding the maternal and child health profiles for health supporting system

Various types of health data are routinely collected and published as national health profiles. However, these vast amounts of data are not always used effectively since they are presented in raw tabular data sets, fragmented, and are not well-equipped with the statistical data inferences and the trend of changes which are essential to explore some unknown previous information. In this paper, we propose how to manage the national open health data sets as a dashboard application to support the decision making for maternal and child health-care services in Indonesia. The research was conducted in four steps. First, we described what maternal and child health parameters are involved in relation to the availability of open and accessible data set. Second, we designed the dashboard application based on Kimball’s four step dimensional modelling. Third, we implemented the schema modelling as data warehouse by executing ETL (extract, transform and load) and OLAP (On Line Analytical Processing) cubes. Fourth, we analyzed the results with the business intelligence approach. We explored the open data sets in four consecutive years (2014-2017) and determined seven types of business process, i.e. analysis of health profile on neonates, infants, and children, analysis of maternal health profile before, during and after pregnancy period, and analysis of health supporting factors. By applying data warehouse and business intelligence, our proposed system has the ability to integrate and extract a number of tabular and fragmented data sets into an easy and quickly inferred information about the current situation of maternal and children health care services.

Design and analysis on data warehouse of personnel administration system using time series algorithm

The purpose of this study was to design and analysis on data warehouse of personnel administration system using time series. The success of the company in performing its business activities is definitely supported by reliable system administration job. Abundant data within the company; employee information, attendance data, leave the data, the increase in the classroom, increase salaries and allowances requires good management in the order. To assist in the management of personnel data in the study, analysis and design on data warehouse is done in the administration section personnel to facilitate the collection of employee data, monthly and annual reports. The method used in the data warehouse design, data retrieval is done form related sources through extraction, transforming, and loading (ETL) process. After the data warehouse is formed, then conducted data analysis of the presence of the employee with a period of time, either the monthly attendance data analysis as well as using the annual time series algorithm. Process analysis is used to look at trend data is the presence of any employee that can be used to analyze the performance of each employee. This process then continues with the creation of reports on performance of employees which can be used for strategic decisions at the management level. The results obtained from this study showed that the application of data warehouse in staffing information systems conducted to obtain information that can support strategic decisions at the management level. Thus, the design and implementation of data warehouse in power system can help collect data, analyze data, create a report and present the data in a form of employment that can be used to aid strategic decisions at the management level.

The application of data warehouse and data mining in fracturing engineering system

With the development of low permeability oil reservoirs in oil fields, the continuous development of fracturing technology and the optimization of fracturing design, the analysis of effect needs to be fast and accurate. The fracturing project is a systematic project that requires full-process, comprehensive data support and analysis. To achieve this, it is necessary to analyse a large amount of data and share resources. The development of data warehouse system and information network technology exactly meets the above requirements. At the same time, data warehouses, with the aid of online analytical processing (OLAP) and data mining tools (DM), can help engineering technologies manage, synthesize and analyse vast amounts of data in a better way to support management decisions. This paper takes fracturing engineering as an example, illustrate the contents of the data warehouse system’s structure, design and implementation, and data presentation, proposes a data warehouse solution for fracturing engineering. Through the use of data warehouse technology and data mining technology, this paper has constructed a data warehouse with specialties in the field of fracturing engineering, providing technical support for engineering management and technical personnel at different levels and departments to provide effective decision analysis.

Research of the possibilities of application of the Data Warehouse in the construction area

Today, in information technologies, the direction associated with the use of Data Warehouse (DW) is evolving very dynamically. Using DW, it is possible to implement two types of data analysis: OLAP-analysis: a set of technologies for the rapid processing of data presented as a multidimensional cube; Data Mining is an intelligent, deep analysis of data to detect previously unknown, practically useful patterns (in our case, the construction area). It is noted, that of all the methods used in technology Data Mining, cluster analysis is especially useful for the construction area. At present, the role of DW has increased, significantly due to the fact, that many methods and approaches of Data Mining have formed the basis of a new, promising method of Big Data. We will specify that, that Data processing from the Data Warehouse with the help of technology Big Data, allows to deduce researches in a building area to the higher level. The purpose of this work is to research of the possibilities of application of the Data Warehouse in the construction area. The article suggests the new approach to data analysis in the construction area, based on the use of Big Data technology and elements of OLAP - analysis. In the section “Discussion” is considering the possibility of the new promising business in the construction field, based on the application of Data Warehouse and technology Big Data.

Application of Data Warehouse in Real Life: State-of-the-art Survey from User Preferences’ Perspective

In recent years, due to increase in data complexity and manageability issues, data warehousing has attracted a great deal of interest in real life applications especially in business, finance, healthcare and industries. As the importance of retrieving the information from knowledge-base cannot be denied, data warehousing is all about making the information available for decision making. Data warehouse is accepted as the heart of the latest decision support systems. Due to the eagerness of data warehouse in real life, the need for the design and implementation of data warehouse in different applications is becoming crucial. Information from operational data sources are integrated by data warehousing into a central repository to start the process of analysis and mining of integrated information and primarily used in strategic decision making by means of online analytical processing techniques (OLAP). Despite the applications of data warehousing techniques in number of areas, there is no comprehensive literature review for it. This survey paper is an effort to present the applications of data warehouse in real life. It focuses to help the scholars knowing the analysis of data warehouse applications in number of domains. This survey provides applications, case studies and analysis of data warehouse used in various domains based on user preferences.

De-Duplication Scheduling Strategy in Real-Time Data Warehouse

Data quality of the data warehouse is crucial to decision-makers. Data duplication is considered one of the critical factors that affect the data quality. Therefore, data de-duplication is an essential process for data warehousing. Par- ticularly, for a real-time data warehouse, it is necessary to ensure not only the data quality in real-time, but also the per- formance of the front-end queries and analysis. The scheduling strategy of de-duplication in a real-time data warehouse should be well studied. In this paper, we firstly investigate the three kinds of data de-duplication scheduling strategies named De-duplication Prior scheduling Strategy (DPS), Real-time scheduling Strategy (RS) and ETL Prior scheduling Strategy (EPS); then propose a new Time-Triggered scheduling Strategy (TTS) which belongs to EPS; finally evaluate the performance of the proposed scheduling strategy through experiments. This work is contributed to the efficient data clean- ing and application of real-time data warehouse.

Multidimensional modeling and analysis of large and complex watercourse data: an OLAP-based solution

This paper presents the application of Data Warehouse (DW) and On-Line Analytical Processing (OLAP) technologies to the field of water quality assessment. The European Water Framework Directive (DCE, 2000) underlined the necessity of having operational tools to help in the interpretation of the complex and abundant information regarding running waters and their functioning. Several studies have exemplified the interest in DWs for integrating large volumes of data and in OLAP tools for data exploration and analysis.Based on free software tools, we propose an extensible relational OLAP system for the analysis of physicochemical and hydrobiological watercourse data. This system includes: (i) two data cubes; (ii) an Extract, Transform and Load (ETL) tool for data integration; and (iii) tools for OLAP exploration. Many examples of OLAP analysis (thematic, temporal, spatiotemporal, and multiscale) are provided. We have extended an existing framework with complex aggregate functions that are used to define complex analysis indicators. Additional analysis dimensions are also introduced to allow their calculation and also for purposes of rendering information. Finally, we propose two strategies to address the problem of summarizing heterogeneous measurement units by: (i) transforming source data at the ETL tier, and (ii) introducing an additional analysis dimension at the OLAP server tier.

Data Mining Architecture System Expansion

How to build a data warehouse and data mining is constantly worth exploring and optimization, not only technically, in commercial applications as well. This article discusses the introduction of new technologies and concepts, the traditional method of data warehouse technology has changed dramatically, based on data warehouse applications are a new development. Each enterprise data warehouse based on the characteristics of different companies, you can use a very flexible method of selection and design selection, implementation. According to some relatively new technical features, talk about data warehousing and data mining architecture.

Study on Application of Data Warehouse in Medical Insurance Fund Mis

Study on Application of Data Warehouse in Medical Insurance Fund Mis

Effective data warehouse for information delivery: a literature survey and classification

Data warehouse is playing an important role in strategic decision making process for complex business solutions. To gain competitive advantage, business executives are increasingly making use of data warehouse concepts as it plays a vital role in analysing, predicting future trends based on past and current scenarios. We as authors have surveyed the various techniques used in building of data warehouse and the methods used for the implementation of techniques. We have conducted an in-depth survey of existing literature from various known international journal papers to come up with a framework which will help the researchers to focus on specific and emerging areas in the field of data warehouse development as well as application of data warehouse in various business domains.

EVALUATION OF BITMAP INDEX USING PROTOTYPE DATA WAREHOUSE

Bitmap indices have become popular access methods for data warehouse applications and decision support systems with large amounts of read-mostly data. This paper could arrive a number of results such as ; Bitmap Index highly improves the performance of Query Answering in Data Warehouses, It highly increases the efficiency of Complex Query processing through using bitwise operations (AND, OR). A prototype of Data Warehouse “STUDENTS DW” has been built according to the conditions of W. Inomn of Data Warehouses. This prototype is built for student's information.

Research of Real-time Data Warehouse Storage Strategy Based on Multi-level Caches

Abstract Real-time data warehouse extend the application of traditional data warehouse. It can not only support tactical queries for enterprise but also provide much variable tactical decision support effectively. For these reasons, it is very meaningful to research on the structure of real-time data warehouses. This paper introduced the background of real-time data warehouse and proposed the strategy of real-time data warehouse which is based on double mirror replication mechanism. The strategy is composed of double steps. First we used double mirror replication mechanism to enable continuous loading data in the real-time data warehouse with minimum impact in query execution time. Second we proposed incorporating multi-level caches into the data warehouse structure which is based on real-time partition and gave the process of design and implementation with details. We differentiated between queries with various data freshness requirements, and used multi-level caches to satisfy these different requirements.

The Application of Data Warehouse in Decision Support System

Decision support systems provide the field of query optimization with increasing challenges in the traditional questions of selectivity estimation that can exploit transformations without exploding search space. The aims of this paper are to understand what the data warehouse and protect the sensitive information stored elsewhere in data warehouse. In the system, management level of system can review all records of historical data for monthly or yearly as user desired item for each dimension. This system is developed to easy access for frequently needed data, to improve end-user respond time. The paper results in accumulation of growing amounts of data in operational databases. The system can help to retrieve complex query analysis report. In this paper, we analyze personnel information by using Data Warehouse and OLAP tools.