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In response of the comprehensive popularization of Institute of Electrical and Electronics Engineers (IEEE) 802.11 in recent years, the rapid increase of Internet of Things (IoT) data has brought about problems such as high network transmission pressure, difficult data management, and high data security risks. Based on years of experience in WiFi wireless Access Point (AP) management and data warehouse implementation, this article expounds a construction method of wireless AP data warehouse based on anomaly identification and lossy compression. The results show that the abnormal identification and lossy compression of data with high repeatability of wireless AP can effectively solve the problems of high storage pressure and communication congestion in distributed data warehouses which caused by the excessive amount of repeated data of network equipment, and greatly reduce the overall cost when the company implement the data warehouse with IoT datas.

Data warehouse technology is useful for managing large data. The purpose of them are analyzing organization’s datasets to formulate a strategy and direction of the organization. In this paper have present method of the development of data warehouse based on Ralph Kimball's methodology, starting from the study of the data source. Process of Extraction, Transformation and Loading (ETL) until we finished to get for summary financial data warehouse of Faculty of Science, Naresuan University. That they have problem about reporting from OLTP database that use for large data of financial management system. We using the data warehouse which is an OLAP database instead report function to analyze and follow up on the disbursement process more conveniently. In the future, data warehouse technology can also be used to analyze data in other fields within the Faculty of Science as well.

Data asset valuation is the foundation of modern data asset management, operation, and data circulation. Based on the theory of the data full lifecycle, starting from first principles, single data asset table was evaluated by assessing their cost, data management, and data application value. Using technologies such as data warehousing and graph algorithms, the cost value of a single data asset table was accurately calculated by using a layer-by-layer allocation method and inheriting the lineage path. Then, the non-economic factor weight of data asset was obtained by using the analytic hierarchy process, and the value of data asset was obtained through a ladder evaluation. Finally, verifies the rationality and feasibility of the new method was verified through an example. [ABSTRACT FROM AUTHOR]

Association rules are used in different data mining applications, including Web mining, intrusion detection, and bioinformatics. This study mainly discusses the COVID‐19 patient diagnosis and treatment data mining algorithm based on association rules. General data The key time interval during the main diagnosis and treatment process (including onset to dyspnea, first diagnosis, admission, mechanical ventilation, death, and the time from first diagnosis to admission, etc.), the cause of death by laboratory examination, and so forth. The frequency of drug use was counted and association rule algorithm was used to analyse and study the effect of drug treatment. The results could provide reference for rational drug use in COVID‐19 patients. In this study, in order to improve the efficiency of data mining in data processing, it is necessary to pre‐process these data. Secondly, in the application of this data mining, the main objective is to extract association rules of COVID‐19 complications. So its properties for mining should be various diseases. Therefore, it is necessary to classify individual disease types. During the construction of association rules database, the data in the data warehouse is analysed online and the association rules data mining is analysed. The results are stored in the knowledge base for decision support. For example, the prediction results of the decision tree can be displayed at this level. After the construction of the mining model, the display interface can be mined, and the decision‐maker can input the corresponding attribute value and then predict it. 0.76% of people had both COVID‐19, CHD and hypertension, while 46.5% of people with COVID‐19 and CHD were likely to have hypertension. This study is helpful to analyse the imaging factors of COVID‐19 disease. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Ekonomi Manajemen Sistem Informasi (JEMSI) is the property of Dinasti Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Business intelligence (BI) is a set of technologies and strategies allowing analyzing data and providing information to help in the decision-making process. Today, BI is used in many sectors and shows marvelous results. In this paper, we are interested in supporting decision-making in higher education. BI concepts when integrated to the process of academic affairs can make significant improvement. The present paper aims at describing a BI solution to support academic affairs at Taibah University. Using BI, we can take advantages of a set of analytical tools that support decision-making for different types of users (students, faculty members, administrators and decision makers). The proposed BI solution consists essentially of three mains tasks: (1) collecting data from different sources using the three operations (Extract, Transform and Load), (2) proposing a multidimensional solution that describes the academic processes, and (3) visualizing results through a set of dashboards and reports. Experiments are made using the SQL Server Data Tools. Three steps are detailed which are: SQL Server Integration Services, SQL Server Analysis Services and SQL Server Reporting Services. The proposed solution provides many statistical and predictive indicators needed in academic tasks. [ABSTRACT FROM AUTHOR]

This paper describes the development and implementation of an anesthesia data warehouse in the Lille University Hospital. We share the lessons learned from a ten-year project and provide guidance for the implementation of such a project. Our clinical data warehouse is mainly fed with data collected by the anesthesia information management system and hospital discharge reports. The data warehouse stores historical and accurate data with an accuracy level of the day for administrative data, and of the second for monitoring data. Datamarts complete the architecture and provide secondary computed data and indicators, in order to execute queries faster and easily. Between 2010 and 2021, 636 784 anesthesia records were integrated for 353 152 patients. We reported the main concerns and barriers during the development of this project and we provided 8 tips to handle them. We have implemented our data warehouse into the OMOP common data model as a complementary downstream data model. The next step of the project will be to disseminate the use of the OMOP data model for anesthesia and critical care, and drive the trend towards federated learning to enhance collaborations and multicenter studies. [ABSTRACT FROM AUTHOR]

The exponential growth of business data coming from heterogeneous sources imposes the use of new generations of database management systems and new data storage architectures. The major players in the big data market have turned to NoSQL (Not only SQL) technology, which provides a flexible data model and high scalability. In this paper, we investigate OLAP (Online Analytical Processing) implementation using columnar databases (a type of NoSQL system). We provide a set of formal transformation rules in order to map the multidimensional conceptual model to a target model that is suitable for the column-oriented model. Then, we propose two OLAP cube operators called MRC-Cube and SC-Cube, which allow to build the OLAP cube using the MapReduce paradigm and Spark respectively. We conduct an experimental comparison of their performance to analogous relational implementation using Oracle OLAP, we focus particularly on read latency metric under different experimental configurations. The obtained results show a clear difference when performing the OLAP cube building between the relational implementation and the columnar one. [ABSTRACT FROM AUTHOR]

Multi-model DBMSs, which support different data models with a fully integrated backend, have been shown to be beneficial to data warehouses and OLAP systems. Indeed, they can store data according to the multidimensional model and, at the same time, let each of its elements be represented through the most appropriate model. An open challenge in this context is the lack of methods for logical design. Indeed, in a multi-model context, several alternatives emerge for the logical representation of dimensions and facts. The goal of this paper is to devise a set of guidelines for the logical design of multi-model data warehouses so that the designer can achieve the best trade-off between features such as querying, storage, and ETL. To this end, for each model considered (relational, document-based, and graph-based) and for each type of multidimensional element (e.g., non-strict hierarchy) we propose some solutions and carry out a set of intra-model and inter-model comparisons. The resulting guidelines are then tested on a case study that shows all types of multidimensional elements. [ABSTRACT FROM AUTHOR]

The main contribution of this paper is to materialize right cubes according to the users' priority at the right time which can significantly reduce delays in information retrieval and achieve faster decision making. Therefore, we propose a PrioDyna algorithm that maintains the two-level cache space to handle the High priority and the long-term user requests dynamically. The first level cache focuses on the short-lived data cubes considering the cube priority as a selection parameter. In contrast, the second level cache targets the long-term trends by capturing frequently used data cubes requested for the longer run. Different experimental results show that our approach significantly outperforms the state-of-the-art methods in terms of overall cost savings by 2-6% in addition to 15-45% fewer cube evictions and better selection of High priority data cubes by 3-8%. [ABSTRACT FROM AUTHOR]

Background Timely and reliable data are crucial for clinical, epidemiologic, and program management decision making. Electronic health information systems provide platforms for managing large longitudinal patient records. Nigeria implemented the National Data Repository (NDR) to create a central data warehouse of all people living with human immunodeficiency virus (PLHIV) while providing useful functionalities to aid decision making at different levels of program implementation. Objective We describe the Nigeria NDR and its development process, including its use for surveillance, research, and national HIV program monitoring toward achieving HIV epidemic control. Methods Stakeholder engagement meetings were held in 2013 to gather information on data elements and vocabulary standards for reporting patient-level information, technical infrastructure, human capacity requirements, and information flow. Findings from these meetings guided the development of the NDR. An implementation guide provided common terminologies and data reporting structures for data exchange between the NDR and the electronic medical record (EMR) systems. Data from the EMR were encoded in extensible markup language and sent to the NDR over secure hypertext transfer protocol after going through a series of validation processes. Results By June 30, 2021, the NDR had up-to-date records of 1,477,064 (94.4%) patients receiving HIV treatment across 1,985 health facilities, of which 1,266,512 (85.7%) patient records had fingerprint template data to support unique patient identification and record linkage to prevent registration of the same patient under different identities. Data from the NDR was used to support HIV program monitoring, case-based surveillance and production of products like the monthly lists of patients who have treatment interruptions and dashboards for monitoring HIV test and start. Conclusion The NDR enabled the availability of reliable and timely data for surveillance, research, and HIV program monitoring to guide program improvements to accelerate progress toward epidemic control. [ABSTRACT FROM AUTHOR]

This research aims to create a data warehouse (Admitted to store and organize Documents) with data management Approach, strategic assessment of the views of senior managers, IT managers and heads of departments of the National Library and Archives of the Islamic Republic of Iran. This is a type of research, and application of research methods, descriptive. The study population consisted of 30 patients (10 senior managers, 10 IT managers and 10 heads of groups), the National Library and Archives Organization of the Islamic Republic of Iran and are 50 National Library and Archives of Iran’s users has been selected sampling method. Data is based on a Likert questionnaire has been developed. Validity and content validity and reliability with Cranach’s alpha coefficients of the views of senior managers by 82%, IT managers perspective with 74% and heads of groups perspective with 78% is estimated. Results indicate that the position of senior managers with an average of 5.35, IT managers with an average of 57.8 and heads of each class with the mean average 39.7 To create a data warehouse with the National Library and Archives of Iran's strategic management processes is positive. Structural communication between senior managers and IT managers plays an important role in implementing the data warehouse of the National Library and Archives of Iran. It is essential that the directors of the National Library and Archives of the Islamic Republic of Iran to establish strategic data warehouse management approach and systematic actions necessary to comply with the principles and practice of the organization needs to take action. Creating data warehousing should be consistent with the organization's strategic management processes, missions, vision, and core objectives. It must also consider the long-term goals of the organization. [ABSTRACT FROM AUTHOR]

Designing a database is a crucial step in providing businesses with high-quality data for decision making. The quality of a data model is the key to the quality of its data. Evaluating the quality of a data model is a complex and time-consuming task. Having suitable metrics for evaluating the quality of a data model is an essential requirement for automating the design process of a data model. While there are metrics available for evaluating data warehouse data models to some degree, there is a distinct lack of metrics specifically designed to assess how well a data model conforms to the rules and best practices of Data Vault 2.0. The quality of a Data Vault 2.0 data model is considered suboptimal if it fails to adhere to these principles. In this paper, we introduce new metrics that can be used for evaluating the quality of a Data Vault 2.0 data model, either manually or automatically. This methodology involves defining a set of metrics based on the best practices of Data Vault 2.0, evaluating five representative data models using both metrics and manual assessments made by a human expert. Finally, a comparative analysis of both evaluations was conducted to validate the consistency of the metrics with the judgments made by a human expert.

Abstract Background We investigated the trend change in vancomycin-intermediate Staphylococcus aureus (VISA)/heterogeneous VISA (hVISA) prevalence among methicillin-resistant S. aureus (MRSA) bacteremia strains and antistaphylococcal antibiotic use together with mutation studies of vancomycin resistance-related gene loci to evaluate the impact of changes in antibiotic use after new antistaphylococcal antibiotics became available. Methods Among 850 healthcare-associated MRSA isolates from 2006 to 2019 at a tertiary hospital in South Korea, hVISA/VISA was determined by modified PAP/AUC analysis, and the identified hVISA/VISA strains were genotyped. Gene mutations at vraSR, graSR, walKR, and rpoB were studied by full-length sequencing. Antistaphylococcal antibiotic use in 2005–2018 was analyzed. Results Two VISA and 23 hVISA strains were identified. The prevalence rate ratio of hVISA/VISA carrying mutations at the two-component regulatory systems among MRSA was 0.668 (95% CI 0.531–0.841; P = 0.001), and the prevalence rate ratio of hVISA/VISA carrying rpoB gene mutations was 1.293 (95% CI 0.981–1.702; 174 P = 0.068). Annual vancomycin use density analyzed by days of therapy (DOT) per 1,000 patient-days did not decrease significantly, however the annual average length of time analyzed by the number of days vancomycin was administered for each case showed a significantly decreasing trend. Conclusions During the 14-year period when the average length of vancomycin therapy decreased every year with the availability of alternative antibiotics, the prevalence of hVISA/VISA did not decrease significantly. This seems to be because the resistant strains carrying the rpoB mutations increased despite the decrease in the strains carrying the mutations at the two-component regulatory systems.

Nowadays, organizations are very interested to gather data for strategic decision-making. Data are disposable in operational sources, which are distributed, heterogeneous, and autonomous. These data are gathered through ETL processes, which occur traditionally in a pre-defined time, that is, once a day, once a week, once a month or in a specific period of time. On the other hand, there are special applications for which data needs to be obtained in a faster way and sometimes even immediately after the data are generated in the operation data sources, such as health systems and digital agriculture. Thus, the conventional ETL process and the disposable techniques are incapable of making the operational data delivered in real-time, providing low latency, high availability, and scalability. As our proposal, we present an innovative architecture, named Data Magnet, to cope with real-time ETL processes. The experimental tests performed in the digital agriculture domain using real and synthetic data showed that our proposal was able to deal in real-time with the ETL process. The Data Magnet provided great performance, showing an almost constant elapsed time for growing data volumes. Besides, Data Magnet provided significant performance gains over the traditional trigger technique.

Com o enorme fluxo de informação gerado pelas organizações, é importante que estas tenham a preocupação de armazenar os dados num sistema só, num data warehouse. Para além da extração, limpeza, transformação e entrega os dados de informação numa só base de dados, este sistema apoia as organizações na gestão e na tomada de decisões estratégicas. O objetivo deste artigo prende-se na exposição dos tópicos mais importantes a ter em conta no momento de implementar este sistema numa organização, como é o caso da escolha da arquitetura, fatores organizacionais internos que garantem o sucesso da implementação de um data warehouse e as questões de segurança, que não devem ser esquecidas. Por fim, é referido, de uma forma breve, uma perspetiva futura, que conjuga as tecnologias 5G e data warehouse, tornando o sistema mais eficiente, seguro e mais barato.

The digital age is responsible for the continuous growth of data volume. Everything related to Information and Communication Technologies contributes to the exponential growth of data, such as social networks, financial technology, and scientific data. Data Mining is a method of extracting data from systems such as Business Intelligence, Big Data, Data Warehouse, so that the patterns extracted during the process are considered information, knowledge, in relation to the theoretical concepts that support them. In this sense, these patterns should be presented as representations of knowledge. In the test case presented, we observed the various phases of the knowledge discovery process and analysed data using regression and classification models, to support companies in the knowledge management. [ABSTRACT FROM AUTHOR]

New technologies and the use of the Internet of Things (IoT) play a remarkable role in the design, construction, and maintenance of Smart Cities. With the exponential increase of data generated in the areas of health, commerce, financial services, science, education, public administration, and society, the challenge of ensuring data security and quality is increasing. IoT and Smart Cities, are in permanent mutation and evolution, towards a new paradigm of digitalization and digital transformation. The existing literature review exposes much of the attention that is given to IoT, but a broad discussion on the characteristics, use, applications, and challenges of these technologies in Smart Cities is still needed. The main contribution of this paper is that it provides a systematic assessment and synthesis of the critical applications and challenges of IoT, in the development of Smart Cities. [ABSTRACT FROM AUTHOR]

The effectiveness of the Business Intelligence (BI) system mainly depends on the quality of knowledge it produces. The decision-making process is hindered, and the user's trust is lost, if the knowledge offered is undesired or of poor quality. A Data Warehouse (DW) is a huge collection of data gathered from many sources and an important part of any BI solution to assist management in making better decisions. The Extract, Transform, and Load (ETL) process is the backbone of a DW system, and it is responsible for moving data from source systems into the DW system. The more mature the ETL process the more reliable the DW system. In this paper, we propose the ETL Maturity Model (EMM) that assists organizations in achieving a high-quality ETL system and thereby enhancing the quality of knowledge produced. The EMM is made up of five levels of maturity i.e., Chaotic, Acceptable, Stable, Efficient and Reliable. Each level of maturity contains Key Process Areas (KPAs) that have been endorsed by industry experts and include all critical features of a good ETL system. Quality Objectives (QOs) are defined procedures that, when implemented, resulted in a high-quality ETL process. Each KPA has its own set of QOs, the execution of which meets the requirements of that KPA. Multiple brainstorming sessions with relevant industry experts helped to enhance the model.EMMwas deployed in two key projects utilizing multiple case studies to supplement the validation process and support our claim. This model can assist organizations in improving their current ETL process and transforming it into a more mature ETL system. This model can also provide high-quality information to assist users inmaking better decisions and gaining their trust. [ABSTRACT FROM AUTHOR]

In the last few decades, precision medicine became a highly weighted subject as the risk of side effects grew with the increasing drug use. The mass of evolving pharmacogenetic data invites to establish a preemptive genotyping approach and the integration of those molecular data into the workflow of an existing hospital information system to improve the safety of drug therapy. The newly introduced software module GraphSAW2-DWHBuilder creates the graph-based data warehouse. For that, molecular data from various sources is assembled in a graph database. Furthermore, an update strategy was developed to ensure that only the latest version of the data is used. To eliminate redundancies and to guarantee efficient querying, a mapper strategy was established. Another new module named GraphSAW2-Check is embedded in an existing drug therapy safety workflow used in hospitals. It contains methods for the pharmacogenetic check, which access the molecular data warehouse of the GraphSAW2-DWHBuilder and execute queries on the graph. These queries contain the drugs and the molecular data of interest and search for connections that represent toxic associations between them. If a connection is found, a warning will be issued to the medical practitioner, who can then reconsider their prescription. The results reveal the added value of molecular data integration to the drug therapy safety. [ABSTRACT FROM AUTHOR]

Copyright of Uporabna Informatika is the property of Slovensko Drustvo Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This literature review is themed on Determination of Executive Information System Data Warehouse, Business Intelligence, and Brainware, which is a scientific article that aims to compile a research hypothesis, namely the influence between variables so that it can be used for further research in the field of executive support systems for business. The writing of this article uses the literature review method or it can be called the library research method. This data is taken from articles such as Google Scholar, Google Scholar, the Mendeley application, which provides articles and scientific journals. The results of writing this article are 1) Information technology has an influence on the Executive Information System, 2) Business Intelligence has an influence on the Executive Information System and 3) Brainware has an influence on the Executive Information System. Outside the variables above also affect the endogenous variables of the Information System executive and there are many other factors that may influence the next variable. [ABSTRACT FROM AUTHOR]

Business Intelligence is the key technology for users to effectively extract valuable information from oceans of data for decision-making. Data warehouses and on-line analytical processing systems have therefore been developed to contribute effectively to the decision-making process. To extract information that is useful to decision-making, decision-makers express their needs in natural language. Such requirements may be formulated in natural language interfaces in free syntax, avoiding unfamiliar language (SQL, MDX). Natural Language queries can stand for WH-questions ("What, Who, Where, Why, etc.") or a set of Keywords. In this paper, we emphasize on the "Why-Question". This type of question provides answers that help in the diagnosis analysis of the data Warehouse. To deal with a Why-Question, we propose a model that mainly captures the components that reflect the multidimensional aspect of the Data Warehouse. When a decision-maker formulates his Why-Question in natural language, he uses his own terms. This decisional need can be not precise because the decision-maker is not always aware of the Data Warehouse's lexicon as well as the Why-Question's model. Consequently, the decision-maker must reformulate his initial question. Otherwise, the Why-Question's answering process will not be triggered. This situation is not obvious for a decision-maker, especially when the reformulation of the question becomes iterative. To handle these issues, we lean towards a Why-Question's recommendation approach based on both the Data Warehouse's content and the decision maker's requirement. This proposal aims to recommend to the decision-maker a set of natural language Why-Questions instead of rephrasing his initial question. To guide the recommendation process, we rely on a grammar that formalizes the Why-Question's model. To validate our approach, a tool called "WQ-Recommender" is developed. An experimental study is presented to evaluate the relevance of the proposal. [ABSTRACT FROM AUTHOR]

Data analysis of weather phenomena to either predict or control human imprint on the environment requires the collection of various forms of observational data ranging from historical and longitudinal to forecast. The objective of this research paper is the development of a data warehouse (DW) based on a new hybrid logical schema, concerning the assimilation and maintenance of historical meteorological data from all operating airports in Greece, along with data in the Greek Flight Information Region related to flight delays and cancellations. SQL is used for querying these data and makes them easily accessible and manageable. The data from the DW are collected and used as training data for the induction of predictive models. In this study, the prediction problem is cast as a classification task, and different decision tree induction techniques are applied to build accurate models that allow flexible scheduling and planning for the minimization of waiting time and inconvenience of passengers. [ABSTRACT FROM AUTHOR]

Schemaless databases offer a large storage capacity while guaranteeing high performance in data processing. Unlike relational databases, which are rigid and have shown their limitations in managing large amounts of data. However, the absence of a well-defined schema and structure in not only SQL (NoSQL) databases makes the use of data for decision analysis purposes even more complex and difficult. In this paper, we propose an original approach to build a document-oriented data warehouse from unstructured data. The new approach follows a hybrid paradigm that combines data analysis and user requirements analysis. The first data-driven step exploits the fast and distributed processing of the spark engine to generate a general schema for each collection in the database. The second requirement-driven step consists of analyzing the semantics of the decisional requirements expressed in natural language and mapping them to the schemas of the collections. At the end of the process, a decisional schema is generated in JavaScript object notation (JSON) format and the data loading with the necessary transformations is performed. [ABSTRACT FROM AUTHOR]

The coal mine massive data has problems such as 'data island', weak correlation, poor data quality due to lack of data management system. It is difficult to make full use of the data and provide analysis and decision-making support for coal mine intelligence. The data warehouse can meet the requirements of multi-source heterogeneous data integration in coal mine, and provide data basis for intelligent application in coal mine. By analyzing the coal mine data types, characteristics and intelligent application requirements of actual data, the intelligent coal mine data warehouse modeling method is studied. Firstly, the layered architecture of intelligent coal mine data warehouse is constructed, and the characteristics of data model of original data layer, detailed data layer, basic index layer, service data layer and public dimension layer are analyzed. Secondly, taking the data of fully mechanized working face as an example, the modeling process of data warehouse is expounded from the aspects of business data analysis, application demand analysis and layered architecture design. Thirdly, the construction method of data model in coal mine data warehouse is introduced. The original data is transformed into data warehouse dimensional model through dimension alignment, dimension association and dimensional index aggregation. The method solves the application problem of coal mine data association in different dimensions. Finally, in order to solve the problem of portability of coal mine data warehouse, the design idea of coal mine parametric data warehouse based on general data warehouse in coal mine industry + parametric ETL (extraction-transformation-load) method is proposed. The platform of coal mine data warehouse in the laboratory environment is set up to process the data of fully mechanized working face of Shanxi Tiandi Wangpo Coal Industry Co., Ltd. The auxiliary mechanism model analysis and visual management cockpit are realized based on the processing data, which verifies the practicability of intelligent coal mine data warehouse. The performance indexes of the original data model and the intelligent coal mine data warehouse are compared. The results show that the data organization, model reuse and iteration difficulty of the intelligent coal mine data warehouse are better than those of the original data model, and the data query response time is shortened by more than 50%.

The advancement of e-commerce marketplace applications has made small and medium entrepreneurs (SMEs) in the e-commerce sector, grow rapidly in the current Era Industrial Revolution 4.0 Era. With limited capital, SMEs businesses must compete by increasing the inventory of various merchandise and large stocks, thus requiring an increase in warehouse capacity to become larger. There is a phenomenon related to the need for large warehouses and inventory for merchandise products of SMEs in the e-commerce sector, to e-commerce marketplace applications that have a virtual warehouse concept. The purpose of this study was to determine the concept of virtual warehouses, inventory management in virtual warehouses and the advantages of virtual warehouses, for SMEs in the E-commerce sector in the Industrial Revolution Era 4.0. This research method is descriptive qualitative. Informants in this study were determined purposively. The Virtual Warehouse is a virtual storage place to accommodate and store inventory data, it’s a product of a digital application system program that functions to accommodate, storage, and record inventory data stock. In the e-commerce marketplace application, the warehouse facility feature is provided virtually by the application system. It’s implementation greatly reduces costs and is very efficient compared to conventional physical warehouses. With mobile device or computer that can operating online and within internet signal range, operating of virtual warehouse can be carried out anytime and anywhere

• Over the past 20 years, hospitals have also adopted and implemented data warehouse technology to facilitate the reuse of administrative and clinical data for research. • Implementation of clinical data warehouses encounters ethical, legislative, technical, human and organizational barriers. • We conducted a focus group at the Medical Informatics in Europe congress to collect and document difficulties and barriers encountered during a clinical data warehouse project. • Based on participants experiences, we proposed 15 practical recommendations, including governance, stakeholder engagement, interdisciplinary collaboration, and external expertise utilization. The increasing implementation and use of electronic health records over the last few decades has made a significant volume of clinical data being available. Over the past 20 years, hospitals have also adopted and implemented data warehouse technology to facilitate the reuse of administrative and clinical data for research. However, the implementation of clinical data warehouses encounters a set of barriers: ethical, legislative, technical, human and organizational. This paper proposes an overview of difficulties and barriers encountered during a clinical data warehouse (CDW) development and implementation project. We conducted a focus group at the 2023 Medical Informatics Europe Conference and invited professionals involved in the implementation of CDW. These experts described their CDW and the difficulties and barriers they encountered at each phase: (i) launching of the data warehouse project, (ii) implementing the data warehouse and (iii) using a data warehouse in routine operations. They were also asked to propose solutions they were able to implement to address the barriers previously reported. After synthesis and consensus, a total of 26 barriers were identified, 10 pertained to tasks, 5 to tools and technologies, 4 to persons, 4 to organization, and 3 to the external environment. To address these challenges, a set of 15 practical recommendations was offered, covering essential aspects such as governance, stakeholder engagement, interdisciplinary collaboration, and external expertise utilization. These recommendations serve as a valuable resource for healthcare institutions seeking to establish and optimize CDWs, offering a roadmap for leveraging clinical data for research, quality enhancement, and improved patient care. [Display omitted] [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic significantly increased e-commerce growth, adding more than 218 billion US dollars to the United States e-commerce sales. With this significant growth, various operational challenges have appeared, including logistic difficulties and customer satisfaction. Businesses that strive to take advantage of increased e-commerce growth must understand data and rely on e-commerce analytics. The large scale of e-commerce data requires sophisticated information technology techniques and cyber-infrastructure to leverage and analyze. This study presents a big e-commerce data platform to address several challenges in e-commerce. The presented platform’s design is based on a distributed system architecture that supports e-commerce analytics applications using historical and real-time data and features a continuous feedback loop to observe the decision-making and evaluation processes to achieve the desired objectives. The platform was validated using two analytical applications. The first application was to identify the periods in which customers prefer to place orders, while the second was used to verify the big e-commerce data platform. The resulting insights and findings promote informed e-commerce decisions. Furthermore, viewing and acting on insight results and findings promote informed decisions that potentially benefit the e-commerce industry. The proposed platform can perform numerous e-commerce applications that potentially benefit the e-commerce industry.