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Health-care database is a collection of health-care data organized for storage, accessibility, and retrieval. Health-care database serves to replace the paper documents, filing cabinets of old, and file folders, which will be more convenient and immediate. At present, health-care system is in a transition from a patient care perspective toward information-based medicine requiring data from various sources such as laboratory tests, genetic tests, medical images, family histories, and patient medical records, which need to be easily stored, integrated, and analyzed. The most commonly used database in health care is the online transaction processing (OLTP) database among different types of databases available. OLTP database is the one that is a single computer application runs on, and the main strength of it is quick and real-time transaction processing. Electronic health record is a prime example of the OLTP, and practitioners enter routine clinical and laboratory data into them during usual practice as a record of the patient's care. Data can be stored externally in a secure place and can be backed up to prevent data loss with these health-care databases. The back-end data can therefore become more accurate and standardized because of the front-end software providing tip text and enforce data. Furthermore, the health-care database allows the rapid processing of classical transactions such as laboratory results and payment claims as the data are computerized.

Managerial responses to online customer reviews not only affect customers who receive the responses but may also influence subsequent customers who observe the responses. This externality arises because of the public nature of online interactions. However, prior studies were mainly in offline settings where such externality rarely exists. In this study, we assess the magnitude of such externality. Using a difference-in-difference-in-differences framework and matched hotels across two large travel agencies, we find that managerial responses indeed have a significant and positive impact on the volume of subsequent customer reviews. The impact on the review valence is not evident, which can be attributed to the unique design of identity disclosure in our research context. Furthermore, our results suggest nuances that were not known in the prior literature. For example, responding to positive and negative reviews may have different effects on future reviews, and managers should provide detailed responses to negative reviews but brief ones to positive reviews. Our results offer managerial implications to service providers on how to improve customer engagement in the interconnected online environment. The online appendix is available at https://doi.org/10.1287/isre.2018.0781 .

An extensive literature in economics and finance has documented home bias , the tendency that transactions are more likely to occur between parties in the same geographical area rather than outside. Using data from a large online crowdfunding marketplace and employing a quasi-experimental design, we find evidence that home bias still exists in this virtual marketplace for financial products. Furthermore, through a series of empirical tests, we show that rationality-based explanations cannot fully explain such behavior and that behavioral reasons at least partially drive this remarkable phenomenon. As crowdfunding becomes an alternative and increasingly appealing channel for financing, a better understanding of home bias in this new context provides important managerial, practical, and policy implications. This paper was accepted by Lee Fleming, entrepreneurship and innovation .

The PANDA experiment has been designed to incorporate software triggers and online data processing. Although PANDA may not surpass the largest experiments in terms of raw data rates, designing and developing the processing pipeline and software platform for this purpose is still a challenge. Given the uncertain timeline for PANDA and the constantly evolving landscape of computing hardware, our attention is directed toward ensuring the futureproofness of the solutions we develop. The PandaR2 is a concept for a framework handling online data processing in heterogeneous and distributed HPC environments. It utilizes the SYCL programming model as the primary technology for parallelization and offloading. Being a new and standalone entity, PandaR2 also interfaces with the PANDA’s original ROOT-based simulation and analysis framework - PandaRoot, connecting the best of both worlds. This contribution aims to present an overview of the PandaR2 SYCL-centric architecture. We will share experiences with SYCL during the codebase design process, particularly highlighting its portability across various hardware platforms and compilers. Additionally, we will showcase the performance results of the initial algorithms implemented in PandaR2.

We propose information-directed sampling —a new approach to online optimization problems in which a decision maker must balance between exploration and exploitation while learning from partial feedback. Each action is sampled in a manner that minimizes the ratio between squared expected single-period regret and a measure of information gain: the mutual information between the optimal action and the next observation. We establish an expected regret bound for information-directed sampling that applies across a very general class of models and scales with the entropy of the optimal action distribution. We illustrate through simple analytic examples how information-directed sampling accounts for kinds of information that alternative approaches do not adequately address and that this can lead to dramatic performance gains. For the widely studied Bernoulli, Gaussian, and linear bandit problems, we demonstrate state-of-the-art simulation performance. The electronic companion is available at https://doi.org/10.1287/opre.2017.1663 .

To provide applications with the ability to analyze fresh data and eliminate the time-consuming ETL workflow, hybrid transactional and analytical (HTAP) systems have been developed to serve online transaction processing and online analytical processing workloads in a single system. In recent years, HTAP systems have attracted considerable interest from both academia and industry. Several new architectures and technologies have been proposed. This paper provides a comprehensive overview of these HTAP systems. We review recently published papers and technical reports in this field and broadly classify existing HTAP systems into two categories based on their data formats: monolithic and hybrid HTAP. We further classify hybrid HTAP into four sub-categories based on their storage architecture: row-oriented, column-oriented, separated, and hybrid. Based on such a taxonomy, we outline each stream’s design challenges and performance issues (e.g., the contradictory format demand for monolithic HTAP). We then discuss potential solutions and their trade-offs by reviewing noteworthy research findings. Finally, we summarize emerging HTAP applications, benchmarks, future trends, and open problems.

The security of online transactions has become a difficult concern for both businesses and consumers. With the increasing volume of transactions occurring online, the need for robust security measures has never been more pressing. Blockchain technology, initially developed to support cryptocurrencies like Bitcoin, has emerged as a powerful tool in enhancing the security of online transactions.

Database growth and Storage problems are main concern for large and small enterprises nowadays, which significantly influences the efficiency of database applications. Database archiving is one of the solutions available for management. Many issues have been identified as a result of the use of archive databases, including the elimination of inactive data from online transaction processing systems (OLTP), performance and integrity management. The aim of this paper is to propose a framework for storing OLTP and archiving data in a distributed database environment as part of an integrated system. Maintaining the integrity between an OLTP database and an archiving database is crucial, but even more critical is the performance of the required query. The main aspect of the proposed framework is that it will not only ensure data integrity for primary and unique keys between OLTP and archive database, but it will also improve query performance by introducing parallel processing and query execution plans to maintain that integrity.

As the sharing economy has grown rapidly and replaced the traditional businesses, new rules and norms for data and digital trade have emerged divergently in many countries. Such divergence in global e-commerce policies may be a major barrier to the internationalization of the sharing economy business. This paper aims to develop an internationalization theory that addresses how the sharing economy firms can internationalize under the condition of the divergence of global e-commerce policies. Drawing on Hayek’s knowledge economy approach, we build a new internationalization theory for the sharing economy firms that facilitate autonomously self-organized business ecosystems and adapt to the lack of harmonized rules and norms for the sharing economy. We first theorize on the attributes of the digital platform-based transactions for the internationalization of the sharing economy firms and then provide some insights into the current international debates of e-commerce policies. Our theory offers two main insights: (1) the competitive advantages of the sharing economy firms stem mainly from digital platform algorithms to catalyze digital platform-based transactions between autonomous actors; (2) the divergence of global e-commerce policies and different internet regimes in different countries may affect the internationalization of business models based on such digital platform-based transactions.