Data Management System Research Articles

Sistem Informasi Inventory Penjualan Barang (Studi Kasus : Toko Aru Raya)

The system for handling product data in a store is one that can facilitate efficient operation. It is still not utilized by the majority of stores, which continue to manage their product data manually. Application for the Goods Data Management System at the Aru Raya Store, which will be sent to the stage of system implementation used to manage transactions at the Aru Raya Store. Numerous flaws in the current system have been discovered during the course of the field research procedure. Despite the fact that there is nothing as a whole, but rather leads to specific issues, it is anticipated to have a significant impact on system users. Because it will be quicker, more accurate, and more precise with this information system, the author of the information system designed the supply chain for Baceno Bag Convection. The author of this new system realized an effective system at Aru Raya Store in Lubuk Begalung by creating it utilizing the PHP, UML, and MySQL Database programming languages.

High-Throughput Vector Similarity Search in Knowledge Graphs

There is an increasing adoption of machine learning for encoding data into vectors to serve online recommendation and search use cases. As a result, recent data management systems propose augmenting query processing with online vector similarity search. In this work, we explore vector similarity search in the context of Knowledge Graphs (KGs). Motivated by the tasks of finding related KG queries and entities for past KG query workloads, we focus on hybrid vector similarity search (hybrid queries for short) where part of the query corresponds to vector similarity search and part of the query corresponds to predicates over relational attributes associated with the underlying data vectors. For example, given past KG queries for a song entity, we want to construct new queries for new song entities whose vector representations are close to the vector representation of the entity in the past KG query. But entities in a KG also have non-vector attributes such as a song associated with an artist, a genre, and a release date. Therefore, suggested entities must also satisfy query predicates over non-vector attributes beyond a vector-based similarity predicate. While these tasks are central to KGs, our contributions are generally applicable to hybrid queries. In contrast to prior works that optimize online queries, we focus on enabling efficient batch processing of past hybrid query workloads. We present our system, HQI, for high-throughput batch processing of hybrid queries. We introduce a workload-aware vector data partitioning scheme to tailor the vector index layout to the given workload and describe a multi-query optimization technique to reduce the overhead of vector similarity computations. We evaluate our methods on industrial workloads and demonstrate that HQI yields a 31× improvement in throughput for finding related KG queries compared to existing hybrid query processing approaches.

New prediction categories in CASP15.

Prediction categories in the Critical Assessment of Structure Prediction (CASP) experiments change with the need to address specific problems in structure modeling. In CASP15, four new prediction categories were introduced: RNA structure, ligand-protein complexes, accuracy of oligomeric structures and their interfaces, and ensembles of alternative conformations. This paper lists technical specifications for these categories and describes their integration in the CASP data management system.

Rancang Bangun Sistem Manajemen Banner Periklanan pada PT. Merah Putih Media Menggunakan Framework Laravel Berbasis Web dan Metode RAD

Abstract
 
 Banner Advertising Management System at PT. Merah Putih Media is designed to facilitate the management of advertising banner data on online media owned by companies. The aim of this research is to design and build an efficient, easy-to-use banner advertising management system. This study uses the Laravel Framework as a development basis and the RAD Method to speed up the system development process. In system development, the RAD method allows for rapid prototyping and testing so that the results can be evaluated immediately. The system consists of banner ad data management and user management modules. The banner ad data management module is used to manage banner ad data on online media, while the user management module is used to manage system users. The result of this research is a banner advertising data management system to manage banner advertising data at PT. Merah Putih Media. This system effectively and efficiently manages advertising banners on online media and can help PT. Merah Putih Media in improving company performance.
 
 Keywords: laravel, banner ads, php, framework, bootstrap, RAD.

PERANCANGAN SISTEM MANAJEMEN STOK BARANG BERBASIS WEB PADA PT.X

Goods stock management is a policy or decision of a company that has the goal of determining the company can provide quality inventory of goods such as managing the amount of goods. Basically, stock management is a process in a warehouse where stock availability is influenced by external factors such as orders from buyers and internal factors such as supply of goods. Every company certainly has a stock of goods for sales. If a company has a lot of stock, of course it needs a stock management system that can be accessed anywhere or web-based. By managing web-based inventory management, users can manage inventory on any platform. One of the keys for a company to have an orderly stock of goods is to use a stock management system. The purpose of designing this system is to form a stock management system and maintenance of a company's data. After already having an application system, it will be easier for the user to find out the details of the stock of goods and information related to the goods of companies that are carrying out maintenance and the process of monitoring stock of goods. The data used in the design of this system is detailed stock data for ink companies located in Jakarta. The data used is the company's inventory data for 2020 january to december. The method used is the SDLC waterfall method. In designing the application system, two stages of design are carried out, namely process design and database design. The tests were carried out using the blackbox testing method and focused beta testing through questionnaires to users at PT.X. After testing, suggestions have been given. Applications that have been made should be given a language change feature because currently the application is still in english, so that some employees who cannot speak english will have difficulty understanding this application.

Technical Perspective: Revisiting Runtime Dynamic Optimization for Join Queries in Big Data Management Systems

Query optimization is the process of finding an efficient query execution plan for a given SQL query. The runtime difference between a good and a bad plan can be tremendous. For example, in the case of TPC-H query 5, a query with 5 joins, the difference between the best and the worst plan is more than 10,000×. Therefore, it is vital to avoid bad plans. The dominating factor which differentiates a good from a bad plan is their join order and whether this join order avoids large intermediate results.

Revisiting Runtime Dynamic Optimization for Join Queries in Big Data Management Systems

Effective query optimization remains an open problem for Big Data Management Systems. In this work, we revisit an old idea, runtime dynamic optimization, and adapt it to a big data management system, AsterixDB. The approach runs in stages (re-optimization points), starting by first executing all predicates local to a single dataset. The intermediate result created by a stage is then used to re-optimize the remaining query. This re-optimization approach avoids inaccurate intermediate result cardinality estimates, thus leading to much better execution plans. While it introduces overhead for materializing intermediate results, experiments show that this overhead is relatively small and is an acceptable price to pay given the optimization benefits.

A quality management system aiming to ensure regulatory-grade data quality in a glaucoma registry.

Disease/patient registries are underutilized despite their multiple advantages over clinical trials in the clinical evaluation of drugs, such as the capacity for long-term curation, provision of patient outcome data in routine clinical practice, and provision of benchmark data for comparison. Ensuring the fit-for-purpose quality of data generated from such registries is important to informing regulatory decision making. Here, we report the construction of a quality management system aiming to ensure regulatory-grade data quality for a registry of Japanese patients with glaucoma to evaluate long-term patient outcomes. The quality management system was established by reference to the risk-based approach in the ICH-E6 (R2) recommendations. The following three-component approach was taken: establishment of governance, computerized system validation (CSV), and implementation of risk assessment and control. Compliance of the system with the recommendations of regulatory guidelines relevant to use of the registry was assessed. Governance by academic collaboration was established. This was followed by the development of a total of 15 standard operating procedures, including CSV, data management, monitoring, audit, and management of imaging data. The data management system was constructed based on a data management plan, which specified data/paper flow and data management procedures. The electronic data capture (EDC) system was audited by an external vendor, and configured and validated using the V-model framework as recommended in the GAMP5 guideline. Informed consent, eligibility assessment and major ophthalmology measurements were determined as Critical to Quality (CTQ) factors. A total of 22 risk items were identified and classified into three categories, and operationalized in the form of a risk control plan, which included training sessions and risk-based monitoring. The glaucoma registry addressed most quality recommendations in official guidelines issued by multiple health authorities, although two recommendations were not met. We established and configured a quality management system for a glaucoma registry to ensure fit-for-purpose data quality for regulatory use, and to curate long-term follow-up data of glaucoma patients in a prospective manner.

Development of a flexible data management system, to implement predictive maintenance in the Industry 4.0 context

In recent years, the way that maintenance is carried out has evolved due to the incorporation of digital tools and Industry 4.0 concepts. By connecting to and communicating with their production system, companies can now gather information about the current and future health of the equipment, enabling more efficient control through a process called predictive maintenance (PdM). The goal of PdM is to reduce unplanned downtimes and proactively address maintenance needs before failures occur. However, it can be challenging for industrial practitioners to implement an intelligent maintenance system that effectively manages data. This paper presents a methodology for developing and implementing a PdM system in the automotive industry, using open standards and scalable data management capabilities. The platform is validated through the presentation of two industry use cases.

The Composable Data Management System Manifesto

The requirement for specialization in data management systems has evolved faster than our software development practices. After decades of organic growth, this situation has created a siloed landscape composed of hundreds of products developed and maintained as monoliths, with limited reuse between systems. This fragmentation has resulted in developers often reinventing the wheel, increased maintenance costs, and slowed down innovation. It has also affected the end users, who are often required to learn the idiosyncrasies of dozens of incompatible SQL and non-SQL API dialects, and settle for systems with incomplete functionality and inconsistent semantics. In this vision paper, considering the recent popularity of open source projects aimed at standardizing different aspects of the data stack, we advocate for a paradigm shift in how data management systems are designed. We believe that by decomposing these into a modular stack of reusable components, development can be streamlined while creating a more consistent experience for users. Towards that goal, we describe the state-of-the-art, principal open source technologies, and highlight open questions and areas where additional research is needed. We hope this work will foster collaboration, motivate further research, and promote a more composable future for data management.

Internship Model 'Freedom Learns Independent Campus' for Nursing Students in the Digital Era of Industry 4.0

This research aims to develop and apply the Internship Model 'Freedom Learns Independent Campus' (MBKM) specifically for nursing students in the digital era of Industry 4.0. This study acknowledges that the role of technology in nursing is increasingly important and that the use of technology in nursing student internships will increase their understanding of patient-centered health care. This study used a mixed approach involving both quantitative and qualitative methods. The research sample consisted of nursing students who are currently undergoing higher education programs and have access to digital technology. Data will be collected through surveys and interviews, which will be analyzed using statistical approaches and thematic analysis. The MBKM Internship Model will include the use of technology such as simulation, electronic data management, telemedicine, and mobile applications in the clinical practice of nursing students. Students will be given training in the use of these technologies and will have the opportunity to apply them in simulated and hands-on nursing situations. During the internship period, nursing students will work with clinical mentors who are experienced in integrating technology into patient care. They will be involved in the collection and analysis of clinical data using electronic data management systems, as well as conducting telemedicine consultations for guidance and feedback from experienced healthcare professionals. It is hoped that the results of this study will provide insight into the effectiveness of the MBKM Internship Model in preparing nursing students to face the challenges and opportunities offered by the digital era of Industry 4.0. In addition, the results of this study can become the basis for the development of curricula and nursing education programs that are more up-to-date and relevant to technological developments.

Hybrid Data Management Systems: Integrating Data Lakes and Data Warehouses

Hybrid Data Management Systems: Integrating Data Lakes and Data Warehouses

An evaluation of measures to reduce corporate air travel emissions: A Griffith University case study

With increasing climate change concerns, there are calls for action to reduce aviation greenhouse gas emissions. Universities are responsible for a significant amount of air travel and this study evaluates approaches to reduce aviation emissions at Griffith University, in the Southeast Queensland area of Australia. The research involved consultation with the Griffith University community. A series of semi-structured interviews with ten relevant stakeholders and a week-long immersive digital discussion with 52 staff using the digital platform Recollective were conducted. University staff described how the COVID disruption gave them the chance to reflect and develop innovative and immersive teaching and research practice. Some university staff were very supportive of the environmental cause relating to air travel, whilst others were more resistant due to work and career-based concerns. It was determined that the travel booking process would be enhanced with tools such as a carbon calculator, an emission impact dashboard, a flight data management system, and improved visualisation of information. Some interesting and innovative ideas also emerged from the study regarding individual incentives and a university-wide campaign effort. Finally, the university has committed to an implementation plan and aviation emissions reduction target of 25% by 2030, based on 2010 levels, following the case study.

Indexing legacy data-sets for global access and processing in multi-cloud environments

Global data access and processing for multi-cloud applications proves challenging where there is a need for efficient access to large pre-existing, legacy data-sets. To address this problem, we created an indexing subsystem, allowing us to index and gather the metadata of legacy data-sets. The gathered information is later incorporated into a multi-cloud data management system to achieve global integration of legacy storage systems containing large data-sets. The solution is based on metadata management, organization and periodic monitoring of legacy data-sets, which makes possible scheme-less co-existence of a legacy storage system and a multi-cloud data management system for managing the same data collections. The approach has been initially evaluated in a multi-cloud deployment scenario. A large data collection consisting of legacy cultural data-sets was exposed to public and commercial infrastructure for data access and processing.

Obituary - Brian Roy Pitkin

Brian Pitkin was one of those relative rarities – an entomologist who excelled on, and made original contributions to, the taxonomy and ecology of several quite disparate groups of insects. Then, mid-career, Brian moved on, to tackle a challenging new world – creating and running data management systems fit for a collection of thirty million specimens and one million names. Along the way he helped numerous colleagues and students alike – always with a smile – and found time not only for his wife and friends, but intensive leisure-time pursuits that included diving, underwater photography, and botanical recording. This appreciation of his life is made up of a series of personal accounts and vignettes, and is completed by a bibliography of his published entomological works.

Data management system for sustainable agriculture among smallholder farmers in Tanzania: research-in-progress

ABSTRACT Smallholder farmers produce about 70% of the world’s food and employ more than one billion people. They therefore have an important role to play in eradicating food insecurity and poverty among the world’s growing population. Although there are different digital services for smallholder farmers, the existing services lack sustainability in the agriculture context and hardly meet their needs. Data management and sharing among different agriculture stakeholders has the potential to make agriculture sustainable, but there is a need to enable access to digital services in an entire farming cycle under one roof. This paper aims to propose the design of a comprehensive data management digital framework to solve common challenges of smallholder farmers in Tanzania and other countries’ agricultural systems. We follow the design science research (DSR) method to develop an artifact that interacts with the problem context. To illustrate the framework’s applicability, we use different case studies in Tanzania.

Evaluation of insertable cardiac monitor placement and safety in the LUX-Dx PERFORM study

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Boston Scientific Corporation Background The LUX-Dx PERFORM Study is designed to evaluate safety and performance of the LUX-Dx insertable cardiac monitor (ICM) in a general patient population. Enrollment and device insertions are complete, and all patients have completed 30 days of follow-up. This abstract describes the study population, characterizes device insertion, and reports on preliminary data for ICM system-related complications. Methods The LUX-Dx PERFORM study is a prospective, multi-center, single arm, post-market, observational study. The ICM system-related complication-free rate (CFR) at 30 days post-insertion was calculated using Kaplan-Meier methodology and the 97.5% one-side lower confidence limit (LCL) was compared to the prespecified performance target of 94%. All ICM system-related adverse events were adjudicated by independent physician reviewers. Results 727 patients were enrolled at 24 centers in the United States from March 2021 to May 2022. The study population was 49% male, 91% white and 66+/-14 years old. 717 patients had device insertions and were successfully registered in the LATITUDE Clarity data management system. There were 9 withdrawals prior to insertion and 1 aborted procedure due to deployment difficulty. ICM insertion was performed in-hospital 76% of the time (N=547) and in-clinic 24% of the time (N=171). 96% were de novo device insertions. A variety of closure techniques were used including surgical glue (62%), adhesive strips (45%), sutures (42%), and staples (9%). Median procedure time was 4 minutes (range 1 - 37). There were 4 ICM system-related complications in the first 30 days (CFR 99%, LCL 99%) which meets the acute safety performance goal. The system-related complications include 1 case of ICM deployment difficulty, 1 case of device expulsion three days post insertion, and 2 infections that were treated with oral antibiotics and resolved without further mitigation. Conclusion The LUX-Dx ICM can be inserted quickly, in both hospital and clinic settings, with a very low complication rate.

A Case Study on Sustainable Quality Assurance in Higher Education

In contemporary academia, accreditation stands as a vital process for universities to uphold their reputation and deliver quality education to students. Key Performance Indicators (KPIs) significantly contribute to this process by enabling institutions to assess their performance in crucial areas, such as student outcomes, faculty engagement, and research effectiveness. To achieve sustainable accreditation, universities necessitate a comprehensive data management system capable of handling extensive data and generating pertinent insights. High-quality education remains paramount for academic institutions to equip students with essential professional skills, thereby enabling societal economic advancement. A sustainable quality management system is an innovative approach emphasizing continuous improvement and long-term planning to enhance an organization’s overall performance and maintain a competitive edge within the sector. This paper presents a case study on sustainable quality assurance in higher education, underscoring the importance of KPIs in accreditation and the need for a robust data management system to ensure sustainability. Additionally, it highlights the significance of delivering high-quality education and the role of a sustainable quality management system in realizing this objective.

How do sonographers improve access to cardiology care? An outpatient study

We report our experience working with cardiac sonographers (SONO) in outpatient settings between April 2019 and December 2022. Our observation focused on 7 outpatient cardiac centers in the Auvergne Rhône Alpes region, 35,743 patients had at least one consultation between April 2019 and December 2022. In total, 23,657 patients an echocardiogram (ECHO) performed (66%). Seven SONOs were involved, supervised by 15 cardiologists. All ECHOs were technically performed based on a standard acquisition protocol. Data was saved on a dedicated cloud-based Picture Archiving and Communication System (PACS) allowing the cardiologist to check the image quality, results and complete the report, automatically included in the electronic health record. Out of a total of 25,686 ECHO (i.e., an average of 1.1 ECHO/patient), ∼19,800 (77%) were performed by SONOs (range from 72% to 92% depending on the medical center). In total, ∼2000 ECHO (∼10%) required technical expertise and additional acquisition of images by the referring cardiologist (range from 5% to 25% depending on the experience level of the SONO). Based on a study sample, the average ECHO acquisition time was 12 ± 7 minutes. Thus, over the entire period of 33-month period, the total medical time saved was estimated up to 4,700 hours. Teamwork is mandatory to improve access to outpatient care in cardiology. Working with cardiac sonographers is efficient and saves cardiologists up to 700 hours/SONO/year of medical time. This organizational workflow should be paired with dedicated imaging data management systems and HER (Fig. 1).

Construction and Application of a Big Data System for Regional Lakes in Coalbed Methane Development.

With the rapid development and widespread application of big data and artificial intelligence, the upgrading of digital and intelligent industries has been rapidly popularized in the oil and gas industry. First, based on the theory of ″regional data lake″, the digital nature of the CBM governance system is analyzed, and the optimization model of CBM governance for different data types is established. Second, considering the geological characteristics and development mode of the CBM reservoir, the regional data lake expansion model is established. Third, a theoretical model of coupling ″on-site data, laboratory data, management data, and data management system″ has been established. The research shows the following: (a) The CBM governance system based on the regional data lake can be divided into four parts: basic support, data life cycle, core governance areas, and governance strategy support. (b) The coupling of the coalbed methane governance model with the BP neural network model in this article has good application results. (c) The computational efficiency of this model has been improved by 12%, which has broad application prospects.