Data Management Structure Research Articles

Causal Inconsistencies Are Normal in Windows Memory Dumps (Too)

Main memory contains valuable information for criminal investigations, e.g., process information or keys for disk encryption. Taking snapshots of memory is therefore common practice during a digital forensic examination. Inconsistencies in such memory dumps can, however, hamper their analysis. In this article, we perform a systematic assessment of causal inconsistencies in memory dumps taken on a Windows 10 machine using the kernel-level acquisition tool WinPmem. We use two approaches to measure the quantity of inconsistencies in Windows 10: (1) causal inconsistencies within self-injected memory data structures using a known methodology transferred from the Linux operating system, and (2) inconsistencies in the memory management data structures of the Windows kernel using a novel measurement technique based on properties of the virtual address descriptor (VAD) tree. Our evaluation is based on a dataset of more than 180 memory dumps. As a central result, both types of inconsistency measurement reveal that a high number of inconsistencies is the norm rather than the exception. We also correlate workload and execution time of the memory acquisition tool to the number of inconsistencies in the respective memory snapshot. By controlling these factors it is possible to (somewhat) control the level of inconsistencies in Windows memory dumps.

UAV, GNSS, Total Station, and Data Management Applied to an Ancient Clay Structure as a Historic Building Information Modeling Proposal: A Case Study of Huaca Arco Iris (Trujillo, Peru)

In light of current risks and environmental impacts, HBIM (historic building information modeling) offers a highly efficient and interactive method for managing historical data and representing the current states of ancient clay structures. In this study, traditional geodetic techniques were employed to digitally locate a structure without compromising its topographic information to create an accurate model. Tools such as total stations, GNSS receivers, and UAVs were utilized to generate detailed topography of the study site and its surroundings. An ontology-based data management structure was also developed to store historical data and site intervention projects, adhering to the ISO 12006-2 standard. This was achieved through automated scripts in Dynamo softwarev.2.18.1. A comparison between the point cloud (279 images) and total station data (600 points) revealed a georeferencing accuracy difference of +/−0.003 m. Consequently, the developed methods can effectively represent similar structures digitally. The proposed ontological structure facilitates automated storage of internal and external information.

Revolutionizing semantic integration of maintenance cost prediction for building systems using artificial neural networks

The maintenance cost management for building services has significantly advanced with the advent of computational techniques and semantic web technologies. Building services, including HVAC, plumbing, firefighting, and electrical systems, ensure building safety and efficiency. However, predicting maintenance costs is challenging due to the complexity and variability in usage patterns and environmental conditions. To address this challenge, this study introduces a Semantically Integrated Knowledge-based Artificial Neural Network (SIKB-ANN) framework. The SIKB-ANN framework integrates Semantic Web Technology (SWT) and Artificial Neural Networks (ANN) to improve the accuracy of maintenance cost predictions. SWT enhances data interoperability and standardization, while ANN manages complex non-linear data relationships. The model was trained with historical maintenance data from a firm (2017–2022) and validated using metrics such as Coefficient of Determination (R2), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The model achieved high predictive accuracy, with R2 values of 0.986 for the test set and 0.944 for the training set, indicating its robustness and reliability. An essential contribution of this research is the innovative integration of semantic ontologies with ANN, significantly enhancing predictive capabilities and providing a structured data management framework. This approach improves prediction accuracy and supports better decision-making and resource allocation in building management. The study highlights the potential of combining semantic technologies with machine learning to address complex predictive challenges in the built environment. Future research should consider integrating real-time data streams, advanced machine learning techniques, and broader applications in facility management.

Lake Environmental Data Harvester (LED) for Alpine Lake Monitoring with Autonomous Surface Vehicles (ASVs)

This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP’s modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles.

Enhancing Business Intelligence with Real-Time Sales Dashboards: Integrating SQL Databases and Power BI

Abstract: In today's fast-paced business environment, the ability to make informed decisions swiftly is crucial for maintaining a competitive edge. This project proposes the integration of SQL databases and Power BI to develop a real-time sales dashboard, aimed at transforming how sales data is visualized and analysed within the organization. By harnessing the robust capabilities of SQL databases for structured data management and Power BI for data visualization and analytics, the project seeks to provide comprehensive insights into sales trends, performance metrics, and customer behaviours. The integration will enable the automatic synchronization of sales data from SQL databases to Power BI, facilitating the creation of interactive and dynamic dashboards. These dashboards will not only display key sales indicators and metrics but also allow users to drill down into specific data points for deeper analysis. The real-time nature of the data presented will empower sales managers and executives to make data-driven decisions rapidly, adjust strategies on the fly, and identify opportunities for growth and improvement with unprecedented speed and accuracy.

Linked List Data Structure and Library Management System

Data Structure and Library Management System, focuses on the implementation of a linked list data structure for a library management, aiming to address the efficiency and functionality of managing library resources through the use of linked lists. The paper explores the advantages of using linked lists in the context of a library management system, discussing how this data structure can optimize operations such as adding or removing books from the library, tracking borrowing history, and managing book availability. By utilizing a linked list data structure, the library management system can efficiently organize and manipulate its resources, leading to improved search and retrieval times. The implementation of a linked list data structure in a library management system streamlines processes, enhances resource management, and ultimately improves the overall efficiency of the system.

Implementation of the Integrated Waste Management Site (TPST) Program

The study analyzed the implementation of waste management at BUMDes Bhirawa in the Mekarsari Integrated Waste Treatment Site (TPST) in Kureksari Village using a qualitative descriptive method. Data was gathered from interviews, observation, documentation, and literature studies, and the informants included the Bhirawa BUMDes Secretary, the Kureksari Village Government, and the TPST users. The research revealed four variables: communication, data source, dispositions, and bureaucratic structure. The results emphasized the importance of effective communication, data management, employee attitudes, and bureaucratic structure for successful waste management programs Highlight : Communication: The study emphasizes the importance of effective communication in waste management programs. Communication was carried out through electronic media, socialization, and training. Data Management: The study highlights the significance of data management in waste management programs. The data source was sufficient, with a special budget for employee salaries and TPST maintenance. Employee Attitudes and Bureaucratic Structure: The study suggests that employee attitudes and bureaucratic structure play a crucial role in successful waste management programs. The results revealed that employees were responsible for their duties, and the bureaucratic structure had clear standard operating procedures and a well-executed division of responsibilities. Keywords: Waste Management, BUMDes Bhirawa, Mekarsari Integrated Waste Treatment Site (TPST), Kureksari Village, Qualitative Descriptive Method

Sistem Informasi Penjualan Dan Stock Bahan Baku Berbasis Web (Studi Kasus Pabrik Krupuk Berkah Jaya)

This research departs from the researcher's interest in the function of websites in the sales process and stock of raw materials at a cracker factory. This research aims to develop an information system that can improve structured data management in business processes, especially in matters regarding sales and stock of raw materials. This research uses the Waterfall method as a development method. The data collection method in this research was carried out using observation, interviews and documentation techniques. The results of this research show that the existence of a website-based information system or sales and raw material recording system facilitates various transaction processes at this cracker factory. Having a database system compiled on a website will make it easier for an owner or business actor to store information on buying and selling transactions, record raw materials, and will make it easier to make reports..

“What About Your Friends?”: How a Collaborative Transdisciplinary Training Approach Supports FAIR Data Sharing Principles in Federally Funded Research

Objective: This paper details a pilot project to establish a baseline for current data management planning activities and offers more targeted data management planning training to researchers.Methods: The authors incorporated a collaborative transdisciplinary approach, leading to the development and delivery of a series of surveys to gain accurate feedback about current workflows, policy adherence, and identifying knowledge gaps.Results: Using formal survey results and informal feedback from researcher interactions to inform targeted training sessions and materials results in a more productive and collaborative experience for researchers and leads to more complete and structured data management plans.Conclusions: Understanding researchers’ current practices and needs is crucial to developing effective training and resources to help improve data management planning and workflows.

Design and Management of a Spatial Database for Monitoring Building Comfort and Safety

As the impacts of climate change on urban environments and buildings become more and more prominent, building comfort and structural safety monitoring becomes crucial. However, efficiently storing and managing the multi-source monitoring data generated during the long-term monitoring process has been an urgent challenge. In order to solve the above problems, this paper designs and develops a spatial database management system for building comfort and structural safety monitoring based on standard database development tools. A conceptual model of a spatial database for building comfort and structural safety monitoring is proposed, and the entities, attributes, and connections in the model are discussed to transform the E-R conceptual model into a logical model supported by an object-relational spatial database management system. Based on this conceptual and logical model, a mainstream backend framework was adopted and combined with common database and programming language tools and BIM (Building Information Modeling) technology for development to establish a spatial database management system with data storage, management, analysis, and visualization functions. We designed building monitoring experiments and proved through the experiments that the database management system can stably store, analyze, and manage the monitoring data and visualize the display, with the advantages of a fast response speed and low error rate. The spatial database system improves the storage and management efficiency of building comfort and structural safety monitoring data, eliminates redundant data, and realizes comprehensive analysis and management of building comfort and structural safety monitoring data. It provides data support for building comfort and structural safety assessment, helps users analyze the formation mechanism and evolution law of the urban heat island effect, assesses the interrelationship between climate change and urban building morphology, and constructs an urban thermal environment that is more adaptable to climate change.

Our public safety system is a perfect storm.

Public safety results from an effective interaction of three separate systems - public health, mental health, and policing. In too many communities today, the crisis in our mental health system creates the perfect storm. Solving the issues of silo-based care necessitates creating an oversight data management structure supporting cross-sector data integration on all levels ensuring that both operational and technical frameworks exist to maintain the security of client data. Safer communities isn't just about being sophisticated, technologically advanced but using an intensifying laser-focus analysis on harmful criminality, and on stakeholders responsible for delivering to those in need of mental health services. The recommendations note that no one agency can solve the crisis in public safety alone. There is an urgent need for one new coordinating agency - a department in government - to eliminate the silos and act like a well-coordinated and effective service delivery network, with public commitment to outcomes and corresponding public accountability.

ShinyLUTS-A Shiny web application for structured data management and analysis for patients with lower urinary tract symptoms (LUTS).

Clinical, time-dependent, therapeutic and diagnostic data of patients with LUTS are highly complex. To better manage these data for therapists' and researchers' we developed the application ShinyLUTS. The statistical programming language R and the framework Shiny were used to develop a platform for data entry, monitoring of therapy and scientific data analysis. As part of a use case, ShinyLUTS was evaluated for patients with non-neurogenic LUTS who were receiving Rezum™ therapy. The final database on patients with LUTS comprised a total of 8.118 time-dependent parameters in 11 data tables. Data entry, monitoring of therapy as well as data retrieval for scientific use, was deemed feasible, intuitive and well accepted. The ShinyLUTs application presented here is suitable for collecting, archiving, and managing complex data on patients with LUTS. Aside from the implementation in a scientific workflow, it is suited for monitoring treatment of patients and functional results over time.

Concept for a digital twin of railway bridges on the example of the new Filstal bridges

AbstractThe preservation of infrastructure is becoming increasingly important. The current reactive maintenance management only initiates maintenance measures after damage has occurred. Short‐term measures can lead to unforeseen restrictions in infrastructure availability and thus cause high social and economic damage.High‐quality measurement technology, automated data analysis, structured data management and collaborative work are just a few technological developments that form the cornerstones for a future predictive maintenance strategy. The digital twin, a digital representation of the real object, combines these developments and is a promising method for a sustainable preservation of infrastructure.Using the example of the Filstal bridges, a concept for a digital bridge twin is introduced. The digital twin combines measurement data, bridge information, inspection results as well as structural safety verifications and aggregates them to continuously display the current bridge condition and thus supports the maintenance of bridges. For intuitive use, all information is located and presented in a way that is consumable by the user.

Will LLMs Reshape, Supercharge, or Kill Data Science? (VLDB 2023 Panel)

Large language models (LLMs) have recently taken the world by storm, promising potentially game changing opportunities in multiple fields. Naturally, there is significant promise in applying LLMs to the management of structured data, or more generally, to the processes involved in data science. At the very least, LLMs have the potential to provide substantial advancements in long-standing challenges that our community has been tackling for decades. On the other hand, they may introduce completely new capabilities that we have only dreamed of thus far. This panel will bring together a few leading experts who have been thinking about these opportunities from various perspectives and fielding them in research prototypes and even in commercial applications.

Multi-Level Data Analyses for Characterizing Rainfall-Induced Landslide Scenarios: The Example of Catanzaro Municipality (South Italy)

This paper presents a GIS-based approach to create a multilevel data system for detailed knowledge of landslide occurrences in small territorial units such as municipalities. The main aim is to collect all the available data (geological, geomorphological, and climatic data, as well as landslide inventory maps and catalogues) in a structured data management system and perform further analyses to identify the typical landslide scenarios of the study area that can be useful in landslide risk management. We demonstrated the use of the methodology analyzing landslide risk in the municipality of Catanzaro (southern Italy), having a surface of 111.7 km2, 20.5% of which was affected by landslides. The spatial and temporal distribution of landslides highlighted that in several cases, they are reactivations of pre-existing phenomena. In fact, in the municipality, approximately 17% of the buildings fall within landslides-affected areas, 7.9% of which are in areas where landslides are classified as active. Furthermore, active landslides involve 8.1% and 9.5% of the roads and railways, respectively. In the 1934–2020 study period, 53% of activations occurred between October and December and were triggered by daily rain which in the highest percentage of cases (49%) showed values between 50 and 100 mm. The proposed GIS platform can be easily updated in order to preserve the landslide history of the area and can be enriched with further thematic layers (i.e., layers concerning flood events, which often occur simultaneously with major landslide events). The case study demonstrates how the platform can support landslide risk management in terms of monitoring, planning remedial works, and the realization/updating of civil protection plans.

506 Allergy Related Clinical Prescribing Alerts in an Electronic Health Record (EHR) System: a descriptive study

Abstract Introduction Globally, allergic responses to medications is a growing concern (1), can result in adverse outcomes of differing levels of severity for patients and if undetected at point of prescription, increase patient risk and potentially increase hospital workload (2). With clinical decision support systems becoming more common as hospitals modernise data management structures to improve overall efficiency and safety, there is an increased ability to carry out drug-allergy interaction monitoring. The alert warnings generated by such clinical decision support systems are based on standard protocols which may not be applicable to all patients. Aim To quantify the types of allergy and intolerance alerts contained in an EHR system at an academic, tertiary care hospital. The objectives were to undertake an analysis of alert appropriateness and the factors associated with warning overrides at point of prescribing. Methods Retrospective analysis of allergy data alerts extracted from an EHR system over an 11-month period between June 2019 and March 2021, comprising data on prescriber actions, prescription description and frequency of allergic response and patient intolerance (i.e., adverse reactions/drug intolerances) to various prescriptions. Variables included provider type, provider speciality, description, context, drug-allergy reactions, drug-allergy contraindication group, importance level, severity, along-with the patients’ sex and age. Descriptive analysis was conducted, as well as unadjusted and adjusted logistic regression analysis with results presented as odds ratios (OR), 95% confidence intervals and p-values. Results In the total dataset (n=53,057), females represented 68.53% (n=36,361) and males represented 31.4% (n=16,696). Overrides made up 61.3% (n=32,520) and heeded/actioned made up 20% (n=10,599) of the total alerts. Prescriber overrides were significantly associated with provider type, provider speciality, description, context, drug-allergy reactions, drug-allergy contraindication group, importance level, severity, along-with the patients’ sex and age. Allergies (adjusted-OR 0.88 [0.82-0.94], p<0.001) were 12% less likely to be overridden than ‘adverse reactions/drug intolerances’, while ‘drug class match’ (adjusted-OR 0.50 [0.35-0.72], p<0.001) and ‘ingredient match’ (adjusted-OR 0.26 [0.18-0.37], p<0.001) were less likely to be associated with an override as compared to cross-sensitive matches. The most frequently recorded reason for overrides was ‘benefit outweighs risk’ (n=12,331; 23.2%), followed by ‘does not apply to patient’ (16.6%) and ‘inaccurate warning’ (4.6%). The key strength of the study is the volume of alerts that allowed an analysis of the factors that were associated with warning overrides. Limitations include the lack of information about actual prescribing decision, patient outcome following the alert action and potential influence of the pandemic. The study adds further evidence to the burden of alerts in EHR systems and identifies factors that may be utilised to improve the design and sensitivity of warnings. Conclusion A high level of alert overrides was seen, with allergies relating to antibiotics least likely to be overridden. Override reasons indicated that prescribers considered the warning to be either inappropriate or inaccurate in one in five cases.

Structured Data Management for Investigating an Optimum Reactive Distillation Design

Reaction and separation are the two most important processes in the chemical industry that significantly affect the overall energy and cost requirements. Therefore, the operation of integrated reactive separation in a single equipment, such as reactive distillation, potentially increases the chemical process efficiency remarkably. To achieve that advantage, it is vital to design an optimum reactive distillation configuration in the preliminary evaluation. This paper proposes structured data management that can be used to encounter an optimum reactive distillation design. The approach simplified and reduced the data necessity from thousands to dozens by applying a systematic data sorting for the simulation results obtained from the Aspen Plus simulator. Using a case study that is highly relevant to the real chemical processes, i.e., the metathesis of 2-pentene, this study showed that the rule of thumb for determining the optimum design of conventional distillation can be adapted for reactive distillation technology.

Data is the New Protein: How the Commonwealth of Virginia Built Digital Resilience Muscle and Rebounded from Opioid and COVID Shocks

During shocks, residents and businesses rely upon the government to ensure health, safety, and the continuity of services. The government’s ability to respond depends upon how well it utilizes its data resources and builds digital resilience. Yet governments often fail to integrate data from different agencies to respond effectively to shocks. We conceptualize digital resilience as a dynamic capability (DC). Although the DC framework provides a theoretical basis, it is unclear what actions managers can take to build DC. Through process tracing, we examine how the Commonwealth of Virginia (COVA) built DCs and rebounded from two shocks—the opioid crisis and the COVID-19 pandemic. COVA managers leveraged statewide data assets, built routines to disseminate data, and reconfigured operational capabilities to build three DCs—relationship building, intelligence creation, and value extraction. Data functioned as the “protein” to build the digital resilience “muscle.” We found that the relationship building DC leveraged the operational capabilities of data management, integration, and governance structure to foster data sharing, the intelligence creation DC leveraged analytics, and the value extraction DC converted analytics into cost savings, revenue generation, and new services. Whereas COVA built robust digital resilience by facilitating data sharing, the agencies exploited data assets to develop scalable solutions.

Auxo: A Scalable and Efficient Graph Stream Summarization Structure

A graph stream refers to a continuous stream of edges, forming a huge and fast-evolving graph. The vast volume and high update speed of a graph stream bring stringent requirements for the data management structure, including sublinear space cost, computation-efficient operation support, and scalability of the structure. Existing designs summarize a graph stream by leveraging a hash-based compressed matrix and representing an edge using its fingerprint to achieve practical storage for a graph stream with a known upper bound of data volume. However, they fail to support the dynamically extending of graph streams. In this paper, we propose Auxo, a scalable structure to support space/time efficient summarization of dynamic graph streams. Auxo is built on a proposed novel prefix embedded tree (PET) which leverages binary logarithmic search and common binary prefixes embedding to provide an efficient and scalable tree structure. PET reduces the item insert/query time from O (| E |) to O ( log | E |) as well as reducing the total storage cost by a log | E | scale, where | E | is the size of the edge set in a graph stream. To further improve the memory utilization of PET during scaling, we propose a proportional PET structure that extends a higher level in a proportionally incremental style. We conduct comprehensive experiments on large-scale real-world datasets to evaluate the performance of this design. Results show that Auxo significantly reduces the insert and query time by one to two orders of magnitude compared to the state of the arts. Meanwhile, Auxo achieves efficiently and economically structure scaling with an average memory utilization of over 80%.

Implementation Of Point Of Sales Using Laravel Framework on Matahari Motor

One of the developments in technology in the modern era is computers as a alternative for human labor. The development of advanced technology must also be balanced with the right application. Web application is one of them because it makes easier to access information on any device as long as it’s connected to the internet. This research aims to create a POS application that can help in facilitating more structured data management with the features provided. The method that this research used is Action Research. Website design using Prototype method, modeling in design using Unified Modeling Language (UML), and database modeling using Entity Relationship Diagram (ERD). Website development using the Laravel framework, and testing using the Black Box method with Equivalence Partioning. The results obtained from this study are the Live Search feature which helps in searching for item data, the notification feature for minimal stock so as not to be late in placing an order, Authentication Login using Jetstream Laravel, there are barcodes and print invoice to assist in the transaction process, error messages feedback or succesfull. successful, the calculation of the income reports, and this research was developed using the Model View Controller (MVC) concept architecture.