Hasil untuk "Management information systems"

Fatemeh Mehrpourbernety, Ramin Fazloula, Alireza Emadi et al.

Extended Abstract Background: Floods are caused by several reasons, including rainfall intensity, vegetation destruction, and encroachment of rivers. The high power of floods damages buildings, bridges, and existing structures, and also reduces the capacity of the river bed. Moreover, the excessive volume of water leads to human and financial losses and the destruction of animal habitats. Structural measures (such as dam construction) and non-structural measures (such as increased vegetation coverage, forecasting, and flood warning systems) are carried out to deal with a flood and its damage. Flood forecasting is the process of estimating the time and place of flood occurrence and the volume of water and, as an efficient and low-cost tool for flood management and damage reduction, has received a lot of attention in recent years. Rainfall-runoff modeling is one of the measures of flood management. Simulation is done using hydrological models to understand the relationship between rainfall and runoff parameters, as well as to determine the peak discharge value and the time to reach the peak discharge. One of the hydrological software packages in this field is the HEC-HMS software. By considering three components of the basin, meteorological, and control specification models, the value of losses, runoff, base flow, and routing are calculated using existing methods, and finally, optimization is performed to reduce the difference between observed and simulated hydrographs. Precipitation is one of the most important input parameters in simulating floods. Therefore, the correct estimation of its amount is considered necessary and important. Considering the number of rain gauge stations and the lack of sufficient stations in Iran, especially in mountainous areas, the use of numerical weather prediction model information and satellite rainfall data plays an important role in flood forecasting. Numerical weather prediction models predict weather conditions using mathematical models. Forecasts are divided into three short-range, medium-range, and long-range categories, and also, into regional and global models. One of these models is the numerical weather prediction model, called GFS, which predicts and provides data such as temperature, wind, and precipitation. Heavy rainfall, destruction of forests, sand and gravel harvesting, and construction in floodplains are among the causes of floods in Mazandaran Province, especially the Tajan River, in recent years. The main goal of this research was to estimate the value of peak discharge by simulating flood events and evaluating the results using the precipitation information of the GFS model in the Tajan watershed located in Sari City, Mazandaran Province. Methods: In this research, data were collected from the hydrometric stations of the Tajan watershed, including the hourly measurements of recorded floods, as well as the information required by the evaporation and rain gauge stations in this area, including precipitation obtained by the Mazandaran Regional Water Company for the 10-year period of 2011-2021. Furthermore, precipitation data were received online (from the following webpage: https://openweathermap.org) through the output of the GFS numerical weather prediction model in the mentioned period. The curve number of each subbasin was determined using land use and soil hydrological group layers in ArcGIS software, and the physiographic characteristics of the Tajan watershed were extracted using the HEC-GeoHMS extension. Then, four events 04 October 2011, 01 December 2011, 14 November 2016, and 01 December 2017 were simulated using the physiographic characteristics of the sub-basins, the precipitation data of the Tajan watershed, and the flood discharge obtained by the Mazandaran Regional Water Company in HEC-HMS software. The Soil Conservation Service curve number method was used to calculate losses, the SCS unit hydrograph method was used to calculate the runoff method, and the lag method was used for routing. Subsequently, sensitivity analysis was performed to determine the sensitivity of the curve number, lag time, and initial abstraction parameters. The optimal values of the parameters in the optimization process were determined using nine objective functions available in the HEC-HMS software, including Mean of Absolute Residuals, Mean of Squared Residuals, Peak-Weighted Root Mean Square Error, Peak-Weighted Variable Power, Percent Error in Peak Discharge, Root Mean Square Error, Sum of Absolute Residuals, Sum of Squared Residuals, and Time-Weighted RMSE. In the next step, validation was performed by event 01 December 2017 using the optimal values of the parameters. Finally, after HEC-HMS software optimization and verification, the aforementioned flood events were simulated using the data of the GFS numerical weather prediction model. Results: The results showed a strong correlation between observed and calibrated hydrographs. Besides, the best objective function was peak-weighted variable power. The results of the sensitivity analysis showed that the peak discharge was more sensitive to the changes in the initial abstraction and curve number parameters. Validation was performed to verify the validity of the results obtained in the calibration process, and the results indicated no significant differences between the averages of the two groups, viz. observed and calibrated flow rates. Moreover, the simulation results using the GFS numerical weather prediction model showed no significant differences (at a 95% confidence level) between the observed and simulated hydrographs. Conclusion: According to the results, using the precipitation data of the GFS numerical weather prediction model and the HEC-HMS rainfall-runoff software makes it possible to simulate the flood with acceptable confidence in predicting the peak discharge of floods.

Brendan Bertone, Paul Wagner, Joshua Pauli

<p>The cybersecurity profession continues to face a significant shortfall of qualified professionals despite steady growth in degree programs. Employers consistently cite experience as the main barrier for entry-level cybersecurity hires. This paper argues that clinic-based experiential learning offers a scalable solution to that preparation gap. A systematic literature review spanning academic and professional literature was conducted to examine: (1) barriers to entry for aspiring cybersecurity professionals; (2) the effectiveness of experiential learning compared to traditional instruction; and (3) the viability and scalability of cybersecurity clinics. Screening emphasized workforce development, experiential pedagogy, and alignment with the NICE Cybersecurity Workforce Framework. Findings show persistent misalignment between curricula and employer demands as entry-level roles frequently require prior professional experience, certifications, and proficiency with industry-standard tools. In contrast, experiential models grounded in Kolb’s learning cycle and informed by traditions in law and health consistently improve technical competence, professional judgment, and job readiness. Cybersecurity clinics, implemented as capstones, semester courses, perpetual or club programs, or internships, provide authentic client work that builds student portfolios and professional networks while delivering public-interest services. This paper examines two Arizona initiatives, Regional Security Operations Centers (RSOCs) and the Arizona Cybersecurity Clinic, as illustrative examples of scaling experiential models to server under-resourced organizations while producing measurable workforce benefits. Contributions include a synthesis of clinic models and tools, a preliminary mapping of clinic activities to NICE roles and KSAs, and a forward agenda defining shared evaluation metrics, open datasets, and longitudinal outcome studies.</p>

Niv Yonat, Igal M. Shohet

The purpose of this manuscript is to provide general system theory concepts and practical tools for management under complexity. Built environments and infrastructure are produced, operated, and maintained by information systems; they are also integral components of information systems themselves. These systems are self-organized and teleonomic. The complexity inherent in built environments and infrastructure systems poses a challenge to research, hindering forecasting and the implementation of managerial tools. The use of faults, which are complex systems’ responses to penetrating risk, provide us with databases of and windows into complex systems. This manuscript presents an explicatory theory (ToF), develops it mathematically, expands it through numerical experiments, validates it by case studies, and relates it to practice by expert contributions. A statistical analysis provides a phase parameter, descriptive statistics elucidate trending and emergent behaviors, digital signal processing expounds the effects of signals on information overload, and a directed-network analysis portray morphology, entropy, and time effects. The novelty of ToF is in the application of complexity theory to construction to produce data analysis tools and a managerial framework.

Muhammad Ibrahim, Olan Naz, Amal Fatima Mohiuddin et al.

<h4>Introduction</h4>In low- and middle-income countries (LMICs), health outcomes are often constrained by inadequate or misdirected resource allocation and limited access to services such as contraception and immunization. We explore the use of spatial heat maps to analyze the stock availability and dispensation/vaccination patterns of contraceptives and vaccines in Pakistan.<h4>Methods</h4>We used data from national contraceptive (cLMIS) and vaccine logistics management information systems (vLMIS). We applied univariate, bivariate, and trivariate spatial heat maps to assess contraceptive and vaccine stock levels, dispensation/vaccination, and wastage across districts. For contraception, we standardized stocks per 100,000 married women of reproductive age (MWRA) and dispensation rates. In immunization, we focused on Pentavalent-3 (Penta3) vaccine outreach, dropout rates, and Bacillus Calmette-Guérin (BCG) vaccine wastage.<h4>Results</h4>Temporal and spatial variations highlighted regional disparities, revealing that developed regions like Punjab had better stock availability, while underserved areas like Balochistan faced higher dispensation rates and stockouts. We also show the effect of inputs (supplies, outreach) on dispensation and utilization of contraceptives and vaccines, respectively. Finally, we depict how these visualizations can help track changes in programming over time.<h4>Conclusions</h4>Our findings show that integrating spatial data visualization with health logistics data identifies critical gaps in health service supply and demand, guiding policymakers in resource allocation, stock management, and service outreach. This scalable approach suits systems with limited analytical resources, as many analyses can be automated and embedded in datasets, providing policymakers with a focused set of visualizations for interpretation, avoiding the need for extensive training or deploying analysis teams at local levels. By leveraging spatial and temporal data, this method supports efficient health system strengthening and resource allocation in LMIC.

Sungjun Seo, Kooktae Lee

This paper addresses the fundamental problem of non-uniform area coverage in multi-agent systems, where different regions require varying levels of attention due to mission-dependent priorities. Existing uniform coverage strategies are insufficient for realistic applications, and many non-uniform approaches either lack optimality guarantees or fail to incorporate crucial real-world constraints such as agent dynamics, limited operation time, the number of agents, and decentralized execution. To resolve these limitations, we propose a novel framework called Density-Driven Optimal Control (D2OC). The central idea of D2OC is the integration of optimal transport theory with multi-agent coverage control, enabling each agent to continuously adjust its trajectory to match a mission-specific reference density map. The proposed formulation establishes optimality by solving a constrained optimization problem that explicitly incorporates physical and operational constraints. The resulting control input is analytically derived from the Lagrangian of the objective function, yielding closed-form optimal solutions for linear systems and a generalizable structure for nonlinear systems. Furthermore, a decentralized data-sharing mechanism is developed to coordinate agents without reliance on global information. Comprehensive simulation studies demonstrate that D2OC achieves significantly improved non-uniform area coverage performance compared to existing methods, while maintaining scalability and decentralized implementability.

Pranav Kasela, Gabriella Pasi, Raffaele Perego

Academic Search is a search task aimed to manage and retrieve scientific documents like journal articles and conference papers. Personalization in this context meets individual researchers' needs by leveraging, through user profiles, the user related information (e.g. documents authored by a researcher), to improve search effectiveness and to reduce the information overload. While citation graphs are a valuable means to support the outcome of recommender systems, their use in personalized academic search (with, e.g. nodes as papers and edges as citations) is still under-explored. Existing personalized models for academic search often struggle to fully capture users' academic interests. To address this, we propose a two-step approach: first, training a neural language model for retrieval, then converting the academic graph into a knowledge graph and embedding it into a shared semantic space with the language model using translational embedding techniques. This allows user models to capture both explicit relationships and hidden structures in citation graphs and paper content. We evaluate our approach in four academic search domains, outperforming traditional graph-based and personalized models in three out of four, with up to a 10\% improvement in MAP@100 over the second-best model. This highlights the potential of knowledge graph-based user models to enhance retrieval effectiveness.

Michele Azzone, Matteo Ghesini, Davide Stocco et al.

Climate-related phenomena are increasingly affecting regions worldwide, manifesting as floods, water scarcity, and heat waves, significantly impairing companies' assets and productivity. It is essential for asset managers to quantify the exposure of their portfolios to such risk. To this aim, we develop a framework based on the Vasicek model for credit risk that introduces downward jumps due to climate phenomena in a company asset's dynamics. These negative shocks are designed to mirror the negative effect of extreme climate events. The model calibration relies on companies' asset intensity and geographical exposure. We apply the new multivariate firm value model with jumps to assess the impact of climate-related extreme events on expected and unexpected portfolio losses. Our findings indicate that expected losses increase over time, with pronounced differences in exposure observed across sectoral indices. From an environmental policy perspective, these results suggest the need for additional capital buffers to offset losses arising from physical climate risks, particularly in sectors with high asset intensity.

Supaporn Sankul, Naratip Supattananon, Raknoi Akararungruangkul et al.

This research paper introduces an adaptive differential evolution algorithm (ADE algorithm) designed to address the multi-compartment vehicle routing problem (MCVRP) for cold chain transportation of a case study of twentyeight customers in northeastern Thailand. The ADE algorithm aims to minimize the total cost, which includes both the expenses for traveling and using the vehicles. In general, this algorithm consists of four steps: (1) The first step is to generate the initial solution. (2) The second step is the mutation process. (3) The third step is the recombination process, and the final step is the selection process. To improve the original DE algorithm, the proposed algorithm increases the number of mutation equations from one to four. Comparing the outcomes of the proposed ADE algorithm with those of LINGO software and the original DE based on the numerical examples In the case of small-sized problems, both the proposed ADE algorithm and other methods produce identical results that align with the global optimal solution. Conversely, for larger-sized problems, it is demonstrated that the proposed ADE algorithm effectively solves the MCVRP in this case. The proposed ADE algorithm is more efficient than Lingo software and the original DE, respectively, in terms of total cost. The proposed ADE algorithm, adapted from the original, proves advantageous for solving MCVRPs with large datasets due to its simplicity and effectiveness. This research contributes to advancing cold chain logistics with a practical solution for optimizing routing in multi-compartment vehicles.

Jacques Matthee, Kenneth Uren, George van Schoor et al.

Simultaneous Localization and Mapping (SLAM) is a crucial component to the push towards full autonomy of robotic systems, yet it is computationally expensive and can rarely achieve real-time execution speeds on embedded platforms. Therefore, a need exists to  evaluate the performance of SLAM algorithms in practical embedded environments – this paper addresses this need by creating  prediction models to estimate the performance that ORB-SLAM3 can achieve on embedded platforms. The paper uses three embedded platforms: Nvidia Jetson TX2, Raspberry Pi 3B+ and the Raspberry Pi 4B, to generate a dataset that is used in training and  testing performance prediction models. The process of profiling ORB-SLAM3 aids in the selection of inputs to the prediction model as  well as benchmarking the embedded platforms’ performances by using PassMark. The EuRoC micro aerial vehicle (MAV) dataset is used to generate the average tracking time that the embedded platforms can achieve when executing ORB-SLAM3, which is the target  of the prediction model. The best-performing model has the following results 2.84%, 3.93%, and 0.95 for MAE, RMSE and R2 score  respectively. The results show the feasibility of predicting the performance that SLAM applications can achieve on embedded  platforms.

Max H. Cohen, Ryan K. Cosner, Aaron D. Ames

Certifying the safety of nonlinear systems, through the lens of set invariance and control barrier functions (CBFs), offers a powerful method for controller synthesis, provided a CBF can be constructed. This paper draws connections between partial feedback linearization and CBF synthesis. We illustrate that when a control affine system is input-output linearizable with respect to a smooth output function, then, under mild regularity conditions, one may extend any safety constraint defined on the output to a CBF for the full-order dynamics. These more general results are specialized to robotic systems where the conditions required to synthesize CBFs simplify. The CBFs constructed from our approach are applied and verified in simulation and hardware experiments on a quadrotor.

Idoia Berges, Jesús Bermúdez, Alfredo Goñi et al.

Over the last years, there has been a change of perspective concerning the management of information systems, since they are no longer isolated and need to communicate with others. However, from a semantic point of view, real communication is difficult to achieve due to the heterogeneity of the systems. We present a proposal which, considering information systems are represented by software agents, provides a framework that favours a semantic communication among them, overcoming the heterogeneity of their agent communication languages. The main components of the framework are a suite of ontologies -- conceptualizing communication acts -- that will be used for generating the communication conversion, and an Event Calculus interpretation of the communications, which will be used for formalizing the notion of a satisfactory conversion. Moreover, we present a motivating example in order to complete the explanation of the whole picture.

Appel Mahmud, Zulqurnain Ali, Md Ashanuzzaman et al.

The psychology of micro-corporate social responsibility (micro-CSR) and employee outcome has emerged in the contemporary literature of interdisciplinary management science. Previous studies have ignored the testing of mediating effects and boundary conditions in the association between micro-CSR and employee outcomes. Drawing on social identity theory (SIT) and social information processing theory (SIPT), this research aims to investigate how, why, and when the perceived CSR community (PCSRc; a micro-CSR activity) affects employee societal behavior (ESB; a voluntary behavior) accounting the mediating role of perceived external prestige (PEP) and moderating role of organizational identification (OI). Our research recruited 452 employees in Bangladesh via questionnaire and tested the proposed measurement model and structural relationships in AMOS. The results report a significant and positive relationship between PCSRc and ESB. It also reveals that PEP mediates PCSRc and ESB link, and OI regulates the straight association of PCSRc and PEP and ancillary links of PCSRc and ESB (via PEP). Finally, we recorded the research implications and future research directions.

Chun-Ho Wu, Stephen Chi-Hung Ng, Keith Chun-Man Kwok et al.

The proliferation of Industry 4.0 (I4.0) technologies has created a new manufacturing landscape for manufacturing, requiring that companies follow I4.0 trends to stay competitive. However, in this novel digital automated environment, these companies must also ensure that lean manufacturing principles are upheld. This study proposes a data-driven framework for analysing raw data across machines in manufacturing systems that can provide a comprehensive understanding of idle time and facilitate adjustments to reduce defect rates. This framework offers an alternative approach to improving manufacturing processes that involves utilising the power of I4.0 technologies in conjunction with lean manufacturing principles. This study’s examination of unprocessed data also provides guidance on improving legislation. The findings of this study provide direction for future research in the field of manufacturing and offer useful advice to businesses wishing to integrate I4.0 technologies into their operations.

Mônica E. T. Alcon-Chino, Salvatore G. De-Simone

Zoonotic-origin infectious diseases are one of the major concerns of human and veterinary health systems. Ticks, as vectors of several zoonotic diseases, are ranked second only to mosquitoes as vectors. Many ticks’ transmitted infections are still endemic in the Americas, Europe, and Africa and represent approximately 17% of their infectious diseases population. Although our scientific capacity to identify and diagnose diseases is increasing, it remains a challenge in the case of tick-borne conditions. For example, in 2017, 160 cases of the Brazilian Spotted Fever (BSF, a tick-borne illness) were confirmed, alarming the notifiable diseases information system. Conversely, Brazilian borreliosis and ehrlichiosis do not require notification. Still, an increasing number of cases in humans and dogs have been reported in southeast and northeastern Brazil. Immunological methods applied to human and dog tick-borne diseases (TBD) show low sensitivity and specificity, cross-reactions, and false IgM positivity. Thus, the diagnosis and management of TBD are hampered by the personal tools and indirect markers used. Therefore, specific and rapid methods urgently need to be developed to diagnose the various types of tick-borne bacterial diseases. This review presents a brief historical perspective on the evolution of serological assays and recent advances in diagnostic tests for TBD (ehrlichiosis, BSF, and borreliosis) in humans and dogs, mainly applied in Brazil. Additionally, this review covers the emerging technologies available in diagnosing TBD, including biosensors, and discusses their potential for future use as gold standards in diagnosing these diseases.

Kailei ZHU, Aijing SUN

In order to extend the lifetime of wireless sensor network (WSN), a clustering routing algorithm for WSN based on Cuckoo Search (CS) algorithm optimized K-means was presented.In the clustering stage, the initial cluster centers were selected by CS algorithm, which make the clustering results of the K-means algorithm more uniform to balance node energy consumption.The remaining energy of the node, the distance from the center of the cluster were comprehensively considered in the cluster election, and the weight according to the remaining energy of the node was dynamically adjusted.In the data communication stage, in order to further balance the load of the cluster head, the remaining energy of the relay node and its load, and the cluster head routing energy consumption were comprehensively considered, CS algorithm was combined to plan routing for the cluster head.The simulation results show that the proposed algorithm is better than LEACH-K, LEACH-improve and DTK-means in terms of energy consumption balance.With the death of the first node as the life cycle of the network, the network lifespan was increased by 173%, 21%, and 6% respectively.The proposed algorithm effectively extending the network life cycle.

Wei WANG, Renqian GU, Li3 PENG et al.

Faced with the need of key regional information transmission in the emergency situation where the perception network lacks of ground base stations, considered about the uncertainty of spatial positioning of emergency internet of things (eIoT) nodes, a robust optimization method for air-based relay of eIoT based on unmanned aerial vehicle (UAV) was proposed.Firstly, the system modeling of this kind of eIoT was carried out.Secondly, according to the fact that the non-convex and nonlinear model has a great correlation with the positioning accuracy of the ground-based eIoT equipment and the tiny disturbance of the positioning information can lead to the invalid solution of the model, the system model was relaxed and the uncertainty description of the location data of the eIoT equipment was introduced, Then the robust equivalent model of the low altitude UAV relay communication power optimization problem was obtained.Thirdly, the model solving algorithm was given.Finally, the effectiveness and robustness of the proposed method were verified from the deployment of UAV and communication energy consumption.At the same time, the factors effecting the effectiveness of the proposed method were analyzed.

Verbivska Lyudmyla V.

Today, information technology plays a special role in the development of business entities, creating new promising management areas, making it possible to optimize business processes and positively affect the operation quality and general efficiency of enterprises. This fact contributes to the scientists’ interest in conducting research in e-business as a new, promising area of economic activity. Given the relevance of this research area, the article considers theories describing the formation and development of e-business as a whole system. Using methodological apparatus of the systemic approach as a universal concept of perceiving systems of different nature, the article formulates its own interpretation of the "e-business system" category. The latter is suggested to be considered as a holistic, single object of study. a set of relationships between economic entities occurring in the process of buying, selling, producing and exchanging goods and services due to the use of information and communication technologies, having its own purpose and complex structure, and interacting with other environmental systems. A model of such a system has also been designed. In addition, the article considers the structural features of such a system. To do this, attributes for grouping the main elements in the e-business system are identified, namely: the content attribute (the method of separating the components of this system depending on economic activities that can be attributed to e-business) and the subjectivity attribute (studying e-business areas by analyzing the peculiarities of the relationship between individual economic entities). Their essence and the corresponding components of the specified system, which are identified based on these attributes, are considered

Giovanni Niero, Tania Bobbo, Simone Callegaro et al.

Extensive summer grazing is a dairy herd management practice frequently adopted in mountainous areas. Nowadays, this activity is threatened by its high labour demand, but it is fundamental for environmental, touristic and economic implications, as well as for the preservation of social and cultural traditions. Scarce information on the effects of such low-input farming systems on cattle health is available. Therefore, the present case study aimed at investigating how grazing may affect the health status of dairy cows by using milk traits routinely available from the national milk recording scheme. The research involved a dairy herd of 52 Simmental and 19 Holstein × Simmental crossbred cows. The herd had access to the pasture according to a rotational grazing scheme from late spring up to the end of summer. A total of 616 test day records collected immediately before and during the grazing season were used. Individual milk yield was registered during the milking procedure. Milk samples were analysed for composition (fat, protein, casein and lactose contents) and health-related milk indicators (electrical conductivity, urea and β-hydroxybutyrate) using mid-infrared spectroscopy. Somatic cell count (SCC) and differential SCC were also determined. Data were analysed with a linear mixed model, which included the fixed effects of the period of sampling, cow breed, stage of lactation and parity, and the random effects of cow nested within breed and the residual. The transition from barn farming to pasture had a negative effect on milk yield, together with a small deterioration of fat and protein percentages. Health-related milk indicators showed a minor deterioration of the fat to protein ratio, differential SCC and electrical conductivity, particularly towards the end of the grazing season, whereas the somatic cell score and β-hydroxybutyrate were relatively constant. Overall, the study showed that, when properly managed, pasture grazing does not have detrimental effects on dairy cows in terms of udder health and efficiency. Therefore, the proper management of cows on pasture can be a valuable solution to preserve the economic, social and environmental sustainability of small dairy farms in the alpine regions, without impairing cows’ health.

Martin Bright Msendma, Wallace Chigona, Benjamin Kumwenda et al.

Medical doctors consider data quality management a secondary priority when delivering health care. Medical practitioners find data quality management practices intrusive to their operations. Using Health Management Information System (HMIS) that uses DHIS2 platform, our qualitative case study establishes that isomorphism leads to legitimization of data quality management practices among health practitioners and subsequently data quality. This case study employed the methods of observation, semi structured interviews and review of artefacts to explore how through isomorphic processes data quality management practices are legitimized among the stakeholders. Data was collected from Ministry of Health's (Malawi) HMIS Technical Working Group members in Lilongwe and from medical practitioners and data clerks in Thyolo district. From the findings we noted that mimetic isomorphism led to moral and pragmatic legitimacy while and normative isomorphism led to cognitive legitimacy within the HMIS structure and helped to attain correctness and timeliness of the data and reports respectively. Through this understanding we firstly contribute to literature on organizational issues in IS research. Secondly, we contribute to practice as we motivate health service managers to capitalize on isomorphic forces to help legitimization of data quality management practices among health practitioners.

Verry Adrian, Intan Rachmita Sari, Hardya Gustada Hikmahrachim

SARS CoV-2 infection and transmission are problematic in developing countries such as Indonesia. Due to the lack of an information system, Provinces must be able to innovate in developing information systems related to surveillance of SARS CoV-2 infection. Jakarta Department of Health built a data management system called Executive Information System (EIS) of COVID-19 Reporting. EIS aimed to provide actual data so that current epidemiological analysis is accurate. The main idea of EIS is to provide valid and actual information to stakeholders, which can then be presented in the form of a dashboard. EIS is utilized to push data flow and management for rapid surveillance purposes. This could be the first time in Indonesia that a system reports near-actual data of nearly half a million people daily using an integrated system through a transparent system. The main data presented is important to monitor and evaluate COVID-19 transmission is the cumulative case dan daily case number. Data in EIS also can offer data geographically so that a more detailed analysis could be done. EIS's data and the dashboard help the government in pandemic control by presenting actual data on bed occupancy and availability across hospitals, especially isolation wards. Stakeholders, academic institutions should utilize EIS data and other elements to help Indonesia fight COVID-19.