Flood Of Information Research Articles

Impact of AI on public health emergency communication in Europe and Central Asia: A Delphi study

Abstract Background Artificial Intelligence (AI) holds the potential to fundamentally transform public health communication - presenting promising opportunities alongside profound challenges and risks. This two-wave Delphi study therefore invited practitioners and scholars to assess the potential impact of AI on the risk communication, community engagement, and infodemic management (RCCE-IM) of public health authorities in European and Central Asian countries. Methods In two successive online surveys, 54 experts - both RCCE-IM practitioners and scholars with expertise in (digital) health communication, risk communication, and AI - from 28 countries provided their assessment of the following analytical dimensions: (1) opportunities, challenges, and risks of AI, (2) conditions for the responsible use of AI, (3) potential future scenarios, and (4) prioritized actions. The first Delphi wave followed an open and exploratory approach, while the second wave sought to prioritize and rank key findings from the previous round. The survey instruments included open-ended and closed-ended questions that were analyzed through statistical analysis, inductive and deductive coding. Results Results showed that AI holds various opportunities for the RCCE-IM of public health communicators. Challenges and risks, such as algorithmic bias or concerns about inclusivity, equally affect RCCE-IM efforts. Experts identified seven principles for the responsible use of AI (e.g., fair, human-centered) and predicted optimistic (e.g., optimized workflows) and pessimistic (e.g., flood of (mis)information) scenarios for the future impact of AI. Prioritized actions range from regulation, resource allocation, and feedback to capacity building, public trust, and education. Conclusions To responsibly navigate the opportunities, challenges, and risks of AI for public health emergency communication, clear guiding principles, ongoing critical evaluation, and multi-sectoral collaboration are needed. Key messages • AI is likely to transform public health emergency communication. • A set of agreed-upon principles will support public health authorities in the responsible management of the opportunities, challenges, and risks of AI.

PENGEMBANGAN DAN IMPLEMENTASI LITERASI DIGITAL UNTUK MENANGKAL HOAKS KEBENCANAAN DI CIANJUR

The flood of information following the earthquake disaster in Cianjur has had an impact on the spread of hoaxes which is difficult to stop. The impact not only causes anxiety and fear, it even triggers division and conflict. To educate the public in preventing hoaxes, the Volunteer Team from Putra Indonesia University and Jayabaya University for three months after the earthquake carried out activities to Strengthen Digital Literacy and Psychosocial Assistance at earthquake victim posts spread across Cianjur. Implementation of community service uses a service-learning method that adapts the learning system to the situation and needs of the community. Community service activities are carried out using teaching aids that can be received by earthquake victims in the form of fun-learning with a duration of two hours. The first session is filled with introductions and storytelling, then learning while playing in the second session. The group was divided into two separate locations, namely the children's group and the parents' group. The aim is for each group to receive different material reinforcement according to their individual needs. Implementation of activities is carried out in collaboration with various parties (between universities, company CSR and local stakeholders). The results of the activities carried out showed that participants were able to understand digital culture, ethics in using social media, and the Personal Data Protection Law.

Sensing Facts and Emotions: Intelligent Technology's Redefinition of News Text Presentation

Intelligent technology represented by artificial intelligence has profoundly changed the boundaries and patterns of journalism. Traditional media reports are constrained by conditions such as the shape of the newspaper layout, presenting independent and objective content of individual reports. Intelligent technology has changed this pattern, the presentation of news text will reflect the characteristics of "information layer of basic facts" and "opinion layer of emotional analysis" co-presentation, while adhering to the separation of the facts and opinions of the report itself, the typical attitudes and emotions of the user feedback and the facts of the media report are jointly presented to the public. While maintaining the separation of facts and opinions in the report itself, the typical attitudes and emotions of user feedback are presented to the public together with the facts of the media report. This will present the public with an intuitive and visualized opinion environment through sentiment and text analysis while quickly distilling and summarizing the main facts in the information flood. At the same time, intelligent technology further shortens the time lag between the occurrence of an event and the release of a news report, shortening the time lag and the perception of the importance of time lag in a cyclical manner, shaping the practitioner's view of time lag.

The Realistic Dilemmas and Solutions of Youth Ideological and Political Education in the Digital Age

As the backbone of society and the hope for the future, the ideological dynamics and values of young people are directly related to the future destiny of the country and the harmony and stability of society. In the context of the digital era, youth ideological and political education is facing the challenges of the loss of youth values in the flood of information, the alienation of youth from real people due to their addiction to virtual socialization, and the disconnection between the traditional education model and the digital era. In the face of the practical difficulties brought by the digital era, it is necessary to place the ideological and political education of youth in a broader social context and historical process, and respond to them by improving the information literacy of youth and cultivating critical thinking, optimizing the social ecology and leading the positive development of network culture, and integrating online and offline and revamping the mode of ideological and political education, in order to better satisfy the needs of the youth for growing up and becoming successful and to cultivate the all-round development of young people. The youth of all-round development will be fostered.

The emotional profile of Slovak freshmen in teaching English as a foreign language.

The article focuses on tracing changes in Slovak pre-service TEFL student teachers' emotional states over the first semester of their university study and detecting possible factors inciting their emotions. It highlights the importance of understanding these emotional states as they significantly impact students' perceptions of their suitability as educators. A mixed-methods approach was employed to collect qualitative and quantitative data through questionnaires, reflections, and interviews. The sample consisted of 67 non-native Slovak first-year TEFL students. It was found that the students experienced a range of emotions, from initial confusion and worry to eventual joy and contentment. Negative emotions were mainly triggered by the flood of new information and study-related factors, while positive emotions were associated with the supportive attitude of teachers and the freedom of university life.

ҒАРИФОЛЛА ЕСІМ: САНА БОЛМЫСЫ

The transition of world authorities into the hands of information in the flow of modern life should make us think. Since humanity cannot exist without each other, without interaction, the information flood has become as necessary as the air. In order to follow the citizens of the state to the end, it is not enough to have one opinion and the opinion of the leader, dependence on him is growing, and the ability of the majority to research and critical thinking is decreasing. Therefore, the urgent problem facing the state has become the propaganda of the historical development of our state, the work to restore consciousness, the spiritual identity of our citizens, the consciousness of the Kazakhs. Because by defining the spiritual nature of the nation and nailing down the essence of consciousness, it becomes possible to feel our national identity. This, in turn, is the biggest tool in consolidating the nation. Therefore, in order to determine the Kazakh national identity, we decided to explain the scientific works of the great scientist, philosopher Garifolla, who has been studying the Kazakh consciousness for more than thirty years. The importance of the work lies in highlighting more than twenty volumes of the scientist's works on the origin of the history of the origins of major representatives of Kazakh philosophy, analyzing the work of Abai, the main ideologist of national identity and describing the scientific potential. How to explain the general understanding of Academician Garifolla's work with a short formula? "Scientific prose", which, in our opinion, is a mixture of the breath of time, the existence of consciousness, Kazakh philosophy, is a scientific assessment of many historical data to young scientists, applicants, analysts, telling about political, cultural and socio-historical problems that took place in the life of society in a systematic way.

A machine learning-based approach for flash flood susceptibility mapping considering rainfall extremes in the northeast region of Bangladesh

Flash floods are catastrophic global events, especially in northeast Bangladesh, and assessing flash flood susceptibility is crucial for preparedness and mitigation. Traditional geographic system-based flash flood susceptibility mapping struggles to capture flash floods’ non-linear and complex nature. However, machine learning models have recently emerged as an efficient alternative to address these limitations. This study evaluates four machine learning models—Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Random Forest – Gradient Boosting (RFGB) Hybrid, and Categorical Boosting (CatBoost)—for flash flood susceptibility. It categorizes areas into five susceptibility levels: very low, low, moderate, high, and very high. Covering 24,424.25 km2 across eight districts, the study uses 400 points (200 flood and 200 non-flood) for training and validation, based on field investigation, historical flood information, Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar data using Google Earth Engine, and insights from the local people. The models’ predictive performances are evaluated by incorporating topographical attributes and rainfall indices and using accuracy, precision, recall, F1 score, and Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) score, with 70% of the data for training and 30% for testing. The ANN model performs best with rainfall indices, achieving high accuracy (maximum AUC = 0.802). The RFGB hybrid model shows excellent training accuracy (AUC ≥ 0.971) but suffers from overfitting during validation (AUC ≤ 0.674), requiring careful hyperparameter adjustment. The CatBoost model effectively uses both rainfall indices and terrain features, achieving AUC = 0.701 in training and AUC = 0.667 in validation. The ANN model conservatively includes the largest area (2198.3 km2) under ’very high’ susceptibility. This study’s flash flood susceptibility maps, which include rainfall extremes, are more robust than those without, helping local administrative authorities and national flood practitioners prepare for flash floods.

The association of adverse birth outcomes with flood exposure before and during pregnancy in Australia: a cohort study

Exposure to floods might increase the risks of adverse birth outcomes. However, the current evidence is scarce, inconsistent, and has knowledge gaps. This study aims to estimate the associations of flood exposure before and during pregnancy with adverse birth outcomes and to identify susceptible exposure windows and effect modifiers. In this cohort study, we obtained all the birth records occurring in Greater Sydney, Australia, from Jan 1, 2001, to Dec 31, 2020, from the New South Wales Midwives Data Collection and in the Brisbane metropolitan region, Australia, from Jan 1, 1995, to Dec 31, 2014, from the Queensland Health Perinatal Data Collection. For each birth, residential address and historical flood information from the Dartmouth Flood Observatory were used to estimate the numbers of days with floods during five exposure windows (Pre-1 was defined as 13-24 weeks before the last menstrual period [LMP], Pre-2 was 0-12 weeks before the LMP, trimester 1 [Tri-1] was 0-12 weeks after the LMP, trimester 2 [Tri-2] was 13-28 weeks after the LMP, and trimester 3 [Tri-3] was ≥29 weeks after the LMP). We estimated the hazard ratios (HRs) of adverse birth outcomes (preterm births, stillbirths, term low birthweight [TLBW], and small for gestational age [SGA]) associated with flood exposures in the five exposure windows using Cox proportional hazards regression models. 1 338 314 birth records were included in our analyses, which included 91 851 (6·9%) preterm births, 9831 (0·7%) stillbirths, 25 567 (1·9%) TLBW, and 108 658 (8·1%) SGA. Flood exposure in Pre-1 was associated with increased risks of TLBW (HR 1·06 [95% CI 1·01-1·12]) and SGA (1·04 [1·01-1·06]); flood exposure during Tri-1 was associated with increased risks of preterm births (1·03 [1·002-1·05]), stillbirth (1·11 [1·03-1·20]), and SGA (1·03 [1·01-1·06]). In contrast, flood exposures during Pre-2 and Tri-3 were associated with reduced risks. Exposures to floods in Pre-1 and Tri-1 are both associated with increased risks of adverse birth outcomes, and the risks increase with a higher exposure. Upon planning for conception and prenatal care, individuals and health practitioners should raise awareness of the increased risks of adverse birth outcomes after experiencing floods. The Australian Research Council and the Australian National Health and Medical Research Council.

Exploring the factors associated with final-year primary school students’ flood knowledge, risk perception, and preparedness in flood-prone areas of South Thailand

Flood frequency has increased globally, and children are among the groups most vulnerable to risk during these events. A significant challenge in mitigating floods worldwide is enhancing flood knowledge, flood risk perception (FRP), and flood preparedness (FP) among communities. Research on FP among primary school students (PSS) in Thailand is limited. This study evaluated the flood knowledge, FRP, and FP of final-year PSS in Nakhon Si Thammarat, a flood-prone area in South Thailand, and identified associated factors. This cross-sectional study surveyed 784 students aged 11–13 years from 61 primary schools. Data collected via a questionnaire included sociodemographic traits, flood experience, learning, training, living arrangements, sources of flood information, feelings about floods, flood knowledge, FRP, and FP. Chi-square and multivariate regression analyses identified the factors linked to the students’ flood knowledge, FRP, and FP. The study revealed that nearly 69.4 % had experienced flooding, and a substantial 91.2 % had never received flood training. Additionally, most students exhibited low levels of flood knowledge, FRP, and FP. The multivariate analysis indicated that the factors significantly associated with flood knowledge included learning experiences, flood information from friends, and Facebook; FRP is influenced by experiences of flooding at home, learning experiences, emotional responses, and sources of flood information from the community and YouTube; and FP is associated with flood training, information from friends, and FRP. The results of this study provide essential guidelines for preparing to address future flooding events among children in the Nakhon Si Thammarat Province and Thailand.

An integrated framework for flood disaster information extraction and analysis leveraging social media data: A case study of the Shouguang flood in China

Flood disasters cause significant casualties and economic losses annually worldwide. During disasters, accurate and timely information is crucial for disaster management. However, remote sensing cannot balance temporal and spatial resolution, and the coverage of specialized equipment is limited, making continuous monitoring challenging. Real-time disaster-related information shared by social media users offers new possibilities for monitoring. We propose a framework for extracting and analyzing flood information from social media, validated through the 2018 Shouguang flood in China. This framework innovatively combines deep learning techniques and regular expression matching techniques to automatically extract key flood-related information from Weibo textual data, such as problems, floodings, needs, rescues, and measures, achieving an accuracy of 83 %, surpassing traditional models like the Biterm Topic Model (BTM). In the spatiotemporal analysis of the disaster, our research identifies critical time points during the disaster through quantitative analysis of the information and explores the spatial distribution of calls for help using Kernel Density Estimation (KDE), followed by identifying the core affected areas using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm. For semantic analysis, we adopt the Latent Dirichlet Allocation (LDA) algorithm to perform topic modeling on Weibo texts from different regions, identifying the types of disasters affecting each township. Additionally, through correlation analysis, we investigate the relationship between disaster rescue requests and response measures to evaluate the adequacy of flood response measures in each township. The research results demonstrate that this analytical framework can accurately extract disaster information, precisely identify critical time points in flood disasters, locate core affected areas, uncover primary regional issues, and further validate the sufficiency of response measures, therefore enhancing the efficiency in collecting disaster information and analytical capabilities.

How to avoid heavy life losses in Liulin-like flash flood events

Abstract. From 11 to 12 August 2021, an extreme rainstorm attacked the Liulin Town, a very common town in Hubei Province, China. The rainstorm triggered flash flood and caused heavy live losses. This study first conducted an analysis on the causes of the flash flood disaster, then, performed a simulation to review the flash flood process, and finally discussed the technical strategies and approaches to avoid life losses in similar cases. The main understandings include: (1) accumulative rainfall of shorter interval and time-sliding is significantly helpful to improve warning accuracy; (2) flash flood risk needs to be evaluated at catchment scale and making room for floods is necessary; (3) residential buildings threatened by flash floods need to rebuild some structures for live-saving in emergency circumstances; and (4) early warning system needs to have capabilities to collect real basin-wide rainfall and flood information, especially in case of rainstorm, and to be smart to judge hazard level.

Perspective on Religious Moderation of Ptkin Students in the Post Truth Era (Case Study on Students of al-Quran and Tafsir Science Study Program Uin Khas Jember)

Information and communication technology which has developed massively has resulted in technological disruption, so that today's society can easily and quickly obtain various information until a information floods emerges. Information in online media has power, both the power to build and the power to destroy. Especially those related to religion, can certainly be disturbing, especially for the younger generation or students. A wrong understanding of religion can be dangerous because it can lead to intolerance and even radicalization. In the end, the concept of religious moderation emerged. This article wants to know the perspective of PTKIN (state Islamic religious college) students in the Al-Qur'an and Tafsir Science Study Program at UIN KHAS Jember regarding religious moderation. The research method used a qualitative research with a case study approach. The data analysis technique used the Miles and Huberman model which consists of Data Collection, Data Reduction, Data Display, and Conclusion. The technique for determining informants was purposive, namely students of the 2020-2021 Al-Qur'an and Tafsir Science Study Program, UIN KHAS Jember. The results of the research show that students of the Al-Qur'an and Tafsir Science Study Program at UIN KHAS Jember already have a good perspective regarding religious moderation up to the highest domain stage, namely evaluation. This can be a good provision for students belonging to the Z generation, to be able to filter various temptations from the flood of information on social media which has a negative impact, especially on the growth of extremism and radicalism thought patterns.

Hyper-resolution flood hazard mapping at the national scale

Abstract. Flood hazard mapping is currently in a transitional phase involving the use of data and methods that were traditionally in the domain of local studies in a regional or nationwide context. Challenges include the representation of local information such as hydrological particularities and small hydraulic structures, as well as computational and labour costs. This paper proposes a methodology of flood hazard mapping that merges the best of the two worlds (local and regional studies) based on experiences in Austria. The analysis steps include (a) quality control and correction of river network and catchment boundary data; (b) estimation of flood discharge peaks and volumes on the entire river network; (c) creation of a digital elevation model (DEM) that is consistent with all relevant flood information, including riverbed geometry; and (d) simulation of inundation patterns and velocities associated with a consistent flood return period across the entire river network. In each step, automatic methods are combined with manual interventions in order to maximise the efficiency and at the same time ensure estimation accuracy similar to that of local studies. The accuracy of the estimates is evaluated in each step. The study uses flood discharge records from 781 stations to estimate flood hazard patterns of a given return period at a resolution of 2 m over a total stream length of 38 000 km. It is argued that a combined local–regional methodology will advance flood mapping, making it even more useful in nationwide or global contexts.

Digital Literacy in Education: An Analysis of Critical Thinking Culture for Preventing the Hoaxes

In the 21st century, a hoax is one of the biggest problems in the world, especially in Indonesia. The post-truth phenomenon and the ease of disseminating information on social media are the primary triggers for this case. Moreover, this disruption era is marked by a flood of information, rapid change, and high use of internet technology. This research intends to analyze the importance of digital literacy, especially critical thinking (CT) culture, in preventing hoax news. A literature review is employed in this research. This method explains how CT skills can be used in digital literacy. The results underscore several points: a) the urgency of digital literacy is based on a big problem in the digital world in Indonesia, namely hoaxes; b) digital literacy can be applied in tips such as those discussed by Facione (1998) regarding the six CT skills that require technical and theoretical skills; c) emphasize technical skills in assessing account identities or media that produce information by Nagler (2017); and d) there are several opportunities for implementing digital literacy. Hence, this research concludes the importance of instilling CT skills in fighting hoax news or misinformation. Apart from that, if we look at the data regarding the low level of digital literacy in Indonesian society, it is urgent to implement digital literacy methods and CT skills integrated with educational institutions or more formal policies.

Named Entity Recognition Using Conditional Random Fields for Flood Detection In Gerbang Kertosusila Based Twitter Data

The national strategic area Gerbang Kertosusila East Java should be aware of floods. One of the existing efforts is to place flood sensors at several flood-prone points. However, that way is constrained by the need for more equipment to handle the many needy areas. So it is necessary to develop technology for the dissemination of flood information. Dissemination of flood information was quickly obtained from social media Twitter. One way is to use Twitter's text data source for a Named Entity Recognition model to help detect flood events and their locations. The Named Entity Recognition (NER) model was constructed using the Conditional Random Fields (CRFs) method to achieve research objectives. This research adds slang word handling at the preprocessing stage to improve model performance and the use of the BIO format in the labeling process and POS Tagging in the Feature Extraction process. Evaluation results with five Kfolds, 80% training data, and 20% test data show that the NER CRFs model performs excellently with a Precision of 0.981, Recall of 0.926, and f-measure of 0.950 so that these results can help the community and government regarding the information on the distribution of floods.

Developing a flood monitoring system by utilizing real-time satellite rainfall estimates and water energy budget-based rainfall-runoff inundation model in West Africa

Abstract. The African regions have been affected by frequent flood-related disasters. To support decision-makers in flood disaster management, a real-time flood monitoring system was established in the Niger and Volta basins under the Water disaster platform to enhance climate resilience in Africa (WADiRE-Africa). To bridge a gap between data unavailability and inaccessibility, the near-real-time satellite rainfall data (GSMaP-NRT) was evaluated and found to have a very low correlation (<0.4) with gauge data at daily scales and a higher correlation (>0.7) at monthly scales. The statistically bias-corrected GSMaP-NRT data was used in the Water and Energy Budget-based Rainfall-Runoff-Inundation (WEB-RRI) model to generate hydrological responses of the basins. As the simulated river discharges were overestimated, an additional correction method was employed for the GSMaP-NRT data to match well with observed discharges. The prototype system provides real-time flood information in both basins.

Automatic identification of role-specific information in product development: a critical review on large language models

AbstractIn the era of digitization and the growing flood of information, the automatic, role-specific identification of information is crucial. This research paper aims to investigate whether the adaptation of LLM is suitable for classifying information obtained from standards for corresponding role profiles. This research reveals that with systematic fine-tuning, prediction accuracy can be increased by almost 100%. The validation was carried out using a two-digit number of standards for three predefined roles and demonstrates the significant potential of LM for labelling content with regard to roles.

Knowledge Management in Parliament. A quantitative overview for Europe

Despite being the fundamental pillar of our democratic systems, there has been a notable scarcity of research on knowledge management within parliamentary settings. The few existing approaches fail to consider the unique structure of parliaments and the challenges newly elected members face. Previous research concluded that knowledge in parliaments still tends to be tacit, informal, and not recorded. In view of the multitude of highly knowledge-intensive crises in the last decade, the need for political knowledge management becomes apparent. This paper investigates how members of the national parliaments in Europe manage their knowledge relevant for policy-making, using original data collected in a quantitative online-survey in mid-2023. I was able to show that both organizational and cultural barriers hinder good knowledge work and that a systematic approach is still not being pursued. I come to the conclusion that modern politics lacks sufficient resources and know-how to adequately evaluate and apply the flood of information in everyday business. This deficiency is especially detrimental to inexperienced MPs and members of the opposition. Building on these findings, a proposed model for political knowledge management is presented. The central pillars are, on the one hand, the creation of awareness, the formulation of a knowledge strategy, the more intensive integration of external expertise and the establishment of IT support.

The coronavirus disease 2019 infodemic: a concept analysis.

This study aimed to analyze the coronavirus disease 2019 (COVID-19) infodemic phenomenon in the medical field, providing essential data to help healthcare professionals understand it. This study utilized a hybrid model for concept analysis. In the theoretical phase (first phase), a literature review was conducted using ScienceDirect, PubMed, CINAHL, ProQuest, Scopus, Web of Science, DBpia, RISS, and KISS. Semi-structured interviews, involving eight physicians and six nurses, were used in the fieldwork phase (second phase). In the final analysis phase (third phase), the results of the preceding phases were combined. Based on the findings of these phases, the COVID-19 infodemic can be defined as "the phenomenon of information flood, reproduction, dissemination, and asymmetry, which occurred during the pandemic through social networks among the public lacking essential knowledge of infectious disease, and is associated with negative and positive effects." Our findings can help the Ministry of Health and Welfare and healthcare professionals to understand the phenomenon of the infodemic and prepare necessary strategies and education programs for the public. Therefore, the provision of basic data is important for developing influential roles for healthcare professionals during infectious disease outbreaks.

Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans

A flood of information has occurred, making it challenging for people to find and filter their favorite items. Recommendation systems (RSs) have emerged as a solution to this problem; however, traditional Appenrecommendation systems, including collaborative filtering, and content-based filtering, face significant challenges such as data scalability, data scarcity, and the cold-start problem, all of which require advanced solutions. Therefore, we propose a ranking and enhancing sequence movie recommendation system that utilizes the combination model of deep learning to resolve the existing issues. To mitigate these challenges, we design an RSs model that utilizes user information (age, gender, occupation) to analyze new users and match them with others who have similar preferences. Initially, we construct sequences of user behavior to effectively predict the potential next target movie of users. We then incorporate user information and movie sequence embeddings as input features to reduce the dimensionality, before feeding them into a transformer architecture and multilayer perceptron (MLP). Our model integrates a transformer layer with positional encoding for user behavior sequences and multi-head attention mechanisms to enhance prediction accuracy. Furthermore, the system applies KMeans clustering to movie genre embeddings, grouping similar movies and integrating this clustering information with predicted ratings to ensure diversity in the personalized recommendations for target users. Evaluating our model on two MovieLens datasets (100 Kand 1 M) demonstrated significant improvements, achieving RMSE, MAE, precision, recall, and F1 scores of 1.0756, 0.8741, 0.5516, 0.3260, and 0.4098 for the 100 K dataset, and 0.9927, 0.8007, 0.5838, 0.4723, and 0.5222 for the 1 M dataset, respectively. This approach not only effectively mitigates cold-start and scalability issues but also surpasses baseline techniques in Top-N item recommendations, highlighting its efficacy in the contemporary environment of abundant data.