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Dataset exploring organizational culture of K-12 schools
Culture can be understood as an explicit social product arising from social interaction as an intentional or unintentional consequence of behavior. Educational Institutions culture differs from other organizational cultures as it impacts teachers' performance and students' learning. In this survey the definition of organizational culture used is given by Schein, The deeper level of basic assumptions and beliefs that are, learned responses to the group's problems of survival in its external environment and its problems of internal integration; are shared by members of an organization; that operate unconsciously; and that define in a basic taken -for-granted fashion in an organization's view of itself and its environment [1]. The data contains 1158 cases collected from K-12 School teachers on their perception of values and beliefs of their organizational culture using the OCTAPACE scale. Convenience sampling is used to obtain the data from teachers. The questionnaire was administered personally to teachers from sixty-five Private aided, Private unaided and Government schools. The eight dimensions measuring values and beliefs of Educational Institutions organizational culture are Pro-action, Authenticity, Openness, Collaboration, Experimenting, Trust, Confrontation and Autonomy. Descriptive statistics are computed from the dataset. The dataset can be used by researchers for meta analysis on organizational culture and school management can explore in depth the need for an organizational culture of autonomy, experimenting, collaboration and openness among teachers. 2022 The Authors -
Database aware memory reconstruction for object oriented programming paradigm
Data storage is a big challenge in front of industries and researchers when its growing enormously. Traditional data storage strategy was fulfilling the business needs till the data was in structured format. But now due to Internet of Things (IoT) compatible devices unstructured data is more than structured one. In such cases traditional data storage strategy won't work efficiently. Initially data storage devices used to store the data irrespective of its logical storage. It means the record was stored either in array format or block format. Such type of storage was not matching physical and logical structure. Logically, structured data is generated as an attribute of particular entity, but physically it gets stored in a sequential memory storage either as file or as memory block. Object Based Storage pattern(OBS) stores the data in the way object gets generated by the programmer and accordingly memory is allocated for that object. Object contains all the data related to that particular entity which makes it easy to retrieve it back. Current study includes comparative advantages, operations and study of different service providers of object-based storage. We also focused on the current need of object-based storage structure for big data analysis and cloud computing. International Journal of Scientific and Technology Research. All rights reserved. -
Data: An Anchor for Decision- Making to Build the Future Workforce Management System
In this digital era, the change in business environments and the nature of work lead to skill gaps. Training the workforce on desired skill sets must fill these skill gaps. Data play a crucial role in identifying the skills needed and helping organizations to plan the future workforce. Data is essential for any organizations growth and success in the dynamic market. Knowing the skill set in advance allows organizations and individuals to plan the business and skill requirements well. The way work is done may be impacted by these structural changes as the world is changing swiftly. Building the abilities necessary for the uncertain environments of the present and future environments is also crucial for training the employees. However, such skills must first be acknowledged and appreciated before being developed. Empirical data must support the methodology for valuing such abilities and skills. This chapter outlines the significance of data in skill identification for individuals to be future-ready. Finding the most relevant abilities in a given environment is the first step toward their formalization and acceptance at the systems level. It also presents the importance of creating skill matrices for students and organizations. The skill matrix objectively quantifies skill value for specific occupations and the possible trajectories to acquire those skill sets. This metric will allow policymakers to navigate this fast-changing workforce landscape and focus resources to ensure that skills are needed as students transition into the workforce and have skills that enable them to transition. 2024 selection and editorial matter, Alex Khang, Sita Rani, Rashmi Gujrati, Hayri Uygun, and Shashi Kant Gupta; individual chapters, the contributors. -
Data: A Key to HR Analytics for Talent Management
The past few years have witnessed a significant rise in job openings across various industries worldwide. This trend has highlighted the need for companies to plan and recruit better talent to keep up with the demand for skilled employees. As a result, Human Resource (HR) professionals are now using workforce planning and HR analytics to address the challenges of finding and retaining the right employees. With the help of technological advancements in HR systems, businesses are leveraging data and insights to understand workplace dynamics better. Workforce planning has thus become crucial for organizations of all sizes to ensure they have the necessary talent to achieve their goals in the present and future. This chapter delves deeper and examines the importance of workforce planning and how HR analytics can help companies achieve their strategic objectives. Talent Management is about analyzing the workforce skill requirements of the organization. It needs a strategic plan to ensure the appropriate people are in the right roles at the right times. Talent Management is a crucial element of every businesss performance. In this process, data play a pivotal role in evaluating the existing workforce and planning for future workforce needs. Based on this, a strategy is developed to fill gaps or prospective shortages. Organizations can achieve their goals by using talent planning and collecting data about upcoming projects and skill requirements based on market needs. For example, talent planning is essential in the healthcare sector to guarantee that hospitals and clinics have enough doctors, nurses, and other healthcare workers to fulfill the rising demand for healthcare services. HR analytics is the key to talent management, enabling organizations to stay competitive, enhance productivity, and achieve long-term strategic objectives. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Data-driven triumph: CRM sales insights revolutionize customer retention
Context: The examination of the CRM data is anchored in a comprehensive analysis of sales performance metrics, with a significant role played. It was found a gap in the literature, considering the scarcity of pertinent case studies within the academic literature. Method: The geographical factor is paramount in this analysis, as it unveils divergent results across different regions. Moreover, the venture into predictive analytics for sales forecasting, capitalizing on CRM primary data spanning from 2018 to 2023, facilitating more informed decision-making. The sample comprises around 1500 Business to Business customer clusters for in-depth analysis is considered. Findings: From the Business Intelligence analysis, it was found the presence of long-standing customers with a lower purchase rate, favouring average industrial product models preferred by the customers. Conclusion:The study also explores the link between CRM can shape business strategies, enhance customer relationships, and boost organizational performance and customer retention. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Data-Driven Transformation of Hospitality Supply Chains Using AI-Powered Segmentation
The increasing complexity of supply chain operations in the hospitality sector demands data-driven strategies for efficient resource utilization and service delivery. This study proposes an artificial intelligence (AI)-driven framework leveraging unsupervised machine learning to uncover hidden patterns in patient-related operational data sourced from a publicly available dataset. The research applies clustering algorithmsK-Means, DBSCAN, and Agglomerative Hierarchical Clusteringto segment patient prof iles based on key variables such as length of stay, procedure type, room category, equipment usage, and staffing needs. Principal Component Analysis (PCA) was employed for dimensionality reduction and cluster visualization. The optimal number of clusters was identified using the Elbow Method, with K-Means yielding the highest silhouette score. Comparative analysis of all clustering models revealed varying strengths in noise detection, interpretability, and handling of sparse features. The results demonstrate how intelligent segmentation can support dynamic resource planning, targeted supply allocation, and improved operational responsiveness in hospital-based hospitality systems. This work contributes to the growing domain of AI-enabled supply chain analytics and of fers a practical pathway for enhancing decision-making in smart hospitality environments. 2025 IEEE. -
Data-Driven Sustainability: Revolutionizing Hospital Supply Chains through Big Data Analytics
Purpose: Despite the growing interest in Big Data Analytics Capabilities (BDAC), its significant impact on hospital operations and supply chains in shaping hospital performance remains elusive. The study investigates the pivotal role of BDAC within the framework of hospital supply chains across India. Drawing upon the Resource-Based View, Dynamic Capability View, and Organisation Information Processing Theory, this research explores the intricate relationships among the organization's capability factors, BDAC, and hospital performance indicators. Design/Methodology/Approach: A conceptual model was developed and empirically tested using survey data collected from 446 hospital managers. The analysis was carried out by using partial least square-structural equation modeling (PLS-SEM). Findings: The results of this study support the significant mediating impact of BDAC on Operational Flexibility, Supply Chain Sustainability, and Organisation Revenue leading to the enhancement of organizational performance. The findings highlight the strategic importance of cultivating BDAC to improve operational efficiency and overall effectiveness in the context of Indian multispeciality hospitals. Originality/Value: This research contributes to the existing knowledge by highlighting the relationship between organization capability factors, BDAC, and performance indicators in the different settings of Indian multispeciality hospitals. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Data-Driven Strategies for Twitter Engagement: Hashtag Recommendations and API Insights
Twitter is a great way to connect with people worldwide, and one of the best ways to do that is by using hashtags. A hashtag is a keyword or phrase attached to a particular topic, and users can use it to find related tweets. Using a hashtag relevant to the needs or for business can increase the tweets visibility and make it easier for people to see the content they want. It can hugely help content creators who want to increase engagement and influence their tweets. This research introduces TagMate, a hashtag recommender system for Twitter that offers significant benefits. By accessing the tweets using Twitter API and after analyzing and performing algorithms, recommendations for hashtags can be obtained. The Twitter API allows access to various information about the account, including followers, tweets, content, etc. This information can be used to generate recommendations for hashtags related to the business. The system will generate hashtags according to the tweet and recommend trending or popular hashtags to increase their reach or engagement on the Twitter platform. Using the API, a dashboard can be created showing which hashtags are being used most frequently and which are most popular. This information can help create more relevant and engaging tweets, attracting more followers and interest. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Data-Driven Malware Detection: Exploring Supervised Machine Learning Approaches
Malicious software must be detected in order to protect sensitive data and systems in the digital era, as sophisticated malware is posing serious risks to cybersecurity. By examining supervised machine learning approaches with a particular focus on Random Forest, Logistic Regression, and Decision Trees, this research proposes a data-driven approach to malware detection. These algorithms are trained to recognize patterns indicating malware by using labeled datasets containing four types of malwares, Ransomware, Trojan, Virus, and Worm. The performance of these algorithms is comprehensively investigated in the paper, with comparisons made between their accuracy, precision, recall, and F1-score. Based on the experimental results, Random Forest (96% accuracy) performed better in terms of robustness and accuracy of detection than both Logistic Regression (91%) and Decision Trees (84%). Logistic Regression provided faster computation at the expense of less accurate detection. Decision trees, while relatively simple to comprehend, performed moderately and they overfit the data. The studys conclusion highlights the significance of choosing the appropriate model in accordance with particular cyber security requirements, outlining the advantages and disadvantages of every approach as well as their practical applicability in real-time malware detection systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Data-Driven Insights into Student Performance: Benchmarking Machine Learning Models for Grade Prediction using Regression and Classification Approaches
This research explores the effectiveness of 17 machine learning models in predicting student performance across Mathematics and Portuguese datasets. The primary goal of this study was to evaluate and compare regression and classification models to identify the most accurate predictors of student grades. A range of algorithms was tested, including linear models (Linear Regression, Elastic Net, Ridge, Lasso), tree-based models (Random Forest, Gradient Boosting, CatBoost, LightGBM), and advanced techniques (Neural Networks, SVM, XGBoost, Naive Bayes, SVR). The methodology involved data preprocessing, feature engineering, and splitting data into training and test sets. Base models were implemented, followed by hyperparameter tuning to optimize performance. Metrics like RMSE, MAE, MSE, R2 (for regression), and accuracy, precision, recall, F1 score (for classification) were used to assess performance. The study found that Gradient Boosting and Elastic Net consistently outperformed other models in regression tasks, achieving the highest R2 scores. For classification, Logistic Regression proved to be the most accurate, followed by Naive Bayes. These findings provide valuable insights for model selection in educational performance prediction, establishing Gradient Boosting and Logistic Regression as benchmark models. 2025 IEEE. -
Data-driven education: Leveraging big data, AI, and machine learning for smarter learning environments
Big Data, artificial intelligence (AI), and machine learning are transforming education with self-paced learning, precise insights, and automated decisions. The effects these technologies have on education are transformative, especially when it comes to improving student achievement, advancing administrative operations, and personalizing the learning experience, as stated in this chapter. Big Data collects and analyzes immense volumes of data, while AI powers automation, makes predictions, automates evaluations, and enables the possibility of adaptive learning. Chatbots, recommendation engines, and tutoring systems enhance student-focused digital education. Still, integrating these technologies comes with challenges such as privacy and ethical issues, algorithm discrimination, and data security concerns. Some of the new trends are explainable AI (XAI) for ethical decision-making, blockchain and federated learning for privacy-preserving analytic systems, and verifiable credentials. In addition, XR, AI-based virtual laboratories, and neurosymbolic AI will have great consequences on the future learning environment. AI in education offers scalability and inclusivity but demands ethical regulation, governance, and resource management. This chapter recommends sustained effort in research, policy changes, and ethical integration of AI for the best possible use thereof. The education sector, by incorporating human-centered AI approaches, can create a just, sustainable future of learning that is accessible to all students around the world. 2026 Elsevier Inc. All rights reserved. -
Data-Driven Drug Discovery Optimization for Breast Cancer Using Interpretable Machine Learning Models
Breast cancer remains one of the most prevalent malignancies worldwide, posing significant therapeutic challenges due to tumor heterogeneity and drug resistance. This study presents a reproducible, data-driven machine learning protocol for predicting drug sensitivity in breast cancer cell lines, with the dual objective of identifying potent single agents and synergistic drug combinations. Using curated datasets from the Genomics of Drug Sensitivity in Cancer (GDSC), two predictive approaches were implemented: a standalone XGBoost regressor and a hybrid Autoencoder-XGBoost pipeline. Preprocessing included label encoding, one-hot encoding, Z-score standardization, missing value imputation, and dimensionality reduction via PCA. Model evaluation demonstrated that XGBoost achieved superior performance (MSE = 1.3789, R2 = 0.8145) compared to the hybrid model (MSE = 4.0322, R2 = 0.4577). Interpretability was addressed using SHapley Additive exPlanations (SHAP), which identified TARGET_PATHWAY, DRUG_ID, TARGET, and CELL_LINE_NAME as key predictive features, aligning with established pharmacological mechanisms. Predicted synergy scores, derived from combining model outputs with DrugComb and SynergyDB data, highlighted promising drug pairs such as Bortezomib + Romidepsin and Paclitaxel + Bortezomib. These findings were further supported by PCA-based pharmacological clustering, revealing biologically relevant groupings of drugs with similar mechanisms of action. The proposed protocol provides a transparent and adaptable framework for precision oncology research, enabling both predictive accuracy and biological interpretability. By integrating rigorous preprocessing, model validation, explainability, and drug synergy analysis, this workflow offers a scalable foundation for translational drug discovery and repurposing in breast cancer treatment. 2025 JoVE Journal of Visualized Experiments. -
Data-Driven Decision Making in the VUCA Context: Harnessing Data for Informed Decisions
Data-driven decision making (DDDM) has evolved from being a strategic advantage to a necessity for organizations aiming to thrive in the dynamic business contexts. It is about using data as a tool to enhance strategic thinking, scenario planning, and adaptation in rapidly changing environments. It involves leveraging data and analytics to navigate the challenges of volatility, uncertainty, complexity, and ambiguity. By embracing DDDM, organizations can enhance their decision-making processes, gain a competitive edge, and navigate the challenges of volatility, uncertainty, complexity, and ambiguity with greater confidence. However, successful implementation requires addressing challenges, fostering a data-driven culture, and continually adapting best practices to meet the evolving demands of the VUCA environment. This chapter discusses how organizations leverage DDDM in VUCA context to support effective and rapid decision making aligned with organizations vision. Particularly, it would offer insights to transit from volatility to vision, uncertainty to understanding, complexity to clarity, and ambiguity to agility. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Data-Driven Decision Making
This book delves into contemporary business analytics techniques across sectors for critical decision-making. It combines data, mathematical and statistical models, and information technology to present alternatives for decision evaluation. Offering systematic mechanisms, it explores business contexts, factors, and relationships to foster competitiveness. Beyond managerial perspectives, it includes contributions from professionals, academics, and scholars worldwide, delivering comprehensive knowledge and skills through diverse viewpoints, cases, and applications of analytical tools. As an international business science reference, it targets professionals, academics, researchers, doctoral scholars, postgraduate students, and research organizations seeking a nuanced understanding of modern business analytics. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Data-driven behaviour finance for mutual fund investment decision making
When it comes to money and investing funds, the individual portfolio investor isn't always as logical as he feels he is, which is why there's a whole school of thought dedicated to explaining why people behave in irrational and weird ways. The primary objective of this research is to investigate the effects of five major behavioural biases on individual investor decisions in a metro city India, with a focus on mutual funds, as well as to examine how individuals make decisions to ensure that their investments generate greater returns for a better future. The statistical evidence shows that a variety of behavioural elements have a significant part in people' investment decision-making patterns, which has an impact on the population's economic situation. The purpose of this study is to illustrate how an individual's perspective, attitude, and conduct affect mutual fund investments. 2023, IGI Global. -
Data- Driven Insights for Decision- Making in the Stock Market by Using Meta- Analyses
The stock market structure is complex, dynamic, and continually evolving. This makes it harder for investors to make smart decisions. Using data-driven insights to inform investment decisions has become increasingly prevalent in recent years. This research study focused on two main parameters: investors behavioral biases and initial public offering (IPO) pricing. Forty-five past studies from 2010 to 2022 were analyzed using meta-analyses. The study initially delves into the difficulties investors face in choosing suitable stock market investments. It then covers the various types of data that are available to investors. The paper proceeds to examine the techniques for analyzing stock market data. Finally, the article concludes by discussing the advantages of implementing data-based insights in investment decision-making. 2025 CRC Press. -
Data visualization: Experiment to impose ddos attack and its recovery on software-defined networks
The entire network is doing paradigm shift towards the software-defined networks by separating forwarding plane from control plane. This gives a clear call to researchers for joining the ocean of software-defined networks for doing research considering its security aspects. The biggest advantage of SDN is programmability of the forwarding plane. By making the switches programmable, it can take live instructions from controllers. The versions of OpenFlow protocol and the compatibility of programmable switches with OpenFlow were the stepping stone making software-defined networks thrashed towards reality. The control plane has come up with multiple options of controllers such as NOX [2], Ryu [3], Floodlight [4], Open- DayLight [6], ONOS [7] and the list is big. The major players are Java based which keeps the doors open for enhancement of features by the contributors. However, more is expected from the practicality of P4Lang programmed switches by bringing skilled people to the industry who can actually implement programmable switches with ease. The obvious reason for delayed progress in the area of software-defined networks is the lack of awareness towards data visualization options existing as of now. The purpose of writing this chapter is to throw light upon the existing options available for data visualization in the area of SDN especially addressing the security aspect by analyzing the experiment of distributed denial of service (DDoS) attack on SDN with clarity on its usage, features, applicability and scopes for its adaptabilities in the world of networks which is going towards SDN. This chapter is a call to network researchers to join the train of SDN and push forward the SDN technology by proved results of data visualization of network and security matrices. The sections and subsections show clearly the experimental steps to implement DDoS attack on SDN and further provide solution to overcome the attack. Springer Nature Singapore Pte Ltd. 2020. -
Data visualization and toss related analysis of IPL teams and batsmen performances
Sports play a very significant role in the development of the human persona. Getting involved in games like Cricket and other various sports help us to build character, discipline, confidence and physical fitness. Indian Premier League, IPL provides the most successful form of cricket as it gives opportunities to young and talented players to show case their talents on various pitch. Decision-makers are the utmost customers for all fundamentals in the sports analytics framework. Sports analytics has been a smash hit in shaping success for many players and teams in various sports. Sports analytics and data visualization can play a crucial role in selecting the best players for a team. This paper is about the Toss Related analysis and the breadth of data visualization in supporting the decision makers for identifying inherent players for their teams. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Data Structure Based Loss-less Image Compression Algorithm
Working capital in any organizations has a significant role in driving the business forward. Hence, there is an imminent need for the management of the working capital. The efficiency with which working capital is managed in a business or organization determines the health of the business or the organization. On having an effective working capital management firms tend to be successful and while ineffective working management leads to the failure of the business. Hence, the management of working capital is of great importance. The research study is to evaluate the effectiveness of working capital management in maximizing the profitability of construction companies in Bangalore. The research will analyze the construction companies to establish an understanding of the significance of effective WCM for maximizing the profitability. The working capital is the life blood of a business and an important function of finance that defines and deals with the liquidity of the firm. Also, profitability of firms is another major aspect of business. The research explores the correlation between the working capital and profitability to understand the effectiveness of working capital management in maximizing the profitability. The construction industry is the second largest industry of the country after agriculture. Construction activity is an integral part of a countrys infrastructure and industrial development. It includes hospitals, schools, townships, offices, houses and other buildings; urban infrastructure (including water supply, sewerage, drainage); highways, roads, ports, railways, airports; power systems; irrigation and agriculture systems; telecommunications etc. Covering as it does such a wide spectrum, construction becomes the basic input for socio-economic development. The construction industry generates substantial employment and provides a growth impetus to other sectors through backward and forward linkages. It is, essential therefore, that, this vital activity is nurtured for the healthy growth of the economy. With the present emphasis on creating physical infrastructure, massive investment is planned during the Tenth Plan. The construction industry would play a crucial role in this regard and has to gear itself to meet the challenges. In order to meet the intended investment targets in time, the current capacity of the domestic construction industry would need considerable strengthening. The construction sector has major linkages with the building material industry since construction material accounts for sizeable share of the construction costs these include cement, steel, bricks/tiles, sand/aggregates, fixtures/fittings, paints and chemicals, construction equipment, petro-products, timber, mineral products, aluminum, glass and plastics. The construction sector is one of the largest employers in the country. In 1999-2000, it employed 17.62 million workers, a rise of 6 million over 1993-94. The sector also recorded the highest growth rate in generation of jobs in the last two decades, doubling its share in total employment. -
Data set on impact of COVID-19 on mental health of internal migrant workers in India: Corona Virus Anxiety Scale (CAS) approach
The article presents a unique dataset on mental health of internal Migrant workers in India. The dataset was constructed during the pandemic when the entire nation was the victim of stringent measures to curtail the spread of Corona Virus in the form of travel restrictions and lockdowns. We collected this data in our pursuit to submit a paper in response to call for paper in the Journal titled Migration and health. Non-availability of authentic data about the internal migrant workers triggered this effort to compile the data. We have recorded 1350 responses out of a 6897 Sample through snowball sampling method. Every respondent is said to be a referee for further driving of sample. The responses were collected between June 2 and August 30, 2020 through the telephonic interviews. Also, the consent of the respondents has been duly obtained for publication of the data without revealing their identity. The interview schedule was adopted by using Corona virus Anxiety Scale (CAS) which uses four dimension model namely Cognitive, Emotional, Behavioral and Psychological. The Interview schedule was originally designed in English but was later translated into three different languages after consulting the language experts. This article provides descriptive statistics of study variables along with socio economic factors. This dataset provides a significant platform for further research related to CAS and in assessment of mental health of vulnerable groups. 2021