Removal Of Redundant Data Research Articles

Time-Aware Missing Healthcare Data Prediction Based on ARIMA Model.

Healthcare uses state-of-the-art technologies (such as wearable devices, blood glucose meters, electrocardiographs), which results in the generation of large amounts of data. Healthcare data is essential in patient management and plays a critical role in transforming healthcare services, medical scheme design, and scientific research. Missing data is a challenging problem in healthcare due to system failure and untimely filing, resulting in inaccurate diagnosis treatment anomalies. Therefore, there is a need to accurately predict and impute missing data as only complete data could provide a scientific and comprehensive basis for patients, doctors, and researchers. However, traditional approaches in this paradigm often neglect the effect of the time factor on forecasting results. This article proposes a time-aware missing healthcare data prediction approach based on the autoregressive integrated moving average (ARIMA) model. We combine a truncated singular value decomposition (SVD) with the ARIMA model to improve the prediction efficiency of the ARIMA model and remove data redundancy and noise. Through the improved ARIMA model, our proposed approach (named MHDP SVD_ARIMA) can capture underlying pattern of healthcare data changes with time and accurately predict missing data. The experiments conducted on the WISDM dataset show that MHDP SVD_ARIMA approach is effective and efficient in predicting missing healthcare data.

Lightweight Deep Neural Network with Data Redundancy Removal and Regression for DOA Estimation in Sensor Array

Lightweight Deep Neural Network with Data Redundancy Removal and Regression for DOA Estimation in Sensor Array

Fused Attentive Generative Adversarial Network based Personalized English Learning Material Recommendation System utilizing Knowledge Graph

The information era has arrived swiftly in the world. Because it is the universal language, English influences how information is transmitted and exchanged throughout the world. Learning English is like having a valuable instrument for learning priceless knowledge. For this reason, it is imperative that English instruction be improved. The Fused Attentive Generative Adversarial Network based Personalized English Learning Material Recommendation System utilizing Knowledge Graph. Initially, input data are collected, given to preprocessing method. In preprocessing remove data redundancy using Generalized Correntropy Sparse Gauss–Hermite Quadrature Filter (GCSG-HQF). In pre-processed output fed to Recommendation System. Here, FAGAN is used to recommend English Learning Material. In general, FAGAN classifier does not express any optimization adaption approaches for determining optimum parameters to assure the Knowledge Graph. Here Clouded Leopard Optimization Algorithm utilized to optimize FAGAN classifier, for Personalized English Learning Material Recommendation System. In Clouded Leopard Optimization Algorithm utilized to optimise weight parameter of the FAGAN. With the use of performance metrics likes accuracy, precision, sensitivity, specificity, F1-score, ROC, computational time are analyzed. The proposed FAGAN-ELMRS-CLOA method attains 30.58%, 28.73% and 35.62%, higher accuracy, 20.48%, 24.73%, 29.32% higher computational time and 30.98%, 26.66% and 21.32% higher ROC analysed with existing models such as personalized English learning material recommendation system depend on knowledge graph (CNN-ELMRS-KG), semantic method for document classification utilizing deep neural networks with multimedia knowledge graph (DNN- ELMRS-MKG), and personalized recommendation of English learning depend on knowledge graph with graph convolutional network (PR- ELMRS-GCN) respectively.

Ensemble machine learning technique-based plagiarism detection over opinions in social media

With the progressive enhancement of social media, several people prefer posting their opinions on various social media instead of posting on radios, television or newspapers. The postings differ in dimensions and include various titles and comments. Nowadays, the formation of plagiarism is increasing tremendously which occurs by rewriting or repeating one’s work. There are many ways to detect plagiarism by browsing through the internet. The significant intention of this paper involves the detection of plagiarism in social media using four different phases, namely the data pre-processing phase, n-gram evaluation, similarity/distance computation analysis and the plagiarism detection phase. The pre-processing includes data cleaning processes, such as the removal of redundant data, upper case letters, noise, irrelevant punctuations and characterizing into a vector form. After pre-processing the data are fed for n-gram evaluation to develop a posting attribution system. Then finally, an ensemble support vector machine-based African vulture optimization (ESVM-AVO) approach is employed to detect plagiarism which signifies that the performance based on detection is enhanced and the execution time in obtaining a high rate of detection accuracy is very low. Finally, the performance evaluation and the comparative analysis are carried out to determine the performance of the proposed system.

Bloom filter empowered smart storage/access in IoMT [edge‐fog‐cloud] hierarchy for health‐care data ingestion

AbstractToday's era of smart technologies has shifted the paradigm of how things work in almost every domain. This change has also affected the utmost need of human beings, that is, sustainability in healthcare. With the help of technologies like the Internet of medical things (IoMT) devices and cloud‐based applications, doctors can now monitor and diagnoses their patients remotely. In the data analytics process for health‐care data, more precision and challenges are present. Data must be transferred from a lower layer to final storage (cloud‐based) during data ingestion so that subsequent operations such as mining, accessing, and streaming can be performed on these data. This article addresses some of the important issues to maintain sustainability in healthcare like authentication, smart scheduling of devices, the removal of redundant data on the final layer, and improving access times for stored data. Bloom filter (a probabilistic data structure)‐based solutions are proposed at different layers of IoMT (edge‐fog‐cloud). The comparative analysis of state‐of‐art techniques with the proposed has shown significant improvement over various metrics. The proposed framework has been evaluated experimentally on real health‐care datasets.

P2S distance induced locally conjugated orthogonal subspace learning for feature extraction

When performing data classification tasks, it often occurs to them the curse of dimensionality problem. To address the issue, a manifold learning method termed locally conjugated orthogonal subspace (LCOS) is put forward for dimensionality reduction or feature extraction in this paper. Note that point to feature space (P2S) distance contributes to mining local geometry information, both a local margin characterizing data apartness and a locally conjugated orthogonal constraint beneficial to removing data redundancy are well studied from the P2S distance metric. They are all exploited to model the proposed LCOS. Then, a low dimensional subspace can be explored by maximizing the P2S distance induced local margin under the constraint. Compared with some other related dimensionality reduction methods, experimental results on benchmark face and object data sets validate the performance of the proposed method.

Super Sparse 3D Object Detection.

As the perception range of LiDAR expands, LiDAR-based 3D object detection contributes ever-increasingly to the long-range perception in autonomous driving. Mainstream 3D object detectors often build dense feature maps, where the cost is quadratic to the perception range, making them hardly scale up to the long-range settings. To enable efficient long-range detection, we first propose a fully sparse object detector termed FSD. FSD is built upon the general sparse voxel encoder and a novel sparse instance recognition (SIR) module. SIR groups the points into instances and applies highly-efficient instance-wise feature extraction. The instance-wise grouping sidesteps the issue of the center feature missing, which hinders the design of the fully sparse architecture. To further enjoy the benefit of fully sparse characteristic, we leverage temporal information to remove data redundancy and propose a super sparse detector named FSD++. FSD++ first generates residual points, which indicate the point changes between consecutive frames. The residual points, along with a few previous foreground points, form the super sparse input data, greatly reducing data redundancy and computational overhead. We comprehensively analyze our method on the large-scale Waymo Open Dataset, and state-of-the-art performance is reported. To showcase the superiority of our method in long-range detection, we also conduct experiments on Argoverse 2 Dataset, where the perception range ( 200m) is much larger than Waymo Open Dataset ( 75m). Code is open-sourced at https://github.com/tusen-ai/SST.

Validating functional redundancy with mixed generative adversarial networks

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query processing. While it has been studied for four decades, existing works on data redundancy mostly focus on syntactic formulations such as normal forms and functional dependencies, which lead to intractable discovery problems. In this work, we propose a new concept, namely functional redundancy, that overcomes the limitations of functional dependencies, especially on continuous data. We design and develop efficient algorithms based on generative adversarial networks to validate any functional redundancy without heavily depending on the number of attributes and the number of tuples like functional dependencies. The core idea is to use the imputation power of generative adversarial networks to model any semantic dependencies between attributes. Extensive experiments on different real-world and synthetic datasets show that our approach outperforms representative baselines, is applicable for first-order and high-order dependencies, and is extensible for different types of data.

Task-Oriented Communication for Multidevice Cooperative Edge Inference

This paper investigates task-oriented communication for multi-device cooperative edge inference, where a group of distributed low-end edge devices transmit the extracted features of local samples to a powerful edge server for inference. While cooperative edge inference can overcome the limited sensing capability of a single device, it substantially increases the communication overhead and may incur excessive latency. To enable low-latency cooperative inference, we propose a learning-based communication scheme that optimizes local feature extraction and distributed feature encoding in a task-oriented manner, i.e., to remove data redundancy and transmit information that is essential for the downstream inference task rather than reconstructing the data samples at the edge server. Specifically, we leverage Tishby’s information bottleneck (IB) principle (Tishby et al., 2000) to extract the task-relevant feature at each edge device, and adopt the distributed information bottleneck (DIB) framework of Aguerri and Zaidi, 2021, to formalize a single-letter characterization of the optimal rate-relevance tradeoff for distributed feature encoding. To admit flexible control of the communication overhead, we extend the DIB framework to a distributed deterministic information bottleneck (DDIB) objective that explicitly incorporates the representational costs of the encoded features. As the IB-based objectives are computationally prohibitive for high-dimensional data, we adopt variational approximations to make the optimization problems tractable. To compensate for the potential performance loss due to the variational approximations, we also develop a selective retransmission (SR) mechanism to identify the redundancy in the encoded features among multiple edge devices to attain additional communication overhead reduction. Extensive experiments on multi-view image classification and multi-view object recognition tasks evidence that the proposed task-oriented communication scheme achieves a better rate-relevance tradeoff than existing methods.

Prediction of chronic liver disease patients using integrated projection based statistical feature extraction with machine learning algorithms

The healthy liver plays more than 500 organic roles in the human body, while a malfunction may be dangerous or even deadly. Early diagnosis and treatment of liver disease can improve the likelihood of survival. Machine learning (ML) is a powerful tool that can assist healthcare professionals during the diagnostic process for a hepatic patient. The standard ML system includes the methods of data pre-processing, feature extraction, and classification. In the feature extraction stage, ML researchers frequently use projection-based feature extraction approaches to remove data redundancy, but this does not produce the desired results. In addition, most statistical projection methods have different purposes when projecting original features. The Indian liver patient dataset (ILPD) from the University of California, Irvin (UCI) repository is used in this study to classify chronic liver disease. The data set has 583 patient disease records; 416 patients have liver disease, and 167 do not. Using several projection methods, we proposed an integrated feature extraction approach to categorize liver patients. In the pipeline, the proposed method first imputes the missing values and outliers for pre-treatment. Then, integrated feature extraction applies the pre-processed data to extract the significant features for classification. A simulation study is also being conducted to strengthen the suggested methodology. The proposed approach incorporates several ML algorithms, including logistic regression (LR), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), multilayer perceptron (MLP), and the ensemble voting classifier. The offered system has an accuracy of 88.10%, a precision of 85.33%, a recall of 92.30%, an F1 score of 88.68%, and an AUC score of 88.20% in predicting liver diseases. Our proposed technique yielded 0.10–18.5% better results than the latest existing studies. The findings suggest that the recommended system could be used to supplement a physician's diagnosis of liver disease.

Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments.

Visual crowdsensing applications using built-in cameras in smartphones have recently attracted researchers’ interest. Making the most out of the limited resources to acquire the most helpful images from the public is a challenge in disaster recovery applications. Proposed solutions should adequately address several constraints, including limited bandwidth, limited energy resources, and interrupted communication links with the command center or server. Furthermore, data redundancy is considered one of the main challenges in visual crowdsensing. In distributed visual crowdsensing systems, photo sharing duplicates and expands the amount of data stored on each sensor node. As a result, if any node can communicate with the server, then more photos of the target region would be available to the server. Methods for recognizing and removing redundant data provide a range of benefits, including decreased transmission costs and energy consumption overall. To handle the interrupted communication with the server and the restricted resources of the sensor nodes, this paper proposes a distributed visual crowdsensing system for full-view area coverage. The target area is divided into virtual sub-regions, each of which is represented by a set of boundary points of interest. Then, based on the criteria for full-view area coverage, a specific data structure theme is developed to represent each photo with a set of features. The geometric context parameters of each photo are utilized to extract the features of each photo based on the full-view area coverage criteria. Finally, data redundancy removal algorithms are implemented based on the proposed clustering scheme to eliminate duplicate photos. As a result, each sensor node may filter redundant photographs in dispersed contexts without requiring high computational complexity, resources, or global awareness of all photos from all sensor nodes inside the target area. Compared to the most recent state-of-the-art, the improvement ratio of the added values of the photos provided by the proposed method is more than 38%. In terms of traffic transfer, the proposed method requires fewer data to be transferred between sensor nodes and between sensor nodes and the command center. The overall reduction in traffic exceeds 20% and the overall savings in energy consumption is more than 25%. It was evident that in the proposed system, sending photos between sensor nodes, as well as between sensor nodes and the command center, consumes less energy than existing approaches due to the considerable amount of photo exchange required. Thus, the proposed technique effectively transfers only the most valuable photos needed.

From traditional leadership to transformative leadership: A conceptual study on the Leadership Style of the School Principals

Introduction: The effectiveness of a school is largely dependent upon its principal. His behavior influences the way a school functions. The most crucial factor in any school is the leadership behavior of the principal and how the principal carries out his leadership behavior and how that reflects the efficiency of school functioning. Today one can notice there is a phenomenal change in the leadership behavior of the principal. Now the environment they are working in is very competitive, advanced, and technical. There are a lot of risks they are encountering in every situation to give their school a sustainable future. In one way they have to address the expectation of society and in the other, they are to meet the expectation of the management. Though their position is tough yet they have to continue their performance up to the satisfaction of the stakeholders. Rationale: The previous research studies were mostly centered on a traditional concept of the leadership style of the principal, which narrows down the role of the principal, and most considered him an instructional leader. Considering this is a research gap the researchers conducted qualitative research on the transformative leadership style of the principal. The ethnographic interview and ethnographic observation established the authenticity and internal consistency of the data and the results thus derived were with a high level of validity. Objective: The purpose of the study was to study the leadership behavior of the school principal concerning transformative leadership style and also to compare the effectiveness of transformative leadership styles concerning the outcomes of the school. Methods: This is a Qualitative Research (QR). Considering the nature of the research, the researchers decided to use a typical research design named concept analysis, since the research tries to identify the insight of the principal towards transformative leadership style through the opinion of their staff members. Research Tools: The Researcher prepared an interview schedule with the questions which were modified from the original questionnaire named, Leadership Behaviors Development Questionnaire (LBDQ) developed by Ohio State University, USA. Data Collection techniques: The data was collected through an Ethnographic interview and Ethnographic observation schedule over an extended period and the writing of extensive field notes by the researchers. Data analysis and interpretation: As this is a qualitative research design, after the data was collected, an in-depth analysis was done, to remove data redundancy, bring stability to the opinion of the respondents, to compare & contrast the opinion of the participants in non-contrived situations. Results: It was found that mostly the principals of urban-based secondary schools are much more aware, motivated, and active in the implementation of transformational leadership style compared to their counterparts in the schools located in the rural area. Again, the ethnographic observations reported that there was a very significant relationship between transformative leadership and the job satisfaction of the staff members. The attrition rate of the staff member was found less and the performance of the school was also continuously better compared to the school leaders having a negative mindset towards transformation. It was also found that transformative leaderships are good for emotional control, social adjustment, and creativity in the professional environment. It was also revealed that the schools with transformative leadership are more advanced in the achievements of many key performance indicators (KPIs). Conclusion: The study concluded that it is very much indispensable for the principals to understand the expectations of the stakeholders and bring innovation to the school. This is required to bring sustainability to the school and rise in a competitive environment.

Dynamic Temporal–Spatial Regularization-Based Channel Weight Correlation Filter for Aerial Object Tracking

Correlation filter (CF) has drawn extensive interest in aerial object tracking due to its remarkable performance. Recently, the popular CF methods based on temporal–spatial regularization have been proved to be able to effectively improve the tracking results. However, the boundary effect and filter template degradation still influence the speed and accuracy of the trackers. To handle the two problems, a novel dynamic temporal–spatial regularization-based channel weighted tracking (DTSCT) method was proposed in this work. First, we attempted to employ the saliency detection technique to describe object variation for weakening the boundary effect. Then, the filter template was introduced to the temporal regularization to alleviate the template degradation. In addition, an adaptive weighting strategy was utilized to remove data redundancy in the feature channels. Experiments on three benchmark datasets showed the competitive performance of our DTSCT approach compared to the state-of-the-art methods.

Water Moth Search Algorithm-based Deep Training for Intrusion Detection in IoT

The economic growth and information technology leads to the development of Internet of Things (IoT) industry and has become the emerging field of research. Several intrusion detection techniques are introduced but the detection of intrusion and malicious activities poses a challenging task. This paper devises a novel method, namely the Water Moth Search algorithm (WMSA) algorithm, for training Deep Recurrent Neural Network (Deep RNN) to detect malicious network activities. The WMSA algorithm is newly devised by combining Water Wave optimization (WWO) and the Moth Search Optimization (MSO). The pre-processing is employed for the removal of redundant data. Then, the feature selection is devised using the Wrapper approach, then using the selected features; the Deep RNN classifier effectively detects the intrusion using the selected features. The proposed WMSA-based Deep RNN showed improved results with maximal accuracy, specificity, and sensitivity of 0.96, 0.973 and 0.960.

A Neural Computing-based Cash Price Prediction Using Multi-layer Perceptron (MLP) and Feature Selection for Health Economics

Background: The present study proceeds to incorporate feature selection as a means for selecting the most relevant features affecting the prediction of cash prices in Iran in terms of health economics. Health economics is an academic field that aids in ameliorating health conditions so as to make better decisions in regard to the economy such as determining cash prices. Methods: Accordingly, a series of search algorithms, namely the Best-First, Greedy-Stepwise, and Ranker methods, are deployed in order to extract the most relevant features from among 500 data samples. The validity of the methods was evaluated via the LMT procedure. The corresponding dataset used for this study constitutes a variety of features including net cash flow, dividends, revenue from short and long-term deposits, cash flow from investment returns, income tax, fixed asset purchases, fixed asset sales, long-term investment purchases, long-term investment sales, total cash flow from investment activities, financial facilities, and repayment of financial facilities. Results: The results were indicative of the superiority of the Ranker model using the RelieFAttribute- Eval tool in Weka over the remaining classification methods. Ergo, the LMT approach could be employed to remove data redundancies and accelerate the estimation process, while saving time and money. The results of the multi-layer perceptron (MLP) further confirmed the high accuracy of the proposed method in estimating cash prices. Conclusions: The present research attempted to reduce the volume of data required for predicting end cash by means of employing a feature selection so as to save both precious money and time.

Intelligent Fog-Enabled Smart Healthcare System for Wearable Physiological Parameter Detection

Wearable technology plays a key role in smart healthcare applications. Detection and analysis of the physiological data from wearable devices is an essential process in smart healthcare. Physiological data analysis is performed in fog computing to abridge the excess latency introduced by cloud computing. However, the latency for the emergency health status and overloading in fog environment becomes key challenges for smart healthcare. This paper resolves these problems by presenting a novel tri-fog health architecture for physiological parameter detection. The overall system is built upon three layers as wearable layer, intelligent fog layer, and cloud layer. In the first layer, data from the wearable of patients are subjected to fault detection at personal data assistant (PDA). To eliminate fault data, we present the rapid kernel principal component analysis (RK-PCA) algorithm. Then, the faultless data is validated, whether it is duplicate or not, by the data on-looker node in the second layer. To remove data redundancy, we propose a new fuzzy assisted objective optimization by ratio analysis (FaMOORA) algorithm. To timely predict the user’s health status, we enable the two-level health hidden Markov model (2L-2HMM) that finds the user’s health status from temporal variations in data collected from wearable devices. Finally, the user’s health status is detected in the fog layer with the assist of a hybrid machine learning algorithm, namely SpikQ-Net, based on the three major categories of attributes such as behavioral, biomedical, and environment. Upon the user’s health status, the immediate action is taken by both cloud and fog layers. To ensure lower response time and timely service, we also present an optimal health off procedure with the aid of the multi-objective spotted hyena optimization (MoSHO) algorithm. The health off method allows offloading between overloaded and underloaded fog nodes. The proposed tri-fog health model is validated by a thorough simulation performed in the iFogSim tool. It shows better achievements in latency (reduced up to 3 ms), execution time (reduced up to 1.7 ms), detection accuracy (improved up to 97%), and system stability (improved up to 96%).

Power Aware Hessian Multi-set Canonical Correlations Based Algorithm for Wireless Eeg Sensor Networks

The miniaturized Electroencephalography (EEG) modules monitors the EEG signals over a smaller area, however, thus modules suffer from poor spatial coverage. The wireless EEG sensor network (WESN) provides an improved spatial coverage with multiple EEG modules, which communicated through a shorter distances. Further, the miniaturized EEG modules tend to create a high energy cost in wireless communication. This paper aims to remove the eye blink artifacts from the WESN EEG channels. The exploitation of correlation among the signals from various EEG modules is taken into consideration for resolving the problem of artifact removal in stringent bandwidth constraints. In this paper, Hessian multi-set canonical correlations based algorithm is used that computes optimal linear combination of the EEG signal between the local EEG channels and other modules. This correlation makes the EEG signal to be correlated at a maximum rate. The use of Hessian multi-set canonical correlations to remove the eye blink artifacts in a distributed realization reduces the transmission cost in the network. The proposed method has been validated against the real and synthetic EEG datasets, collected from the WESN shortest distance communication. The removal of redundant data from the wireless nodes, the algorithm attains an improved performance to remove the eye blink artifact with reduced power consumption in the wireless networks.

Map-optimize-reduce: CAN tree assisted FP-growth algorithm for clusters based FP mining on Hadoop

Over the past era, Frequent Pattern Mining (FPM) is emerging as a significant approach to discover fascinating knowledge concealed in the data. However, preceding works failed to address the validation of FPM with user queries and also achieving better scalability and execution time is still bottleneck owing to difficulties in handling large dataset. To address this downside, our proposed work establishes FPM using extend version of MapReduce framework in Hadoop environment. Our proposed work comprises of five processes that are: 1) Preprocessing 2) Affinity Propagation (AP) based Clustering 3) Load Balancing 4) Map-Optimize-Reduce 5) Mining User Queries. Primarily, our proposed work performs preprocessing to remove data redundancy. To speed up the MapReduce framework, we propose AP clustering which generates effective clusters from the given dataset. Load balancing is executed to balance load among different blocks concerning where reputation is computed. To avoid oversight in scanning and minimal searching space in MapReduce, optimizer is included between Mapper and Reducer where Emperor Penguin Colony (EPC) optimization is used. Frequent patterns are mined using CANonical order (CAN) tree based Frequent Pattern (FP) growth which reduces execution time and frequent tree construction. User provides Mining_Request to the Hadoop and frequent patterns are mined for given query which is send back to the user. If user given query is not present in the CAN tree, then it sends Relevance Feedback as a recommendation to the user. Finally, we validate our proposed work performance with the previous works for succeeding metrics that are Execution Time, Response Time, Load Balancing Rate, and Scalability.

Abnormal Detection of Wind Turbine Based on SCADA Data Mining

In order to reduce the curse of dimensionality of massive data from SCADA (Supervisory Control and Data Acquisition) system and remove data redundancy, the grey correlation algorithm is used to extract the eigenvectors of monitoring data. The eigenvectors are used as input vectors and the monitoring variables related to the unit state as output vectors. The genetic algorithm and cross validation method are used to optimize the parameters of the support vector regression (SVR) model. A high precision prediction is carried out, and a reasonable threshold is set up to alarm the fault. The condition monitoring of the wind turbine is realized. The effectiveness of the method is verified by using the actual fault data of a wind farm.

Support Vector Machine Based Data Classification to Avoid Data Redundancy Removal before Persist the Data in a DBMS

Data Base Management System is one of the growing fields in computing world. Grid computing, internet sharing, distributed computing, parallel processing and cloud are the areas store their huge amount of data in a DBMS to maintain the structure of the data. Memory management is one of the major portions in DBMS due to edit, delete, recover and commit operations used on the records. To improve the memory utilization efficiently, the redundant data should be eliminated accurately. In this paper, the redundant data is fetched by the Quick Search Bad Character (QSBC) function and intimate to the DB admin to remove the redundancy. QSBC function compares the entire data with patterns taken from index table created for all the data persisted in the DBMS to easy comparison of redundant (duplicate) data in the database. This experiment in examined in SQL server software on a university student database and performance is evaluated in terms of time and accuracy. The database is having 15000 students data involved in various activities.