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1 *Corresponding author: [email protected]
2 †Corresponding author: [email protected]
2
A Framework for Big Data Knowledge Engineering
Devi T.1* and Ramachandran A.2
1Department of Computer Science & Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India
2Department of Computer Science & Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, India
Abstract
Analytics and analysis from a massive database using various approaches and techniques are experimented, and ongoing research brings its main focus toward the domain such as big data. Economic growth and technological growth combined with its data production are also highlighted in big data approaches. Data are analyzed from social media, online stock markets, healthcare data, etc., which can be collaborated with artificial intelligence by developing the automated learning algorithms and development in cloud computing as well. Data can be either discrete or continuous, which are independent of the various processes for understanding the decision-making that relies on knowledge engineering. The proposed work converges in transforming the observed sequential data analysis from weather forecasting dataset. These systems can perform the cognitive task in improving the performance along with integrity of data using the enhanced framework. The prediction of natural disasters is a challenge for customers accessing forecast data, since fluctuations in data occur frequently, which fail to update the localization, that are identified as sensor latitude and longitude that are updated as a sequence on regular intervals from various directions. These four hidden states are the features that differentiate the probability of distributions for calculating the best cognitive tasks. Improved Bayesian Hidden Markov Frameworks (IBHMFs) have been proposed to identify the exact flow of state and detect the high congestion, which leads to earthquakes, tremor, etc. As the data from the analysis are unsupervised and features are converted to discrete and sequential data (independent variables), IBHMF can utilize in increasing the performance and produce the accuracy results in state estimation.
Keywords: Artificial intelligence, big data, Improved Bayesian Hidden Markov Frameworks (IBHMF), hidden state, knowledge engineering, weather forecasting
2.1 Introduction
Catastrophic damage has been caused by natural hazards along with loss in a socioeconomic way, thereby depicting the increase in trend. Several disasters pose challenges to officials working in the disaster management field. These challenges may include resources unavailability and limited workforce, and these limitations force them from changing the policies toward managing the disasters [1].
The amount of data generated is huge in size including the real along with the simulation data. These data can be used in supporting disaster management. The technological advancement like data generated from social media as well as remote sensing is huge in size and also is real data. In certain times, these real data are scarce and lead us to usage of simulation data. Several computational models can be used in generation of simulation data that can be used in estimation of impact produced due to disaster. It is much necessary to acquire big data, manage it, and process within a short time span for effective management of disaster irrespective of the type of data being used. For this reason, artificial intelligence (AI) methods can be employed for analyzing the huge volume of data for extracting useful information. Such methods have gained popularity while they support the process of making decisions in case of disaster management [5, 6].
The usage of big data for managing disasters is still evolving. The main challenge for a scientist in today’s technological world is handling the huge volume of information that is being generated during disaster time. When the volume of data is being increased, traditional systems (Figure 2.1) employed for storing and data processing are not able to perform better. The factors affecting their working include scalability along with data availability [8].
The storage systems at the present time are diverse in nature, and when it comes to collaboration, they provide much less scope. This leads to the necessity of methods that can be employed for integration, aggregation, and visualization of data. Also, the decisions taken need to be optimized as their quality is based on the available data quality. It is much necessary to organize data followed by storage and analysis of disaster data for further investigation [11].
Figure 2.1 Traditional Bayesian Neural Network disaster prediction from the dataset.
2.1.1 Knowledge Engineering in AI and Its Techniques
AI can be useful for disaster management, wherein it can be further classified as the following categories: supervised model, unsupervised model, deep learning, and reinforcement learning along with optimization.
2.1.1.1 Supervised Model
Training is done using the human input on the data that is pre-existing in case of algorithms, representing the supervised model. Such models can represent a function by utilizing the methods such as classification for predicting the output value. This is due to the training data that is labeled and also the input as well as output pairs. The main advantages of these models include extraction of information and recognition of objects, patterns, and speech [17].
2.1.1.2 Unsupervised Model
Statistical methods are being used for extraction of hidden structure from the unlabeled data in unsupervised models. Human input is also absent here, and they are utilized in detection of abnormal data along with reduction of dimension of the data. Its applications include clustering as well problems such as data aggregation. The unlabeled data can be partitioned as several groups depending on the similarity feature. This recognition of patterns can be done using the clustering algorithms. On the other hand, the algorithms employed for reduction of dimension like PCA (Principal Component Analysis) play a significant role in data complexity reduction, which, in turn, helps in avoiding overfitting [18].
2.1.1.3 Deep Learning
Input data can be used for extracting the features by using the multiple layers and such algorithm classes constitute deep learning [20]. The learning performance is improved with a wide scope of application [3, 10]. The main disadvantage of using the algorithms in deep learning is that they take more time for training the data. These algorithms can be employed for solving problems such as assessment of damage, detecting motion, recognizing facial expressions, prediction of transportation,