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Any data set contains large volumes of information and complex data is called Big Data (BD). BD is unlike other traditional data sets, so it requires special processing to manage it. BD faces many challenges starting from data capture through to the final results. BD exists in many subject areas such as business, governments, sciences, healthcare a...
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... Thus, the basic characteristics of big data are the three Vs-'Volume', 'Veracity', and 'Variety' [13]. Over time, the three Vs have been extended to five Vs by the addition of 'Value,' the value of the data (often relative to a point in time), and 'Velocity,' the rapid increase in the volume of the data [14]. Big data processing often encounters the limitations of traditional databases, software technologies, and established processing methodologies [15]. ...
The paper presents possible approaches for reducing the volume of data generated by simulation optimisation performed with a digital twin created in accordance with the Industry 4.0 concept. The methodology is validated using an application developed for controlling the execution of parallel simulation experiments (using client–server architecture) with the digital twin. The paper describes various pseudo-gradient, stochastic, and metaheuristic methods used for finding the global optimum without performing a complete pruning of the search space. The remote simulation optimisers reduce the volume of generated data by hashing the data. The data are sent to a remote database of simulation experiments for the digital twin for use by other simulation optimisers.
... The studies that addressed the privacy policy of the data collection phase are [78,79], and there are no studies that addressed privacy-preserving data collection related technologies. In data storage phase, encryption is addressed in all studies excluding [80][81][82][83], and access control in storage phase is addressed in all studies excluding [79,80,84]. In addition, the studies addressed audit trail are [78,82,[85][86][87]. ...
... Finally, they mentioned the big data life cycle's importance but not presented the big data life cycle. Alwan et al. [83] presented a big data life cycle (i.e., data collection, data cleaning, data classification, data modeling, and data delivery). In addition, they analyzed big data in specific domains such as smart grid and IoT. ...
The use of big data in various fields has led to a rapid increase in a wide variety of data resources, and various data analysis technologies such as standardized data mining and statistical analysis techniques are accelerating the continuous expansion of the big data market. An important characteristic of big data is that data from various sources have life cycles from collection to destruction, and new information can be derived through analysis, combination, and utilization. However, each phase of the life cycle presents data security and reliability issues, making the protection of personally identifiable information a critical objective. In particular, user tendencies can be analyzed using various big data analytics, and this information leads to the invasion of personal privacy. Therefore, this paper identifies threats and security issues that occur in the life cycle of big data by confirming the current standards developed by international standardization organizations and analyzing related studies. In addition, we divide a big data life cycle into five phases (i.e., collection, storage, analytics, utilization, and destruction), and define the security taxonomy of the big data life cycle based on the identified threats and security issues.
Businesses need all data to be controlled centralized, and most corporations utilize analysis to learn where their company stands in the market. Big data tools and approaches are being used by researchers and practitioners to compute performance utilizing various algorithms. It is obvious that organisations require strong understanding of their consumers, commodities, and laws; nevertheless, with the aid of big data, organisations may find new methods to compete with other organizations. This study will focus on big data techniques and algorithms to find patterns to apply on the business cases which are lagging. Technology is simply a tool used by the business elite to keep their clientele close by. It has successfully aided the organisation in achieving cost savings, making quicker, better decisions using business big data cycle and collaborative filtering.
Dataset is a set of data that becomes the standard for showing the behaviour of something. In this case, the industry. For this situation, the industry. Be that as it may, each dataset consistently has restrictions to help the prediction. The prediction is about the current conduct of the industry, where yet just exists in enormous information like in big data. This paper aims to construct a concept that describes a method for developing a dataset.
