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Big Data in Cloud Computing - A Defense Mechanism
Published in Abid Hussain, Garima Tyagi, Sheng-Lung Peng, IoT and AI Technologies for Sustainable Living, 2023
N. Ramachandran, Salini Suresh, Sunitha, V. Suneetha, Neha Tiwari
The exhaustive use of the Internet, the IoT, cloud computing and other evolving IT advancements have significantly contributed to the increase of data of diverse types and structures, generated from various sources at exponential. Big Data can play an essential part in supporting nations’ macroeconomic stability and improving enterprises’ competitive edge. With the emergence of Big Data, researchers have easy access to information and knowledge directly applied to tackling societal problems such as public health and economic growth. Smarter forecasts of future trends can be accomplished through efficient integration and concise analysis of multi-source heterogeneous Big Data. Big Data analytics enables us to examine and analyze the tremendous amount of data collected and offers better insights for the business. On a large scale, Big Data helps companies make appropriate decisions simultaneously, leading to better customer service and successful business outcomes.
Internet of Things Enabled by Artificial Intelligence
Published in Lavanya Sharma, Towards Smart World, 2020
Rinku Sharma Dixit, Shailee Lohmor Choudhary
The next wave of disruptive technologies came with the term Big Data. Big data refers to data sets that are very large and cannot be processed by traditional software and hardware systems. Big data is characterized by the 7 Vs—volume, velocity, variety, variability, veracity, visualization, and value—and may exist in forms such as structured, semi-structured, or completely unstructured [22]. The smart systems that were created due to coalition of IoT and other technologies together with social media, are generating vast amounts of data that has now taken the form of big data. This huge data store can be analyzed for predicting and forecasting trends, patterns, and correlations. This data soon took the form of Open Data, which is machine readable, nonproprietary in nature and freely available for use and republishing. Companies have started releasing parts of their data that can be used for igniting the creativity of the masses. This will require creating a data infrastructure for sharing and maintenance of data.
The Future for Sensor Networks—Cloud and IoT
Published in Vidushi Sharma, Anuradha Pughat, Energy-Efficient Wireless Sensor Networks, 2017
So let us start with an understanding of these terms. What is big data, one may ask. There are many different definitions and thoughts around this term, while most agree that it started as a term used in distributed computing but the meaning and purpose of it is evolving. In the simplest terms, big data refers to very large data sets that cannot be processed using traditional methods to draw intelligence from them. One may ask, so what is the definition of very large.? This is a good question to ask because the boundaries defining large data sets are ever expanding. A few years ago, tens of terabytes were considered very large; today we talk in terms of petabytes of data. Big data can be characterized by not just the size but also by the other two major characteristics that make it challenging to process big data—first, the speed at which data is being generated keeps increasing with the proliferation of devices like wireless sensors, and second, the type and variety of data being generated and captured are large and at many times with gaps in between. The digitization of commerce and communications along with the adoption of social media by the ever-increasing number of people has fueled the growth of big data (Yaqoob et al. 2016).
The assessment of factors influencing Big data adoption and firm performance: Evidences from emerging economy
Published in Enterprise Information Systems, 2023
Mahak Sharma, Ruchita Gupta, Rajat Sehrawat, Karuna Jain, Amandeep Dhir
Consistent with Xu, Nash, and Whitmarsh (2020), perceived cost ranks sixth in the present research. The cost of data collection can become a major deterrent for big data and tourism research. Indeed, this data collection requires a high initial investment to purchase devices (e.g. GPS loggers and Bluetooth sensors) and recruit volunteers. However, web search data has a comparatively lower cost and significant application in tourism research (J. Li et al. 2018). Big data offers substantial benefits to businesses in enhancing decision-making, generating revenue, managing risk and reducing cost (Shin 2015). We posit, however, that firms struggle to invest significantly in the resources and time required for big data’s fruitful implementation. The main reason firms delay adopting big data is that the returns from the predictions generated by big data are unclear. The data can only predict the ‘what’ aspect of decision-making while completely ignoring the ‘how’ and ‘why’. This study’s findings also confirm the other two factors, observability and trialability, as essential factors for BDA. However, they ranked eighth and eleventh, respectively. While these factors are extremely critical in other sectors, such as IT and academia (Gangwar, Date, and Ramaswamy 2015), in the context of big data in hospitality firms, they are thus outranked by other factors that are more decisive for its adoption.
Analyse vehicle–pedestrian crash severity at intersection with data mining techniques
Published in International Journal of Crashworthiness, 2022
The majority of the previous research on crash severity modelling applied statistical regression models, whose performance may not be good when handling the mass complicated crash data with many discrete variables or variables with a large number of categories. These models also usually rely on strict statistical assumptions such as the linearity assumption, which is difficult to satisfy in crash circumstances [28,29]. As such, the non-parametric data mining techniques is a potential solution. The data mining analytic process is designed to explore large amounts of data or big data to search for structures, commonalities, and hidden patterns or rules in the dataset. It is able to handle large, complicated datasets, requiring relatively short data preparation time, providing satisfiable accuracy, as well as addressing the non-linear effects and interactions among predictors [30,31].
Machine Learning Techniques and Big Data Analysis for Internet of Things Applications: A Review Study
Published in Cybernetics and Systems, 2022
Fei Wang, Hongxia Wang, Omid Ranjbar Dehghan
Techniques such as Apache HBase, Apache Cassandra, Apache Flink, Apache Storm, Apache Spark and Apache Hadoop can be used to process data classified as big data (Kotenko, Saenko, and Branitskiy 2018). The IoT and big data are so intertwined that billions of Internet-connected objects will generate large amounts of data. However, this in itself will not be part of another industrial revolution, will not change digital everyday life, or will not provide an early warning system to save the planet. However, existing big data techniques alone lack large-scale processing, making this efficient big data analysis difficult (Martis et al. 2018). In this context, the use of a combination of machine learning and big data techniques to enhance the data analysis of IoT devices has been introduced. In recent years, machine learning techniques have become widely used due to features such as ensemble unsupervised training with faster processing (Rezaeipanah, Mojarad, and Fakhari 2022). Big data analysis by machine learning techniques includes classification, clustering, association rule mining, and regression, as shown in Figure 2. In most existing research, machine learning and big data techniques focus separately on IoT data analysis.