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Big Data is used to refer to very large data sets having a large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. The research into large amounts of data to reveal hidden patterns and secret correlations named Big Data analytics. This is useful information for companies...
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Hyperchaotic maps are generally used in the encryption to generate the secret keys. The number of hyperchaotic maps has been implemented so far. These maps involve a large number of state and control parameters. The major concern is the estimation of these parameters. Because the estimation requires extensive computational search. In this paper, a...
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... In general, it is defined as a large set of data features with specific properties that require the use of new mechanisms in order to store and analyse it successfully [1]. The generated datasets by healthcare sources are characterized by the main aspects that are well known for big data, which are known as the 5 V's; Value, Volume, Velocity, Variety, and Veracity [2,3]. This type of data is widely spreading, as it is generated by different sources such as healthcare systems, research outputs, health insurance companies, government agencies, etc. ...
... In a study on big data in the field of health care that have been published between 2010 and 2015, the researchers defined it as containing large data sets from health care institutions, in addition to requiring adequate storage, analysis, and visualization to make productive and positive decisions [15]. Given the importance of big data in the healthcare field, researchers from India have undertaken a study mainly aimed at providing them with a comprehensive meaning [16][17][18], where they considered the big data in healthcare as critical in its importance, as the use of modern technologies and their application to them, has brought incredible benefits to the entire society, which has been achieved in various health care institutions around the world [2]. ...
Big data is a massive amount of information, measurements, and observations, where it has the power to provide a solution to the impossibilities. Recently, it has become the most trending topic in the field of data analysis because of its amazing potentials in extracting the hidden facts. Which attracted various sectors all over the world to collect and analyze the big data in order to improve their services and introduce high valuable products. Specifically, in the healthcare industry, different sources generate big data such as; hospital records, medical records of patients, and results of medical examinations. This type of data is related to the population healthcare, and it requires analysis in order to extract valuable knowledge. Nowadays, with the available high-end computing solutions for big data analysis. It becomes easy for researchers to have solutions that improve the healthcare level of the population. The promising thinking to give new technologies, high services, and big profits for healthcare, can revolutionize the medical solutions and help the community in overcoming the impossible cases. This research discusses essential clinical big data matters related to the healthcare sector by introducing a clear definition and features of the clinical big data in healthcare and its process. Also, by presenting analytics, applications, benefits, challenges, and future of the clinical big data technologies in the healthcare sector. This survey aims to review state of the art for the application of the clinical big data in the healthcare sector, in which it would be an apparent reference, where authors can refer to in their future research.
... It is useful to group a lot of data points, data relationship understanding, address questions in real-time, and determine where to focus research quickly [8]. The main objective of data visualization is also explained by [9] which is to communicate information through graphics clearly and effectively [10]. Data visualization plays a key role in the process of data discovery and better decision making as it can collect several data points, grasp data relationships, address problems in real-time, and determine where to concentrate analysis more quickly [7]. ...
The existing iCEPS webpage continues to display program information in a list format, requiring users to manually scroll through the list to access all the available UiTM programs. This presentation of data in a list format is inefficient for analysis as it necessitates users to imagine and visualize the geographical location of the campuses themselves. This study aims to investigate the feasibility of utilizing a dot distribution map to assist users (candidates) in their search and selection of campuses for pursuing part-time distance learning at the diploma or degree level, as offered by the Institute of Continuing Education and Professional Studies (iCEPS) at Universiti Teknologi MARA (UiTM). In this study, a methodology to enhance the user experience in selecting campuses for part-time distance learning programs at the diploma or degree level was implemented. This includes initializing a map using the Google Maps JavaScript API, generating markers on the map to represent campus locations, and implementing tooltips to provide essential information when users interact with the markers. The webpages with the dot distribution maps are presented as the findings of study. It is effectively visualizing the locations of iCEPS campuses across various branches in Peninsular Malaysia, Sabah and Sarawak. These dot distribution maps offer an interactive and user-friendly platform that allows users to access detailed information about the courses offered at each campus. The implications of this study point to a significant reduction in the time, costs, and health-related concerns associated with users’ travelling. By providing an efficient means of campus selection through dot distribution maps, prospective learners can minimize the time spent commuting, reduce expenses on accommodation, food, and travelling thus promoting convenience, financial savings, and well-being.
... In general, Big Data can be explained according to three V's: Volume, Velocity and Variety [3]. Also, the other characteristics of Big Data described in [4] are volume, variety, velocity, veracity, valence, and value. Later on, in [5] 10V's volume, variety, velocity, veracity, variability, viscosity, volatility, viability, validity, and value are exposed. ...
... Big data is a term for online database providers with a broad scope. The advantage of having big data is that it has characteristics known as 5V, namely volume, variety, veracity, velocity, and value (Hadi et al., 2015). One of the most widely accessed sources of big data is social media. ...
Along with the development of science and technology, using big data, map makers can take advantage of crowdsourcing social media data on Twitter to obtain user location when uploading tweets, which can be called geolocated tweets. Earthquakes that occur very often in Indonesia often grab people's attention, especially netizens who use social media like Twitter. One of the major earthquakes that occurred in Indonesia in 2018 was the Lombok earthquake, which occurred twice in a row from July to August 2018. Using Twitter data, information and social responses related to the 2018 Lombok earthquake can be obtained, which can be used as evaluation material for public handling and responding. The information is then visualized in various forms, and one of the best visualization methods is selected.This study uses Twint package in Python as a way of obtaining location data from Twitter. The method used to collect Twitter data is a case study on the social impact of the Lombok earthquake in Indonesia in 2018. The data observation method used is a simulation of several types of map visualization and survey methods in selecting the best type of visualization. The method of analysis used is by mapping the data on the number of tweets as the main object using various types of maps, as well as calculating survey results by scoring each group of questions.The results of spatial data extraction from Twitter in this study obtained 2032 tweets that had been selected and cleaned from 11,584 tweets. Map visualization with the theme of the social impact of the Lombok earthquake in 2018 was compiled using five types of visualization, namely choropleth maps, proportional symbol maps, dot maps, hexagonal tessellation maps, and heat maps. Based on the results of the survey on selecting the best visualization, it was found that the choropleth map is the best visualization method according to respondents with a cartography background and respondents who are unfamiliar with cartography because the information displayed is easier to read and understand.
... The five prominent attributes of big data are variety, volume, value, veracity, and velocity, which are known as 5Vs. [117]. Hence, any significant data movement from intelligent devices and equipment to the cloud may not be efficient. ...
These days, the development of smart cities, specifically in location-aware, latency-sensitive, and security-crucial applications (such as emergency fire events, patient health monitoring, or real-time manufacturing), heavily depends on more advanced computing paradigms to address these requirements. In this regard, fog computing, a robust cloud computing complement, plays a preponderant role by virtue of locating closer to the end-devices. Nonetheless, utilized approaches in smart cities are frequently cloud-based, which causes not only the security and time-sensitive services to suffer but also its flexibility and reliability to be restricted. In order to obviate the limitations of cloud and other related computing paradigms such as edge computing, this paper proposes a study for the state-of-the-art fog-based approaches in smart cities. Furthermore, according to the reviewed research content, a classification is proposed that falls into three classes: service-based, resource-based, and application-based. This study also investigates the evaluation factors, used tools, evaluation methods, merits, and demerits of each class. Types of proposed algorithms in each class are mentioned as well. Above all else, by taking various perspectives into account, comprehensive and distinctive open issues and challenges are provided via classifying future trends and issues into practical sub-classes.
... FIGURE 1. Big Data Types [20] III. BIG DATA CHARACTERISTICS The characteristics of big data are sets of parameters that describe big data with the analytical approach in which the main features of big data mostly refer as 5vs i.e. volume, variety, velocity, Veracity and value. ...
The data is growing daily due to the revolution in Information Technology, and the data size is much bigger than traditional data. Big data is the combination of data sets which is huge and diverse. Big data has multifaceted characteristics like Volume, Variety, Veracity, Velocity and Value (5Vs). Due to these characteristics and versatility, many issues and challenges occur in data retrieving and manipulation. The data management and processing issues are also facing data analysts and researchers. The existing traditional tools and algorithms are not capable of resolving these issues. In this survey paper, we will provide the current issues and challenges in the field of big data and discuss in detail the big data tools (Hadoop, Apache Spark) to use in different applications to serve the end users.
... Velocity. The period/duration in which Big Data can be processed is referred to as BD velocity (Hadi et al., 2015). The rate at which data is generated is referred to as velocity (Johnson et al., 2017). ...
This chapter focuses on the critical success factors of Big Data solutions in marketing, particularly in assisting decision-making and optimising business processes. In the information age, such solutions may help a company gain a competitive advantage by utilising Big Data to understand customer needs, increase the efficiency of the entire decision-making process, and improve marketing activities. Discussions in this chapter highlight the characteristics of Big Data which include value, variety, volume, variability, veracity, velocity and valence, and the major critical factors of Internet of Things (IoT), statistical applications, business intelligence, amongst others. The application of Big Data in various areas, challenges as well as the benefits to contemporary organisations are also uncovered . Recommendations are included for effective utilisation and management of big data to improve marketing activities in Africa.KeywordsBig DataStructuredUnstructuredSemi-structuredTechnology
... These data coming from different client offices is process by planning office and realizing these ample amount of inputs to create an output, the term "Big Data" can be applied. Big data is used to refer to very large data sets having large, more varied and complex structure with the difficulties of storing, analysing and visualizing for further processes or results [3]. First introduced to the computing world by Roger Magoulas from O'Reilly media in 2005, aiming to define a great amount of data, traditional data management techniques cannot afford to easily manage and process due to the complexity and size of data involved [3] However, processing and collection of data in the planning office was done manually and LAN messenger is the fastest way of communication and means of data collection. ...
... Big data is used to refer to very large data sets having large, more varied and complex structure with the difficulties of storing, analysing and visualizing for further processes or results [3]. First introduced to the computing world by Roger Magoulas from O'Reilly media in 2005, aiming to define a great amount of data, traditional data management techniques cannot afford to easily manage and process due to the complexity and size of data involved [3] However, processing and collection of data in the planning office was done manually and LAN messenger is the fastest way of communication and means of data collection. The office is staffed by an officer, a part time secretary, and sometimes a student assistant. ...
Human Relations in an organization are of substantial value in any workplace. The way people relate to one another is one of the key factors that helps to easily comply with the required job need to be done. Planning office of BatStateU has its manual data management and the most pressing problem encountered is the late submission of reports coming from its client offices and observance of standard format on the processing as well as on the construction of data. Due to this, a follow up on each concerned office is personally done by the officer. All in one man, officer process all the required reports with the assistance of a part time secretary. Follow ups on concerned offices is professionally accepted, though expend an extra task to the officer. This study focus on evaluating the major concerns of the planning office, namely: prompt submission, accuracy and consistency of the report, observance of the standard format and ethical response of the client. Self-evaluation on the performance of the client offices was done to balance the concerns. Their recommendations were collated and triangulation was made concerning the different data source in time, space and person. Improvement of the protocol, and development of information system was suggested since Big Data is already involve in the process.
... Data value refers to a measure of the usefulness of the collected data in decision-making [16]. It is closely related to volume and variety because it depends on the events or processes, such as stochastic, probabilistic, regular, or random [17]. ...
Recently, Big Data analytics has been one of the most emerging topics in the business field. Data is collected, processed and analyzed to gain useful insight for their organization. Big Data analytics has the potential to improve the quality of life and help to achieve Sustainable Development Goals (SDG). To ensure that SDG goals are achieved, we must utilize existing data to meet those targets and ensure accountability. However, data quality is often left out when dealing with data. Any types of errors presented in the dataset should be properly addressed to ensure the analysis provided is accurate and truthful. In this paper, we have addressed the concept of data quality diagnosis to identify the outlier presented in the dataset. The cause of the outlier is further discussed to identify potential improvements that can be done to the dataset. In addition, recommendations to improve the quality of data and data collection systems are provided.
... The volume refers to the amount of big data instantly generated, such as keyword search queries on search engines, website clicks or browsing [31]. ...
... Veracity deals with the validity and the trust towards the big data, such as self-reported data versus data gathered automatically. The value refers to what is the added value of the data gathered that can contribute to the process [31]. The revenue from big data is estimated to be 103 Billion USD in 2027 ( Fig. 10.2). ...
Artificial Intelligence in Consumer Behavior.
E-commerce is one of the fastest changing industries and consumers try to purchase most of the goods online. Following the internet revolution, retail and promotion, daily data generation led to a point where marketers could not further process, using the traditional statistical ways, the new demands of vast data volume. The use of big data analytics emerged, and the rise of AI came along with predictive analytics including machine learning and data mining solutions. Artificial Intelligence has both evolved and affected the way marketing is implemented. AI is proven to be a sophisticated way of analyzing and processing data as well as decision making. The privilege of its use is considered an asset to the business executives who understand its potential. Considering AI as a tool, it may provide massive data processing and prediction accuracy. Referring to marketing science, marketers have witnessed the benefits of AI by predicting consumers behavior. Since consumers behavior varies, brands struggle for customer satisfaction, they invest time and money to highlight their products or services potentials, better define their market share, and classify customers’ needs. Marketers have been in pursuit of customer satisfaction using AI tools to read web metrics and optimize reach and conversions strategies. Machine learning, natural language processing, expert systems, voice, vision, planning, and robotics are the main AI branches that companies use to stay ahead of the competition. Business objectives aim to attract new customers, predict consumer behavior, along with the capability to personalize and predict demand using “smart” systems which allow them to increase sales, mitigate the decision-making risk and increase customer satisfaction, customer loyalty, and sales predictions. This chapter tries to map the stages of AI contribution to consumer behavior describing the essential marketing milestones.
