Various artificial intelligence, data mining, machine learning and statistical analysis‐based solutions have been successfully applied in thematic domains like climate science, energy management, transport, air quality management and weather pattern analysis. It was found that the recommended Efficient Stitching Algorithm (ESA) produced an average of 46 panoramas per second, and the reconstruction phase could reach a rate of 90 frames per second, which is significantly higher than the 30 frames per second standard video format system. The current technology and market trends demand an efficient framework for video big data analytics. The proliferation of multimedia devices over the Internet of Things (IoT) generates an unprecedented amount of data. As an example of such a theorem we mention the uniqueness theorem for best Chebyche# approximation C[0, 1] by (algebraic) polynomials p. Join ResearchGate to find the people and research you need to help your work. Smart Data first appeared in the literature in 2009 and Strategy, Resources, Operations, and Design. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Finally, we identify and articulate several open research issues and challenges, which have been raised by the deployment of big data technologies in the cloud for video big data analytics. The objective of this work is the design of an architecture for the management and storage of data that are exponentially increasing and coming from different sources. Data for ‘big data,’ however, came mostly from computer-based systems (e.g. After comparing various time series forecasting algorithms with the Mean Absolute Percentage Error (MAPE) results, we adopted Long Short-Term Memory (LSTM) as the methodology to further predict share price. To the best of our knowledge, this is the first study that presents the generalized view of the video big data analytics in the cloud. Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance - Kindle edition by Marr, Bernard. Due to the relative infancy of smart cities, and the diversity of approaches and implementations of smart information systems (Big Data and AI),many of the ethical challenges are still being defined. This is a particularly interesting demonstration of the rise of big data largely because we are using big data to actually track the same. This includes Internet of Things (IoT) technology, smart sensors, smart transport, and more. ope, and complexity, the transformation will be unlike anything humankind has experienced before. of the journey. In the first section, the Cross/Coddington model is outlined. modeling technique to model and analyze the Invoicing case study proposed for the Invoice'98 Workshop. The term Big Data was introduced in 1980 and contains a number of different fields This research aims at establishing forecasting models with deep learning technology for the share price prediction in logistic industry. In the context of a large volume of video data, we specialize the generic big data characteristics. The proposed system, in this paper, merges feature extraction stitching techniques with geometric relational matrix calculations to reduce the time complexity limitations of traditional mosaicking. The proposed LSTM model was trained with different hyperparameters and validated by Root Mean Square Error (RMSE). This is a well-needed practical introduction to actually putting the topic into practice. Big Data SQL. A significant amount of improvement will be seen when the desired data turns a city into a much safer place. Evaluation measures have been used to indicate the efficiency of the recommended system. The acronym SMART has been used with different meaning in numerous environments with great success. data. Created to the level of big data to smart city application of big data using various purposes a reliable, such as consumers of service. Section 2 defines the boundary energy of a potential curve and proves that the problem of determining a metric from a single-time flow boundary energy cannot have a unique solution. The 4th industrial revolution has entered the 2nd wave of growth and productivity that is fundamentally alter the way we live, work, our economy and how we relate to one another. Section 1 refines the theory of potential curves determined by a flow and a Riemannian metric. It is shown that fixed bargaining costs can be dealt with by a simple transformation of the demand spg~e, which allows all the results when there are no such costs. To serve this purpose, we present this study, which conducts a broad overview of the state-of-the-art literature on video big data analytics in the cloud. Architecture of Big data Analytics for IOT based smart city Asad Ur Rehman, Tauseef Rana, and Muhammad Umer Sajjad Department of Computer Software Engineering, Military College of Signals National University of Sciences and Technology, Islamabad, Pakistan. operations, customised promotion, and new business models, the success of Big Data In fact, by 2020, we will have over 50 billion devices that Complete real-world projects designed by industry experts, covering topics from asset management to trading signal generation. tion strategies, and how to apply to outcompete and outperform. Finally, we identify and articulate several open research issues and challenges, which have been raised by the deployment of big data technologies in the cloud for video big data analytics. There is so much buzz around big data. In Big Data, you have access to techniques for converting raw information into real business results. Meter readings to big cities are selected urban environment characteristics that the applications will be as well as smart grids. (2020). Download it once and read it on your Kindle device, PC, phones or tablets. In the technological context, the concept of Fourth Logistics Generation includes all tools and techniques used for implementation processes that are supported by modern, mostly autonomous systems facilitating the implementation of individual activities159 . Fixed bargaining costs are reintroduced in the sixth section. Big Data will give you a clear understanding, blueprint, and step-by-step approach to building your own big data strategy. This paper provides a critical Keywords: Smart Data, Big Data, Business Performance, Data Analytics. Consequently, the world has stepped into the era of big data. With data of all kinds being produced in record amounts every year, collating and analyzing this information will give businesses more insights than ever before into their customers and their industries, and perhaps even let them predict what might happen in the future. It is disrupting the every industry and economy in every country. Corpus ID: 60683870. 1 Introduction In this paper we use an integrated informal Object-Oriented (OO) and formal, Introduction Many theorems in analysis are of the form (or can be transformed into the form): F (x) = 0 G(x) = 0 where X is a complete separable metric space (CSM--space for short) and F, G : X IR are constructively definable (and therefore continuous) functions. Big data systems introduce efficiency into a complex data infrastructure. In this study, we found various optimal parameters of the proposed LSTM model for six different logistic stocks in Hong Kong. In this study, we clarify the basic nomenclatures that govern the video analytics domain and the characteristics of video big data while establishing its relationship with cloud computing. in line with data governance, and Data Aggregation. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by E. Siegel. The type of data generated by various sources such as text, picture, video, voice, and logs are known as Variety, ... Based on that, traditional BI is left behind by forward-looking businesses that are keen to gain competitive advantage from unstructured business data floating around, within, and beyond their enterprise. excellence of automation, connectivity and alignment across the value chain. The OO models are expressed in the Unified Modeling Language (UML). However, the current work is too limited to provide a complete survey of recent research work on video big data analytics in the cloud, including the management and analysis of a large amount of video data, the challenges, opportunities, and promising research directions. Some features of the site may not work correctly. The study was conducted from September-October, (i.e., fall) 2019 in the city of Daejeon, South Korea. Access scientific knowledge from anywhere. With the increasing role of ICT in enabling and supporting smart cities, the demand for big data analytics solutions is increasing. The UML models we produced were analyzed by transforming them to Z specifications. Marr brings complex concepts down to earth, so you can use data the SMART way to transform everything you do. In ‘Big Data: Using Smart Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance’, Marr emphasizes that before thinking about data, you must think about your strategy and the big questions big data can help you answer. But what will set you apart from the rest is actually knowing how to USE big data to get solid, real-world business results - and putting that in place to improve performance. The paper adopted a systematic review by exploring literature on digital innovations applications such as Big Data Analytics, Cloud Computing and Internet of Things (IoTs). The tone of this book is most definitely pro Big Data (and analytics). 1 Review. Only useful information for solving the problem is presented. The experimental work tests numerous state of the art feature extraction algorithms for stitching, Scale Invariant Feature Transform (SIFT), Speed Up Robust Feature (SURF) and Oriented FAST and Rotated BRIEF (ORB) with different orders of stitching. The final section is devoted to a discussion of the appropriate choice of initial demand from a game theory standpoint. The proposed system created panoramas with an average of 99% similarities with the traditional mosaicking systems while being highly faster than these conventional methods. The main areas of intervention are focused on demonstrating how a solution of a service architecture is able to abstract itself from any software that produces or receives data and guarantees a correct flow of them regardless of the speed or quantity of input, also guaranteeing fault-tolerance. Compression ratios and data rate savings, reflecting the gain in data stored and transferred, were calculated, reporting an average of 2.66 and 0.62 per frame, respectively, when applied to standard datasets. Big Data will take you through the five steps of the SMART model: Start with Strategy, Measure Metrics and Data, Apply Analytics, Report Results, Transform. When the'bargainers are similar, the elimination of inadmissible strategies in this game severely restricts the possible outcomes. A reduction in “volume” takes place with Smart Data. Whilst Big Data can assist in improving performance, efficient operations, customised promotion, and new business models, the success of Big Data relies heavily on Smart Data. In order to meet the goal of competitiveness, companies need to apply new tools and methods to improve their logistics processes. Co-chaired and led by the Henrik von Scheel, originator of the 4th Industrial Revolution that in a workgroup setting. The conceptual model developed suggested that deploying digital innovation capabilities promotes organizations to benefit in the area the of managerial decision making, enhancing information technology infrastructure alignments, operational activities and overall firm performance. © 2008-2020 ResearchGate GmbH. ResearchGate has not been able to resolve any references for this publication. 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