scholarly journals The future of film-making: Data-driven movie-making techniques

2020 ◽  
Vol 10 (2) ◽  
pp. 167-174
Author(s):  
Nadide Gizem Akgülgil Mutlu
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Driven ◽  
Art World ◽  
Human Beings ◽  
Digital Era ◽  
Production And Distribution ◽  
Pros And Cons ◽  
Film Making

Since the term ‘big data’ came to the scene, it has left almost no industry unaffected. Even the art world has taken advantage of the benefits of big data. One of the latest art forms, cinema, eventually started using analytics to predict their audience and their tastes through data mining. In addition to online platforms like Netflix, Amazon Prime and many more, which act on a different basis, the industry itself evolved to a new phase that uses AI in pre-production, production, post-production and distribution phases. This paper researches software, such as Cinelytic, ScriptBook and LargoAI, and their working strategies to understand the role of directors and producers in the age of the digital era in film-making. The research aims to find answers to the capabilities of data-driven movie-making techniques and, accordingly, it makes a number of predictions about the role of human beings in the production of an artwork and analyses the role of the software. The research also investigates the pros and cons of using big data in the film-making industry.   Keywords: Artificial intelligence, cinema, data mining, film-making.

scholarly journals Out of the loop? On the radical and the routine in urban big data

Urban Studies ◽  
2021 ◽  
pp. 004209802110140
Author(s):  
Sarah Barns
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Computational Models ◽  
Data Driven ◽  
Urban Systems ◽  
Out Of The Loop ◽  
Urban Futures ◽  
Urban Behaviour ◽  
The City

This commentary interrogates what it means for routine urban behaviours to now be replicating themselves computationally. The emergence of autonomous or artificial intelligence points to the powerful role of big data in the city, as increasingly powerful computational models are now capable of replicating and reproducing existing spatial patterns and activities. I discuss these emergent urban systems of learned or trained intelligence as being at once radical and routine. Just as the material and behavioural conditions that give rise to urban big data demand attention, so do the generative design principles of data-driven models of urban behaviour, as they are increasingly put to use in the production of replicable, autonomous urban futures.

Big data-driven supply chain and performance: a resource-based view

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shampy Kamboj ◽  
Shruti Rana
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Small And Medium Enterprises ◽  
Data Driven ◽  
Equation Modeling ◽  
Firm Age ◽  
Content Type ◽  
Sustainable Performance ◽  
Medium Enterprises

PurposeThe main objective of this paper is to study the role of supply chain performance (SCP) as a mediator between big data-driven supply chain (BDDSC) and firm sustainable performance. In addition, the role of firm age as a moderator between BDDSC and SCP as well as between SCP and firm sustainable performance has also been explored.Design/methodology/approachThe 200 managers of medium or senior level positions in micro, small and medium enterprises (MSMEs) located at Delhi-NCR have been contacted. Further, collected data have been confirmed with confirmatory factor analysis (CFA). In this paper, structure equation modeling (SEM) has been employed to empirically check the proposed hypotheses and their relationships.FindingsThe findings confirmed that SCP mediates the link between BDDSC and firm sustainable performance. Additionally, firm age moderates the association between BDDSC and SCP as well as between SCP and firm sustainable performance.Research limitations/implicationsThe role of SCP and firm age between BDDSC and sustainable performance have been examined in the context of MSMEs in Delhi-NCR and thereby limit the generalization of results to other industries and country contexts.Originality/valueThe present study adds to the existing literature via recognizing the blackbox using SCP and firm age to comprehend BDDSC and firm sustainable performance relationship.

scholarly journals The limits of computation: A philosophical critique of contemporary Big Data research

2018 ◽  
Vol 5 (2) ◽  
pp. 205395171881184 ◽  
Author(s):  
Petter Törnberg ◽  
Anton Törnberg
Keyword(s):  
Big Data ◽  
Social Life ◽  
Quantitative Methods ◽  
Qualitative Change ◽  
Philosophical Discussion ◽  
Philosophical Critique ◽  
Social Phenomena ◽  
Social Patterns ◽  
Digital Era

This paper reviews the contemporary discussion on the epistemological and ontological effects of Big Data within social science, observing an increased focus on relationality and complexity, and a tendency to naturalize social phenomena. The epistemic limits of this emerging computational paradigm are outlined through a comparison with the discussions in the early days of digitalization, when digital technology was primarily seen through the lens of dematerialization, and as part of the larger processes of “postmodernity”. Since then, the online landscape has become increasingly centralized, and the “liquidity” of dematerialized technology has come to empower online platforms in shaping the conditions for human behavior. This contrast between the contemporary epistemological currents and the previous philosophical discussions brings to the fore contradictions within the study of digital social life: While qualitative change has become increasingly dominant, the focus has gone towards quantitative methods; while the platforms have become empowered to shape social behavior, the focus has gone from social context to naturalizing social patterns; while meaning is increasingly contested and fragmented, the role of hermeneutics has diminished; while platforms have become power hubs pursuing their interests through sophisticated data manipulation, the data they provide is increasingly trusted to hold the keys to understanding social life. These contradictions, we argue, are partially the result of a lack of philosophical discussion on the nature of social reality in the digital era; only from a firm metatheoretical perspective can we avoid forgetting the reality of the system under study as we are affected by the powerful social life of Big Data.

Knowledge Management and Business Analytics

2019 ◽  
pp. 1-42
Author(s):  
Nirali Nikhilkumar Honest ◽  
Atul Patel
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Business Intelligence ◽  
Business Performance ◽  
Virtual Organization ◽  
Data Driven ◽  
Business Analytics ◽  
Broad Category ◽  
Business Operations

Knowledge management (KM) is a systematic way of managing the organization's assets for creating valuable knowledge that can be used across the organization to achieve the organization's success. A broad category of technologies that allows for gathering, storing, accessing, and analyzing data to help business users make better decisions, business intelligence (BI) allows analyzing business performance through data-driven insight. Business analytics applies different methods to gain insight about the business operations and make better fact-based decisions. Big data is data with a huge size. In the chapter, the authors have tried to emphasize the significance of knowledge management, business intelligence, business analytics, and big data to justify the role of them in the existence and development of an organization and handling big data for a virtual organization.

Combining Data-Driven and User-Driven Evaluation Measures to Identify Interesting Rules

2009 ◽  
pp. 38-55 ◽  
Author(s):  
Solange Oliveira Rezende ◽  
Edson Augusto Melanda ◽  
Magaly Lika Fujimoto ◽  
Roberta Akemi Sinoara ◽  
Veronica Oliveira de Carvalho
Keyword(s):  
Data Mining ◽  
Data Driven ◽  
Objective Measures ◽  
Post Processing ◽  
Rule Mining ◽  
Evaluation Measures ◽  
Combining Data ◽  
Pros And Cons ◽  
The Subject

Association rule mining is a data mining task that is applied in several real problems. However, due to the huge number of association rules that can be generated, the knowledge post-processing phase becomes very complex and challenging. There are several evaluation measures that can be used in this phase to assist users in finding interesting rules. These measures, which can be divided into data-driven (or objective measures) and user-driven (or subjective measures), are first discussed and then analyzed for their pros and cons. A new methodology that combines them, aiming to use the advantages of each kind of measure and to make user’s participation easier, is presented. In this way, data-driven measures can be used to select some potentially interesting rules for the user’s evaluation. These rules and the knowledge obtained during the evaluation can be used to calculate user-driven measures, which are used to aid the user in identifying interesting rules. In order to identify interesting rules that use our methodology, an approach is described, as well as an exploratory environment and a case study to show that the proposed methodology is feasible. Interesting results were obtained. In the end of the chapter tendencies related to the subject are discussed.

scholarly journals Recommender System using Content Based Filtering for News Portal in Indonesia

2020 ◽  
Vol 8 (6) ◽  
pp. 173-178
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Recommender System ◽  
Information Overload ◽  
Cosine Similarity ◽  
News Article ◽  
The Internet ◽  
New Model ◽  
Digital Era ◽  
Content Based Filtering

Nowadays there is much news on the internet. It makes the reader become information overload. The reader does not know the most important news for them. The digital era, especially in Indonesia, generated data in Bahasa very fast that referred to as big data. Data mining by process big data can collect the data insight that the reader already read. This paper proposes a new model to proceed with Bahasa news and use the TF-IDF method to collect the feature of the article. Cosine similarity from the news article used to rank the new unknown articles to recommend articles based on their preference. we can filtering the stream of information and highlight the most likely article they will read but based on their preference that we already collect implicitly from the article that they read it, it’s a scroll depth of the article they read.Then we can serve the news more personalized from what they love to read.

scholarly journals Computational Simulation and Prediction on Electrical Conductivity of Oxide-Based Melts by Big Data Mining

Materials ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1059 ◽  
Author(s):  
Ao Huang ◽  
Yanzhu Huo ◽  
Juan Yang ◽  
Guangqiang Li
Keyword(s):  
Data Mining ◽  
Electrical Conductivity ◽  
Big Data ◽  
Molten Slag ◽  
Computational Simulation ◽  
Chemical Properties ◽  
Data Driven ◽  
Gradient Boosting ◽  
Big Data Mining ◽  
Physical Chemical

Electrical conductivity is one of the most basic physical–chemical properties of oxide-based melts and plays an important role in the materials and metallurgical industries. Especially with the metallurgical melt, molten slag, existing research studies related to slag conductivity mainly used traditional experimental measurement approaches. Meanwhile, the idea of data-driven decision making has been widely used in many fields instead of expert experience. Therefore, this study proposed an innovative approach based on big data mining methods to investigate the computational simulation and prediction of electrical conductivity. Specific mechanisms are discussed to explain the findings of our proposed approach. Experimental results show slag conductivity can be predicted through constructing predictive models, and the Gradient Boosting Decision Tree (GBDT) model is the best prediction model with 90% accuracy and more than 88% sensitivity. The robustness result of the GBDT model demonstrates the reliability of prediction outcomes. It is concluded that the conductivity of slag systems is mainly affected by TiO2, FeO, SiO2, and CaO. TiO2 and FeO are positively correlated with conductivity, while SiO2 and CaO have negative correlations with conductivity.

ERP Integrated Instruments – Opportunities and Challenges for Audit

2021 ◽  
Vol 19 (163) ◽  
pp. 574-586
Author(s):  
Casiana Maria DARIE ◽  
Keyword(s):  
Big Data ◽  
Large Volume ◽  
Business Intelligence ◽  
External Environment ◽  
Financial Accounting ◽  
Computer Assisted ◽  
Questionnaire Data ◽  
Digital Era ◽  
It Systems

The digital era affects all the fundamental areas known so far. In meeting the high levels of competition and industry pressures, the organizations used IT systems to help them achieve market advantages by saving resources, developing domestically and adapting to the challenges posed by the external environment. This paper includes in the first part a description of the role of systems such as ERP, Business Intelligence, "Analytics", "Big Data" and Computer Assisted Audit Techniques – CAAT's in the activity of auditors but also in collecting and processing a large volume of data by those in charge with the financial accounting field. In the second part, with the help of the questionnaire, data on the use of these systems by Romanian auditors were collected and analysed.

Biological and Medical Big Data Mining

2016 ◽  
pp. 246-262
Author(s):  
George Tzanis
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Technological Development ◽  
Medical Data ◽  
Human Beings ◽  
Medical Data Mining ◽  
Big Data Mining ◽  
Efficient Management ◽  
Medical Big Data ◽  
Major Emphasis

This chapter discusses the concept of big data mining in the domain of biology and medicine. Biological and medical data are increasing at very rapid rates, which in many cases outpace even Moore's law. This is the result of recent technological development, as well as the exploratory attitude of human beings, that prompts scientists to answer more questions by conducting more experiments. Representative examples are the advances in sequencing and medical imaging technologies. Challenges posed by this data deluge, and the emerging opportunities of their efficient management and analysis are also part of the discussion. The major emphasis is given to the most common biological and medical data mining applications.

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