Predictive Modelling and Analytics on Big Data for Diabetic Management

2017 ◽  
Author(s):  
V. Sujatha ◽  
V. Dharanidharan
Keyword(s):  
Big Data ◽  
Predictive Modelling

scholarly journals Big data and predictive modelling for the opioid crisis: existing research and future potential

2021 ◽  
Vol 3 (6) ◽  
pp. e397-e407
Author(s):  
Chrianna Bharat ◽  
Matthew Hickman ◽  
Sebastiano Barbieri ◽  
Louisa Degenhardt
Keyword(s):  
Big Data ◽  
Predictive Modelling ◽  
Opioid Crisis ◽  
Future Potential

scholarly journals Social Media Big Data Analytics for Demand Forecasting

2020 ◽  
Vol 28 (1) ◽  
pp. 103-120 ◽  
Author(s):  
Rehan Iftikhar ◽  
Mohammad Saud Khan
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply Chain ◽  
Demand Forecasting ◽  
Big Data Analytics ◽  
Predictive Modelling ◽  
Product Demand ◽  
Emotion Model ◽  
Improving Accuracy

Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.

Positioning big data analytics capabilities towards financial service agility

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abeeku Sam Edu
Keyword(s):  
Big Data ◽  
Financial Services ◽  
Data Analytics ◽  
Business Models ◽  
Empirical Test ◽  
Big Data Analytics ◽  
Predictive Modelling ◽  
Financial Service ◽  
Content Type ◽  
It Capability

PurposeEnterprises are increasingly taking actionable steps to transform existing business models through digital technologies for service transformation such as big data analytics (BDA). BDA capabilities offer financial institutions to source financial data, analyse data, insight and store such data and information on collaborative platforms for a quick decision-making process. Accordingly, this study identifies how BDA capabilities can be deployed to provide significant improvement for financial services agility.Design/methodology/approachThe study relied on survey data from 485 banking professionals' perspectives with BDA usage, IT capability development and financial service agility. The PLS-SEM technique was used to evaluate the underlying relationship and the applicability of the research framework proposed.FindingsBased on the empirical test from this study, distinctive BDA usage grounded on the concept of IT capability viewpoint proof that financial service agility could be enhanced provided enterprises develop technical capabilities alongside other relevant resources.Practical implicationsThe study further highlights the need for financial service managers to identify BDA technologies such as data mining, query and reporting, data visualisation, predictive modelling, streaming analytics, video analytics and voice analytics to focus on financial knowledge gathering and market observation. Financial managers can also deploy BDA tools to develop a strategic road map for data management, data transferability and knowledge discovery for customised financial products.Originality/valueThis study is a useful contribution to the burgeoning discussion with emerging technologies such as BDA implication to improving enterprises operations.

Towards benefitting from diverse inferred features and foreseen behaviours of hydroclimatic variables in predictive modelling contexts

2021 ◽  
Author(s):  
Georgia Papacharalampous ◽  
Hristos Tyralis
Keyword(s):  
Time Series ◽  
Big Data ◽  
Time Series Analysis ◽  
Large Scale ◽  
Predictive Modelling ◽  
Global Scale ◽  
Multiple Models ◽  
Forecast Combinations ◽  
Hydroclimatic Variability ◽  
Hydroclimatic Variables

<p>We discuss possible pathways towards reducing uncertainty in predictive modelling contexts in hydrology. Such pathways may require big datasets and multiple models, and may include (but are not limited to) large-scale benchmark experiments, forecast combinations, and predictive modelling frameworks with hydroclimatic time series analysis and clustering inputs. Emphasis is placed on the newest concepts and the most recent methodological advancements for benefitting from diverse inferred features and foreseen behaviours of hydroclimatic variables, derived by collectively exploiting diverse essentials of studying and modelling hydroclimatic variability and change (from both the descriptive and predictive perspectives). Our discussions are supported by big data (including global-scale) investigations, which are conducted for several hydroclimatic variables at several temporal scales.</p>

Social Media Big Data Analytics for Demand Forecasting

2022 ◽  
pp. 902-920
Author(s):  
Rehan Iftikhar ◽  
Mohammad Saud Khan
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply Chain ◽  
Demand Forecasting ◽  
Big Data Analytics ◽  
Predictive Modelling ◽  
Product Demand ◽  
Emotion Model ◽  
Improving Accuracy

Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.

scholarly journals Opportunities for the Use of Business Data Analysis Technologies

2016 ◽  
Vol 28 (1) ◽  
pp. 20-25 ◽  
Author(s):  
Signe Bāliņa ◽  
Rita Žuka ◽  
Juris Krasts
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Predictive Modelling ◽  
Data Sources ◽  
Data Source ◽  
Business Data

Abstract The paper analyses the business data analysis technologies, provides their classification and considers relevant terminology. The feasibility of business data analysis technologies handling big data sources is overviewed. The paper shows the results of examination of the online big data source analytics technologies, data mining and predictive modelling technologies and their trends.

scholarly journals Big data and artificial intelligence in future patient management. How is it all started? Where are we at now? Quo tendimus?

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Ashraf Mina
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Patient Management ◽  
Predictive Modelling ◽  
Wearable Devices ◽  
Pharmaceutical Companies ◽  
Early Disease ◽  
Key Points ◽  
Disparate Data ◽  
Future Patient

AbstractBackgroundThis article is focused on the understanding of the key points and their importance and impact on the future of early disease predictive models, accurate and fast diagnosis, patient management, optimise treatment, precision medicine, and allocation of resources through the applications of Big Data (BD) and Artificial Intelligence (AI) in healthcare.ContentBD and AI processes include learning which is the acquisition of information and rules for using the information, reasoning which is using rules to reach approximate or definite conclusions and self-correction. This can help improve the detection of diseases, rare diseases, toxicity, identifying health system barriers causing under-diagnosis. BD combined with AI, Machine Learning (ML), computing and predictive-modelling, and combinatorics are used to interrogate structured and unstructured data computationally to reveal patterns, trends, potential correlations and relationships between disparate data sources and associations.SummaryDiagnosis-assisted systems and wearable devices will be part and parcel not only of patient management but also in the prevention and early detection of diseases. Also, Big Data will have an impact on payers, devise makers and pharmaceutical companies. BD and AI, which is the simulation of human intelligence processes, are more diverse and their application in monitoring and diagnosis will only grow bigger, wider and smarter.OutlookBD connectivity and AI of diagnosis-assisted systems, wearable devices and smartphones are poised to transform patient and to change the traditional methods for patient management, especially in an era where is an explosion in medical data.

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