scholarly journals Finding the right approach to big data-driven medicinal chemistry

2015 ◽  
Vol 7 (10) ◽  
pp. 1213-1216 ◽  
Author(s):  
Scott J Lusher ◽  
Tina Ritschel
Keyword(s):  
Big Data ◽  
Medicinal Chemistry ◽  
Data Driven ◽  
The Right

Data-driven medicinal chemistry in the era of big data

2014 ◽  
Vol 19 (7) ◽  
pp. 859-868 ◽  
Author(s):  
Scott J. Lusher ◽  
Ross McGuire ◽  
René C. van Schaik ◽  
C. David Nicholson ◽  
Jacob de Vlieg
Keyword(s):  
Big Data ◽  
Medicinal Chemistry ◽  
Data Driven

scholarly journals Adoption of Big Data by Global Chemical Industries

2021 ◽  
Author(s):  
Ashiff Khan ◽  
A Seetharaman ◽  
Abhijit Dasgupta
Keyword(s):  
Big Data ◽  
Data Science ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Time Data ◽  
New Era ◽  
Potential Applications ◽  
Data Driven Approach ◽  
Process Plants ◽  
The Right

The new era of Big Data (BD) is influencing the chemical industries tremendously, providing several opportunities to reshape the way they operate and for shifting towards smart manufacturing. Given the availability of free software, and the large amount of real-time data generated and stored in process plants why many chemical industries are still not fully adopting BD? The industry is just starting to realize the importance of a large amount of data that they own to make the right decisions and to support their strategies. This article is exploring the importance of professional competencies and data science that influence BD in chemical industries for shifting towards smart manufacturing in a fast and reliable manner. This article utilizes a literature review and identifies potential applications in the chemical industry to shift from conventional methods towards a data-driven approach.

scholarly journals Complex Event Processing of Market Data through Data Modeling in Big Data

2019 ◽  
Vol 8 (5S3) ◽  
pp. 11-17
Keyword(s):  
Big Data ◽  
Stock Exchange ◽  
Mathematical Finance ◽  
Complex Event Processing ◽  
Data Driven ◽  
Event Processing ◽  
Data Sets ◽  
Market Data ◽  
Complex Events ◽  
The Right

Mathematical Finance utilizes advance refined mathematic models and advanced computer techniques to forecast the movement of worldwide markets. To possess an ability to react intelligently to the fast-paced changes in the business is a winning factor. Complex event processing with advanced toolchains plays a crucial role in the explosive growth and diversified forms of market data. To resolve such issues, we have developed a model based on Big Data that processes the intricate tasks to assess the market data. The model executes complex events in a data-driven mode in parallel computing on copious data sets, this model is known as StatCloud. To implement StatCloud, we have used datasets from the Bombay Stock Exchange to determine the performance. We execute the model with the help of Data analysis techniques and Data Modelling. The experiment results show that this model obtains high throughput and latency. It executes data dependent tasks through a data-driven strategy and implements a standard style approach for developing Mathematical Finance analysis models. This integrated model facilitates the work process of complex events in a financial organization to enhance the efficiency to implement the right strategies by the financial engineers.

scholarly journals Objective, Quantitative, Data-Driven Assessment of Chemical Probes

2017 ◽  
Author(s):  
Albert A. Antolin ◽  
Joe E. Tym ◽  
Angeliki Komianou ◽  
Ian Collins ◽  
Paul Workman ◽  
...  
Keyword(s):  
Big Data ◽  
Biomedical Research ◽  
Quantitative Data ◽  
Medicinal Chemistry ◽  
Objective Assessment ◽  
Data Driven ◽  
Target Validation ◽  
Chemical Probes ◽  
Chemistry Data ◽  
Assessment Resource

ABSTRACTChemical probes are essential tools for understanding biological systems and for target validation, yet selecting tools for biomedical research is largely biased and subjective. Here we describe the Probe Miner: Chemical Probes Objective Assessment resource – capitalising on the plethora of public medicinal chemistry data to empower quantitative, objective, Big Data-driven assessment of chemical probes. We assess >1.8m compounds for their suitability as chemical tools against 2,220 human targets and dissect their biases and limitations.

scholarly journals Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts

2021 ◽  
Vol 11 (13) ◽  
pp. 6047
Author(s):  
Soheil Rezaee ◽  
Abolghasem Sadeghi-Niaraki ◽  
Maryam Shakeri ◽  
Soo-Mi Choi
Keyword(s):  
Big Data ◽  
Augmented Reality ◽  
User Study ◽  
Spatial Information ◽  
Ease Of Use ◽  
Tourist Attraction ◽  
Gender And Education ◽  
Time Distance ◽  
Data Source ◽  
The Right

A lack of required data resources is one of the challenges of accepting the Augmented Reality (AR) to provide the right services to the users, whereas the amount of spatial information produced by people is increasing daily. This research aims to design a personalized AR that is based on a tourist system that retrieves the big data according to the users’ demographic contexts in order to enrich the AR data source in tourism. This research is conducted in two main steps. First, the type of the tourist attraction where the users interest is predicted according to the user demographic contexts, which include age, gender, and education level, by using a machine learning method. Second, the correct data for the user are extracted from the big data by considering time, distance, popularity, and the neighborhood of the tourist places, by using the VIKOR and SWAR decision making methods. By about 6%, the results show better performance of the decision tree by predicting the type of tourist attraction, when compared to the SVM method. In addition, the results of the user study of the system show the overall satisfaction of the participants in terms of the ease-of-use, which is about 55%, and in terms of the systems usefulness, about 56%.

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