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Understanding Social And Collaborative Business Intelligence

datapine

This is done using interactive Business Intelligence and Analytics dashboards along with intuitive tools to improve data clarity. Popularity is not just chosen to measure quality, but also to measure business value. What is Social Business Intelligence? Analytical tools are used to achieve user understanding and comfort.

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Enhancing Knowledge Discovery: Implementing Retrieval Augmented Generation with Ontotext Technologies

Ontotext

This dramatically simplifies the interaction with complex databases and analytics systems. Join us as we demystify the methodologies empowering such implementations, shed light on their range of capabilities, and detail how Ontotext is harnessing these technologies to bring transformative enhancements to our data interaction landscape.

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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. Nevertheless, A/B testing has challenges and blind spots, such as: the difficulty of identifying suitable metrics that give "works well" a measurable meaning.

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Performing Non-Compartmental Analysis with Julia and Pumas AI

Domino Data Lab

Domino Lab supports both interactive and batch experimentation with all popular IDEs and notebooks (Jupyter, RStudio, SAS, Zeppelin, etc.). TIME – time points of measured pain score and plasma concentration (in hrs). The analyses shown below are accessible in the NCA project on Domino’s trial site. and 3 to 8 hours.

Metrics 59
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Understanding Social And Collaborative Business Intelligence

datapine

This is done using interactive Business Intelligence and Analytics dashboards along with intuitive tools to improve data clarity. Popularity is not just chosen to measure quality, but also to measure business value. What is Social Business Intelligence? Analytical tools are used to achieve user understanding and comfort.

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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

In each case, users engage with the service at will and the service makes available a rich set of possible interactions. But the fact that a service could have millions of users and billions of interactions gives rise to both big data and methods which are effective with big data. And an LSOS is awash in data, right?

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

The need for interaction – complex decision making systems often rely on Human–Autonomy Teaming (HAT), where the outcome is produced by joint efforts of one or more humans and one or more autonomous agents. but it generally relies on measuring the entropy in the change of predictions given a perturbation of a feature.

Modeling 139