computer science

PolyAnalyst

PolyAnalyst is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand PolyAnalyst rather than just read about it. In short: PolyAnalyst is a data science software platform developed by Megaputer Intelligence that provides an environment for text mining, data mining, machine learning, and predictive analytics. It is used by Megaputer to build tools with applications to health care, business management, insurance, and other industries.

Key takeaways

PolyAnalyst belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect PolyAnalyst to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of PolyAnalyst from memory before moving on to harder problems.

Reference excerpt

PolyAnalyst is a data science software platform developed by Megaputer Intelligence that provides an environment for text mining, data mining, machine learning, and predictive analytics. It is used by Megaputer to build tools with applications to health care, business management, insurance, and other industries. PolyAnalyst has also been used for COVID-19 forecasting and scientific research.

Overview

PolyAnalyst's graphical user interface contains nodes that can be linked into a flowchart to perform an analysis. The software provides nodes for data import, data preparation, data visualization, data analysis, and data export. PolyAnalyst includes features for text clustering, sentiment analysis, extraction of facts, keywords, and entities, and the creation of taxonomies and ontologies. Polyanalyst supports a variety of machine learning algorithms, as well as nodes for the analysis of structured data and the ability to execute code in Python and R. PolyAnalyst also acts as a report generator, which allows the result of an analysis to be made viewable by non-analysts. It uses a client–server model and is licensed under a software as a service model.

Business Applications

Insurance PolyAnalyst was used to build a subrogation prediction tool which determines the likelihood that a claim is subrogatable, and if so, the amount that is expected to be recovered. The tool works by categorizing insurance claims based on whether or not they meet the criteria that are needed for successful subrogation. PolyAnalyst is also used to detect insurance fraud.

Health care

PolyAnalyst is used by pharmaceutical companies to assist in pharmacovigilance. The software was used to design a tool that matches descriptions of adverse events to their proper MedDRA codes, determines if side effects are serious or non-serious, and to set up cases for ongoing monitoring if needed. PolyAnalyst has also been applied to discover new uses for existing drugs by text mining ClinicalTrials.gov, and to forecast the spread of the COVID-19 virus in the United States and Russia.

Business management PolyAnalyst is used in business management to analyze written customer feedback including product review data, warranty claims, and customer comments. In one case, PolyAnalyst was used to build a tool which helped a company monitor its employees' conversations with customers by rating their messages for factors such as professionalism, empathy, and correctness of response. The company reported to Forrester Research that this tool had saved them $11.8 million annually.

SKIF Cyberia Supercomputer PolyAnalyst is run on the SKIF Cyberia Supercomputer at Tomsk State University, where it is made available to Russian researchers through the Center for Collective Use (CCU). Researchers at the center use PolyAnalyst to perform scientific research and to management the operations of their universities. In 2020, researchers at Vyatka State University (in collaboration with the CCU) performed a study in which PolyAnalyst was used to identify and reach out to victims of domestic violence through social media analysis. The researchers scraped the web for messages containing descriptions of abuse, and then classified the type of abuse as physical, psychological, economic, or sexual. They also constructed a chatbot to contact the identified victims of abuse and to refer them to specialists based on the type of abuse described in their messages. The data collected in this study was used to create the first ever Russian-language corpus on domestic violence.

References

External links Official website

Illustrations

Worked examples

Example 1 — a first encounter with PolyAnalyst

Start with the simplest possible case. Write down what PolyAnalyst claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to PolyAnalyst before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about PolyAnalyst ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of PolyAnalyst

In research: PolyAnalyst appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses PolyAnalyst in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: PolyAnalyst is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1994 software, Business software, Computing platforms, so understanding it makes those chapters shorter.
In everyday life: Look for PolyAnalyst outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study PolyAnalyst in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what PolyAnalyst means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain PolyAnalyst out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is PolyAnalyst in simple terms?

Why does PolyAnalyst matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study PolyAnalyst?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on PolyAnalyst.