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Advanced visual data mining and prediction system that enables you to analyse diverse, multi-dimensional and complex datasets.
Better analysis of complex data
Deep Topological Analysis is an evolution of cutting edge data science which combines Topological Data Analysis and Deep Learning. This approach delivers provable performance gains over competing technologies.Faster workflow
The simplicity of the DataRefiner platform saves time. Automated checks for data consistency, automatic selection of model parameters and other features make your data analysis pipeline orders of magnitude faster.Explainable Artificial Intelligence
We have unique algorithms designed to make the process visual and accessible. Understand and communicate results with your organisation, clients and regulators.How much do you know about your data?
Most data is not organised or structured making it hard to find the important patterns.Your data is the biggest asset of your business
High level data segmentation can help any business thrive.But what if its sources are user activities, sensors or texts?
Current business intelligence systems do not allow you to segment or discover patterns in such data. That’s where DataRefiner comes in: it provides tools to understand such data.Transform your data into a data map
Imagine you could transform your data into a data map in just a few clicks and without specialist knowledge.Deep Topological Analysis
Using machine learning, we group complex data into clusters, which allows users to discover hidden patterns.State-of-the-art data segmentation
DataRefiner uses a unique algorithm to group complex data into clusters using deep topological analysis. It maps the segments and describes them to any business user without specialist training.Visualise patterns in your data
You’re in control of the results: you can identify anomalies, predict behaviour, target audiences or eliminate engineering errors.Customer and user activities
Extract personas from your user activities to understand their needs, experiences, behaviours and goals.Internet of Things
Telemetry from your sensors provides important patterns for your equipment. Understanding of such patterns allow you to make improvements.Bank transactions or crypto currency analysis
Spot anomalies or unusual patterns in your data. Perform deep analysis of such patterns using most advanced technology available on the market.Discover things you haven't even started looking for
Patterns in your data are visualised to make them clear to anyone: from data scientists to business users.Explainable AI
Achieve explainable results. DataRefiner visualises features in your data and makes them clear to data scientists or business users. It allows you to easily understand and communicate results within your organisation, to clients and regulators.Сomply with GDPR
Make sure your machine learning systems are not biased against different groups of people. DataRefiner helps you verify the results of your algorithm.Feed
AI for AI (artificial insemination) — Deep Topological Analysis for sensors
Find out how TDA and Deep Learning classifies events and discovers hidden patterns in IoT activity sensors’ data streams
March 10, 2020
Using topological text analysis for COVID-19 Open Research Challenge
My take on COVID-19 Kaggle challenge analysis of scientific white papers. This research is a first step to help specialist in virusology, pharmacy and microbiology to find answers to the problem
March 24, 2020
What 6.5 million of #coronavirus tweets reveal about people thoughts during the pandemic
We apply Topological Data Analysis and Deep Learning to a large volume of text data to reveal hidden patterns in discussions
June 15, 2020
Use Cases
User activity segmentation
DataRefiner allows you to visualise complex user activities and to find activity patterns
that are important for your business. Group your users and tailor your app or a website to their needs.
Example: Find “personas” in your activity data
Example: Find “personas” in your activity data
Add to A/B test
User Activities
Activities, captured from user interactions with the app
S3, RedShift or in-house DBs
Activities are stored in the database or a message queue
DataRefiner
DataRefiner performs segmentation of user activities and extracts users' personas
Export segmentation result
Download a user segment using DataRefiner API
Perform a targeted marketing campaign
Target user groups precisely according to their activity patterns and be GDPR compliant
Analysis of text or user comments
Perform segmentation of the posts, user comments or any other user generated content.
Fight spammers, scammers and other unwanted behaviour on your platform. Using DataRefiner you're able
to visualise and understand common patterns much faster than with traditional NLP approach.
Example: Fight spammers and scammers by analysing your users' text data
Example: Fight spammers and scammers by analysing your users' text data
Collection of texts or messages
User comments or posts, stored in your databases
CSV file with the texts
Prepare a collection of comments or user posts as CSV file
DataRefiner
DataRefiner performs segmentation of texts and extracts common topics
A data analyst reviews the structure
An analyst understands the formed clusters and extracts the anomalies
Present the findings
Use DataRefiner player to show your findings to internal or external audiences
Analysis of sensors data (IoT) and
anomaly detection
Analyse telemetry from your hardware equipment: civil aerospace, defence, power systems, robotics and any
other source of complex IoT data. Find the unexpected operational patterns and improve the performance.
Example: Find the unexpected operational patterns in aircraft engines
Example: Find the unexpected operational patterns in aircraft engines
Time series data streams from sensors
A stream of real-time data from sensors
AWS or Azure cloud infrastructure
Raw sensor data is stored and pre-processed
DataRefiner
DataRefiner performs segmentation of telemetry data
A data analyst reviews the structure
A subject matter expert reviews the structure and identifies the unexpected patterns
Present the findings
The findings are presented to equipment engineers for further review and re-configuration
Analysis of the bank or blockchain transactions
Identify patterns in the bank or blockchain transactions. Visualise complex schemes: fight money laundering and fraud.
Understand how your initial coin offering performs and identify real users and bots among your clients.
Example: Find money laundering schemes
Example: Find money laundering schemes
or
Crypto currency ledger
A list of account transactions with related metadata
Bank account ledger with metadata
A list of account transactions with related metadata
DataRefiner
DataRefiner finds account activity patterns
Data Analyst / Regulator reviews the structure
Data structure reveals different account patterns including undesirable ones
Bank security review the threat
A list of suspicious accounts is reviewed in details
Analysis of graph data for ratings,
votes or questionnaires
Analyse a wide range of graph and network data: social graph, user activity graph, reviews, telecom network statuses.
Visualise and understand interaction and differences between the subgroups.
Example: Find the different groups of audiences using their ratings or likes on your platform
Example: Find the different groups of audiences using their ratings or likes on your platform
Collection of user ratings or answers
User rating for movies, music, cafes; user votes or answers to multiple selection questionnaires
CSV file with the votes
Collection of user votes and ratings in one CSV file for analysis
DataRefiner
DataRefiner finds common patterns and presents its structure
A data analyst describes user groups
An analyst reviews users grouped by their preferences and identifies the key questions or important issues for a community
Present the structure to a monitoring team or a client
Use DataRefiner player shows your findings to the internal or external audiences
Cyber security (analysis of the packets and flows)
Identify activity patterns in your network using data from a wide range of agents. Allow your security
analysts to identify the dynamic threats by going beyond the predefined security patterns.
Example: Identify the dynamic threats in your corporate network
Example: Identify the dynamic threats in your corporate network
Agent 1
Agent 2
Agent 3
Agent X
Reports on previously suspected activity
Update the rules
Network monitoring agents
A large number of network monitoring agents are performing flows and packets monitoring
Aggregated signals from agents
The stream of signals from monitoring agents
DataRefiner
DataRefiner constantly learns on a data stream and produces activity patterns
A data analyst reports suspicious patterns
An analyst identifies suspicious network behaviour and marks it
Cyber security system
DataRefiner reports finding to an internal cyber security system used by security analysts.
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