Apache, Hadoop, HDFS, Spark are trademarks of the Apache Software
Foundation in the United States and/or other countries.
All trademarks, logos and brands are property of their respective owners.
© 2024 MWARE SOLUTIONS. All rghts reserved.
Analyze 4G and 5G call trace data to compute optimal base station radio parameters and detect anomalies (~30,000+ radio signaling events/second)
Identify problematic areas and suggest optimal radio parameters
Instant collection of radio data, enrichment, correlate and analyze huge amounts of records into actionable data structures, extracting and storing information in the RAN data lake
Near-realtime RAN data processing for predicting events and instant optimization
Vendor agnostic, can be used for traditional vendors like Huawei, Ericsson or Open RAN like Mavenir
Can complete/replace the traditional drive testing, reducing the number of trips up to 3x
Improves Customers QoE (with Revenue impact)
Full API/SDK support for application developers and automations
Complements existing solutions with unstructured data capabilities
Fast integration in customer environments to connect existing data and services
SaaS Private/Hybrid Cloud
Can perform Network audits for seamless performance across MNOs of the same GROUP
Collect real-time location data from MDT messages
Work with location data, no regression or triangulation
Deep-learning AI model to predict geo-location based on CELL_ID, TA and RSRP
Predict location when MDT location is not available
Connect the system to live data
Create accurate UE geo-location model
Determine optimal antenna params based on aggregated UE locations, TA, measurement reports and historical traffic patterns
Report anomalies
Deliver predefined or custom reports
Each red dot is one UE
Each UE placed on the map is based on geolocation (lat/long and location predicted with AI)
Triangulation is avoided because of low resolution
20x20m geo raster is overlayed on the map
Each road to be evaluated by “instant drive testing” is defined by yellow raster (from CDG-BV. AVIATORILOR)
The intersection between UEs and yellow raster coordinates will create the sum of measurements for the road alone
The UEs ‘on the road’ are identified as the ones in greed contour
Only the green ones will be analyzed for the road drive testing.
Everything else is useful for indoor and performance analytics, like traffic heat maps or outlier identification (anomalies detection)
Instant collection of radio data with automatic enrichment & correlationTurn radio data into actionable structures
Create a RAN data lake
Manage the complete data lifecycle, before and after optimization from collection to AI/ML to visualization
Completes traditional drive testing and reduces the number of trips up to 3x
The AI assisted network solution Improve Customers QoE (with Revenue impact), create Efficient Network density (with Capex impact), Reduce power consumption (Opex impact) with high impact for migration to 5G
Complements existing solutions with unstructured data capabilities
Fast integration in customer environments to connect existing data and services
Offers Network audits, for seamless performance across MNOs of the same GROUP
Vendor agnostic, can be used for traditional vendors, like Huawei, Ericsson or Open RAN like Mavenir
Analyze 4G and 5G call trace data to compute optimal base station radio parameters and detect anomalies (~30,000+ radio signaling events/second)
Identify problematic areas and suggest optimal radio parameters
Instant collection of radio data, enrichment, correlate and analyze huge amounts of records into actionable data structures, extracting and storing information in the RAN data lake
Near-realtime RAN data processing for predicting events and instant optimization
Vendor agnostic, can be used for traditional vendors like Huawei, Ericsson or Open RAN like Mavenir
Can complete/replace the traditional drive testing, reducing the number of trips up to 3x
Improves Customers QoE (with Revenue impact)
Full API/SDK support for application developers and automations
Complements existing solutions with unstructured data capabilities
Fast integration in customer environments to connect existing data and services
SaaS Private/Hybrid Cloud
Can perform Network audits for seamless performance across MNOs of the same GROUP
Intelligence Agencies across the world face significant challenges to identify, gather and interpret content from various sources.
Extracting and summarizing meaningful and relevant information from huge amounts of data is a big challenge.
Social Networks are tightening their data-collection policies, restricting more and more automated data collection capabilities
Predicting outcomes from existing data points is often not possible or too difficult
Deriving intelligence from existing data sources is time-consuming and performed manually
To collect data from all available sources, both online and in-house
To identify information of interest and how it’s connected (persons/groups, associates, organizations, locations, phone numbers etc.)
To find what you need in a matter of seconds
To predict what will happen next so you can prevent
To get the exact big-picture of the situation
Identify data sources
Determine feasible access and acquisition methods
Data acquisition
Language translation if necessary
Data extraction Early detection (real-time processing)
Link Analysis, Spatial Analysis
Text, Images, Videos, Audio
Documents, Web Content, Social Content, Dark Web Content
Object detection, speech recognition, entity recognition, relationship extraction, sentiment analysis
Image/video/audio analytics
Knowledge discovery: data mining, search, data visualization, data sciencePrediction
Collaboration
Watch list
Continuous monitoring
Intelligence reports
Real-time alerts
Automated actions
Dashboards
Enforcement
Gap analysis
Highly secure, multi-model, massively-scalable datastore
OSINT and SOCINT data collectors and rotating proxy systems
Store massive amounts of data, never delete a single thing
Sub-second interrogation capability
Trainable AI models
Integrates with your existing systems and databases
Low-code ecosystem, extensible, no vendor lock-in !
Efficient and effective information retrieval from online and in-house data sources
Information extraction from all types of data: video, image, audio and multilingual textual content
Real-time event correlation, detection and visualization
Strategize on possible future trends and hot-topics before they happen
Central knowledge system that grows over time
Customer satisfaction (CSAT) is the ultimate measure of customer loyalty
Self-service is the biggest opportunity for organizations to reduce costs
Customers abandon self-service when they lose confidence in their ability to resolve an issue without the help of another person.
Low-effort customer interaction is the main driver for growth and acquisition:
A low-effort, faster resolution process has three critical functions:
- Focusing on customer outcomes
- Managing customers’ perception of the outcome
- Staying ahead of foreseeable issues
Deploy AI to recognize a customer’s intent, to provide answers, understanding requests that aren’t a part of a predetermined menu of options
Quickly identify the customer, their products, services and previous interactions
Accurately respond with relevant answers
Automate tasks like paying a bill, completing orders, and providing instructions to customers without channel switching
Send automatic alerts when an issue is solved
Proactively alert customers when something is wrong
Instant collection of radio data, enrichment, correlation and analysis of huge amounts of records into actionable data structures, extracting and storing information in the RAN data lake;
Manages the complete data lifecycle, before and after optimization from collection to AI/ML to visualization;
Self-service & no-code (zero touch) for technical and business users;
Processing near real time RAN data for predicting events and fast/instant optimization;
Can complete/replace the traditional drive testing and reduce three times the number of trips;
The AI assisted network solution improves Customers QoE (with Revenue impact), creates Efficient Network density (with Capex impact), reduces Power consumption (Opex impact) with high impact for migration to 5G.
Full API/SDK support for application developers and automations;
Complements existing solutions with unstructured data capabilities;
Fast integration in customer environments to connect existing data and services;
SaaS Private/Hybrid Cloud;
Provides Network audits, for seamless performance across MNOs of the same GROUP;
The solution is vendor agnostic, can be used for traditional vendors, like Huawei, Ericsson or Open RAN like Mavenir. The unstructured data will be mapped for each parameters, otherwise lost after each call;
The BigConnect implementation outcomes will impact Coverage, Mobility, Capacity and Interference;
Always build regression models to predict TA from RSRP
Triangulate
Sensitive to geography
Location might not be accurate or incomplete (no road for drive testing)
May not be reliable for further optimizations
Collect location data from MDT messages directly
Work with location data, no regression or triangulation
Train a deep-learning model to predict geo-location based on CELL_ID, TA and RSRP
Use the model to predict location when MDT location is not available
Connect the system to live data • Create accurate UE geo-location model
Determine optimal antenna params based on aggregated UE locations, TA, measurement reports and historical traffic patterns
Report anomalies
Deliver predefined or custom reports
Money Laundering – Moving from People to Corporations, Shell Companies and Back
Credit Card – Present or Not Present/Skimming
Identity Theft – Synthetic IDs and Account Takeovers
Combinations – Identity theft + Credit Card + Money Laundering Fraud we don’t know about yet...
Smurfing: moving money between small accounts
Transactions: Amounts, Locations, Types of Goods, Stores
Actors: People, Companies, Goods and Services
Locations: Physical, Corporate identities, Zip Codes
Devices: Mac Address, IP, IMEI, IMSI etc
Identify data sources
Semi / Fully automated
Looks for risk patterns “we have seen before”
Write programmatic rules
Fraudsters constantly working to “crack the code”
Expert + interactive
Link analysis
Case analysis
Charts / Dashboards
Connect with other data sources
Learn from existing patterns
Look at new data sources (social media, web)
Semi / Fully automated
Looks for risk patterns “we have not seen before”
Looks for recurring patterns
Does not need rules
Difficult to “crack”
Traditional fraud analytics looks for outliers but cannot detect patterns (e.g. Fraud Rings)
Pattern matching
Relationship & association analysis
Real-time monitoring and decisions
Can adapt to any change or data
Organized in groups
Synthetic identities
Stolen identities
Hijackd devices
Order of operations
Find things closely related to each-other (e.g. friends, affiliations, groups)
Find similar paths or patterns
Highlight variant of dependencies
Find the most connected or relevant nodes
Find distribution problems and identify optimizations