Oracle Stream Analytics - Industry Examples

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Turn Data into Action in Real-time !
Oracle Stream nalytics

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Oracle Stream Analytics
Golden Gate CDC or
Txn Logs
Sensor Data
Social Media
Click Stream
Geo Location
Filter
Aggregate
Transform
Correlate/Enrich
Geo-fence
Time Windows
Data Patterns
Spatial Analytics
Anomalies
Classification
Clustering
Statistical Inference
Regression Models
Business Rules
Policies
Conditional Logic
Notify/Publish
Invoke/Execute
Visualize
Persist
Data Ingestion Pre-processing Analysis &
Prediction Decisions Actions
Data Processing Stages in an OSA Pipeline

Oracle Stream Analytics - Functional Components
OSA
Positioned as a Leader by Forrester and Bloor
Runs 100% on Spark
Complex Event
Processing
Operational
Intelligence
Real-time OLAP for
Event & Time-series
Data
Prediction &
Forecasting

The New OSA Stack
Oracle Confidential 4
Stream Explorer Web UI
Spark SQL MLLIB GraphX
RETE Rule
Processor
Continuous
Query
Processor
Spatial
Cartridge
Spark Streaming
Spark Core
Oracle Stream Analytics Engine

Kafka Kafka
Data Ingestion & Target Systems
Downstream Apps
ICS Flows
BPM Processes
Databases
REST

Kafka Kafka
Real-time Analytics on Transaction Data using Golden Gate
Downstream Apps
ICS Flows
BPM Processes
Databases
REST
Transaction
Logs
Insert/Update/Delete
Inflight analytics using transaction
context

Why Oracle Stream Analytics ?
1. Build Complex Event Processing applications
– Filters and aggregates on streaming data
– Sliding Windows (Time Windows, Row Windows, Value Windows)
– Shifting Windows (This Day, This Hour, This Minute, etc…)
– Pattern Matching and Recognition
– Geo-spatial Analytics
– Business Rules
– ML, Event Scoring, and Prediction
2. Visualize streams and build operational dashboards
3. Run ad hoc queries on CEP results (real-time and historical)
– Sub second response to all ad hoc queries
4. Predict or forecast future values based on historical data and PMML models

Oracle Stream Analytics Industry Examples

Retail
Some examples from Gartner Research
1. Supplier Analysis
– Compute transaction losses, margins, etc. in real-time to renegotiate with suppliers
2. Markdown optimization
– Continuously track demand/inventory-levels and gradually lower prices for better margins instead or
randomly marking down at end of season
3. Dynamic pricing and forecasting
– Readjust prices based on demand, inventory levels, sentiments and user feedback in social media
4. Personalized offers
– Make real-time offers based on customer presence, store vicinity, customer profile, and spend patterns
5. Real-time shelf-space management in stores
– Based on customer traffic and point-of-sale data
6. Purchase and consumption trends from social media
– Empower marketing and sales by identifying top selling products and services in real-time

Retail…contd
1. Shopping cart defections
– Identify lull in shopping cart activity and improve conversion rates
2. Recommendations using fuzzy match
– Based on what other customers are browsing/purchasing in similar categories and at the same time
3. Improved mall experience
– Upsell/cross-sell based on customer profile and current location in the mall
– Make special offers for checking in at more than 10 stores or making purchases worth 100$ or more
4. Monetize retail data
– Stream quantity sold, buying patterns, etc. to partners and suppliers in real-time
5. Fraud detection and prevention
– Prevent fraudulent return of merchandize

Financial and Banking Services
1. Fraud detection
– Flag and block transactions from stolen and misused credit cards
2. Money laundering
– Many small transactions from same source but to different destination accounts in a small window
3. Upsell products and services
– Based on customer’s financial profile, seize the tiny window of opportunity when customer is online
4. Real-time risk management
– Evaluate portfolio risk from real-time equity and commodity prices
5. Tick analytics & social media feeds
– Blend tick data with social media to identify securities/commodities likely to be affected in next few
minutes or hours
6. ATM Service Optimization
– Identify optimal period and amount to be stocked based on bill-sizes and withdrawal patterns
– Obtain real-time alerts on malfunction for preventive maintenance Some examples from Gartner Research

Transportation & Logistics
1. Asset utilization and tracking
– Average time spent on dock loading/unloading merchandise
2. Asset handling and staff monitoring
– Number of driving violations with trailer on road
3. Asset maintenance
– Check deviation of operating parameters and proactively schedule maintenance
4. Turnaround planning
– Prepare dock, staff, etc. and optimize workflows based on estimated time of arrival
5. Real-time route optimization
– Calculate optimal delivery sequence based on items being shipped, traffic, weather, customer
profile, etc..

Telecommunications
1. Wifi offloading
– Free cellular bandwidth by automatically switching users with data plan to hotspots
2. Lower support costs
– Monitor network outages and proactively inform affected customers via automated text and VMs
3. Reduce customer churn and improve loyalty
– Address dropped calls and proactively inform customers of data usage when roaming before they
further breach the thresholds
4. Fraud detection on prepaid cards
– Detect and block stolen/misused cards
5. Distributed denial of service attacks
– Prevent attacks on network by continuously monitoring source and destination IP addresses
6. Improve end-user application security
– Prevent session hijacking by monitoring IP and associated cookies

Utilities
1. Optimize energy distribution with demand pricing
– Move homes to different price slabs based on their current energy consumption
• E.g. Current energy consumption of home X is 30% higher than the global median so move to different slab
2. Energy theft
– Continuously monitor unusual usage patterns and variations in meter signatures
3. Peak-demand analytics
– Anticipate and plan for real-time energy demand by monitoring weather and neighborhood events
(e.g. Superbowl), etc.
4. Monetize energy consumption data
– Sell energy consumption data to producers, distributors, and appliance manufacturers in real-time

Public Sector
1. Education
– student performance improvement
• Correlate student performance with learning data (use of library, lab, classroom, etc.) for better scores in weak areas
2. Public Safety
– Listen to social media chatter and identify areas likely to be affected by crime or terrorism
3. Traffic and infrastructure optimization
– Analyze real-time traffic flow and infrastructure usage
4. Law enforcement efficiency
– Use real-time traffic for better dispatch policies

Insurance
1. Usage-based premiums
– Price premium on driver behavior and data from telematics
2. Damage prevention alerts
– Proactively alert subscribers in vulnerable areas on inclement weather
– Proactively alert subscribers to avoid areas of social unrest, epidemics, etc. using social media feeds
3. Reduce customer churn
– Track behaviors that reveal an impending cancelation
4. Fraud detection
– Detect parameter fiddling to reduce premium
• E.g. Continuous changes to work distance parameter when seeking quotes

Manufacturing
1. Reduce costs using real-time parts flow monitoring
– Plan and schedule production resources based on current location of parts
2. Preventive maintenance of manufacturing equipment
– Continuously monitor operating parameters for deviation and likely breakdown
3. Preventive maintenance of manufactured products
– Continuously monitor usage and proactively inform consumers of upcoming maintenance or
expected malfunction
4. Improve staff safety using data from sensors
– Crucial in hazardous environments, poor air quality, employee fatigue, etc.

Life Sciences and Health Care
1. Supplies, facilities, equipment, and staff
– Continuously monitor life-saving equipment and prevent breakdowns
– Continuously monitor life saving supplies and eliminate costs of overstock and understock
– Continuously monitor drugs for expiration dates
2. Adaptive treatment
– Continuously monitor the effects of medication through sensors and adapt dosage
3. Healthcare fraud detection
– Large invoices with no matching purchase orders or vendors
– Identify excessive billing by a single physician
– Alert on illegal staff overtime charges

Media and Entertainment
1. Right content at right location and right time
– Change ad on bus based on its location, time-of-day, and other contextual data
2. Personalized offers for online gaming
– Track customers on losing streak and offer coupons from local stores
3. Targeted ads based on viewing preferences and location
– Target ads based on user location and content being viewed
• E.g. Place local restaurant ads when customer is viewing Food Network
• E.g. Place ads from local nursery when customer is viewing Home and Garden
4. Monetization of content analytics
– Collect and stream viewing stats by category to content providers in real time

Oracle Stream Analytics - Industry Examples

Oracle Stream Analytics - Industry Examples

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