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Smack presentation sneak_preview


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Smack presentation sneak_preview

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Smack presentation sneak_preview

  1. 1. Challenges in modern data-processing Data is getting bigger with ever- INCREASING data sources Data models from 1 hour ago are practically obsolete Analysis gets TOO SLOW to get any ROI Data freshness matters more than data volume
  2. 2. SMACK Stack components 3 Processing Engine Cluster Manager Concurrency Model Decentralized Data Store Message Broker  ETL/ Machine Learning  In-memory data fabric  Cluster management  Resource management  Actor-based concurrency framework  NoSQL Columnar high availability database  Solid data fabric for Spark compute jobs  Handle high-volume data push  Process streams of real-time data
  3. 3. SMACK Stack – Functional Flow Decentralized Data Store Read the data Write the model Read the Model Data Source, Files, DB Extracts Batched Data Real Time APIs Streaming Data Services Model Alerts and Notification Fast Analytics Event Processing Machine Learning
  4. 4. Machine Learning – Real World Applications
  5. 5. A glimpse of what we have done DECISION SUPPORT SYSTEM BEHAVIORAL ANALYTICS PREDICTIVE ANALYTICS REFERENCE DATA ANALYTICS Healthcare Media Analytics Platform (HMAP) Sentiment Analysis of Social Media Data Recommender System Fraud Management System for Banking 6
  6. 6. OUR DEMO
  7. 7.  End-User Scenario  A doctor remotely monitoring 9 patients with a cardiac condition  Patient involved in performing different types of activities  Real-time information on several parameters captured and sent to the backend platform  Analytics Component  As soon as an anomaly is identified, the patient is indicated with a red indicator.  The doctor can view a patient’s info.  Patient’s activities  Heart rate during those activities  Anomaly score (Probability) Demo Use Case 11