Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

Partner Webcast – Oracle Streams Analytics platform - Is it time to reverse traditional analytics?

799 visualizaciones

Publicado el

Real Time, Live, Immediate or Instant are all big keywords in everyday business. All data originates in a flash, whether it is from Internet-of-Things (IoT) devices and sensors or Mobile applications or customer web clicks and transactions. But traditional analytics is done much, much later. Why wait?

Oracle Stream Analytics (OSA) is a new tool, provided as a part of Oracle Event Processing technology platform.

Milomir Vojvodic & Alessandro Cagnetti – EMEA Data Integration Product Team

[

Publicado en: Tecnología
  • Inicia sesión para ver los comentarios

  • Sé el primero en recomendar esto

Partner Webcast – Oracle Streams Analytics platform - Is it time to reverse traditional analytics?

  1. 1. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Safe Harbor Statement The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.
  2. 2. Oracle Data Integration Solutions (DIS) Oracle Stream Analytics – Is it time to reverse analytics? Milomir Vojvodic & Alessandro Cagnetti – EMEA Data Integration Product Team
  3. 3. Eight Core Products Cloud or On- Premise
  4. 4. Business Friendly Extreme Performance Spatial Awareness Oracle Stream Analytics DB Web / Devices Data Event Data & Transaction Streams Downstream (eg; Hadoop) Data Event Oracle Stream Analytics is a powerful analytic toolkit designed to work directly on data in motion – simple data correlations, complex event processing, geo-fencing, and advanced dashboards run on millions of events per second. Innovative dual model for Apache Spark or Coherence grid Simple to use spatial and geo- fencing features an industry first Includes Oracle GoldenGate for streaming transactions
  5. 5. Traditional Analytics Architecture Target DB OGG Source DB AnalyticsTransactions • Data set Architecture • Data Static & Query Dynamic • Streaming Architecture • Query Static & Data Dynamic
  6. 6. 7 • What it does: • Compelling, friendly and visually stunning real time streaming analytics user experience for Business users to dynamically create and implement Instant Insight solutions • Key Features • Analyze simulated or live data feeds to determine event patterns, correlation, aggregation & filtering • Patterns library for industry specific solutions • Streams, References , Maps & Explorations abstracted integration , Data Discovery Canvas • Benefits •Accelerated delivery time from months to minutes • Hides all the challenges & complexities of underlying real time event driven analytics infrastructure OSA Introduction
  7. 7. Adapter Cache ProcessorPOJO EPN (Event Processing Network) Elements Channel Input event streams Output event streams • Application logic contained in processor nodes • Programmed in Java and Continuous Query Language (CQL) Event Processing Network Of OSA
  8. 8. • The following streaming operators are the fundamental building blocks of streaming analytics: • Transformation - narrow the incoming stream. A light version of traditional extract, transform, load (ETL) for streaming applications. • Correlation - combine data from multiple sources. • Enrichment - reference data to provide additional context. • Time windows - snapshot of the stream over an arbitrary time period. Perform time series analysis in real time, such as running totals, weighted moving averages. • Pattern matching - patterns that only emerge as new streaming data arrives. • Business logic - the result of stream analysis is to inform applications with real-time context. Blocks Of Streaming Analytics
  9. 9. • New Extensive Patterns Library, including Spatial, Statistical, General industry and Anomaly detection, streaming machine learning • Streaming Expression and Business Rules Analysis • Simplistic definition of Event Streams and References • Actions to act on Analysis, push downstream to various event sinks, including exporting to Jdeveloper • Visual GEOProcessing with GEOFence relationship spatial analytics • Abstracted visual façade to interrogate live real time streaming data and perform intuitive in-memory real time business analytics • Graphical representations of tabular streaming information • Catalog Topology Viewer and Navigation • An array of new streaming end point connections/targets, including Kafka • Catalog perspectives for major industries Main Features Of OSA
  10. 10. First Time Oracle platform on this report for 7 years Oracle delivers Two distinct unique pieces that are critical for the future of analytics: Stream Explorer for ingesting and interrogating data as it lands in the cloud or the enterprise; and Oracle Edge Analytics (OEA) for filtering, aggregating, and preprocessing data on embedded devices. Report based on last years (March), first release of Stream Explorer product, with no Spark Streaming, no Kafka, and without the array of new features in the Oracle Stream Analytics product released this April 2014 Analysts
  11. 11. • Enhanced Patterns Library • New Geo-spatial pattern • Integrated Expression Builder • Support for Business Rules in Explorations • New streaming end point connections/targets • New Event Stream sources and targets, such as MQTT, Apache Kafka and Twitter • Scaling-Out with Spark Streaming • Better Insights with Catalog Topology Viewer and Navigation New Features Of OSA
  12. 12. Utilities & Oil and GasInternet of ThingsPublic Sector Financial Services Transportation & Logistics Telecommunications Manufacturing & Retail Vertical Adaption
  13. 13. • Fire emergencies. Immediately isolate the fire location and define exclusion zones around the incident. In parallel, the identification of available fire resources, that are best equipped and in nearest vicinity is vital. • Toll System - Charging a single price for every vehicle, regardless of time of day or is not an efficient model. Tolls can be computed dynamically based on congestion, accidents, and strategies for optimizing traffic. • Airlines: Monitor all airline's operational events to eliminate flight delays, create passenger alerts , detect baggage location due to local or destination-city weather, ground crew operations, airport security, etc. Analyze event data directly aircraft, as soon as they land, and together with historical trending event data, the flight readiness can be instantly determines for the next flight. • Air Defense - the flight readiness can be instantly determines for the next flight. • Vehicle telematics : Reduce fuel cost alerting on element of the vehicle, Improved safety by out-of- hours usage, transgressing unscheduled locations • Supply Chain and Logistics: Detect and report on potential delays in arrival. Use Cases
  14. 14. • IT. Entire system infrastructure running at optimum performance ( online retail ). Analyze : SNMP traps indicating HW status, OS warning and error events (log “tailing”), MW events, EDN, ESB and events from the executing critical Apps • Financial Services: Banking. Immediate Action – Payments processed more than 60 minutes without ACK or Bank Error. Ability to perform real-time risk analysis, securities trading and foreign exchange prices. Online Fraud - anticipating trends prior to successive attempts. Insurance: In conjunction with Oracle Real Time Decisions, ability to learn to detect potentially fraudulent claims. • Retail: Proximity based marketing to provide personalized offers • Telco: Location based offers, real-time call detail (CDR) record monitoring • Utilities - Smart Meters “influence” individual meters to reduce home consumption levels. • Oil&Gas- monitor oil pressure levels in real time and weather conditions and various resources nearest to the oil pipe with the correct skills to correct the problems. • Healthcare: Medical Device Data to help save lives. Smart beds : Body Use Cases
  15. 15. DEMO Oracle Stream Analytics in action
  16. 16. • Use separate optimized platforms for each workflow stage: – Oracle R Enterprise (ORE) component of Oracle Advanced Analytics (OAA) for model development and building – Oracle Stream Explorer (OSX) for streaming data scoring, stream pre/post-processing Input event stream Model Builder Model Scoring Model transfer from ORE to OSX using PMML Oracle R Enterprise Oracle Stream Explorer Preprocessing, model import & scoring function creation Training data Prediction stream Streaming Machine Learning Architecture
  17. 17. Header Data Dictionnary Data Transformations Scope of Fields Mining Schema Targets Outputs Mapping of user data in suitable form for the DM model Definitions for all fields used by the DM model (data types, ranges …) Taxonomies Multiple Models Model Verification Fields usage (active/target), policies for handling missing/invalid values, outliers … …. Common syntax for handling target categories & post- processing Suitable form of output fields Version/timestamp/model development environment … • Classes of models supported by PMML • Association Rules • Baseline Models • Decision Trees • Center- & Distribution-based Clustering • Regression & General Regression • k-Nearest Neighbors • Neural Networks • Naïve Bayes • Sequences • Text • Times Series • Support Vector Machines PMML
  18. 18. Q&A Milomir Vojvodic & Alessandro Cagnetti – EMEA Data Integration Product Team Migration Center blog: Migration Center email:
  19. 19. Oracle Partner Hub ISV Migration Center Partner Hub Team Info, Events/Activities Schedule, etc Migration Center Team Blog Webcasts, Howto, Demos, Guides, etc Youtube: OracleIMCteam Slideshare: Oracle_IMC_team Center-4535240