UN VISTAZO AL PASADO AL PRESENTE Y AL FUTURO DEL SOFTWARE

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1 UN VISTAZO AL PASADO AL PRESENTE Y AL FUTURO DEL SOFTWARE

2 Evolución del poder del ser humano Sociedad Agrícola Sociedad Industrial Sociedad información NEC Confidential

3 Evolución de la sociedad de la información Hoy En 5 años En 10 años Hace 20 años Hace 15 años - PC Obligatorio - Internet Opcional - PC Obligatorio - Internet Obligatorio - Cloud Opcional - PC Obligatorio - Internet Obligatorio - Cloud Obligatorio - Desarrollo Opcional - PC Obligatorio - Internet Opcional - Cloud Obligatorio - Desarrollo Obligatorio - PC Opcional - Internet Opcional

4 Una nueva definición del software Procesos Automatización Clasificación Orden Buenas Ideas Código IaaS Big Data PaaS Servicio Masificación Economía de Escala SaaS Analítica Big Data

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6 Cloud Computing Computación en la nube Símbolo por defecto de Internet en los diagramas Cloud Computación Lógica de coordinación Alamacenamiento y procesamiento Computing Mover la computación del simple PC o centro de datos hacia Internet Cloud Computing

7 Cloud Computing Cloud Computing es un modelo para habilitar acceso por red a un conjunto de recursos computacionales configurables y compartidos (ergo. Redes, servidores, almacenamiento, aplicaciones y servicios) de manera conveniente, por demanda y ubicua que pueden ser rápidamente aprovisionados y liberados con un esfuerzo administrativo y una interacción con el proveedor del servicio mínimos. El modelo Cloud Computing promueve disponibilidad y está compuesto por cinco características esenciales, tres modelos de despacho y cuatro modelos de despliegue.

8 Cloud Computing

9 Cloud Computing Según la última definición de Cloud Computing del NIST, las características esenciales son: - Autoservicio por demanda - Acceso amplio a red - Conjunto de recursos - Rápida elasticidad - Servicio medido

10 Economías de escala

11 Economías de escala

12 Economías de escala

13 Situación mundial y megatendencias De acuerdo con las Naciones Unidas, se espera que la población mundial llegue de aproximadamente 7 mil millones de personas a partir del 2011 a 8 mil millones de personas en 2025 y hasta 9,6 mil millones en También se prevé que, a medida que más personas se trasladan a las zonas urbanas, la población urbana mundial aumentará de 3,5 mil millones en 2011 para 6,3 mil millones en Como resultado, la demanda de alimentos, agua y energía aumentará 1,7 veces, 1,6 veces y 1,8 veces respectivamente. Se espera que las emisiones de gases de efecto invernadero aumenten hasta 1,5 veces. Esto significa que si el estilo de vida urbana de hoy persiste, en 2050 vamos a necesitar más del doble de recursos que la tierra entera puede suministrar. Por lo tanto, con el fin de construir un mundo sostenible, no tenemos más remedio que mejorar la productividad de los recursos, modificar la estructura social y estilo de vida, y reducir el consumo de recursos.

14 Situación mundial y megatendencias

15 El Concepto - Smart City Una Ciudad Inteligente se concibe como un espacio urbano que, por medio de millones de sensores, es capaz de escuchar y comprender lo que está sucediendo en la ciudad para tomar decisiones con la suficiente información, a la vez que proporciona la información adecuada a los ciudadanos. Estadios Entretenimiento Residentes La ciudad debe ser un espacio para vivir, trabajar y visitar Seguridad Bienestar Eficiencia Lugar eficiente de trabajo. Facilidad de desplazamiento. Elasticidad en la expansión de la ciudad. Sostenibilidad Deben existir sistemas que garanticen la seguridad ciudadana mediante vigilancia y reconocimiento de parámetros biométricos. Es crucial que la ciudad no solamente sea capaz de atraer personas y talento, sino también que sea capaz de retenerlo.

16 Centro de operaciones de la ciudad Centro de operaciones de la ciudad basado en Cloud (CCOC) Ciudad Sistemas Operaciones APIs para servicios externos Detección Información tiempo real Tablero de control de la ciudad Analítica de Big Data Data Normalisation (FI-WARE) Modelo de Ciudad Monitorización y gestión Sensores de Ciudad Fuentes de Información

17 Perspectiva TIC Servicios de gobierno Seguridad Educación Transporte Salud Servicios Modelo de ciudad digitalizada Big data Agricultura 3G,LTE Wi-Fi ZigBee IrDA M2M network Specified Low Power Radio Bluetooth WiMAX xdsl,ftth Z-Wave Sensores Sociedad real

18 Big Data Creación de nuevo Valor Presión sangre Herencia Ejercicio Salud Cuerpo Temperatura Nutricional Dieta Medicacion Luz del sol Temperatura Humedad Agricultura Vel. Viento Dióxido de carbono Ecosistema Calidad sueño Peso corporal Precipitacion Precios mercado Energía Marketing Clima Temperatura Industrial actividades Hospital Estación Tiempo Moda Publicidad Electricidad Transporte Region Internet Gas Agua Edad Tendencias Page 18

19 SEP 2014

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33 Perspectiva TIC Servicios de gobierno Seguridad Educación Transporte Salud Servicios Modelo de ciudad digitalizada Big data Agricultura 3G,LTE Wi-Fi ZigBee IrDA M2M network Specified Low Power Radio Bluetooth WiMAX xdsl,ftth Z-Wave Sensores Sociedad real

34 Big Data Típicamente caracterizado por las tres V, nominalmente, Volumen Escalabilidad, Variedad No estructurado, Velocidad Tiempo real, En un sentido más amplio, datos usados para otros propósitos más allá de los propósitos para los que originalmente se recolectaron. Page 34

35 Source: IDC Digital Universe Study, 2010; IDC 2012; Big Data/Predictive Analytics Big Data = Grandes retos en TIC Big Data: Es definitivamente la fuente de ventaja competitiva entre todas las industrias - Forbes Los malos datos o de pobre calidad le cuestan a las compañías de EEUU $600KM anualmente Para el 2020 las transacciones de negocios por internet: B2B y B2C alcanzarán 450KM por día Implementar mejores prácticas en calidad de datos puede crecer los ingresos de una compañía hasta 66% Data generated by sensors, machines, etc. Images, various measurement data Corporate data Transaction data Log data Digitization of analog data Media data Internet Films, images TV, radio Social media, etc. Increased unstructured data

36 Estrategia BigData

37 NEC s Technologies to change BIGDATA into Values Media Processing Timely and accurate recognition of diversified real world events. Analytics Finding new rules, and optimization with predictions. Data Capture Prediction Social Value Creation Vibration Sensor Invariant Analysis Financial Efficiency Tax, Social Security Real World Voice Recognition Media Processing Face Recognition Natural Language Recognition Heterogeneous Mixture Learning Analytics RAPID Machine Learning Textual Entailment Recognition Optimization/Control Predictive Medication Healthcare Energy Optimization Accident, Crime, Disaster Mgmt. Food Production Optimization Analysis Process Management

38 NEC s BigData Offerings

39 FAILURE SIGN MONITORING SYSTEM(INVARIANT ANALYSIS)

40 Failure Sign Monitoring System(Invariant Analysis) * Collection and analysis of large volumes of sensor data to detect when operations are different than normal * Detection of abnormalities through comparison of past data and real- time data Detection of signs of failure and/or abnormalities in domains in which failures could have a high economic and/or social impact Social infrastructure (Bridges, expressways, etc.) Power plants IT systems, data centers, telecom networks Manufacturing plants (Assembly, chemical, etc.) Automobiles, trains, aircraft, ships, etc. Sensor data Operation logs Provision of safety and security solutions that contribute to society

41 Detection of abnormalities by comparing past data and real- time data * Automatic modeling of complex systems using machine learning * Early detection of abnormalities by comparing current observational data with values predicted using a model to determine whether or not operations are different than normal Visualization of normal operations [Invariant model] Detection of signs indicating that operations are different than normal [Real- time failure detection] Extent of abnormality Visualization of operations Detection of abnormalities Mechanical and automatic visualization of relationships that even experts would have difficulty noticing Comprehensive viewing of all relationships enables abnormalities to be detected in 1 / 20 th the amount of time it would take a human

42 Chugoku Electric Power Co., Inc. Effectiveness of Failure Sign Monitoring System for Large- scale Plants utilizing invariant analysis confirmed through field trials at Shimane Nuclear Power Station Real- Time No. of sensors per power plant: 3, of data from each sensor in a second Advanced Analysis Discovery of signs indicating that operations are different than normal from correlations between 3,500 x 3,499 sets of sensors On- site know- how Realization of improved accuracy of failure sign monitoring through close collaboration between experts in power plant operations working for the customer and NEC s own analysts.

43 HETEROGENEOUS MIXTURE LEARNING

44 Heterogeneous Mixture Learning Technology High speed optimization of data partitioning and pattern learning based on an original algorithm FAB Inference Raw data * * **** * * *** B ** ** ** ** ** * * ** ** * ** * ** ** B * ** * **** * * C B **** ** *** ** ** ** * ** ***** * ** * * * ***** ** A * * ** * ** * ** ** * ** * *** * * A ** ** ** * * * * * * A Cand. 1 (Scr. = 0.6) Cand. 3 (Scr. = 0.7) Discovering heteromixture patterns Cand. 2 (Scr. = 0.8) Optimal data partitioning without experts hypotheses Accurate extractions of patterns mixed in the data Fujimaki, Hayashi, Factorized Asymptotic Bayesian Hidden Markov Models ICML2012 Fujimaki, Morinaga, Factorized Asymptotic Bayesian Inference for Mixture Modeling AISTATS2012. Fujimaki, Sogawa, Morinaga, Online heterogeneous mixture modeling with marginal and copula selection KDD2011. Fujimaki, Morinaga, Momma, Aoki, Nakata, Linear Time Model Selection for Mixture of Heterogeneous Components ACML2009.

45 Disposal loss problems in the retail industry Food retail Fresh products Due to short shelf-lives and high frequency of orders, losses due to disposal of unsold items significantly affect costs. Although appropriate product demand forecasting is needed to avoid losses due to disposal, it also requires the analysis of a variety of factors that affect each other in complex manner. Calendars Weather Events Inability to comprehend patterns from past experiences even if they have been included in manuals. Human judgment based on intuition and experience has limitations due to the large number and complexity of products.

46 Sales Amount Prediction at Retail Stores Various elements affect sales of each item. The day of the week, Weather, Temperature, Popularity trend, The Hetero Mixture Learning can model complex and composite data. Real Prediction Learning Prediction For semi-automatic and effective ordering

47 Auction Price Prediction Current Customer s Price Prediction for Trade-in CertainProducts Determine Trade-in Price Other Consideratio ns Price Prediction Condition & Parameter s Input Investigate Products Customer s Problem Existing prediction model can not handle dynamic changes in the market, and Error Ratio is significantly high. Low Profit & High Operational Cost NEC is now proposing a new Price prediction system using Heterogeneous mixture learning technology.

48 Price Prediction using Heterogeneous Mixture Learning Higher Precision and Low Maintenance Cost <Proposing System> The BigData Data mining Calculation Formula Existing Auction Results Sales Data, etc. (Preprocessing) Input Data Cleansing Format Change Existing Logic Formula:P1* x P2* P3 P3* Z P:parameter * ** ** ** B ** D ** ** ** *** * * * C A Pattern Extraction Fixed Formula(common for all products) Parameters can be updated Output a Formulas for each products. Difference Product A: Formula A Product B: Formula B Product C: Formula C Update every week Inquirly Result User s Application Our Logic Product A/Typeα:P1*x P2*P3 P3*Z Product A /Typeβ:P3*Z Product C /Typeγ:P1*x P2*P3 P3*Z P4*α + Formula and parameters are both adaptively generated by machine learning PoC Effort proved the effectiveness of our technology. Error Ratio is Reduced from 21%to 10 %! Further improvement in Progress

49 Electricity Consumption Prediction Hetero- mixture Learning (found three patterns and predicts based on them) Electricity consumption Learning period Sat. Sun. Predicting period Mon. Tue. Wed. Thu Fri. Average relative error 2.7% time Trial 1: Day- of- the- week- wise data partitioning ( 7 conventional predictors) Electricity consumption Learning period Change points of the relationships and day-of-theweek seems not to be equal Sat. Sun. Predicting period Mon. Tue. Wed. Thu Fri. Average relative error5.8% large error time

50 RAPID MACHINE LEARNING (DEEP LEARNING) Page 50 NEC Corporation 2013

51 Recommendation Systems: Job Matching and other applications Machine Learning Department January

52 Recommender Systems Job matching is a type of a recommender function Traditional Recommender Systems: Personalized: Based on preferences, buying history, browsing history Collaborative: Based on behavior of others (w hat they buy, watch, ) Similarity metrics: User User: User s ratings of the same product determines distance between users. Result: Clusters of similar people. User User metrics tend to shift quickly Item Item ( Job - Job) : Distance is based on rating of two products, how closely do users using both agree in rating? Result: Clusters of similar items. Item Item metrics tend to be more stable (can precompute most). Used most by retailers (Amazon, Netflix, ) Our Approach: Generic matching: Personality categories trained with job classifications Online training and matching: Make recommendations relevant for present situation (expressed preferences, job history, )

53 Education Characterize Person 360 degree view of a person Structured data: Education, Tests: SPI, OPQ (Japanese standardized tests) Training (length, university) Location Unstructured data: Work experience: Usually in free text Recommendations: Free text Behavior: What is searched, clicked on web Attitude: From essays, behavior Standard Personality Categories: Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism Need finer personality characterization How do we get a better understanding of the personality of an applicant? Combine traditionally used data (standardized tests, questionnaires, ) with information extracted from unstructured text: Essays, behavior on recruiting web site, information from social networks.

54 Recommendations: NEC Approach Competitors: Collaborative Filtering (CF) NEC: Supervised Semantic Indexing ( SSI) All Learned: Mapping between candidate and job Supervised Learning: Train system for specific targets Flexible: Integrates different types of data Tests with Customer data: Reduce error by 50% over CF alone Candidate: Qualifications Personality Philosophy Behavior (dynamic) Features X Trained Model Matrix X Features Job, Company: Requirements Philosophy Context Context 19

55 Job Matching Process Training phase Candidate profile Qualifications, Personality, Preferences Candidate Features Labels Training programs: Supervised learning Unsupervised learning Candidate behavior Clicks, Page Views Dynamic Job profile Requirements Philosophy Context Preprocessing Text data and non- text data Candidate behavior features Job Features Labels Supervised Semantic Indexing ( SSI) Collaborative Filtering ( CF) Compute time: Hours Trained Mo T d ra e i l n s ed Mo T d ra e i l n s ed Models Test phase, Daily use Candidate profile Qualifications, Personality, Preferences Candidate behavior Clicks, Page View Dynamic Context Preprocessing Text data and non- text data Candidate Features Candidate behavior features Trained Model X Job Features X Candidate features Compute time: Milliseconds Recommendation Score Decision

56 Feature Generation All data about a candidate are encoded in a vector ( similar for jobs) : Structured data describing the candidate s qualifications are encoded in binary or numeric values. Unstructured text data are encoded with text analysis algorithms: The individual words are encoded in a dictionary ( most common words in Japanese), syntactic information is also encoded. Candidate qualifications: Structured data 21 years, 4 year college, Computer Science,. Candidate Essay 株 式 会 社 ワールドインターナ(1995 年 4 月 ~2004 年 4 月 )%n 事 Text Analysis Age Code for: Education Field Words (dictionary) Code for: Syntactic elements 0,0,0,1,0,0 0,0,4,0,0, 0,0,0,1,0, 0,0,0,1,0,0, 0 0,0,0, 0,1,1,0,0, 0,0,0,0,1, Overall: hundreds of features Overall: thousands of features 21

57 HR Functions NEC Data Analytics supports all steps of the recruiting process Steps of Recruiting Function NEC Analytics Value NEC provides WEB site Tests: SPI; OPQ Short Essays Recruiting Social Network Introduce companie s Identify candidates with suitable qualifications Evaluate interactions of users Analyze discussion boards Place advertisements Filter candidates Analyze test results and essays Combine information from tests with analysis of essays Attract qualified candidates Better candidates for specific jobs Refined selection of candidates Deeper personal profile of candidate Interview Identify match between job and candidate Support for interviewer Direction of personality questions Personality match, not just qualification match Job Offer Attract desired candidate Personalize offer Highlight points of interest for candidate Higher acceptance rate Provisional job Confirm satisfactory match of job and candidate Identify potential problems because of personality Propose changes Higher satisfaction of company and candidate 22

58 Application: Hotel Recommendation We conducted a POC with a customer They want a more detailed understanding why a customer is giving a certain rating to a hotel. What is wrong or what is good. Test: Jalan Web Site Predict rating from text: 96.5%accuracy Identify reason form complaint or praise: Highly accurate

59 Benchmark: Tweet Analysis SemEval 2013: International Competition to determine sentiments of Tweets Several hundred competitors participated Our Deep Learning technology with a recent new development for highorder feature learning beat all competitors: NECLA Best competitor 12.7% error 14.2% error 24

60 Technology The following are examples of original technology, developed by NECLA, that is used for a wide range of applications. Strength of this technology: Analyze unstructured data of many different types. Can be used to generate structured data or for data retrieval, data interpretation, etc. Analysis is done based on semantics (content, meaning, ) Here Deep Learning technology is stressed. This technology has received a lot of attention recently. NECLA has been a leading contender for many years in this field. 25

61 Deep Semantic Networks: SENNA Totally new approach for text analysis Trained from raw text: No handcrafted features Tasks: Part of Speech Chunking Name Entity Recognition Semantic Role Labeling Parsing Easy to adapt to different languages. We have networks for English, Chinese, German, Spanish (Japanese with Open Source pre-processing) Advantages of our technology: Much faster than alternatives Benchmark results: 10 to 100 times faster than state of the art algorithms. Can run on mobile devices Compact code packages: SENNA, SSI Well protected IP 1/28/2014

62 Industry Leading Performance Ref: Natural Language Processing (Almost) from Scratch; R. Collobert et al., J. Machine Learning Research 12 (2011) Accuracies: (Data: Penn Tree Bank) Task Benchmark SENNA Part of Speech (POS) (Accuracy) % % Chunking (CHUNK) (F1) % % Named Entity Recognition (NER) (F1) % % Parse Tree level 0 (PT0) (F1) % % Semantic Role Labeling (SRL) (F1) % % Speed, storage requirements: Orders of magnitude faster than state of the art!! Can run on mobile devices 27

63 Deep Semantic Networks: SSI, SSE Supervised Semantic Indexing (SSI) and Sparse Sequence Embedding (SSE) are both NECLA technologies using Deep Learning. They are used for semantic analysis of a wide range of data types, including text and bioinformatics data. These algorithms are used in a variety of applications. SSI Very successful technology for data retrieval based on semantic content SSE This is the most efficient technology for data retrieval based on n- grams

64 Interpretation of Essays Starts with standard text analysis techniques, such as TF- IDF representation of query ( essay) and document ( job posting) Structured features describing user or job ( preferred job attributes, preferred location) can be added Adds syntactic and semantic features of the essay and the job posting Syntactic SSI: Extension of SSI to improve Japanese queries: Learn relations not only between words but use also structural features that are typical for Japanese

65 Human Resource Matching(RAPID Machine Learning) Matching Company Demands and Job Seekers Increasing Brokering Efficiency with Minimal Costs Job Seekers Entry Broker RAPID Machine Learning Register Recruiter Companies Company Choices CV SNS Job Description Best Candidates RAPID Machine Learning: NEC original Technology based on Deep Leaning

66 NEC A n alytics Multi-modal: Different Data Types; structured and unstructured Multi-Lingual text analysis: Adapting by training High-Level Reasoning Performance for Big Data analysis NEC Strengths Text, machine data, web data: Numeric, categorical, textual, binary, sensors Japanese, English, Chinese, Spanish, German 1. Statistics 2. Classification, Regression 3. Relationships, Causality Extensive parallelization: Vectorization, Cluster (Hadoop), Parallelization of algorithms

67 InfoFrame Elastic Relational Store (IERS): Next Generation Database Solution

68 Big Data s Impact on Database Systems Traditional Relational Database limitations became apparent: Scalability Relational Databases (RDBs) have difficulty accessing data in distributed storage Performance Increase in processing power faster access rates NoSQL (Not Only SQL) - alternative database technology Pros: Better scalability and performance than RDBs Cons: Lacks SQL support Non-adherence to ACID (Atomicity, Consistency, Isolation, Durability) Some view as severe limitations, so enterprise RDBMS users are hesitant to switch to it.

69 CAP Theorem (Eric Brewer autumn 1998) In theoretical computer science, the CAP theorem, also known as Brewer's theorem, states that it is impossible for a distributed computer system to simultaneously provide all three of the following guarantees: Consistency (all nodes see the same data at the same time) Availability (a guarantee that every request receives a response about whether it succeeded or failed) Partition tolerance (the system continues to operate despite arbitrary partitioning due to network failures)

70 NewSQL Database Systems New type of database emerged, called NewSQL, targeting the workload growth of Online Transaction Processing (OLTP) systems. Key characteristics of NewSQL: Highly scalable High performance SQL support ACID adherence

71 RDBMS vs. NoSQL vs. IERS IERS combines the best of relational DB functionality with high scalability and performance Traditional DB (RDBMS) NoSQL IERS Scalability Performance SQL Support ACID Adherence Mission Critical HA/RA

72 Overview : What is IERS? IERS is a distributed DBMS for OLTP (On-Line Transaction Processing) using multiple commodity servers Large number of transactions

73 IERS s unique multi-tier architecture IERS is designed to have a capability of Scale-out, SQL interface and ACID transaction Each layer is independent and able to scale individually Partiqle Server Transaction Server Storage Server SQL Interface Elastic scale-out ACID transaction In-memory operation to maximize throughput and minimize latency High availability Elastic scale-out Data durability Destructed data detection using checksum Elastic scale-out

74 IERS Key Strengths Multi-level IERS architecture and in-memory implementation offers a high performance, cost effective DBMS environment suitable for OLTP workloads. Enhanced high-speed transaction functionality enabled by NEC's innovative, patented "MicroSharding" Technology. Elastically scalable: scale-in/scale-out, shared-nothing architecture, capable of running on a large number of nodes without suffering bottlenecks. Performance and costs can be managed through resource management. SQL support reduces system development requirements and enables utilization of current SQL applications (enterprise legacy applications). High reliability and 24x7 availability for Mission Critical applications. Analytics support using Hadoop MapReduce framework. High-skilled IT personnel are not required for IERS system development and support.

75 IERS Key Features and Functions Feature Function Function overview Advanced scaling capability High volume read-write transactions capability Ease of system development Reliability and availability for missioncritical applications Elastic scale-out mechanism Automatic resource reallocation Transaction engine Memory cache SQL interface Hadoop integration Security function Data protection Availability System resources can be added while system is LIVE, enabling the system to be re-configured without downtime. This allows customers to manage workload performance and costs effectively. Balancing data processing load among transaction servers and storage servers enables balanced system operation of all the scaled-out servers. (Available in the next release) Data to be saved in transaction servers can be updated at once, as one transaction, by using the patented MicroSharding technology. Transaction servers save the data by using the high-reliability memory cache which adopts in-memory database technology. SQL support (compliant with the SQL-89 standard). Data stored in storage servers can be accessed directly from Hadoop. Settings on user authentication and access privileges equivalent to those for relational databases are available. Data in the transaction server and storage server are stored in multiple servers for improved reliability. When a failure occurs in the transaction server, slave is automatically promoted to master, enabling business continuity without service interruption.

76 IERS: Benefits IERS empowers you to: Operate Your Business with Enhanced Speed and Elastic Scalability Optimize Your Mission Critical Applications with High Availability, Reliability, and Security Scale rapidly while Maintaining Simplicity for your New/Existing Applications Reduce TCO, enabling Faster ROI Leverage strategic business analytics via Hadoop interoperability

77 IERS Use Case: OLTP Transactions Retail, Financial Services, e-commerce/internet Business Problem: Need to perform analytics on massive amounts of data generated daily after customer s RDB s could not scale. NEC Solution: IERS offered elastic scalability which enabled customer to perform various data analytics using the data accumulated in system. After InfoFrame Elastic Relational Store Online Extract/Modify Data Update Matching, Transfer, etc. Store all including the past data DWH AP DB (1 Day) Before Other sources Streamline the data and store it to DWH (Delete raw data) Program to Extract Data DB For DWH Night batch processing

78 IERS Use Case: Marketing Financial Services, CPG, Healthcare, Communications, Telecom, Media, Services, e-commerce/internet Business Problem: Direct Marketing Application Need to create Target Client lists easily and quickly using analysis from required customer data. As data volume increases, analytics batch processing must be completed within a finite timeframe. NEC Solution: Using IERS and Hadoop API, customer accelerated data extraction in parallel and minimized batch processing time frame using both online and offline processing of one consolidated database. Before Online AP After Online AP Add another axis for analysis RDB Client information 1 Batch Process InfoFrame Elastic Relational Store Batch Process Extract RDB Client Information 2 RDB Client Information n Extract Omit RDB History data Target Client Client info 1 Client info 2 Client info n History data map/ reduce Omit Target Client

79 IERS Use Case: M2M Data Manufacturing/Utilities Business Problem: Accumulating and analyzing M2M Data Need to provide a highly scalable system and leverage a large amount of data aggregated from many machines and sensors on a 24x7 basis. NEC Solution: IERS performs data transformation to prepare data for analysis and transfers data to RDB/DWH for nightly analytics batch processing. Devices Before Requires DB re-sizing when enhance the system Requires system down Partner RDB Sales Replace Night batch (Map/Reduce) DB for Analytics After Data generated from machines, sensors, etc (Images, remote sensing, etc) IERS IERS IERS IERS IERS IERS InfoFrame Elastic Relational Store Scaling out without service downtime Shop

80 NEC reúne e integra tecnología y experiencia para crear la sociedad del mañana por medio de las TIC. Colaboramos estrechamente con socios y clientes de todo el mundo, orquestando cada proyecto para asegurar que todas sus partes estén bien afinadas a las necesidades locales. Cada día, nuestras soluciones innovadoras para la sociedad contribuyen a una mayor seguridad, eficiencia e igualdad, y posibilitan vivir vidas más brillantes a las personas.

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