NTT DATA Big Data Reference Architecture Ver. 1.0

Size: px
Start display at page:

Download "NTT DATA Big Data Reference Architecture Ver. 1.0"

Transcription

1 NTT DATA Big Data Reference Architecture Ver. 1.0 Big Data Reference Architecture is a joint work of NTT DATA and EVERIS SPAIN, S.L.U.

2 Table of Contents Chap.1 Advance of Big Data Utilization... 2 Chap.2 NTT DATA Big Data Reference Architecture 3 Chap.3 Use cases of Big Data Reference Architecture Forecast of variation of financial market index by using SNS data Automation Tool for System Development in Design Phase Real-time Bridge Monitoring System Traffic Congestion Control System... 9 Chap.4 Challenges of Big Data utilization and the features of BDRA 10 Figure Fig.1:Cases of Big Data use... 2 Fig.2:NTT DATA Big Data Reference Architecture (BDRA)... 3 Fig.3:Layers of NTT DATA Big Data Reference Architecture... 4 Fig.4:Patterns of analysis scenario

3 Chap.1 Advance of Big Data Utilization It has been said that the world will be filled with a substantial amount of data and thus, the utilization of Big Data will drive the competitiveness of enterprises. In fact, the Ministry of Internal Affairs and Communications in Japan stated that the estimated value of the amount of transition of data distribution in enterprises expanded 8.7 times in 9 years from 2005 to In data utilization, there are some use cases: Ad technology, which is applied for Internet advertising and demand forecasting for individuals in marketing domain; and accuracy improvement in design for the manufacturing industry and improvement of operation efficiency in the transportation industry in operational management and quality control domain etc. (Figure 1) The IoT (Internet of Things) is one of the most important subjects of Big Data utilization. Every single product is connected to network, and equipped sensor with to understand the situation of each product. Therefore, we can collect that information in real time from a remote location and manipulate the product. New services that utilize this generated information in real time will soon follow. In response to this situation, a lot of enterprises work on the construction of the mechanism of accumulating, analyzing and utilizing Big Data more than before. Figure 1: Cases of Big Data use Utilization domain Use case Marketing DSP (Demand-Side Platform) for Internet advertising (Ad technology) Demand forecasting for individual produce management Business management and quality control Accuracy improvement in designing/machining operators in manufacturing industry Forecasting and management for growth conditions of livestock Optimization of operation schedule by onboard GPS data and number of passengers Source: (Information and Communications in Japan, Ministry of Internet Affairs and Communications, Japan, 2014) 2

4 Chap.2 NTT DATA Big Data Reference Architecture Looking at the mechanism of data utilization in the world, individual technologies have been provided such as the Hadoop, which is the infrastructure supporting distributed processing for large amounts of data, and CEP (Complex Event Processing), which supports real time analysis. Furthermore, some technologies are distributed as open source technology, so anyone can easily use these technologies. However, the key for utilizing Big Data for business is not only about gathering elemental technologies but also constructing the mechanism to fit the purpose of business by promptly combining these elemental technologies, and then flexibly expanding and developing it. Thus, NTT DATA Group systematizes the Big Data Reference Architecture (BDRA), which makes use of the global experience of developing Big Data solutions. (Figure 2) By using BDRA, we can represent the policy of Big Data utilization in accordance with the purpose and situation of the existing systems in each enterprise. Figure 2: NTT DATA Big Data Reference Architecture (BDRA) 3

5 This section describes the introduction of the framework of BDRA, which helps understanding use cases in the following section. The features of BDRA will be discussed later. BDRA is composed of three platforms and seven layers. The first platform, which has a role processing the various data for analysis, contains three layers: Information Gathering, Information Store, and Data Processing. The second one is the analytics platform, which is the core function for data utilization, and contains two layers: Data Analytics and Information Utilization. The third one is the management platform for total management and contains two layers: Governance and Infrastructure. (Figure 3) Figure 3: Layers of NTT DATA Big Data Reference Architecture Category Layer Overview Data Platform Information Gathering Information Store Data Processing This layer contains functions that gather various data generated and stored in various data sources such as web media, sensors and databases, changing them into a form that can be easily analyzed. It implements integration of different types of data by ETL, and deals with the improved reliability, availability, and accessibility by messaging/replication and shared information between different resources such as software and hardware in this layer. This layer contains database functions for flexibly storing and processing massive amounts of data. For example, distributed data store which realizes the processing of massive amounts of data, an in-memory database which realizes processing at high-speed, and NoSQL which realizes high scalability and flexibility, are contained in this layer. This layer contains a function for high-speed processing of massive amounts of data collected and a pre-processing function for analysis. For example, the core functions of Big Data solution such as distributed parallel processing which realizes massive data processing technology and complex event processing technology that realizes processing at high-speed, are in this layer. 4

6 Category Layer Overview Analytics Platform Data Analytics This layer contains functions for analyzing stored and collected data such as correlation analysis, natural language analysis and machine learning. For example, text mining and data mining are contained in this layer. Moreover, the analytics method, BICLAVIS originally developed by NTT DATA, optimizes various analytical methods and utilizes them in multiple ways. Information Utilization This layer contains functions for decision support with the results of analysis. Data visualization, OLAP, and business process management are contained in this layer. Management Platform Governance This layer contains functions for data management like data quality control and data protection. It realizes data quality management through data management such as information lifecycle management, data profiling, master data management, and metadata management. From the point of data protection view, it contains security management and auditing. Infrastructure This layer contains functions that realize both operation management and system management for the purpose of managing reliability, availability, performance, and scalability. Details about the data analytics method, BICLAVIS will be described in Challenges of Big Data utilization and the features of BDRA. 5

7 Chap.3 Use cases of Big Data Reference Architecture We introduce four main cases using BDRA mentioned in the previous section Forecast of variation of financial market index by using SNS data The following section describes the Twitter sentiment index, developed by real time analytics with a huge amount of data, that revealed the relation between a stock index and our Twitter sentiment index consisting of Twitter data. Recently, information utilization of SNS data such as Twitter data among financial sectors is becoming popular in the United States. There is also increasing demand in Japan for such utilization. In order to meet this demand, NTT DATA and NTT DATA Mathematical Systems developed the Twitter sentiment index, which is a numerical indicator of the proportion of positive or negative sentiments expressed in tweets relating to the stock market by extracting and analyzing Twitter data in real time. We verified that there is a statistically significant correlation between the Twitter sentiment index and the Nikkei 225 volatility index by extracting several millions of stock-related tweets for 35 months (from January 2011 to November 2013). Key points of Big Data utilization in this case are efficiently maintaining real time analysis and selecting analytical technologies. In order to analyze in real time, it has to construct a mechanism for quickly extracting data from high volume data. Besides, it takes more time to process Japanese text than other languages because it is necessary to take a process to judge the smallest word units by the context in Japanese while there are separations among words in English etc.therefore, analyzing tweets in real time is realized by Distributed Parallel Processing in the Data Processing layer and Distributed Data Store in the Information Store layer (specifically, utilizing the Hadoop Distributed File System) in BDRA. In addition, integrating various technologies such as Text Mining and Data Mining in the Data Analytics layer and Rule Engine in the Data Processing layer is one of the features in this case. Furthermore, the use of the data analytics method, BICLAVIS systematized by NTT DATA, assists in the selection of efficient analysis methods. In this case, an Evaluation and Important Analysis type scenario pattern is used in evaluating the correlation between the Twitter sentiment index and the Nikkei 225 volatility index. Details about the data analytics method, BICLAVIS will be described in Issues of Big Data utilization and the features of BDRA. 6

8 3.2. Automation Tool for System Development The following section describes the case in which we introduced the automation tool in system development by flexible data model construction and the use of metadata management. NTT DATA provides a total solution for open system development called TERASOLUNA, which realizes a conventional IT system with high quality in a short term due to a change in the business environment such as the progress of the globalization. We developed TERASOLUNA DS as one of the solutions that enables gathering information for system development such as design information and contributes to optimizing system development and quality assurance by implementing the consistency check of design documents and an accumulation of the design know-how. TERASOLUNA DS provides various functions: automating consistency and notation variability check among design documents, accelerated full-text searching of design documents and source codes, influential range analysis in changing specifications, and supporting input design document. It drastically improves productivity in the design phase by reducing reviews and supporting the identification of the influence range of specification changes or bug occurrences. In this case, Key points of Big Data utilization are that; complex schemas due to the difference in document formats depending on projects and the redesign of the schema due to the new document format being added. In this case, all documents are firstly converted into XML files by ETL processing in the Data Gathering layer, and are then stored in the NoSQL database. This constructs a flexible and schema-independent data model. In addition, "Metadata Management" in the Governance layer enables to automate a consistency check on design documents with efficiency and accuracy. The amount of design documents in large scale system development is enormous, extending to 40,000 files and 400,000 pages. By applying a mechanism of managing metadata such as structure, attribute, and recorded information about these design documents, it realizes the check and analysis with accuracy more than a manual review. 7

9 3.3. Real-time Bridge Monitoring System The following section describes the case of applying high speed processing for massive amounts of data based on the service of monitoring the Tokyo Gate Bridge in Japan and Can Tho Bridge in Vietnam. Bridges and roads are social infrastructure supporting the life of people and are thought to be safe anytime. Therefore, the road administrator is required to detect defect or damage in bridges, and thus make a decision on the road traffic flow or specify available routes. NTT DATA works on continuously collecting and analyzing various data in real time by using several sensors placed on the bridges such as strains of bridge beams and piers. The key point of Big Data utilization in this case is processing large amounts of sensor data in a short period. By using Complex Event Processing in the Data Processing layer in BDRA, this system can quickly analyze the sensor data of more than 100 bridges with just one server, in case of large-scale disasters, when the road administrators need to panoramically monitor multiple bridges. This system is realized by combing technologies in each layer: Data Mining in the Data Analytics layer to extract abnormal patterns; and Data Visualization in the Information Utilization layer to clearly visualize the anomalies in the detected results. Additionally, we can improve the accuracy in anomaly detection by using the data analytics method BICLAVIS developed by NTT DATA. The abnormal values detected from the sensor data include measurement failures because of the sensor malfunction, and external forces such as high winds and earthquakes. In order to distinguish between abnormal values and defects in bridges, we implemented pre-processing for low-frequency component removal and determination logic using lag correlation based on the positional relation between the sensors. Anomaly detection uses BICLAVIS scenario patterns: Outlier Detection if it is possible to define the abnormal patterns and Incorrect Detection if it is difficult to define them. 8

10 3.4. Traffic Congestion Control System The following section describes the case to ease traffic congestion by utilizing simulation technology of massive data and using a prediction/control analytics model. Traffic congestion is one of the biggest problems for both developed and developing countries. Congestion causes environmental problems like fossil fuel consumption and CO 2 emissions as well as enormous time and financial losses. Many countries have a strong interest in reducing and easing traffic congestion, however, most measures are expensive and the effectiveness of each measure is unclear. In addition, the problem is that these measures tend to be only partial optimization and not overall optimization. In order to solve the problem, NTT DATA developed a traffic simulation system that can evaluate the effectiveness of measures for easing traffic congestion such as traffic light control and traffic restriction. This system uses GPS data collected from car navigation systems and smart phones in each vehicle for a traffic simulation we tested in Jilin, China, and achieved a 27 percent improvement in bus service times by using the simulation results to ease traffic congestion. The technology we developed is based on the statistical traffic models with vehicles, roads, intersections, traffic lights and reproduces the traffic environment on a computer. Also, it enables to control the traffic lights by the best pattern that a light turns to green to minimize traffic congestions. The pattern are produced by traffic simulations, and they are evaluated through turning relevant parameters. Multi-agent simulation technology sets multiple system construction factors to operate in the computer and predicts the future. A traffic administrator can judge the effectiveness of traffic measures on some scenarios in advance with this system. Also, a traffic administrator is able to detect the causes of the current traffic condition such as road and time slots which tend to cause traffic congestion. In this case, the key point of Big Data utilization is high-speed processing for the traffic simulation platform to simulate a large amount of traffic volume. By utilizing Distributed Parallel Processing in the Data Processing layer, this system can handle over one million vehicles. In addition to Distributed Parallel Processing, we combine functions in each layer of BDRA: Real Time Capture in the Data Gathering layer and Data Visualization in the Information Utilization layer. This realizes efficient and appropriate development of architecture. Moreover, in this case, we use analysis method BICLAVIS for the data analysis of prediction and control, and adopt the Risk Simulation scenario pattern for this system. 9

11 Chap.4 Challenges of Big Data utilization and the features of BDRA The following chapter describes common issues of Big Data utilization found in the cases previously mentioned and the features of BDRA. Issues of Big Data utilization (1) The combination of multiple IT infrastructure technologies Single use of IT infrastructure technology is not enough and thus, the combination of data gathering, data storing, data processing, and data analysis is necessary when enterprises utilize Big Data. Especially, it becomes common to select items of realizing data storing among various technologies including relational database, NoSQL database etc. Therefore it is required to provide how to store data and/or how to use stored data. (2) Data analysis of various industries Various enterprises utilize Big Data such as finance, IT, social infrastructure etc. Furthermore, data analysis methods become complicated as enterprises require more advanced data analysis results. It is important to select the correct data analysis method in order to respond to these requirements without slowing business speed down. (3) Assurance of the data quality Stored data by enterprises is not originally assumed to be analyzed as we mentioned in the case of the automation of system development. Therefore, it is required to verify the availability of data for analysis by data profiling. Also, it is important to properly manage the lifecycle of data in order to get significant results from data analysis. The features of BDRA BDRA has the features as below to solve the three problems above. (1) Comprehensive framework to realize rapid and flexible technology integration BDRA systematized the knowledge of the Big Data utilization with the deep understanding as well as the combination of this knowledge. BDRA verified the combination of products by vendors and open source software (OSS) and thus, it can support selecting the combination of products by different vendors. The combination of products with high frequency of use is provided as a set; besides, it is possible to select products fitting with existing IT systems. (2) BICLAVIS to realize systematic and efficient approach to analysis NTT DATA developed cross-industrial data analytical methods BICLAVIS generated based on data analysis implemented over 200 cases. Data analysis work tends to be individualistic and a wide range of industries and business seek data analysis. Therefore, NTT DATA constructs the mechanism to gather know-how of data analysis and systematizes the analytics model in order to utilize this information in cross-industrial. Specifically, we organize them as patterns of analysis scenario based on the analysis purpose by categorizing and summarizing purposes, procedures, and techniques of data analysis with the template of those scenarios. (Figure 4) Thus, we can get results about requests from any industries. 10

12 Figure 4: Patterns of analysis scenario Scenario Pattern Portent Overall Detect the signs of structure change and situation change from Big Data. Anomaly Detection Automatically detect an abnormal pattern in real time and stimulate early crisis response through alerting. False Detection Detect an illegal or outlier situation that is fitted to the definition of an abnormal pattern. Outlier Detection Detect a deviation from the standard or normal situation. Prediction and Control By clarifying relation between the cause and effect at work and estimating the change in result due to manipulating causes, understand the appropriate standard for the cause. Profit Simulation Estimate the effect of work restructuring measures and prioritize them by simulation. Risk Simulation Assess risks by business modeling with uncertainties and prioritize them. Optimization Select the measures that maximize performance by the optimization method. Risk Hedge Support risk reduction with the risk scattered method Targeting Extract the targets to be approached, such as a potential customer in order to maximize the cost-effectiveness. Credit Control Determine the default risk of individuals or bankruptcy risk of enterprises. Evaluation and Factor Analysis Context Awareness Weigh up the various objects and identify the factors. Recommend the product and the service through analysis of behavior and preferences in advance. Process Trace Extract the process of growth and development and identify the accelerator or inhibitor. 11

13 (3) Governance layer to establish Big Data governance BDRA has abundant governance functions required for the utilization of Big Data, such as improving reliability of data and security. As the words Garbage in, Garbage out, only meaningless results come out from the inaccurate data. Especially about functions to improve reliability of data, we define the implementation of data profiling before data cleansing and fixing rules. Also, the system to manage master data has been confirmed. Furthermore, various functions are arranged from the viewpoint of security in order to protect data. Recently, there have been various discussions about personal data, therefore security is necessary to utilize Big Data with peace of mind. BDRA has security management as a series of methods with various audit points such as IT audit, information security audit, and Data- Centric Audit and Protection (DCAP). As we mentioned above, BDRA is aggregating know-how about various architecture and technology integration for the utilization of Big Data. NTT DATA has, thus far, been providing a lot of architecture by BDRA, and we are continuously going to improve it. NTT DATA Corporation Toyosu Center Building, 3-3, Toyosu 3-chome, Koto-ku, Tokyo , Japan The display of the (TM) mark or the (R) mark might be omitted in this paper.

Fujitsu Big Data Software Use Cases

Fujitsu Big Data Software Use Cases Fujitsu Big Data Software Use s Using Big Data Opens the Door to New Business Areas The use of Big Data is needed in order to discover trends and predictions, hidden in data generated over the course of

More information

Information Technology Engineers Examination. Network Specialist Examination. (Level 4) Syllabus. Details of Knowledge and Skills Required for

Information Technology Engineers Examination. Network Specialist Examination. (Level 4) Syllabus. Details of Knowledge and Skills Required for Information Technology Engineers Examination Network Specialist Examination (Level 4) Syllabus Details of Knowledge and Skills Required for the Information Technology Engineers Examination Version 2.0

More information

Offload Enterprise Data Warehouse (EDW) to Big Data Lake. Ample White Paper

Offload Enterprise Data Warehouse (EDW) to Big Data Lake. Ample White Paper Offload Enterprise Data Warehouse (EDW) to Big Data Lake Oracle Exadata, Teradata, Netezza and SQL Server Ample White Paper EDW (Enterprise Data Warehouse) Offloads The EDW (Enterprise Data Warehouse)

More information

White Paper. How Streaming Data Analytics Enables Real-Time Decisions

White Paper. How Streaming Data Analytics Enables Real-Time Decisions White Paper How Streaming Data Analytics Enables Real-Time Decisions Contents Introduction... 1 What Is Streaming Analytics?... 1 How Does SAS Event Stream Processing Work?... 2 Overview...2 Event Stream

More information

Big Data Collection and Utilization for Operational Support of Smarter Social Infrastructure

Big Data Collection and Utilization for Operational Support of Smarter Social Infrastructure Hitachi Review Vol. 63 (2014), No. 1 18 Big Data Collection and Utilization for Operational Support of Smarter Social Infrastructure Kazuaki Iwamura Hideki Tonooka Yoshihiro Mizuno Yuichi Mashita OVERVIEW:

More information

Social Innovation through Utilization of Big Data

Social Innovation through Utilization of Big Data Social Innovation through Utilization of Big Data Hitachi Review Vol. 62 (2013), No. 7 384 Shuntaro Hitomi Keiro Muro OVERVIEW: The analysis and utilization of large amounts of actual operational data

More information

2015 Analyst and Advisor Summit. Advanced Data Analytics Dr. Rod Fontecilla Vice President, Application Services, Chief Data Scientist

2015 Analyst and Advisor Summit. Advanced Data Analytics Dr. Rod Fontecilla Vice President, Application Services, Chief Data Scientist 2015 Analyst and Advisor Summit Advanced Data Analytics Dr. Rod Fontecilla Vice President, Application Services, Chief Data Scientist Agenda Key Facts Offerings and Capabilities Case Studies When to Engage

More information

Information Technology Engineers Examination. Information Security Specialist Examination. (Level 4) Syllabus

Information Technology Engineers Examination. Information Security Specialist Examination. (Level 4) Syllabus Information Technology Engineers Examination Information Security Specialist Examination (Level 4) Syllabus Details of Knowledge and Skills Required for the Information Technology Engineers Examination

More information

W H I T E P A P E R. Deriving Intelligence from Large Data Using Hadoop and Applying Analytics. Abstract

W H I T E P A P E R. Deriving Intelligence from Large Data Using Hadoop and Applying Analytics. Abstract W H I T E P A P E R Deriving Intelligence from Large Data Using Hadoop and Applying Analytics Abstract This white paper is focused on discussing the challenges facing large scale data processing and the

More information

DATA EXPERTS MINE ANALYZE VISUALIZE. We accelerate research and transform data to help you create actionable insights

DATA EXPERTS MINE ANALYZE VISUALIZE. We accelerate research and transform data to help you create actionable insights DATA EXPERTS We accelerate research and transform data to help you create actionable insights WE MINE WE ANALYZE WE VISUALIZE Domains Data Mining Mining longitudinal and linked datasets from web and other

More information

Market Trends: IoT Enables Smart City to Better Manage Public Infrastructures

Market Trends: IoT Enables Smart City to Better Manage Public Infrastructures G00262860 Market Trends: IoT Enables Smart City to Better Manage Public Infrastructures Published: 26 September 2014 Analyst(s): Nagayoshi Nakano Monitoring public infrastructures has emerged as a critical

More information

Reimagining Business with SAP HANA Cloud Platform for the Internet of Things

Reimagining Business with SAP HANA Cloud Platform for the Internet of Things SAP Brief SAP HANA SAP HANA Cloud Platform for the Internet of Things Objectives Reimagining Business with SAP HANA Cloud Platform for the Internet of Things Connect, transform, and reimagine Connect,

More information

Moving Large Data at a Blinding Speed for Critical Business Intelligence. A competitive advantage

Moving Large Data at a Blinding Speed for Critical Business Intelligence. A competitive advantage Moving Large Data at a Blinding Speed for Critical Business Intelligence A competitive advantage Intelligent Data In Real Time How do you detect and stop a Money Laundering transaction just about to take

More information

Amplify Serviceability and Productivity by integrating machine /sensor data with Data Science

Amplify Serviceability and Productivity by integrating machine /sensor data with Data Science Data Science & Big Data Practice INSIGHTS ANALYTICS INNOVATIONS Manufacturing IoT Amplify Serviceability and Productivity by integrating machine /sensor data with Data Science What is Internet of Things

More information

VIEWPOINT. High Performance Analytics. Industry Context and Trends

VIEWPOINT. High Performance Analytics. Industry Context and Trends VIEWPOINT High Performance Analytics Industry Context and Trends In the digital age of social media and connected devices, enterprises have a plethora of data that they can mine, to discover hidden correlations

More information

Big Data and Advanced Analytics Technologies for the Smart Grid

Big Data and Advanced Analytics Technologies for the Smart Grid 1 Big Data and Advanced Analytics Technologies for the Smart Grid Arnie de Castro, PhD SAS Institute IEEE PES 2014 General Meeting July 27-31, 2014 Panel Session: Using Smart Grid Data to Improve Planning,

More information

Luncheon Webinar Series May 13, 2013

Luncheon Webinar Series May 13, 2013 Luncheon Webinar Series May 13, 2013 InfoSphere DataStage is Big Data Integration Sponsored By: Presented by : Tony Curcio, InfoSphere Product Management 0 InfoSphere DataStage is Big Data Integration

More information

Alexander Nikov. 5. Database Systems and Managing Data Resources. Learning Objectives. RR Donnelley Tries to Master Its Data

Alexander Nikov. 5. Database Systems and Managing Data Resources. Learning Objectives. RR Donnelley Tries to Master Its Data INFO 1500 Introduction to IT Fundamentals 5. Database Systems and Managing Data Resources Learning Objectives 1. Describe how the problems of managing data resources in a traditional file environment are

More information

Exploration on Security System Structure of Smart Campus Based on Cloud Computing. Wei Zhou

Exploration on Security System Structure of Smart Campus Based on Cloud Computing. Wei Zhou 3rd International Conference on Science and Social Research (ICSSR 2014) Exploration on Security System Structure of Smart Campus Based on Cloud Computing Wei Zhou Information Center, Shanghai University

More information

Smarter Energy: optimizing and integrating renewable energy resources

Smarter Energy: optimizing and integrating renewable energy resources IBM Sales and Distribution Energy & Utilities Thought Leadership White Paper Smarter Energy: optimizing and integrating renewable energy resources Enabling industrial-scale renewable energy generation

More information

ENZO UNIFIED SOLVES THE CHALLENGES OF OUT-OF-BAND SQL SERVER PROCESSING

ENZO UNIFIED SOLVES THE CHALLENGES OF OUT-OF-BAND SQL SERVER PROCESSING ENZO UNIFIED SOLVES THE CHALLENGES OF OUT-OF-BAND SQL SERVER PROCESSING Enzo Unified Extends SQL Server to Simplify Application Design and Reduce ETL Processing CHALLENGES SQL Server does not scale out

More information

Contents. Condition-based Maintenance for High-speed Fleet 1 M2M approach to the CBM Solution... 2. References 4 About Author... 4

Contents. Condition-based Maintenance for High-speed Fleet 1 M2M approach to the CBM Solution... 2. References 4 About Author... 4 Contents Condition-based Maintenance for High-speed Fleet 1 M2M approach to the CBM Solution.............................................. 2 References 4 About Author............................................................

More information

End to End Solution to Accelerate Data Warehouse Optimization. Franco Flore Alliance Sales Director - APJ

End to End Solution to Accelerate Data Warehouse Optimization. Franco Flore Alliance Sales Director - APJ End to End Solution to Accelerate Data Warehouse Optimization Franco Flore Alliance Sales Director - APJ Big Data Is Driving Key Business Initiatives Increase profitability, innovation, customer satisfaction,

More information

Achieving Integrated IT Service Management

Achieving Integrated IT Service Management Achieving Integrated IT Service Management V Akihiko Sakurai (Manuscript received January 22, 2007) Increasingly, IT Infrastructure Library (ITIL) practices are being adopted to improve the quality of

More information

Big Data and Healthcare Payers WHITE PAPER

Big Data and Healthcare Payers WHITE PAPER Knowledgent White Paper Series Big Data and Healthcare Payers WHITE PAPER Summary With the implementation of the Affordable Care Act, the transition to a more member-centric relationship model, and other

More information

Effective Data Integration - where to begin. Bryte Systems

Effective Data Integration - where to begin. Bryte Systems Effective Data Integration - where to begin Bryte Systems making data work Bryte Systems specialises is providing innovative and cutting-edge data integration and data access solutions and products to

More information

Big Data & Analytics for Semiconductor Manufacturing

Big Data & Analytics for Semiconductor Manufacturing Big Data & Analytics for Semiconductor Manufacturing 半 導 体 生 産 におけるビッグデータ 活 用 Ryuichiro Hattori 服 部 隆 一 郎 Intelligent SCM and MFG solution Leader Global CoC (Center of Competence) Electronics team General

More information

Data Science & Big Data Practice

Data Science & Big Data Practice INSIGHTS ANALYTICS INNOVATIONS Data Science & Big Data Practice Manufacturing Internet of Things (IoT) Amplify Serviceability and Productivity by integrating machine /sensor data with Data Science What

More information

Architecting an Industrial Sensor Data Platform for Big Data Analytics: Continued

Architecting an Industrial Sensor Data Platform for Big Data Analytics: Continued Architecting an Industrial Sensor Data Platform for Big Data Analytics: Continued 2 8 10 Issue 1 Welcome From the Gartner Files: Blueprint for Architecting Sensor Data for Big Data Analytics About OSIsoft,

More information

Bringing Together ESB and Big Data

Bringing Together ESB and Big Data Bringing Together ESB and Big Data Bringing Together ESB and Big Data Table of Contents Why ESB and Big Data?...3 Exploring the Promise of Big Data and ESB... 4 Moving Forward With ESB and Big Data...5

More information

Syslog Analyzer ABOUT US. Member of the TeleManagement Forum. info@ossera.com +1-916-290-9300 http://www.ossera.com

Syslog Analyzer ABOUT US. Member of the TeleManagement Forum. info@ossera.com +1-916-290-9300 http://www.ossera.com Syslog Analyzer ABOUT US OSSera, Inc. is a global provider of Operational Support System (OSS) solutions for IT organizations, service planning, service operations, and network operations. OSSera's multithreaded

More information

INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY

INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK A SURVEY ON BIG DATA ISSUES AMRINDER KAUR Assistant Professor, Department of Computer

More information

Chapter 6. Foundations of Business Intelligence: Databases and Information Management

Chapter 6. Foundations of Business Intelligence: Databases and Information Management Chapter 6 Foundations of Business Intelligence: Databases and Information Management VIDEO CASES Case 1a: City of Dubuque Uses Cloud Computing and Sensors to Build a Smarter, Sustainable City Case 1b:

More information

Big Data Analytics. Copyright 2011 EMC Corporation. All rights reserved.

Big Data Analytics. Copyright 2011 EMC Corporation. All rights reserved. Big Data Analytics 1 Priority Discussion Topics What are the most compelling business drivers behind big data analytics? Do you have or expect to have data scientists on your staff, and what will be their

More information

Big Data Processing in Cloud Environments

Big Data Processing in Cloud Environments Big Data in Cloud Environments Satoshi Tsuchiya Yoshinori Sakamoto Yuichi Tsuchimoto Vivian Lee In recent years, accompanied by lower prices of information and communications technology (ICT) equipment

More information

Big Data Use Cases Update

Big Data Use Cases Update Big Data Use Cases Update Sanat Joshi Industry Solutions Manufacturing Industries Business Unit 1 Data Explosion Web & social networks experienced it first Infographic by Go-gulf.com 2 Number Of Connected

More information

Streaming Analytics and the Internet of Things: Transportation and Logistics

Streaming Analytics and the Internet of Things: Transportation and Logistics Streaming Analytics and the Internet of Things: Transportation and Logistics FOOD WASTE AND THE IoT According to the Food and Agriculture Organization of the United Nations, every year about a third of

More information

Chapter 6 8/12/2015. Foundations of Business Intelligence: Databases and Information Management. Problem:

Chapter 6 8/12/2015. Foundations of Business Intelligence: Databases and Information Management. Problem: Foundations of Business Intelligence: Databases and Information Management VIDEO CASES Chapter 6 Case 1a: City of Dubuque Uses Cloud Computing and Sensors to Build a Smarter, Sustainable City Case 1b:

More information

The Internet of Things

The Internet of Things The Internet of Things Vijay Sethia Senior Product Manager, IBM Software Group 2014 IBM Corporation Agenda The Internet of Things The IBM IoT On-Prem Cloud Sample IoT Application 1 The Internet of Things

More information

International collaboration to understand the relevance of Big Data for official statistics

International collaboration to understand the relevance of Big Data for official statistics Statistical Journal of the IAOS 31 (2015) 159 163 159 DOI 10.3233/SJI-150889 IOS Press International collaboration to understand the relevance of Big Data for official statistics Steven Vale United Nations

More information

The Recipe for Sarbanes-Oxley Compliance using Microsoft s SharePoint 2010 platform

The Recipe for Sarbanes-Oxley Compliance using Microsoft s SharePoint 2010 platform The Recipe for Sarbanes-Oxley Compliance using Microsoft s SharePoint 2010 platform Technical Discussion David Churchill CEO DraftPoint Inc. The information contained in this document represents the current

More information

Challenges for Big Data Applications in Japan: Hopes and Concerns

Challenges for Big Data Applications in Japan: Hopes and Concerns Challenges for Big Data Applications in Japan: Hopes and Concerns Kibi International University Takushi OTANI In this presentation, I will discuss three challenging big data applications that are thought

More information

Nagarjuna College Of

Nagarjuna College Of Nagarjuna College Of Information Technology (Bachelor in Information Management) TRIBHUVAN UNIVERSITY Project Report on World s successful data mining and data warehousing projects Submitted By: Submitted

More information

REAL-TIME OPERATIONAL INTELLIGENCE. Competitive advantage from unstructured, high-velocity log and machine Big Data

REAL-TIME OPERATIONAL INTELLIGENCE. Competitive advantage from unstructured, high-velocity log and machine Big Data REAL-TIME OPERATIONAL INTELLIGENCE Competitive advantage from unstructured, high-velocity log and machine Big Data 2 SQLstream: Our s-streaming products unlock the value of high-velocity unstructured log

More information

BIG DATA: BIG CHALLENGE FOR SOFTWARE TESTERS

BIG DATA: BIG CHALLENGE FOR SOFTWARE TESTERS BIG DATA: BIG CHALLENGE FOR SOFTWARE TESTERS Megha Joshi Assistant Professor, ASM s Institute of Computer Studies, Pune, India Abstract: Industry is struggling to handle voluminous, complex, unstructured

More information

Concept and Project Objectives

Concept and Project Objectives 3.1 Publishable summary Concept and Project Objectives Proactive and dynamic QoS management, network intrusion detection and early detection of network congestion problems among other applications in the

More information

Development and Runtime Platform and High-speed Processing Technology for Data Utilization

Development and Runtime Platform and High-speed Processing Technology for Data Utilization Development and Runtime Platform and High-speed Processing Technology for Data Utilization Hidetoshi Kurihara Haruyasu Ueda Yoshinori Sakamoto Masazumi Matsubara Dramatic increases in computing power and

More information

The 4 Pillars of Technosoft s Big Data Practice

The 4 Pillars of Technosoft s Big Data Practice beyond possible Big Use End-user applications Big Analytics Visualisation tools Big Analytical tools Big management systems The 4 Pillars of Technosoft s Big Practice Overview Businesses have long managed

More information

How to Enhance Traditional BI Architecture to Leverage Big Data

How to Enhance Traditional BI Architecture to Leverage Big Data B I G D ATA How to Enhance Traditional BI Architecture to Leverage Big Data Contents Executive Summary... 1 Traditional BI - DataStack 2.0 Architecture... 2 Benefits of Traditional BI - DataStack 2.0...

More information

Data Warehousing Fundamentals for IT Professionals. 2nd Edition

Data Warehousing Fundamentals for IT Professionals. 2nd Edition Brochure More information from http://www.researchandmarkets.com/reports/2171973/ Data Warehousing Fundamentals for IT Professionals. 2nd Edition Description: Cutting-edge content and guidance from a data

More information

StreamStorage: High-throughput and Scalable Storage Technology for Streaming Data

StreamStorage: High-throughput and Scalable Storage Technology for Streaming Data : High-throughput and Scalable Storage Technology for Streaming Data Munenori Maeda Toshihiro Ozawa Real-time analytical processing (RTAP) of vast amounts of time-series data from sensors, server logs,

More information

The IBM Solution Architecture for Energy and Utilities Framework

The IBM Solution Architecture for Energy and Utilities Framework IBM Solution Architecture for Energy and Utilities Framework Accelerating Solutions for Smarter Utilities The IBM Solution Architecture for Energy and Utilities Framework Providing a foundation for solutions

More information

Harnessing the Data Flood: Oracle s Visionary Platform from Device to Data Center. Chris Baker Senior Vice President Worldwide ISV/OEM Java Sales

Harnessing the Data Flood: Oracle s Visionary Platform from Device to Data Center. Chris Baker Senior Vice President Worldwide ISV/OEM Java Sales Harnessing the Data Flood: Oracle s Visionary Platform from Device to Data Center Chris Baker Senior Vice President Worldwide ISV/OEM Java Sales Canvas Lumber Compass Sextant 1851 America s Cup The oldest

More information

International Journal of Advanced Engineering Research and Applications (IJAERA) ISSN: 2454-2377 Vol. 1, Issue 6, October 2015. Big Data and Hadoop

International Journal of Advanced Engineering Research and Applications (IJAERA) ISSN: 2454-2377 Vol. 1, Issue 6, October 2015. Big Data and Hadoop ISSN: 2454-2377, October 2015 Big Data and Hadoop Simmi Bagga 1 Satinder Kaur 2 1 Assistant Professor, Sant Hira Dass Kanya MahaVidyalaya, Kala Sanghian, Distt Kpt. INDIA E-mail: simmibagga12@gmail.com

More information

Complex Event Processing (CEP) Why and How. Richard Hallgren BUGS 2013-05-30

Complex Event Processing (CEP) Why and How. Richard Hallgren BUGS 2013-05-30 Complex Event Processing (CEP) Why and How Richard Hallgren BUGS 2013-05-30 Objectives Understand why and how CEP is important for modern business processes Concepts within a CEP solution Overview of StreamInsight

More information

IoT Analytics Today and in 2020

IoT Analytics Today and in 2020 Competitive Edge from Edge Intelligence IoT Analytics Today and in 2020 Aapo Markkanen: Principal Analyst Competitive Edge from Edge Intelligence IoT Analytics Today and in 2020 INTRODUCTION Across the

More information

Big Data, Physics, and the Industrial Internet! How Modeling & Analytics are Making the World Work Better."

Big Data, Physics, and the Industrial Internet! How Modeling & Analytics are Making the World Work Better. Big Data, Physics, and the Industrial Internet! How Modeling & Analytics are Making the World Work Better." Matt Denesuk! Chief Data Science Officer! GE Software! October 2014! Imagination at work. Contact:

More information

Towards Smart and Intelligent SDN Controller

Towards Smart and Intelligent SDN Controller Towards Smart and Intelligent SDN Controller - Through the Generic, Extensible, and Elastic Time Series Data Repository (TSDR) YuLing Chen, Dell Inc. Rajesh Narayanan, Dell Inc. Sharon Aicler, Cisco Systems

More information

Performance testing as a full life cycle activity. Julian Harty

Performance testing as a full life cycle activity. Julian Harty Performance testing as a full life cycle activity Julian Harty Julian Harty & Stuart Reid 2004 Scope of Performance Performance What is performance testing? Various views 3 outcomes 3 evaluation techniques

More information

Tracking System for GPS Devices and Mining of Spatial Data

Tracking System for GPS Devices and Mining of Spatial Data Tracking System for GPS Devices and Mining of Spatial Data AIDA ALISPAHIC, DZENANA DONKO Department for Computer Science and Informatics Faculty of Electrical Engineering, University of Sarajevo Zmaja

More information

Chapter 6 Basics of Data Integration. Fundamentals of Business Analytics RN Prasad and Seema Acharya

Chapter 6 Basics of Data Integration. Fundamentals of Business Analytics RN Prasad and Seema Acharya Chapter 6 Basics of Data Integration Fundamentals of Business Analytics Learning Objectives and Learning Outcomes Learning Objectives 1. Concepts of data integration 2. Needs and advantages of using data

More information

The future of Big Data A United Hitachi View

The future of Big Data A United Hitachi View The future of Big Data A United Hitachi View Alex van Die Pre-Sales Consultant 1 Oktober 2014 1 Agenda Evolutie van Data en Analytics Internet of Things Hitachi Social Innovation Vision and Solutions 2

More information

Bringing Big Data into the Enterprise

Bringing Big Data into the Enterprise Bringing Big Data into the Enterprise Overview When evaluating Big Data applications in enterprise computing, one often-asked question is how does Big Data compare to the Enterprise Data Warehouse (EDW)?

More information

Understanding traffic flow

Understanding traffic flow White Paper A Real-time Data Hub For Smarter City Applications Intelligent Transportation Innovation for Real-time Traffic Flow Analytics with Dynamic Congestion Management 2 Understanding traffic flow

More information

Knowledgent White Paper Series. Developing an MDM Strategy WHITE PAPER. Key Components for Success

Knowledgent White Paper Series. Developing an MDM Strategy WHITE PAPER. Key Components for Success Developing an MDM Strategy Key Components for Success WHITE PAPER Table of Contents Introduction... 2 Process Considerations... 3 Architecture Considerations... 5 Conclusion... 9 About Knowledgent... 10

More information

III Big Data Technologies

III Big Data Technologies III Big Data Technologies Today, new technologies make it possible to realize value from Big Data. Big data technologies can replace highly customized, expensive legacy systems with a standard solution

More information

FOR IMMEDIATE RELEASE

FOR IMMEDIATE RELEASE FOR IMMEDIATE RELEASE Introducing "Operational Management Innovation Service", a Service that Improves the Utilization Rate of Factories, Plants, and Public Infrastructure Facilities, while also Achieving

More information

Formal Methods for Preserving Privacy for Big Data Extraction Software

Formal Methods for Preserving Privacy for Big Data Extraction Software Formal Methods for Preserving Privacy for Big Data Extraction Software M. Brian Blake and Iman Saleh Abstract University of Miami, Coral Gables, FL Given the inexpensive nature and increasing availability

More information

Understanding the Value of In-Memory in the IT Landscape

Understanding the Value of In-Memory in the IT Landscape February 2012 Understing the Value of In-Memory in Sponsored by QlikView Contents The Many Faces of In-Memory 1 The Meaning of In-Memory 2 The Data Analysis Value Chain Your Goals 3 Mapping Vendors to

More information

Big Data Are You Ready? Jorge Plascencia Solution Architect Manager

Big Data Are You Ready? Jorge Plascencia Solution Architect Manager Big Data Are You Ready? Jorge Plascencia Solution Architect Manager Big Data: The Datafication Of Everything Thoughts Devices Processes Thoughts Things Processes Run the Business Organize data to do something

More information

Blueprints and feasibility studies for Enterprise IoT (Part Two of Three)

Blueprints and feasibility studies for Enterprise IoT (Part Two of Three) Blueprints and feasibility studies for Enterprise IoT (Part Two of Three) 1 Executive Summary The Internet of Things provides a host of opportunities for enterprises to design, develop and launch smart

More information

Make the right decisions with Distribution Intelligence

Make the right decisions with Distribution Intelligence Make the right decisions with Distribution Intelligence Bengt Jensfelt, Business Product Manager, Distribution Intelligence, April 2010 Introduction It is not so very long ago that most companies made

More information

Big Data and Analytics: Challenges and Opportunities

Big Data and Analytics: Challenges and Opportunities Big Data and Analytics: Challenges and Opportunities Dr. Amin Beheshti Lecturer and Senior Research Associate University of New South Wales, Australia (Service Oriented Computing Group, CSE) Talk: Sharif

More information

Chapter ML:XI. XI. Cluster Analysis

Chapter ML:XI. XI. Cluster Analysis Chapter ML:XI XI. Cluster Analysis Data Mining Overview Cluster Analysis Basics Hierarchical Cluster Analysis Iterative Cluster Analysis Density-Based Cluster Analysis Cluster Evaluation Constrained Cluster

More information

Big Data. Copyright 2014 Engineering Group, SpagoBI Competency Center. All rights reserved. www.spagobi.org

Big Data. Copyright 2014 Engineering Group, SpagoBI Competency Center. All rights reserved. www.spagobi.org Big Data Overview on SpagoBI suite A comprehensive suiteoffering a full set of analytical and reporting tools. Innovative themes and solutions: Location Intelligence, Free inquiry, KPI, Interactive cockpits,

More information

Interactive data analytics drive insights

Interactive data analytics drive insights Big data Interactive data analytics drive insights Daniel Davis/Invodo/S&P. Screen images courtesy of Landmark Software and Services By Armando Acosta and Joey Jablonski The Apache Hadoop Big data has

More information

Paper 064-2014. Robert Bonham, Gregory A. Smith, SAS Institute Inc., Cary NC

Paper 064-2014. Robert Bonham, Gregory A. Smith, SAS Institute Inc., Cary NC Paper 064-2014 Log entries, Events, Performance Measures, and SLAs: Understanding and Managing your SAS Deployment by Leveraging the SAS Environment Manager Data Mart ABSTRACT Robert Bonham, Gregory A.

More information

COMP9321 Web Application Engineering

COMP9321 Web Application Engineering COMP9321 Web Application Engineering Semester 2, 2015 Dr. Amin Beheshti Service Oriented Computing Group, CSE, UNSW Australia Week 11 (Part II) http://webapps.cse.unsw.edu.au/webcms2/course/index.php?cid=2411

More information

Attunity Better Data Movement For The Internet Of Things

Attunity Better Data Movement For The Internet Of Things Attunity Better Data Movement For The Internet Of Things Internet of Things North America Chicago 4-15-2015 Kevin Petrie Senior Director Attunity 4/15/15 Telemetry Has Come A Long Way Weather Sensors on

More information

Understanding the impact of the connected revolution. Vodafone Power to you

Understanding the impact of the connected revolution. Vodafone Power to you Understanding the impact of the connected revolution Vodafone Power to you 02 Introduction With competitive pressures intensifying and the pace of innovation accelerating, recognising key trends, understanding

More information

Big Data Services From Hitachi Data Systems

Big Data Services From Hitachi Data Systems SOLUTION PROFILE Big Data Services From Hitachi Data Systems Create Strategy, Implement and Manage a Solution for Big Data for Your Organization Big Data Consulting Services and Big Data Transition Services

More information

Cloud Computing Based on Service- Oriented Platform

Cloud Computing Based on Service- Oriented Platform Cloud Computing Based on Service- Oriented Platform Chiseki Sagawa Hiroshi Yoshida Riichiro Take Junichi Shimada (Manuscript received March 31, 2009) A new concept for using information and communications

More information

I D C V E N D O R S P O T L I G H T

I D C V E N D O R S P O T L I G H T I D C V E N D O R S P O T L I G H T Workstyle Innovation with Mobile/Cloud, and Fuji Xerox Next Generation Managed Print Services December 2014 By Eiji Ishida, Atushi Kikuchi Sponsored by Fuji Xerox Co.,

More information

Predictive Analytics: Turn Information into Insights

Predictive Analytics: Turn Information into Insights Predictive Analytics: Turn Information into Insights Pallav Nuwal Business Manager; Predictive Analytics, India-South Asia pallav.nuwal@in.ibm.com +91.9820330224 Agenda IBM Predictive Analytics portfolio

More information

Using Predictive Maintenance to Approach Zero Downtime

Using Predictive Maintenance to Approach Zero Downtime SAP Thought Leadership Paper Predictive Maintenance Using Predictive Maintenance to Approach Zero Downtime How Predictive Analytics Makes This Possible Table of Contents 4 Optimizing Machine Maintenance

More information

BIG DATA & ANALYTICS. Transforming the business and driving revenue through big data and analytics

BIG DATA & ANALYTICS. Transforming the business and driving revenue through big data and analytics BIG DATA & ANALYTICS Transforming the business and driving revenue through big data and analytics Collection, storage and extraction of business value from data generated from a variety of sources are

More information

BIG DATA GREAT VALUE.

BIG DATA GREAT VALUE. BIG DATA GREAT VALUE. For those who want to be ahead of the field. T-Systems Big Data 21.06.2013 1 ON THE LOOKOUT FOR NEW SOURCES OF VALUE CREATION. WHAT WILL DRIVE BUSINESSES IN FUTURE? From the black

More information

An Agent-Based Concept for Problem Management Systems to Enhance Reliability

An Agent-Based Concept for Problem Management Systems to Enhance Reliability An Agent-Based Concept for Problem Management Systems to Enhance Reliability H. Wang, N. Jazdi, P. Goehner A defective component in an industrial automation system affects only a limited number of sub

More information

Big Data must become a first class citizen in the enterprise

Big Data must become a first class citizen in the enterprise Big Data must become a first class citizen in the enterprise An Ovum white paper for Cloudera Publication Date: 14 January 2014 Author: Tony Baer SUMMARY Catalyst Ovum view Big Data analytics have caught

More information

Craig McWilliams Craig Burrell. Bringing Smarter, Safer Transport to NZ

Craig McWilliams Craig Burrell. Bringing Smarter, Safer Transport to NZ Craig McWilliams Craig Burrell Bringing Smarter, Safer Transport to NZ World Class Transport. Smarter, Stronger, Safer. Bringing Smarter Safer Transport to NZ Craig Burrell Infrastructure Advisory Director

More information

EBERSPÄCHER ELECTRONICS automotive bus systems. solutions for network analysis

EBERSPÄCHER ELECTRONICS automotive bus systems. solutions for network analysis EBERSPÄCHER ELECTRONICS automotive bus systems solutions for network analysis DRIVING THE MOBILITY OF TOMORROW 2 AUTOmotive bus systems System Overview Analyzing Networks in all Development Phases Control

More information

Big Data-Challenges and Opportunities

Big Data-Challenges and Opportunities Big Data-Challenges and Opportunities White paper - August 2014 User Acceptance Tests Test Case Execution Quality Definition Test Design Test Plan Test Case Development Table of Contents Introduction 1

More information

Where is... How do I get to...

Where is... How do I get to... Big Data, Fast Data, Spatial Data Making Sense of Location Data in a Smart City Hans Viehmann Product Manager EMEA ORACLE Corporation August 19, 2015 Copyright 2014, Oracle and/or its affiliates. All rights

More information

DAMA NY DAMA Day October 17, 2013 IBM 590 Madison Avenue 12th floor New York, NY

DAMA NY DAMA Day October 17, 2013 IBM 590 Madison Avenue 12th floor New York, NY Big Data Analytics DAMA NY DAMA Day October 17, 2013 IBM 590 Madison Avenue 12th floor New York, NY Tom Haughey InfoModel, LLC 868 Woodfield Road Franklin Lakes, NJ 07417 201 755 3350 tom.haughey@infomodelusa.com

More information

Service Middleware for Performing and Optimizing On-site Work

Service Middleware for Performing and Optimizing On-site Work Service Middleware for Performing and Optimizing On-site Work V Tsuneo Katsuyama V Youji Kohda (Manuscript received April 20, 2007) The growing importance of services is placing greater emphasis on company

More information

What do Big Data & HAVEn mean? Robert Lejnert HP Autonomy

What do Big Data & HAVEn mean? Robert Lejnert HP Autonomy What do Big Data & HAVEn mean? Robert Lejnert HP Autonomy Much higher Volumes. Processed with more Velocity. With much more Variety. Is Big Data so big? Big Data Smart Data Project HAVEn: Adaptive Intelligence

More information

Meeting the challenges of today s oil and gas exploration and production industry.

Meeting the challenges of today s oil and gas exploration and production industry. Meeting the challenges of today s oil and gas exploration and production industry. Leveraging innovative technology to improve production and lower costs Executive Brief Executive overview The deep waters

More information

The big data revolution

The big data revolution The big data revolution Friso van Vollenhoven (Xebia) Enterprise NoSQL Recently, there has been a lot of buzz about the NoSQL movement, a collection of related technologies mostly concerned with storing

More information