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Results 26 - 38 of 38Sort Results By: Published Date | Title | Company Name
By: SAS     Published Date: Jun 06, 2018
Data integration (DI) may be an old technology, but it is far from extinct. Today, rather than being done on a batch basis with internal data, DI has evolved to a point where it needs to be implicit in everyday business operations. Big data – of many types, and from vast sources like the Internet of Things – joins with the rapid growth of emerging technologies to extend beyond the reach of traditional data management software. To stay relevant, data integration needs to work with both indigenous and exogenous sources while operating at different latencies, from real time to streaming. This paper examines how data integration has gotten to this point, how it’s continuing to evolve and how SAS can help organizations keep their approach to DI current.
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SAS
By: Exablox     Published Date: Jan 27, 2015
As your organization grows, you need the ability to quickly and non-disruptively scale storage capacity to meet the increasing amounts of high-density graphic images, streaming media and other unstructured data. However, when using traditional storage methods, it can be complicated and expensive for you to scale capacity to meet your needs. In this second of a series of informative e-books from Exablox, we take a look at the di?iculties of traditional storage approaches, and o?er simple, practical ways to make your data storage easier to scale.
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enterprise storage, unstructured data, data mangement, data recovery, data availability, storage performance, exablox, oneblox, data management
    
Exablox
By: IBM     Published Date: Aug 05, 2014
There is a lot of discussion in the press about Big Data. Big Data is traditionally defined in terms of the three V’s of Volume, Velocity, and Variety. In other words, Big Data is often characterized as high-volume, streaming, and including semi-structured and unstructured formats. Healthcare organizations have produced enormous volumes of unstructured data, such as the notes by physicians and nurses in electronic medical records (EMRs). In addition, healthcare organizations produce streaming data, such as from patient monitoring devices. Now, thanks to emerging technologies such as Hadoop and streams, healthcare organizations are in a position to harness this Big Data to reduce costs and improve patient outcomes. However, this Big Data has profound implications from an Information Governance perspective. In this white paper, we discuss Big Data Governance from the standpoint of three case studies.
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ibm, data, big data, information, healthcare, governance, technology, it management, data management
    
IBM
By: IBM     Published Date: Jan 13, 2016
IBM InfoSphere Information Server connects to many new ‘at rest’ and streaming big data sources, scales natively on Hadoop using partition and pipeline parallelism, automates data profiling, provides a business glossary, and an information catalog, plus also supports IT.
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ibm, data integration, infosphere, information server, partition, data profiling, knowledge management, data management
    
IBM
By: SAS     Published Date: Aug 28, 2018
Data integration (DI) may be an old technology, but it is far from extinct. Today, rather than being done on a batch basis with internal data, DI has evolved to a point where it needs to be implicit in everyday business operations. Big data – of many types, and from vast sources like the Internet of Things – joins with the rapid growth of emerging technologies to extend beyond the reach of traditional data management software. To stay relevant, data integration needs to work with both indigenous and exogenous sources while operating at different latencies, from real time to streaming. This paper examines how data integration has gotten to this point, how it’s continuing to evolve and how SAS can help organizations keep their approach to DI current.
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SAS
By: Phunware     Published Date: Aug 11, 2014
Mobile devices are streaming millions of location data points in real-time. These data points are extremely valuable in their own right because the very apps that help generate data can also be used to act on insights and deliver relevant messages. Download these insights and examples to turn mobile data into actions.
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phunware, mobile, physical marketing analytics, location analytics, business intelligence, space management, customer service, crm, loyalty, analytics for mobile, analytics on mobile, app analytics, app tracking analytics, mobile analytics tool, mobile app analytics, mobile app usage analytics, mobile data analytics, mobile device analytics, mobile location analytics, knowledge management
    
Phunware
By: Impetus     Published Date: Mar 15, 2016
Streaming analytics platforms provide businesses a method for extracting strategic value from data-in-motion in a manner similar to how traditional analytics tools operate on data-at rest.
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impetus, guide to stream analytics, real time streaming analytics, streaming analytics, real time analytics, big data analytics, monitoring, network architecture, business activity monitoring, business analytics, analytical applications, data warehousing
    
Impetus
By: IBM     Published Date: Jul 07, 2015
In this book you will also learn how cognitive computing systems, like IBM Watson, fit into the Big Data world. Learn about the concept of data-in-motion and InfoSphere Streams, the world’s fastest and most flexible platform for streaming data.
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big data, mobility, compute-intensive apps, virtualization, cloud computing, scalable infrastructure, reliability, data management
    
IBM
By: SAS     Published Date: Apr 25, 2017
If you’re in the data world, you know it’s full of discord. Multiple data sources, inconsistent standards and definitions, inaccurate reports and a lack of governance are enough to derail any organization. What’s an enterprise architect to do? With the right data governance and master data management (MDM) solution, you can set and enforce policies and establish a consistent view of your data without micromanaging it. You can eliminate duplicate and inconsistent data. You can combine traditional data and new big data sources – like streaming data from the IoT – into one harmonious view. Read this e-book for expert advice and case studies that will show you new ways to manage your big data – and make sure everyone’s on the same page.
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SAS
By: SAS     Published Date: Apr 25, 2017
Today, data is constantly flowing in and out of organizations from electrical and mechanical sensors, RFID tags, smart meters, scanners, mobile devices, vehicles, live social media, machines and other objects. Did you know that a modern plane with more than 10,000 sensors just in the wings is expected to generate more than 7 terabytes a day? And Bain predicts that by 2020 annual revenues could exceed $470 billion for the internet of things (IoT) vendors selling hardware, software and comprehensive solutions. Analysts believe that all of this data will drive a new type of industrial revolution – one that’s driven by highly accurate, real-time analysis, alerts and actions. Increasingly, machines will automate decisions and simply notify humans with instructions. Consider the promise of the IoT, where any object can be connected to the internet and continuously send and receive data. Gartner says that by 2020, 21 billion IoT devices will be in use worldwide.
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SAS
By: WiChorus     Published Date: Nov 05, 2007
Rising demand for multimedia applications and mobile usage requires new paradigm to shift voice-oriented cellular architecture into data-oriented networks in order to serve bandwidth hungry packet based applications which include but not limited to multimedia gaming, mobiTV, streaming media, P2P, etc. Data oriented network requires 20-fold fatter air link and backhaul as compared to typical voice communication.
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wimax, access controller, asn-gw, gateway, base station, ofdma, edge router, farpoint group, controller, wichorus, wireless, wifi, wireless communications, wireless infrastructure, wireless service providers
    
WiChorus
By: SAS     Published Date: Apr 16, 2015
ITS technology is a general term. Two common and related forms of ITS communication technology using event stream processing are referred to as vehicle-to-vehicle (V2V) and vehicle to-infrastructure (V2X) in the US, and car-to-infrastructure (Car2X) in Europe. The two types of connected-car research and development programs often overlap and can be integrated. Car2X enables vehicle communication with the road transportation infrastructure and provides the ability to send or receive local information about traffic conditions, geo-markers (e.g. to identify pothole locations), road hazards, alerts, safety vehicles, etc. V2V focuses on connected-car technology and the anonymous communication of sensor data continuously transmitted to and from cars. Using event stream processing, this streaming data enables the real-time synthesis of information to communicate what will improve and promote driver safety, reduce crashes, and improve vehicle transportation efficiency.
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SAS
By: AWS     Published Date: May 18, 2018
We’ve become a world of instant information. We carry mobile devices that answer questions in seconds and we track our morning runs from screens on our wrists. News spreads immediately across our social feeds, and traffic alerts direct us away from road closures. As consumers, we have come to expect answers now, in real time. Until recently, businesses that were seeking information about their customers, products, or applications, in real time, were challenged to do so. Streaming data, such as website clickstreams, application logs, and IoT device telemetry, could be ingested but not analyzed in real time for any kind of immediate action. For years, analytics were understood to be a snapshot of the past, but never a window into the present. Reports could show us yesterday’s sales figures, but not what customers are buying right now. Then, along came the cloud. With the emergence of cloud computing, and new technologies leveraging its inherent scalability and agility, streaming data
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AWS
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