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.
Published By: Gomez IT
Published Date: Dec 19, 2011
The Gomez Application Performance Management (APM) platform is the industry's leading solution for managing and optimizing the performance, availability and quality of web, non-web, mobile, streaming and cloud applications.
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.
Published 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.
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.
Historically, the speed of a wired connection at the access edge has always been faster than that of a wireless connection. This means workers had to choose between the performance of wired connectivity and the convenience of a wireless connection. Consequently, workers often had to modify the way they worked— first, finding a wired port to use for high-bandwidth applications, such as video or streaming media, and then shifting to wireless connections when the need to be mobile was more important than having the performance of wired.
Today, though, the industry sits on the precipice of the biggest innovation in the history of wireless LAN: the release of 802.11ac Wave 2. Wave 1 of 802.11ac brought network speeds that were 1.3 Gbps (Gigabits per second), which is on par with wired networking speeds. However, 802.11ac Wave 2 shatters the gigabit barrier with speeds of up to 6.8 Gbps.
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.
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.
Analytics is now an expected part of the bottom line. The irony is that as more companies become adept at analytics, it becomes less of a competitive advantage. Enter machine learning. Recent advances have led to increased interest in adopting this technology as part of a larger, more comprehensive analytics strategy. But incorporating modern machine learning techniques into production data infrastructures is not easy.Businesses are now being forced to look deeper into their data to increase efficiency and competitiveness. Read this report to learn more about modern applications for machine learning, including recommendation systems, streaming analytics, deep learning and cognitive computing. And learn from the experiences of two companies that have successfully navigated both organizational and technological challenges to adopt machine learning and embark on their own analytics evolution.
Managing UC means delivering streaming video fluidly, transmitting voice with clarity, moving IM's briskly, and send emails promptly. Easier said than done. To succeed from setup to show time, take these three tips to achieve the high performance end-users expect.
Published 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.
TV 2.0 offers TV Viewing PLUS complete control of all PC applications from the comfort of your couch. The complete convergence of the PC and TV has arrived. Read more in this white paper by Icron Technologies Corporation.
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.
This white paper identifies some of the challenges COBOL applications face when working with large XML documents and discusses how these challenges can be addressed using a technique called "streaming".
Learn how easy it is to create and send a single PDF Portfolio using Adobe Acrobat X Pro. See how to create a rich, branded PDF Portfolio containing many types of documents, each with a rich preview experience. Discover how to embed web pages and streaming media and even output the entire portfolio experience as a web page.
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