Tag: event stream processing
“Quick response forecasting (QRF) techniques are forecasting processes that can incorporate information quickly enough to act upon by agile supply chains” explained Dr. Larry Lapide, in a recent Journal of Business Forecasting column. The concept of QRF is based on updating demand forecasts to reflect real and rapid changes in demand, both
The fourth edition of the series brings me to Rijkswaterstaat, the Dutch government agency responsible for the main connecting infrastructure in the Netherlands (roads, bridges, waterways and water systems). I talked with Bas van Essen who’s responsible for RWS Datalab, the big data lab within Rijkswaterstaat (RWS). Company Overview RWS
If you’ve been waiting for the buzz to settle around the Internet of Things before deciding how to invest in this new technology space, now’s the time to stop waiting. I’ve been working in the technology sector for a few decades, and the innovation and excitement I’m seeing around IoT
Will the IoT live up to the hype? Yes. A most resounding YES. In fact, it will exceed the hype, because we don't even know all the IoT possibilities yet. We don’t know what we don’t know, and that lack of imagination limits even our hype. Where we are with
For many industries, products and features are no longer the most crucial differentiators in the minds of customers. Take mobile telecommunications, for example. The recent market shift from virtually no unlimited data plans to announcements of unlimited data offerings by every major US wireless carrier in a short span of
Let me start by posing a question: "Are you forecasting at the edge to anticipate what consumers want or need before they know it?" Not just forecasting based on past demand behavior, but using real-time information as it is streaming in from connected devices on the Internet of Things (IoT).
„Durchsatz ist wichtig, jaja“, Supply-Chain-Leiter Herr Aklit lehnt sich zurück, faltet seine Hände über dem üppigen Bauch und sagt zu Lenin: „Sie haben ja schon einiges in Fluss gebracht mit Ihren Projekten zur Datenanalyse im Internet of Things.“ Er atmet tief durch und schaut aus dem Fenster: „Alles fließt …“,
Jim Harris discusses how the lines between data management and analytics are fading.
Lenin und ich sitzen im Publikum und applaudieren heftig: Seine Chefin hat ihren Vortrag beendet über „Datenqualität als Erfolgsfaktor im Internet of Things“. „Kein Datenqualitätsprojekt ohne Hilfe von oben“, raunt Lenin mir zu, "Unterstützung vom Boss ist manchmal wichtiger als tolle Software." Ich will beleidigt darauf hinweisen, dass seine Chefin
@philsimon says that old stalwarts sometimes just don't cut it.
David Loshin extends his exploration of ethical issues surrounding automated systems and event stream processing to encompass data quality and risk considerations.
Unless you’ve been living under a rock, you've surely noticed the increasing numbers of headlines about big data, Hadoop, internet of things (IoT) and, of course, data streaming. For many companies, this next generation of data management is clearly marked "to play with later." That's because adopting the next wave
I’m drawn to immersive analytics (IA) because it covers areas I’ve been looking at since 2012, and have been publishing on since early 2014, like virtual reality and data worlds. I’m retroactively applying the cool new term IA (not to be confused with AI for artificial intelligence) to all of my activities
We've all seen it before – a truck on the side of the road with the hood up and the driver desperate to figure out what’s wrong. In this situation, not only is a customer not receiving goods on time, but the problem is exacerbated by the fact that most
Streaming technologies have been around for years, but as Felix Liao recently blogged, the numbers and types of use cases that can take advantage of these technologies have now increased exponentially. I've blogged about why streaming is the most effective way to handle the volume, variety and velocity of big data. That's
Hadoop may have been the buzzword for the last few years, but streaming seems to be what everyone is talking about these days. Hadoop deals primarily with big data in stationary and batch-based analytics. But modern streaming technologies are aimed at the opposite spectrum, dealing with data in motion and
It’s nearly impossible to avoid the debate. From politicians and pundit commentary, to dinner table discussions across the United States, the hot topic for the last several years has been the rising cost of health care. Consider that health care expenditures in the US were $3 trillion in 2014 and are
You could say we've been working toward the internet of things (IoT) since computers were first invented. Look at how airplanes have changed from flying by wire to now, quite literally, flying by IoT (or connected plane). The connected car is another example of how big data analytics is the
Applying analytics to IoT data provides opportunities for cities to use information from sensors, citizens and connected infrastructure in unprecedented ways.
Do you have a need for real-time, streaming analytics? What technology are you considering? How are you going to enable rapid development and deployment of your analytical models all in real-time? SAS has partnered the the Hortonworks HDF team to develop a nifi processor that allows SAS machine learning models and
Some of the most common questions from customers are about their analytics administration. Often regarded as house-keeping, administration failures can nevertheless cause real pain. The typical day-to-day tasks of analytics administration include checking availability and health of the analytics platform components, allocating proper resources for users like memory, file systems,
“Good afternoon, Mr. Yakamoto. How did you like that three-pack of tank tops you bought last time you were in?” Washington D.C. Year 2054. Chief of PreCrime John Anderton is running from the law for a crime he has not committed yet. After a risky eye transplant in order to
The numbers are daunting. More than 40 million Americans have their identities stolen each year. Credit card companies lose more than $200 billion annually due to fraud. Cybercrime-related losses exceed $3 million per claim for large companies. If you’re like me, those stats are enough to give pause. To fuel the concern,
.@philsimon says that it's never too early to think about the IoT and data management.