Big Data and Cloud Computing

"The BlueBorne attack vector requires no user interaction, is compatible to all software versions, and does not require any preconditions or configurations aside of the Bluetooth being active. Unlike the common misconception, Bluetooth enabled devices are constantly searching for incoming connections from any devices, and not only those they have been paired with. This means a Bluetooth connection can be established without pairing the devices at all. This makes BlueBorne one of the most broad potential attacks found in recent years, and allows an attacker to strike completely undetected."

Cybersecurity threats continue to escalate. The growing number, frequency and impact of cyber attacks have propelled cybersecurity to the top of the CFO and board agendas. Today’s cybercriminals are well-funded, organized structures with the patience to infiltrate a company’s systems over time to obtain the information they desire. The infiltrators have gone beyond focusing on credit card details. They have long-term strategies, which often mean they are targeting to manipulate a company’s share price, alter a company’s financial data or undertake or interfere with large-scale financial transactions. CFOs fully understand that cyber threats are real. They have seen the financial and reputational damage victims have faced. And as the world becomes increasingly digital, the number and severity of these threats will only intensify. Cybersecurity is fundamentally about protecting an organization’s shareholder value. CFOs must play a much greater role. The first step tow...

import errno import os import signal import time class TimeoutError(Exception): pass class timeout: def __init__(self, seconds=1, error_message='Timeout'): self.seconds = seconds self.error_message = error_message def handle_timeout(self, signum, frame): raise TimeoutError(self.error_message) def __enter__(self): signal.signal(signal.SIGALRM, self.handle_timeout) signal.alarm(self.seconds) def __exit__(self, type, value, traceback): signal.alarm(0) with timeout(seconds=3): time.sleep(4)

Cross-occurrence allows us to ask the question: are 2 events correlated. To use the Ecom example, purchase is the conversion or primary action, a detail page view might be related but we must test each cross-occurrence to make sure. I know for a fact that with many ecom datasets it is impossible to treat these events as the same thing and get anything but a drop in quality of recommendations (I’ve tested this). People that use the ALS recommender in Spark’s MLlib sometimes tell you to weight the view less than the purchase. But this is nonsense (again I’ve tested this). What is true is that *some* views lead to purchases and others do not. So treating them all with the same weight is pure garbage. What CCO does is find the views that seem to lead to purchase. It can also find category-preferences that lead to certain purchases, as well as location-preference (triggered by a purchase when logged in from some location).  And so on. Just about anything you know about users or c...

f you’re like me, you run a WordPress blog and are terrified of the thought of something going wrong.  With core updates, theme updates, plugin updates, and server component updates, there is a lot of room for error.  This is where a WordPress backup could help ease your mind. WordPress recommends taking a backup of your blog before any of these are done and there are even some popular plugins that will do this for you.  For example, you could use the popular  UpdraftPlus  or similar, but I believe there is room for error in those as well.  While I could be wrong, I think WordPress must be in good shape for backup plugins to be successful. The alternative would be to create your own backup scripts that run on a cron schedule.  We’re going to see how to do this for WordPress instances running on a Linux machine. Creating the Backup Script There are two core components that need to be backed up in case of a catastrophe.  You need to back...

In order to write a tutorial about classification, it was necessary to find an example that was broad enough that it would need to be sub-divided. Since I actually care about whether you remember this stuff, it needed to be something that a lot of people like and would relate to. And since I have a lot of international subscribers, it needed to be cross-cultural as well. So what is universal, cross-cultural, and dearly loved? Beer. There’s American beer, Mexican beer, German beer, Belgian beer….hell, even the Japanese make beer. There’s IPA, Lager, Pilsner. Dark, light, stout. There are so many ways to classify beer that we could spend weeks doing it (so naturally, I did). Now, before you can classify anything you have to determine the characteristics that you’re going to use. For beer you could use country of origin, color, alcohol content, type of hops, type of yeast, and calorie count among other things. That way you could sort based on any of those characteristics t...

n today’s world of big data and the internet of things, it is common for a business to find itself sitting atop a mountain of data. Possessing it is one thing, but leveraging it for data driven decision making is a much different ball game. Gut-feelings and institutionalized heuristics have traditionally been used to guide development of protocol and decision making, but the world of artificial intelligence and big disparate data is changing that. Everyone is trying to make sense of, and extract value from, their data. Those that are not will be left behind. This challenge (and opportunity) is not limited to certain industries. For instance, most companies are exploring how they can use data to make better marketing decisions, most retailers are using data to optimize their supply chains, and most manufacturers are using data for quality control of final products.  Almost all business problems (with surrounding data) can be broken down into two categories: supervised and unsu...