Rule-based temporal query languages provide the expressive power and flexibility required to capture in a natural way complex analysis tasks over streaming data. Stream processing applications, however, typically require near real-time response using limited resources. In particular, it becomes essential that the underpinning query language has favourable computational properties and that stream processing algorithms are able to keep only a small number of previously received facts in memory at any point in time without sacrificing correctness. In this paper, we propose a recursive fragment of temporal Datalog with tractable data complexity and study the properties of a generic stream reasoning algorithm for this fragment. We focus on the window validity problem as a way to minimise the number of time points for which the stream reasoning algorithm needs to keep data in memory at any point in time.

In the last few years, Artificial Intelligence (AI) has gained momentum in every industry. Healthcare, transportation, manufacturing, logistics, retail, and more have started to use AI-based applications to improve productivity and performance. According to PwC, Artificial Intelligence will contribute $15.7 trillion to the global economy by 2030. So businesses can reap huge benefits from investing in AI. The MIT Sloan Management Review's 2017 Artificial Intelligence Global Executive Study and Research Project found that 85% of executives believe that AI will help their businesses gain or sustain competitive advantage.

Click to learn more about author Dr. Michael Zeller. As volume, velocity and variety of data – often referred to as the "three V's of Big Data" – continue- to increase, many businesses are unprepared to match the influx of this data with tools for collection and analysis. Even more surprising is that most organizations are not in a position to truly leverage their own operational data for more intelligent decision making, which is the means by which data converts into business value. Basically, the current, standard business processes mostly leverage hard-coded rules and make fixed, pre-defined decisions. This classical "rules-based" approach is in many cases ill-suited for today's Cloud-centric, data-driven enterprise environment where customers demand a more personalized response of IT systems.

Artificial intelligence in cybersecurity has recently made several headlines: "The Future of Cybersecurity: Artificial Intelligence" (Cybersecurity.CIOReview), "How AI is the Future of Cybersecurity" (Infosecurity Magazine) and "AI in Cyber Security Market to Grow at an Exorbitant Pace" (P&S Market Research). These headlines make seasoned cybersecurity professionals wary. We've seen other emerging technologies receive similar attention, and we've seen many of them fail to live up to their expectations. In this article, we will build a real-world perspective on AI in cybersecurity. We will explore where scepticism regarding AI in cybersecurity is justified, how the technology can provide tangible value, and what to look for in an AI-driven cybersecurity provider.

New Zealand Prime Minister Jacinda Ardern said those are the things she has obsessed over the most as a new parent, and that the experience of focusing on such basic needs for her baby girl has helped her appreciate why people with young families may not find time for politics. "So it's our job to make sure that we are serving the needs of people, regardless of whether they have time to engage with what we're doing or not," she said. "That's something that's been really amplified for me." Ardern spoke on Thursday at her bungalow in Auckland as she prepared to return to the capital, Wellington, after six weeks of leave following the birth of her daughter, Neve. Ardern, 38, is just the second elected world leader in recent history to give birth while holding office, and her story has provided inspiration for working mothers around the world.

AUCKLAND, New Zealand – New Zealand Prime Minister Jacinda Ardern said Thursday the world is watching closely to see if recent optimism over North Korea leads to the elimination of its nuclear weapons program. In an interview with The Associated Press, Ardern also said New Zealand is among many nations concerned about the tit-for-tat trade war between China and the U.S. She said the idea of New Zealand becoming a republic is not a priority for her government. Ardern spoke with the AP at her home in Auckland as she prepared to return to the capital, Wellington, after six weeks of leave following the birth of her daughter, Neve. Ardern, 38, is just the second elected world leader in recent history to give birth while holding office.

Prime Minister Shinzo Abe and his Maltese counterpart Joseph Muscat agreed Wednesday to closely cooperate in maintaining maritime order based on the rule of law. In a news conference after their meeting in Tokyo, Abe said the two leaders confirmed the importance of the issue. Malta is the home of the International Maritime Law Institute aimed at training specialists in the field. Abe said they also agreed to work together in resolving outstanding issues surrounding North Korea, including Japanese nationals abducted by Pyongyang in the 1970s and 1980s. On the economic front, Muscat, the first Maltese prime minister to visit Japan in 28 years, expressed hope for an early implementation of a free trade agreement Japan and the European Union signed last month.

When Charles Givre, lead data scientist at Deutsche Bank, teaches security teams about the benefits of applying security data science techniques, he often focuses on a common malware tactic: domain-generation algorithms. Used by malicious programs to establish contact with a command-and-control server, domain-generation algorithms, or DGAs, create a list of domain names as potential contact points using pseudo-random algorithms. The domains change often -- usually daily -- and can look random or use random words. For humans, finding a single computer's call to a random domain is a difficult problem. Yet data analysis can quickly call out the anomalous communications.

Deep learning video analysis has increasingly become a trend in security. With hardware advances and an overwhelming amount of data, their future growth potential is not to be ignored. That was one of the main points discussed by Memoori in its recent blog post titled "Will AI Video Analytics Finally Add'Real Intelligence' to Video Surveillance?." Needless to say, artificial intelligence (AI) and deep learning have garnered the attention of security vendors and users alike. The technology learns different objects, behavior or character traits and can detect abnormalities and irregularities with more precision and accuracy than video content analysis technologies employed in the past. "Video analytics has eaten a few free lunches over the last 15 years. Whilst it has certainly added some value to video installations, there has been much debate about exactly'how intelligent' the technology really is and whether it provides satisfactory ROI. But in 2018, there is now a growing belief that video analytics could finally move beyond what has been achieved through conventional rule-based systems," the post said.

It's a timeless manufacturing goal: to produce high quality products at a minimum cost. Factory 4.0 is already demonstrating its value by enabling manufacturers to reach this goal more successfully than ever, and one of the core technologies driving this new wave of ultra-automation is Industrial AI and Machine Learning. Data has become a valuable resource, and it's cheaper than ever to capture and store. Through the use of artificial intelligence, specifically Machine Learning, manufacturers can use data to significantly impact their bottom line by greatly improving efficiency, employee safety, and product quality. Maintenance represents a significant part of any manufacturing operation's expenses.