Netflix has mysteriously been removed from the American film industry's search engine for legal streaming sites. WhereToWatch, which was set up by the Motion Picture Association of America (MPAA) in 2014, lets users search a range of legal streaming services for their favourite TV shows and films. The idea was to provide internet users with a handy resource which would steer them away from illegal streams and downloads, protecting them from potential legal trouble and helping the studios at the same time. However, as TorrentFreak reports, Netflix has been removed from the WhereToWatch search results, despite being the one of the most-used legal streaming services in the world. Netflix results have also been removed from the UK equivalent of the site, FindAnyFilm, although some Netflix results are still available on GoWatchIt, the search engine which powers WhereToWatch.

Apache Spark is increasingly thought of as the new jack-of-all-trades distributed platform for big data crunching – what with everything from traditional MapReduce-like workloads, streaming, graph computation, statistics, and machine learning all in one package. Except for Spark Streaming, with its micro-batches, Spark is focused for the most part on higher-latency, rich/complex analytics workloads. What about using Spark as an embedded, web-speed / low-latency query engine? This post will dive into using Apache Spark for low-latency, higher concurrency reporting / dashboard / SQL-like applications - up to hundreds of queries a second! Launching Spark applications on a cluster, or even on localhost, has a pretty high overhead.

As data grows, organizations are increasingly seeking for an intelligent information discovery and analytics platform that goes beyond keyword searches and better understands users' intent. With Google Now and Cortana, advanced question answering systems are starting to become ubiquitous. Recently, Gartner has also started discussing'insight engines,' a new technology that can provide natural, total, and proactive search, analytics, and discovery. Please let us know the email address we should be sending a PDF copy of the white paper to. A download link will be immediately emailed to you - please check your junk mail if you have a strong email filter.

At your day job as a professional code Googler – I mean developer – you probably search for quick snippets multiple times a day to find the best way to perform a particular task. Almost always as developers we end up on Stack Overflow or Mozilla Developer Network, but now Microsoft's Bing has given us something even better: executable code directly in search results. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May. Thanks to a collaboration with HackerRank, if you search for something like string concat C#, you'll get an interactive code editor with a result that can be run directly from that page to see how it works. It's a seriously fantastic feature that I hope Google adds soon – I'm not sure I'd switch search engine for this, but I'm incredibly jealous.

The face of SEO is changing, and it's mainly due to incredible research and development into artificial intelligence (AI) baked into search algorithms. A decade ago, search engine marketers could create a site with some keywords in specific places, drop some links around the web, and start ranking within a short amount of time. The web has grown tremendously since that time, and now these methods are no longer viable. Instead of focusing on keywords, search engine algorithms attempt to "understand" your content. The latest news of Google's new RankBrain algorithm is just one example of why the future of SEO will change Internet marketing. Before getting into the details of 2016 SEO, it helps to understand Google's market.

Tutorials, books, articles, data sets, certifications, you name it. All about data science, machine learning and related topics. You can find them with a simple keyword search: enter the keyword "free" in the DSC's search box, and here are the results. Below is a screenshot of the DSC search results page, for the keyword "free". It shows the top 6 results, out of dozens of highly relevant search results.

The way you add a bot is the same way you'd add a contact; indeed, the Add Contact screen in Skype now has two categories: "People" and "Bots." As of right now, the only Bots available to add on Skype are Bing Music, Bing News, Bing Images, Getty Images and Build Bot (which is mostly centered around the Build conference). They're mostly in Preview mode at the moment (in other words, they're still sort of in beta), but they do appear to be functional. As you might expect, the chat bots respond to very specific keywords. For example, with the Bing Music bot, typing in "Hello" would bring up Adele's song (a link to her YouTube video, it looks like) rather than polite small talk.

An Iranian charged with hacking the computer system that controlled a New York dam used a readily available Google search process to identify the vulnerable system, according to people familiar with the federal investigation. The process, known as "Google dorking," isn't as simple as an ordinary online search. Yet anyone with a computer and Internet access can perform it with a few special techniques. Federal authorities say it is...

We represent knowledge as integrity constraints in a formalization of probabilistic spatio-temporal knowledge bases. We start by defining the syntax and semantics of a formalization called PST knowledge bases. This definition generalizes an earlier version, called SPOT, which is a declarative framework for the representation and processing of probabilistic spatio-temporal data where probability is represented as an interval because the exact value is unknown. We augment the previous definition by adding a type of non-atomic formula that expresses integrity constraints. The result is a highly expressive formalism for knowledge representation dealing with probabilistic spatio-temporal data. We obtain complexity results both for checking the consistency of PST knowledge bases and for answering queries in PST knowledge bases, and also specify tractable cases. All the domains in the PST framework are finite, but we extend our results also to arbitrarily large finite domains.

I am a newbie to Bigdata and would like to highlight some significant advantages if incorporated in a company's supply-chain management strategies, expecting the reader's views and suggestions. Because, in recent past I have developed a online supply-chain management systems in which sellers and customers are matched using an algorithm. It acted as a decision support system and I needed to dig deeper on the available data to get more insights over the data pattern (even for evaluating its correctness). We know that data is generated throughout the supply chain – So, for sure the manufacturers of consumer goods or services analyze key performance indicators to ensure the resources are delivering at its capacity. So, there is a need for efficient data storage/management and information retrieval techniques. Current technology can track and capture data effectively and efficiently, even in vast quantities.