Data Mining


TigerGraph, a graph database born to roar

ZDNet

One thing that may have helped in getting for example what TigerGraph says is the largest transaction graph in production in the world at Alipay, with more than 100 billion vertices, 600 billion edges and 2 billion daily real time updates, is TigerGraph's backing. According to TigerGraph's benchmark, TigerGraph runs queries from 4 to almost 500 times faster than the competition, loads data from 2 to 25 times faster, and uses about 80 percent less space to store that data. By having a native C graph storage engine (GSE) work side-by-side with a graph processing engine (GPE) to handle of data and algorithms and by using parallelism and a distributed architecture. TigerGraph offers a browser-based SDK called GraphStudio to enable users to create graph models, map and load data sources and build graph queries.


Sweet IoT Journey: How One Solution Provider Helped Implement Microsoft Azure Machine Learning At Hershey

#artificialintelligence

An early dive into the Internet of Things landscape yielded sweet rewards for The Hershey Company once the chocolate manufacturer began tracking the weight of Twizzlers during production. Morinigo said the advent of scalable cloud-based analytics – Microsoft Azure Machine Learning, in this case – became a catalyst for the Twizzlers project because it reduced technical barriers involved in implementing data science capabilities. At New Signature, a Microsoft systems integrator, Morinigo said he was able to build an IoT and advanced analytics team because he felt the scalability of the cloud finally enabled business intelligence initiatives for significant success. Completed in just two weeks, the first model built by Hershey and New Signature achieved 60 percent accuracy – not perfect, but a step in the right direction when it came to implementing IoT.


[session] Leveraging AI @CloudExpo #AI #Chatbots #DX #ArtificialIntelligence

#artificialintelligence

With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-4, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. Join Cloud Expo @ThingsExpo conference chair Roger Strukhoff (@IoT2040), October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, for three days of intense Enterprise Cloud and'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets. Accordingly, attendees at the upcoming 21st Cloud Expo @ThingsExpo October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track.


Meet The Startups That Bring Artificial Intelligence To Log Management And Analysis

#artificialintelligence

By bringing Machine Learning to log analysis, the systems become smart by becoming proactive. Sensing the opportunity in algorithmic log management, a few startups have built viable business models in the space of Security Information and Event Management (SIEM). Perspica, the San Jose-based startup was found in 2014 by JF Huard, ex-CTO of Netuitive, Inc. Like most of its competitors, it promises to deliver artificial intelligence powered analytics and observability for TechOps and DevOps. It integrates with mainstream monitoring systems, log aggregation engines and Big Data platforms.


Industry News: Machine Learning and Artificial Intelligence for the week ending September 18, 2017

#artificialintelligence

There are different frameworks, libraries, applications, toolkits, and datasets in the machine learning world that can be very confusing, especially if you're a beginner. Add to that investment professionals' continuing struggle to fully incorporate the low, or negative, interest rate environment into the portfolio management process. It seems, like everything else in business, that the buzzwords of AI, big data and machine learning continue to generate noise as the saviours of human roles struggling to keep afloat with the challenges that the same tech revolution has created…. Detecting iceberg orders using ordinary machine learning methods is difficult, but obvious upon human inspection.


Five lessons banks can learn from disrupted industries

#artificialintelligence

Online banking has made access to banking services easier than ever for millions of people and in the process reduced the need for an extensive branch network. Patients can also take photos of meals, which are then available for their doctor to view, giving much more insight into patients' lifestyles and potential risks," says Rowan Scranage at Couchbase. "Early applications for AI have spread through many industries, from healthcare where providers are starting to use cognitive analytics to aid in the diagnosis of patients, to consumer products such as Apple's Siri, with varying degrees of success," says Dr Richard Harmon, director of Europe, Middle East and Africa financial services at Cloudera. By making it easier to access banking on the go and present pertinent products to users on mobile, based on extensive customer data, conventional banks can utilise the most user-friendly elements of startup banks.


Towards Artificial General Intelligence in Enterprise – Data Science Driven by Statistics Requires New Qualitative Analytics to Model Disruptive Changes

@machinelearnbot

Statistical solutions use inductive rationale to predict outcome based on voluminous historical data. Disruptive changes require deductive and abductive rationale to find solutions on new facts when historical data on such facts does not exists. We propose a comprehensive solution that would enable enterprises to use Artificial General Intelligence (AGI) to discover new relevant subjects, so as to discover and augment existing quantitative analysis with new or previously unknown domain variables. The real-time aspect of tactical and strategic execution requires new analytic methods on qualitative data.Meta Vision Analysis and Bionic Fusion Analysis enable enterprise organizations to transcend this limitation and dynamically discover relevant domain variables, drawing insights for real-time execution by "connecting the dots".


Machine Learning APIs power a more intelligent Internet of Things at the Edge

@machinelearnbot

Where these IoT devices are in fact already doing some limited analytics at or very near the point of capture (as in the case with true Edge Computing systems), there is opportunity to create a more intelligent, more relevant, and more positive experience or outcome from the Internet of Things by using Haven OnDemand Machine Learning APIs to perform early analytics and computing that enhances or augments the data that is being acquired and aggregated at the edge. It achieved this by analyzing local law enforcement open data crime statistics to detect specific crime trends and specific crime anomalies. A more intelligent IoT solution would analyze still images to detect the presence of faces, recognize and extract text via Optical Character Recognition (OCR), identify corporate logos and even read barcodes. Examples include counting customers, analyzing customer demographics, analyzing customer personal effects to detect logos and determine brand preferences, analyzing real-time social media check-in mentions for sentiment, and point-of-sale data trend analysis.


Adding Stanford CoreNLP To Big Data Pipelines (Apache NiFi 1.1/HDF 2.1) Part 1 of 2 - Hortonworks

@machinelearnbot

The latest version of Stanford CoreNLP includes a server that you can run and access via REST API. CoreNLP adds a lot of features, but the one most interesting to me is Sentiment Analysis. You can call the Stanford Server via wget and curl. Another simple option for Sentiment Analysis and NLP integration is to use Apache NiFi's ExecuteScript to call various Python libraries.