{"id":26359,"date":"2021-06-02T06:00:00","date_gmt":"2021-06-02T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=26359"},"modified":"2021-06-03T09:38:39","modified_gmt":"2021-06-03T16:38:39","slug":"knowledge-graphs-2-0-high-performance-computing-emerges","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2021\/06\/02\/knowledge-graphs-2-0-high-performance-computing-emerges\/","title":{"rendered":"Knowledge Graphs 2.0: High Performance Computing Emerges"},"content":{"rendered":"\n<p>The increasing reliance on knowledge graphs parallels that of Artificial Intelligence for three irrefutable reasons. They\u2019re the most effective means of preparing data for statistical AI, creditable knowledge graph platforms utilize supervised and unsupervised learning to accelerate numerous processes, and their smart inferences are a form of machine intelligence.<\/p>\n\n\n\n<p>Coupling knowledge graphs with high performance computing enables organizations to not only avail themselves of sophisticated techniques to optimize AI, but also employ it at the scale and speed of contemporary data demands. According to <a href=\"https:\/\/www.katanagraph.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Katana Graph<\/a> CEO Keshav Pingali, \u201cThere is a need for high performance graph computing\u2026in two ways. One is the volume of data, and the other is time to insight.\u201d<\/p>\n\n\n\n<p>Scaling knowledge graphs with high performance computing is a means of rapidly analyzing the tremendous data quantities organizations routinely contend with for informed, low latent action across numerous use cases including \u201cintrusion detection, fraud detection, and Anti-Money Laundering,\u201d Pingali noted.<\/p>\n\n\n\n<p>Moreover, this synthesis allows them to do so with established and emerging AI approaches that are imperative for accurately producing desired results in these and other deployments.<\/p>\n\n\n\n<p><strong>Scaling Out High Performance Computing<\/strong><\/p>\n\n\n\n<p><a href=\"https:\/\/www.gartner.com\/en\/information-technology\/glossary\/high-performance-computing-hpc\" target=\"_blank\" rel=\"noreferrer noopener\">High performance computing<\/a> typically involves clusters with multiple processing units swiftly performing complicated calculations. It has a lengthy history in science use cases that are too computationally intense for conventional approaches. \u201cPeople are used to the notion of high performance computing in the context of computational science applications, where you\u2019re solving very large systems of PDEs and you use something like finite elements to ultimately generate systems of linear and non-linear equations,\u201d Pingali explained.<\/p>\n\n\n\n<p>This computing paradigm is critical for deployments in which copious amounts of storage and compute are required. The ability to distribute workloads among multiple servers is fundamental to high performance computing, particularly for apportioning a knowledge graph engine among machines for parallel processing tasks. According to Pingali, top options in this space \u201cscale to 256 machines\u201d as required for computational demands at scale. This capability is primed for AI deployments involving event stream processing and other applications.<\/p>\n\n\n\n<p><strong>Performant Knowledge Graphs<\/strong><\/p>\n\n\n\n<p>Parallel processing with high performance computing is ideal for addressing the size of knowledge graphs in the post big data era. \u201cA scale out solution is essential in some verticals\u2026in fintech, security identity,\u201d Pingali reflected. \u201cWe\u2019re talking about very big graphs, very big topologies, in some cases maybe a trillion edges. And also, lots of property data on nodes and edges.\u201d The overflowing quantities of predominantly unstructured data inundating the enterprise via external, cloud, social media, and IoT sources are directly responsible for the expansiveness of contemporary knowledge graphs.<\/p>\n\n\n\n<p>Pingali echoed the notion that \u201cmore than half of the world\u2019s data was created in the last two years, but less than 2 percent of it has been analyzed. Some of this data is of course structured data\u2026but a lot of that data is also unstructured and can be viewed usefully as graphs and processed usefully with graph algorithms.\u201d Fortified by high performance computing, knowledge graphs successfully represent and process this data via:<\/p>\n\n\n\n<ul><li><strong>Nodes and Edges:<\/strong> Graphs represent data and their relationships via nodes and edges. \u201cThe nodes represent entities of some kind and the edges represent binary relations between these entities,\u201d Pingali commented.<\/li><li><strong>Labeled Properties:<\/strong> Users can annotate graphs with labeled properties that are helpful for data provenance and recording confidence scores, both of which enhance machine learning use cases. \u201cIn a lot of applications, the nodes and edges also have a lot of property data,\u201d Pingali revealed. \u201cFor example, if there is a node that represents a person, the property associated with that node could be the first name, the last name, the social security number, the date of birth, where the person resides, citizenship, and so on.\u201d<\/li><li><strong>Graph Algorithms:<\/strong> There are several algorithms that excel in <a href=\"https:\/\/blogs.gartner.com\/andrew_white\/2021\/02\/23\/top-trends-in-data-and-analytics-for-2021\/?_ga=2.153415864.1896793072.1621459665-340082403.1618942319\" target=\"_blank\" rel=\"noreferrer noopener\">graph settings for understanding data<\/a>. Specific algorithm types include \u201cpath finding, node ranking, community detection, structural properties, and graph mining algorithms,\u201d Pingali disclosed. \u201cMost of them run on CPUs, GPUs, as well as distributed CPUs and GPUs.\u201d<\/li><\/ul>\n\n\n\n<p><strong>Timely Insights<\/strong><\/p>\n\n\n\n<p>The responsiveness of knowledge graphs underpinned by high performance computing greatly exceeds that of other methods. These performance gains are often the vital distinction between simply amassing immense knowledge graphs and actually deriving low latent action from them. \u201cA lot of the time there is a window of opportunity within which, if your analytics completes, you can get insights and you can act on those insights,\u201d Pingali remarked. \u201cThen, you benefit from the analytics. But if the answer comes too late outside of that window of opportunity, then you might as well not have done the analytics.\u201d<\/p>\n\n\n\n<p>Pingali described a use case in which the <a href=\"https:\/\/www.darpa.mil\/\" target=\"_blank\" rel=\"noreferrer noopener\">Defense Advanced Research Projects Agency (DARPA)<\/a> utilized knowledge graphs enhanced by high performance computing for real-time intrusion detection in their computer networks. \u201cThey build interaction graphs and then you are pattern mining within that graph to find what are called forbidden patterns,\u201d Pingali mentioned. \u201cIf you find a forbidden pattern then you raise an alarm, somebody steps in, and so on.\u201d<\/p>\n\n\n\n<p><strong>Distributed Computations<\/strong><\/p>\n\n\n\n<p>The capacity to rapidly traverse extensive topologies laden with labeled properties at the speed of high performance computing is primarily based on the following three considerations for distributing workloads among machines.<\/p>\n\n\n\n<ul><li><strong>Sharding:<\/strong> Sharding is a means of partitioning workloads among different machines. Once that\u2019s done \u201ceach of the machines has a small portion of the graph, and so you can do graph computing on that single machine,\u201d Pingali specified.<\/li><li><strong>Dynamic Load Balancing:<\/strong> Unlike the case with many computer science applications, the computations <a href=\"https:\/\/www.forbes.com\/sites\/cognitiveworld\/2020\/08\/02\/knowledge-graphs-and-ai-interview-with-chaitan-baru-ucsd\/?sh=1deacf08ab5b\" target=\"_blank\" rel=\"noreferrer noopener\">for workloads in graph computing<\/a> aren\u2019t always predictable or static. Load balancing systems can rectify this issue for users.<\/li><li><strong>In-Memory:<\/strong> In-memory capabilities are fundamental for the quick processing high performance computing is acclaimed for. According to Pingali, sharding also allows an \u201cin-memory compute engine to run on each machine.\u201d Credible options in this space also have runtime capabilities for inter-machine communication.<\/li><\/ul>\n\n\n\n<p><strong>Advancing Knowledge Graphs<\/strong><\/p>\n\n\n\n<p>Although the knowledge graph idiom is widely proclaimed by a number of vendors with varying approaches, pairing this technology with high performance computing is a significant development for meeting the needs of the contemporary data ecosystem. It addresses the burgeoning size of knowledge graphs, the real-time responsiveness required to succeed with them, and the computational demands required for AI in mission-critical use cases at enterprise scale.<\/p>\n\n\n\n<p>\u201cThat is where the real intelligence comes in, to figure out what might happen in the future and mitigate any bad things that might happen and ensure you can exploit all the good things that might happen,\u201d Pingali posited. \u201cThis is going to require using lots and lots of knowledge graphs, as well as AI. Knowledge graphs and AI are really made for each other\u2026in a platform where you can quickly spin up those kinds of applications and exploit the enormous amount of data that we all have.\u201d<\/p>\n\n\n\n<p><strong>About the Author<\/strong><\/p>\n\n\n\n<div class=\"wp-block-image is-style-default\"><figure class=\"alignleft size-large\"><img decoding=\"async\" loading=\"lazy\" width=\"125\" height=\"125\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/10\/Jelani-Harper.jpg\" alt=\"\" class=\"wp-image-23475\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/10\/Jelani-Harper.jpg 125w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/10\/Jelani-Harper-110x110.jpg 110w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/10\/Jelani-Harper-50x50.jpg 50w\" sizes=\"(max-width: 125px) 100vw, 125px\" \/><\/figure><\/div>\n\n\n\n<p><em>Jelani Harper is an editorial consultant servicing the information technology market. He specializes in data-driven applications focused on semantic technologies, data governance and analytics.<\/em><\/p>\n\n\n\n<p><em>Sign up for the free insideBIGDATA&nbsp;<a rel=\"noreferrer noopener\" href=\"http:\/\/insidebigdata.com\/newsletter\/\" target=\"_blank\">newsletter<\/a>.<\/em><\/p>\n\n\n\n<p><em>Join us on Twitter:&nbsp;@InsideBigData1 \u2013 <a href=\"https:\/\/twitter.com\/InsideBigData1\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n\n\n\n<p><br><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this contributed article, editorial consultant Jelani Harper discusses The how increasing reliance on knowledge graphs parallels that of Artificial Intelligence for three irrefutable reasons. They\u2019re the most effective means of preparing data for statistical AI, creditable knowledge graph platforms utilize supervised and unsupervised learning to accelerate numerous processes, and their smart inferences are a form of machine intelligence.<\/p>\n","protected":false},"author":10513,"featured_media":22407,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,115,87,180,258,56,1],"tags":[106,429,888,96],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Knowledge Graphs 2.0: High Performance Computing Emerges - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2021\/06\/02\/knowledge-graphs-2-0-high-performance-computing-emerges\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Knowledge Graphs 2.0: High Performance Computing Emerges - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"In this contributed article, editorial consultant Jelani Harper discusses The how increasing reliance on knowledge graphs parallels that of Artificial Intelligence for three irrefutable reasons. 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As computing tools and expertise used in conducting scientific research continue\u2026","rel":"","context":"In &quot;Big Data&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2016\/09\/4295284527_aaa1686515_z.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":27144,"url":"https:\/\/insidebigdata.com\/2021\/09\/17\/the-next-wave-of-cognitive-analytics-graph-aware-machine-learning\/","url_meta":{"origin":26359,"position":2},"title":"The Next Wave of Cognitive Analytics: Graph Aware Machine Learning","date":"September 17, 2021","format":false,"excerpt":"In this contributed article, editorial consultant Jelani Harper discusses a number of compelling and timely topics including manifold learning, graph embeddings, and cognitive computing.","rel":"","context":"In &quot;Analytics&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":29025,"url":"https:\/\/insidebigdata.com\/2022\/04\/13\/accelerating-graph-technology-adoption\/","url_meta":{"origin":26359,"position":3},"title":"Accelerating Graph Technology Adoption","date":"April 13, 2022","format":false,"excerpt":"In this contributed article, Keshav Pingali, CEO and co-founder of Katana Graph, believes that graph technology\u2019s need is apparent \u2013\u00a0and will be even more so in the coming years with 95% of businesses stating that managing unstructured data is a serious challenge. Highlighting successes and promoting interoperability with other libraries\u2026","rel":"","context":"In &quot;Big Data&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":29598,"url":"https:\/\/insidebigdata.com\/2022\/06\/15\/the-worlds-most-ambitious-knowledge-graph\/","url_meta":{"origin":26359,"position":4},"title":"The World\u2019s Most Ambitious Knowledge Graph\u00a0","date":"June 15, 2022","format":false,"excerpt":"In this contributed article, editorial consultant Jelani Harper highlights how knowledge graphs have become one of the foremost expressions of Artificial Intelligence today. Almost everyone\u2014from vendors to organizations, analysts to regulators\u2014relies on these applications at some point to compile, harmonize, and scrutinize specialized business information.","rel":"","context":"In &quot;Analytics&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":24534,"url":"https:\/\/insidebigdata.com\/2020\/06\/05\/data-science-demystified-the-data-modeling-proposition\/","url_meta":{"origin":26359,"position":5},"title":"Data Science Demystified: The Data Modeling Proposition","date":"June 5, 2020","format":false,"excerpt":"In this contributed article, editorial consultant Jelani Harper discusses how data modeling is the foundation of the data science discipline that\u2019s responsible for the adaptive, predictive analytics that are so critical to the current data ecosystem. Before data scientists can refine cognitive computing models or build applications with them to\u2026","rel":"","context":"In &quot;Big Data&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2019\/11\/Data_Modeling_shutterstock_500404219.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/26359"}],"collection":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/users\/10513"}],"replies":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/comments?post=26359"}],"version-history":[{"count":0,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/26359\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media\/22407"}],"wp:attachment":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media?parent=26359"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/categories?post=26359"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/tags?post=26359"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}