December, 2017
The enterprise knowledge graph for entity 360-views has emerged as one of the most useful graph database technology applications when buttressed by W3C standard semantic technology, modern artificial intelligence, and visual discovery tools. Read this IEEE publication by Dr. Jans Aasman to learn more about Knowledge Graphs.
For additional information, see here
Today, innovative approaches, such as Hadoop, Spark, NoSQL, and NewSQL, are being used in addition to more established technologies, such as the mainframe, and relational and MultiValue database systems. In addition, artificial intelligence and machine learning capabilities are some of the newer approaches being introduced in products. To help bring these resources to light, each year, Database Trends and Applications magazine looks for offerings that promise to help organizations derive greater benefit from their data, make better decisions, work more efficiently, achieve greater security, and address emerging challenges. In total, this list of forward-looking products helps illuminate the path on which the data management market is headed.
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The 22nd Annual Enterprise Data World (EDW) Conference hosted by DATAVERSITY® is recognized as the most comprehensive educational conference on data management in the world. Join hundreds of data professionals from around the globe to attend this unique conference. Your transformation to data-driven business starts here!
Franz CEO Jans Aasman will be presenting "Taking Graphs to the Next Level with Artificial Intelligence and Machine Learning".
Graphs and Knowledge Management have gained significant visibility with the rebirth of artificial intelligence and emergence of cognitive computing. By combining artificial intelligence, big data, graph databases, and dynamic visualizations, we will discuss deploying Graph based AI applications as a means to help predict future events across numerous types of industries.
Knowledge creation via AI and Graphs stems from the capability to combine the probability space (i.e. statistical inference on a user’s data) with a knowledge base of comprehensive industry terminology systems. AI using Graphs are remarkable not just because of the possibilities they engender, but also because of their practicality. The confluence of knowledge via machine learning, visual querying, graph databases, and big data not only displays links between objects, but also quantifies the probability of their occurrence. We believe this approach will be transformative across numerous business verticals.
During the presentation we will describe the Graph based AI concepts that also incorporate Hadoop, along with analytics via R, SPARK ML and other AI techniques for practical Enterprise predictive analytics use cases.
For additional information, see here
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New Features Include:
For additional information, see the Gruff Documentation
Semantic Graph Databases are now common in many industries, including life sciences, healthcare, the financial industry and in government and intelligence agencies. Graphs are particularly valuable in these sectors because of the complex nature of the data and need for powerful, yet flexible data analytics.
Attributes, user attributes and static filters are a new mechanism for graph databases to protect sensitive information. This combination provides the right amount of power and flexibility to address high-security use cases, such as: HIPAA access controls, privacy rules for banks, security models for policing, intelligence and the government. In addition, this set of methods is far easier to use, provides more expressiveness than security methods in relational databases and doesn’t suffer from performance degradations.
To read the full article, see here
The Enterprise Knowledge Graph concept strikes at the core of what every data-driven organization is trying to do: translate data assets into a competitive advantage unique to those assets and the company itself.
By effectively connecting enterprise-wide data—both internal and external—into a sole repository reusable for a variety of use cases across an organization, Enterprise Knowledge Graphs are the single most effective mechanism for accomplishing this objective. The proliferation of use cases spanning Silicon Valley’s finest proves this point as well as the business value of this methodology.
To read the full article, see here
Franz Inc., an early innovator in Artificial
Intelligence and leading supplier of Semantic Graph Database
technology - AllegroGraph, announced that it has been named to
The Silicon Review 50 Fastest Growing Tech Companies 2017
- Delivering Scalable Knowledge Graph solutions Franz
Inc...
Used by Fortune 500 companies that span healthcare, intelligence agencies, life sciences, telecommunications, and research organizations, Franz provides AllegroGraph, a high-performance and transactional semantic graph database, and Allegro CL, a Lisp programming environment to create complex applications for solving real-world problems.
AllegroGraph is a database technology that enables businesses to extract sophisticated decision insights and predictive analytics from highly complex, distributed data that cannot be uncovered with conventional databases. Unlike traditional relational databases or other NoSQL databases, AllegroGraph employs semantic graph technologies that process data with contextual and conceptual intelligence. AllegroGraph is able run queries of unprecedented complexity to support predictive analytics that help organizations make more informed, real-time decisions.
To read the full article, see here
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