AI 100 Top Company – Franz Inc.

Franz Inc. is proud to announce it has been named an “AI 100 Top Company.

AllegroGraph provides organizations with essential Knowledge Graph solutions, including Large Language Models (LLMs), Graph Neural Networks, Graph Virtualization, GraphQL, Apache Spark graph analytics, and Kafka streaming graph pipelines. These capabilities exemplify AllegroGraph’s leadership in empowering data analytics professionals to derive business value out of Knowledge Graphs.

“Today, AI has the potential to impact almost every part of an organization’s structure and operations, including their customer-facing presence,” remarked Tom Hogan Jr., publisher of KMWorld. “We see AI reaching into marketing, customer service, legal, finance, human resources, compliance, fleet maintenance, manufacturing, sales, and many other business units.”

“Franz Inc. is continually innovating and we are honored to receive this acknowledgement for our efforts to deliver leading AI solutions in Data management,” said Dr. Jans Aasman, CEO, Franz Inc. “Organizations across a range of industries are realizing the critical role that Knowledge Graphs play in creating rich, yet flexible AI-driven applications. AllegroGraph with its patented FedShard™ technology uniquely provides companies with the foundational environment for delivering Graph based AI solutions with the ability to continually enrich and contextualize the understanding of data.”

Knowledge Graph Standards in Ambient Computing

By Jans Aasman, CEO

Ambient computing is a broad term that describes an environment of smart devices, data, AI decisions, and human activity that enables computer actions alongside everyday life, without the need for direct human commands or intervention. Ambient computing represents an unparalleled opportunity to enhance almost every sphere of society – from the professional to the personal. And in my opinion, it is also the ultimate use case for which semantic knowledge graphs were created.

With knowledge graph standards, ambient computing is no longer a mere ideal or science fiction fantasy on television or in books. It’s a real computational model involving Internet of Things (IoT) endpoints, AI analytics, machine reasoning, orchestration, and low latent event processing at the edge to anticipate users’ desires and perform timely action – without explicit commands.

For example, a motion detector might identify a homeowner’s return from work at night, open the garage accordingly, and trigger a thermostat to increase the air conditioning to a desired temperature while smart gadgets in the kitchen begin preheating the oven for dinner.

Each of these actions happens without someone deliberately engaging with these disparate systems. One’s interactions with his or her environment dictate which events occur, relegating the computational process to the background to benefit humans.

Different vendors currently have varying degrees of ambient computing in place. Amazon has several household devices that interact with Alexa, for example. Still, the larger vision of ambient computing can’t be restricted to one vendor and must include timely data exchanges between vendors, products, and operating systems.

Doing so requires systemic interoperability, the likes of which the universal standards powering semantic graph technology have provided for years. This smart data approach is integral to the mainstream adoption of ambient computing, which is impending.

Read the full article at Dataversity.

Essilor’s Knowledge Graph for Global Supply Chain Risk Management

Essilor, part of the EssilorLuxottica SA group (which sells global brands such as Ray-Ban, Oakley and Varilux among many others), is a French-based vertically integrated, multinational ophthalmic optics company and the world leader in the design, manufacture and distribution of lenses to correct or protect eyesight. Including sundries, Essilor carries hundreds of thousands of stock and finished products that are fabricated at many different labs in different countries and are sold all over the world.

“Tracking product packaging and fulfilling orders efficiently had always been difficult in the past,” said Mel Yuson, Director Enterprise Architecture, Essilor AMERA. “We tried to modernize our product tracking system with 3rd party software solutions and in-house relational database applications but without success because relational databases lack the ability to model complex relationships. We needed the freedom of a schemaless graph database, like Franz’s AllegroGraph, which uniquely provides us the flexibility to evolve our data model and seamlessly add new applications to address rapid growth and changing needs at Essilor.”

Here is a sliver of the complex data model of the Essilor application viewed in Gruff.

“We developed and deployed to production our first AllegroGraph based application in only a few months after engaging Franz,” said Yuson. “We found AllegroGraph’s W3C standard SPARQL query language is much easier to use than SQL but most importantly, AllegroGraph is a very stable and highly scalable platform with its Multi-Master Replication cluster feature. Today, we deploy several AllegroGraph servers in the cloud, which easily handle 100,000 concurrent queries per minute at peak hours. We are very pleased with our partnership with Franz Inc. and are believers in the power of Semantic Graph Database technology.”

System architecture at Essilor


Essilor’s success in deploying production systems with AllegroGraph has made them a firm believer in the power of semantic graph database technology.

Read more in our Supply Chain Risk Management white paper.

Read the Supply and Demand Chain Risk Management Article.

No-Code Queries Can Accelerate AI and Data Analytics

By Dr. Jans Aasman, CEO

The low-code, no-code methodology is becoming highly sought-after throughout the modern IT ecosystem—and with good reason. Options that minimize manually writing code capitalize on the self-service, automation idiom that’s imperative in a world in which working remotely and doing more with less keeps organizations in business.

Most codeless or low-code approaches avoid the need for writing language-specific code and replace it with a visual approach in which users simply manipulate on-screen objects via a drag-and-drop, point-and-click interface to automate code generation. The intuitive ease of this approach — which is responsible for new standards of efficiency and democratization of no-code development — has now extended to no-code query writing.

No-code querying provides two unassailable advantages to the enterprise. First, it considerably expedites what is otherwise a time-consuming ordeal, thereby accelerating data analytics and AI-driven applications and second, it can help organizations overcome the talent shortage of developers and knowledge engineers. Moreover, it does so by furnishing all the above benefits that make codeless and low-code options mandatory for success.

Read the full article at DZone.

Why Young Developers Don’t Get Knowledge Graphs

Dr. Aasman recently interviewed for this Datanami article.

Business is booming these days for graph databases–maybe it took COVID to show us how connected everything is–and that’s good news for Franz, which develops a semantic graph database called AllegroGraph. Just the same, you won’t find CEO Jans Aasman spending much time convincing developers of a certain age to use it.

“If you live in our world of semantic graph databases, I only talked to people over 35, 40,” Aasman tells Datanami. “I never talk to young developers.”

The problem with younger developers, he explains, is that they’re usually interested in using the graph database to build point solutions to solve specific problems, as opposed to creating a wide base of knowledge that can not only solve a specific problem, but be used with future solutions too. Plus, building point solutions exacerbates the data silo problem, he says.

“In our community of the semantic graph databases, literally everything is about integration and making sure that everything can interoperate,” the Franz CEO continues. “And there’s not a single young programmer that cares about that. Seriously. You’re young, you want to do a fun project, your managers are saying, in three months I need this thing done. You do whatever you want to do. Well, they get it done. And then you have new a data silo.”

Read the full article at Datanami.

AiThority Interview with Dr. Jans Aasman

Jans Aasman, please tell us about your current role and the team / technology you handle at Franz.

As CEO of Franz Inc., I drive the overall technology vision for our Enterprise Knowledge Graph solutions and ensure our customer projects deliver the ROI results expected with graph based architectures.

Franz Inc. is composed of an expert team with skills in Graph Databases, Semantic technologies, Graph Visualization, AI, NLP and Machine Learning.  Our domain knowledge encompasses large enterprises in Healthcare, Pharma, Customer Support, and Intelligence Agencies.

Our main business today revolves around AllegroGraph, a Semantic Graph platform that allows infinite data integration through a patented approach unifying all data and siloed knowledge into an Entity-Event Knowledge Graph solution that can support massive big data analytics. AllegroGraph’s FedShard feature utilizes patented federated sharding capabilities that drive 360-degree insights and enable complex reasoning across a distributed Knowledge Graph. AllegroGraph is utilized by dozens of the top Fortune 500 companies worldwide.

We also offer a popular data visualization and no-code query builder called Gruff – the most advanced Knowledge Graph visualization application on the market, which we recently integrated into Franz AllegroGraph. Gruff enables users to create visual Knowledge Graphs that display data relationships in views that are driven by the user. Ad hoc and exploratory analysis can be performed by simply clicking on different graph nodes to answer questions. Gruff’s unique ‘Time Machine’ feature provides the capability to explore temporal context and connections within data. The visual query builder within Gruff empowers both novice and expert users to create simple to highly complex queries without writing any code.

Read the full interview at AIThority.

The Future of AI: Machine Learning and Knowledge Graphs

Bringing knowledge graph and machine learning technology together can improve the accuracy of the outcomes and augment the potential of machine learning approaches. With knowledge graphs, AI language models are able to represent the relationships and accurate meaning of data instead of simply generating words based on patterns.

Read this special report to dive into key uses cases, best practices for getting started, and technology solutions every organization should know about.

The Future of AI: Machine Learning and Knowledge Graphs

Gartner Case Study: Entity-Event Knowledge Graph for Powering AI Solutions (Montefiore)

Gartner featured Franz’s customer, Montefiore Medical Center, in a research report on Montefiore’s Entity-Event Knowledge Graph:

“AI solutions are often hindered by fragmented data and siloed point solutions,” according to Gartner’s Chief Data and Analytics Officer Research Team. “Montefiore’s data and analytics leader used semantic knowledge graphs to power its AI solutions and achieved considerable cost savings as well as improvements in timeliness and the prediction accuracy of AI models.” Source: Gartner Case Study: Entity-Event Knowledge Graph for Powering AI Solutions (Montefiore) – Subscription required.

Copy Available from Montefiore/Einstein.

KMWorld 100 Companies that Matter Most – Franz Inc.

Franz Inc., is proud to announce that it has been named to The 100 Companies That Matter in Knowledge Management by KMWorld. The annual list reflects the urgency felt among many organizations to provide a timely flow of targeted information. Among the more prominent initiatives is the use of AI and cognitive computing, as well as related capabilities such as machine learning, natural language processing, and text analytics.

“Flexibility, agility, and the ability to pivot are attributes that have become critical to forward-thinking companies—and that is particularly the case now. Successful organizations don’t want to merely survive; they want to dominate their market sectors. But to do that, they need the right tools and products,” said Tom Hogan, Group Publisher at KMWorld. “Amidst the dramatic changes taking place today, innovative organizations are seeking new approaches to improve their processes. The 2021 KMWorld 100 is a list of leading-edge knowledge management companies that are helping their customers to expand access to information, leverage new opportunities, and accelerate growth.”

Read More about Franz Inc.

Data Fabrics and Knowledge Graphs — A Symbiotic Relationship

Dr. Jans Aasman’s recent article in Dzone.

The data fabric notion is gaining credence throughout the analyst community, in much the same way knowledge graphs have done so for years. Both technologies link all relevant data for a specific business purpose, which is why the most successful companies in the world employ them.

Amazon’s knowledge graph retains metadata about its vast product array; Google’s captures data about an exhaustive list of web entities of interest. Lesser-known organizations regularly deploy these mechanisms for everything from comprehensive customer views to manufacturing processes.

Data fabrics have a unique, symbiotic relationship with the knowledge graph movement because they substantially streamline the processes to extract data from the myriad sources that populate these platforms. In turn, knowledge graphs provide some of the fundamental capabilities enabling data fabrics to accomplish this objective.

Read the Full Article at Dzone.