AllegroGraph Named to KMWorld’s 2022 Trend Setting Products
Franz Inc. Delivers AI Knowledge Fabric Solutions for the Enterprise
Lafayette, Calif., September 21, 2022 — Franz Inc., an early innovator in Artificial Intelligence (AI) and leading supplier of Graph Database technology for Entity-Event Knowledge Graph Solutions, today announced it has been named to the “2022 Trend Setting Products” by KMWorld.
AllegroGraph provides organizations with essential Knowledge Graph solutions, including 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.
“Solutions ranging from AI and Knowledge Graphs to remote and hybrid working environments are shaping the capabilities of products and services for 2022,” said Tom Hogan, Group Publisher, KMWorld. “To help showcase advanced products and services, each year KMWorld magazine looks for innovative offerings that are helping organizations become better equipped to compete in this ever changing competitive work environment. This is KMWorld’s Trend-Setting Products list of 2022!”
“Franz Inc. is continually innovating and we are honored to receive this acknowledgement for our efforts in setting the pace for AI Knowledge Graph Solutions,” said Dr. Jans Aasman, CEO, Franz Inc. “We are seeing demand for Intelligent Data Fabrics take off across industries along with recognition from top technology analyst firms that Knowledge Graphs provide the critical foundation for Data Fabric solutions. AllegroGraph with FedShard 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.”
Graph Neural Networks with AllegroGraph
“The ability to create Graph Neural Networks within the AllegroGraph platform opens up the next level of AI to data analytics professionals with the ability to produce the best prescriptive outcomes,” said Dr. Jans Aasman, CEO of Franz Inc. “GNNs are ideal for applying machine learning’s advanced pattern recognition to high-dimensional, non-Euclidian datasets that are too complex for other machine learning types. Organizations get two forms of reasoning in one framework by fusing GNN reasoning capabilities around relationship predictions, entity classifications, and graph clustering, with classic semantic inferencing available in AllegroGraph Knowledge Graphs. Automatically mixing and matching these two types of reasoning is next level AI and is the basis for predicting the best prescriptive outcome for any business event based on context at scale.”
With AllegroGraph, users can create Graph Neural Networks (GNNs) and take advantage of a mature AI approach for Knowledge Graph enrichment via text processing for news classification, question and answer, search result organization, event prediction, and more. GNNs created in AllegroGraph enhance neural network methods by processing the graph data through rounds of message passing, as such, the nodes know more about their own features as well as neighbor nodes. This creates an even more accurate representation of the entire graph network. AllegroGraph GNNs advance text classification and relationship extraction for enhancing enterprise-wide Data Fabrics.
AllegroGraph’s GraphQL Support
GraphQL is an open-source data query language for APIs and a runtime for fulfilling queries with data. It allows API clients to query data as a graph irrespective of how the data is stored, making it possible to loosely couple data sources with client applications. GraphQL provides a complete and understandable description of the data in the API, gives clients the power to ask for exactly what they need and nothing more, and makes it easier to evolve APIs over time. Using GraphQL APIs within AllegroGraph can lower integration costs and minimize redundancy in enterprise systems, while improving the value of data-driven applications.
Graph Database Adoption Expected to Skyrocket
Industry analysts predict the graph database market to experience skyrocketing adoption over the next several years. In a SiliconANGLE 2022 analyst prediction interview, IDC Research Vice President Carl Olofson said, “I regard graph database as the next truly revolutionary database management technology.” Olofson said he expects the graph database market to “grow by about 600% over the next 10 years.” He listed a broad set of use cases for graphs including: “entity resolution, data lineage, social media analysis, customer 360, fraud prevention, cybersecurity… supply chain is a big one. There is explainable AI and this is going to become important because a lot of people are adopting AI. Then we’ve got data governance, data compliance, risk management. We’ve got recommendation, we’ve got personalization, anti-money-laundering, that’s another big one, identity and access management. There’s also root cause analysis and fraud detection is a huge one.”
About Franz Inc.
Franz Inc. is an early innovator in Artificial Intelligence (AI) and leading supplier of Graph Database technology with expert knowledge in developing and deploying Knowledge Graph solutions. The foundation for Knowledge Graphs and AI lies in the facets of semantic technology provided by AllegroGraph and Allegro CL. AllegroGraph 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 to run queries of unprecedented complexity to support predictive analytics that help organizations make more informed, real-time decisions. AllegroGraph is utilized by dozens of the top Fortune 500 companies worldwide. To learn more about Franz and AllegroGraph, go to franz.com.
All trademarks and registered trademarks in this document are the properties of their respective owners.