Franz Inc., is proud to announce it has been named the “Best Knowledge Graph” in the 2023 KMWorld Readers’ Choice Award voting.

According to KMWorld,  Technologies such as knowledge graphs, cloud computing and storage, data mesh and data fabric, chatbots, natural language processing, machine learning, and, most recently, generative AI (GenAI) have come to the forefront in our attempts to manage the myriad formats and knowledge silos rampant within organizations.

Business practices are changing fast, and so are knowledge management offerings. To put the spotlight on the innovative and dependable products and services that KMWorld readers depend on, the publication presents the KMWorld Readers’ Choice Award winners. After all, who best to know what products serve them best as they wrestle with so many changes happening so quickly?

In the November 2023 issue, KMWorld magazine announces the winners of the 2023 KMWorld Readers’ Choice Awards. The categories for competition were wide-ranging. In all, there were 13 areas in which products and technologies could be nominated and ultimately voted upon. They include business process management, cognitive computing and AI, customer service and support, e-discovery, knowledge graphs, text analytics, and NLP.

With the diverse array of knowledge management products, services, and technologies to consider, and the stakes getting higher for information-driven success, it can be challenging to make the right choices. There are many ways to learn more about what is available, including white papers, research reports, and webinars, as well as consulting with experts and peers. We hope the KMWorld Readers’ Choice Awards list provides an additional resource to help make the job of identifying solutions to investigate easier.

Franz Inc., is proud to announce it has been named the “Best Knowledge Graph” in the 2022 KMWorld Readers’ Choice Award voting.

According to KMWorld,  Global enterprises are making substantial investments in developing innovative approaches and strategies for competing successfully in a knowledge-based market. Such innovative practices, resulting in the development of knowledge-intensive products and services, are prevalent among enterprises in North America and Europe.

In the November 2022 issue, KMWorld magazine announces the winners of the 2022 KMWorld Readers’ Choice Awards. The categories for competition were wide-ranging. In all, there were 13 areas in which products and technologies could be nominated and ultimately voted upon. They include business process management, cognitive computing and AI, customer service and support, e-discovery, knowledge graphs, text analytics, and NLP.

With the diverse array of knowledge management products, services, and technologies to consider, and the stakes getting higher for information-driven success, it can be challenging to make the right choices. There are many ways to learn more about what is available, including white papers, research reports, and webinars, as well as consulting with experts and peers. We hope the KMWorld Readers’ Choice Awards list provides an additional resource to help make the job of identifying solutions to investigate easier.

Data management agility has become of key importance to organizations as the amount and complexity of data continues to increase, along with the desire to avoid creating new data silos. The concept of creating a ‘data fabric’ as an agile design concept has been proposed by leading analysts, such as Mark Beyer, Distinguished VP Analyst at Gartner. “The emerging design concept called ‘data fabric’ can be a robust solution to ever present-day management challenges, such as the high-cost and low-value of data integration cycles, frequent maintenance of earlier integrations, the rising demand for real-time and event-driven data sharing, and more,” says Mark Beyer.

As a data fabric readily connects and provides singular access to all data sources distributed throughout the enterprise, semantic knowledge graphs provide the foundation that makes this design possible. Semantic knowledge graphs and aspects of AI are necessary for the data fabric architecture to work. According to Gartner, “The semantic layer of the knowledge graph makes it more intuitive and easy to interpret, making the analysis easy for D&A leaders. It adds depth and meaning to the data usage and content graph, allowing AI/ML algorithms to use the information for analytics and other operational use cases.” In this respect, graph applications are the enabler of both data fabrics and the AI that supports them.

Data fabrics involve additional tooling like respective layers for data integration and run-time orchestration, in addition to active metadata management. Nonetheless, these capabilities would fail to properly function without the semantic layer, and data cataloging value, of semantic knowledge graphs that are foundational to realizing this grand data management vision.

Read the full article at Data Science Central.

AllegroGraph also wins Finalist position for “Best Cognitive Computing and AI Platform”.

Franz Inc., is proud to announce it has been named the “Best Knowledge Graph” in the 2021 KMWorld Readers’ Choice Award voting. Additionally, AllegroGraph was considered a “Finalist” in the category of Best Cognitive Computing and AI platforms for the Readers’ Choice awards.

According to KMWorld, the world of knowledge management continues to expand with the steady influx and evolution of innovative products and technologies to help organizations extract the right information for use by the right people at the right time. The value of knowledge management solutions and services is reflected in growth projections for the global knowledge management market, which was valued at about $206.9 billion in 2016 and is expected to reach more than $1,232 billion by 2025, representing a compound annual growth rate of more than 22%, according to Zion Market Research.

In this November issue, KMWorld magazine announces the winners of the 2021 KMWorld Readers’ Choice Awards. The categories for competition were wide-ranging. In all, there were 14 areas in which products and technologies could be nominated and ultimately voted upon. They include business process management, cognitive computing and AI, customer service and support, e-discovery, knowledge graphs, text analytics and NLP.

“As the stakes get higher for information-driven successes, businesses must make technology decisions from an increasingly diverse array of knowledge management offerings,” said Tom Hogan, Group Publisher at KMWorld. “The Readers’ Choice Awards put the spotlight on innovative and dependable solutions and services that can help companies solve pressing challenges and take advantage of new opportunities.”

“Franz Inc. is continually innovating and we are honored to receive this acknowledgement for our efforts in setting the pace for 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.”

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

AllegroGraph 7.2, provides organizations with essential Data Fabric tools, including Graph Neural Networks, Graph Virtualization, Apache Spark graph analytics, and streaming graph pipelines. These new capabilities exemplify AllegroGraph’s leadership in empowering data analytics professionals to derive business value out of Knowledge Graphs.

Graph Neural Networks

With AllegroGraph 7.2, 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.

Graph Virtualization

AllegroGraph 7.2 allows users to easily virtualize data as part of their AllegroGraph Knowledge Graph solution. When graphs are virtual, the data remains in the source system and is easily linked and queried with other data stored directly in AllegroGraph.

Any data source with a supported JDBC driver can be integrated into an AllegroGraph Knowledge Graph, including Databases (i.e. Apache Cassandra, AWS Athena, Microsoft SQL Server, MongoDB, MySQL, Oracle Database); BI Tools (i.e. IBM Cognos, Microsoft PowerBI, RapidMiner, Tableau); CRM Systems (i.e. Dynamics CRM, Netsuite, Salesforce, SugarCRM); Cloud Services (i.e. Active Directory, AWS Management, Facebook, Marketo, Microsoft Teams, SAP, ServiceNow) and Shared Data Files (i.e. Box, Gmail, Google Drive, Office365).

Streaming Graph Pipelines using Kafka

Enterprises that need real-time experiences are starting to adopt streaming pipelines to provide insights that adapt to new data in real-time rather than processing data in batches. AllegroGraph is often used as an Entity Event Knowledge Graph platform in diverse settings such as call centers, hospitals, insurance companies, aviation organizations and financial firms.

AllegroGraph 7.2 can be used seamlessly with Apache Kafka, an open-source distributed event streaming platform for high-performance data pipelines, streaming analytics, data integration and mission-critical applications. By coupling AllegroGraph with Apache Kafka, users can create a real-time decision engine that produces real-time event streams based on computations that trigger specific actions. AllegroGraph accepts incoming events, executes instant queries and analytics on the new data and then stores events and results.

Graph Analytics with Apache Spark

AllegroGraph 7.2 enables users to export data out of the Knowledge Graph and then perform graph analytics with Apache Spark, one of the most popular platforms for large-scale data processing. Users immediately gain machine learning and SQL database solutions as well as GraphX and GraphFrames, two frameworks for running graph compute operations on data.

A key benefit of using Apache Spark for graph analytics within AllegroGraph is that it is built on top of Hadoop MapReduce and extends the MapReduce model to efficiently use more types of computations. Users can access interfaces (including interactive shells) for programming entire clusters with implicit data parallelism and fault-tolerance.

Availability of AllegroGraph 7.2

AllegroGraph 7.2 is immediately available directly from Franz Inc. For more information, visit the AllegroGraph Quick Start page for cloud and download options.

Examples

Visit our Github AllegroGraph Examples page.

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.

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.

Data fabrics are emerging as the most effective means of integrating data throughout the enterprise. They deliver a single access point for all data regardless of location — whether it’s at rest or in motion. Experts agree that data fabrics are the future of data analytics and management. Gartner recommends:

“Data and analytics leaders must upgrade to a data fabric design that enables dynamic and augmented data integration in support of their Data Management strategy.”

Forrester states that “Enterprise Architecture (EA) pros should use data fabric to democratize data across the enterprise for various use cases.”

However, the adoption rate of data fabrics hinges on the ROI of their use cases. One such use case is to make it easier to do advanced Data Science on available data sources. Currently, extracting machine learning features is an exacting, time-consuming process because relevant data is trapped in silos. Data fabrics and knowledge graphs have a unique, symbiotic relationship because they substantially streamline the processes to extract data from the myriad sources that populate these platforms. Knowledge graphs are key to providing fundamental capabilities enabling data fabrics to accomplish this objective.

Read the full article at Dataversity.

Data and the subsequent knowledge derived from information are the most valuable strategic asset an organization possesses. Despite the abundance of sophisticated technology developments, most organizations don’t have disciplines or a plan to enable data-centric principles.

DCAF 2021 will help provide clarity.

Our overarching theme for this conference is to make it REAL. Real in the sense that others are becoming data-centric, it is achievable, and you are not alone in your efforts.

Join us in understanding how data as an open, centralized resource outlives any application. Once globally integrated by sharing a common meaning, internal and external data can be readily integrated, unlike the traditional “application-centric” mindset predominantly used in systems development.

The compounding problem is these application systems each have their own completely idiosyncratic data models. The net result is that after a few decades, hundreds or thousands of applications implemented have given origin to a segregated family of disparate data silos. Integration debt rises and unsustainable architectural complexity abounds with every application bought, developed, or rented (SaaS).

Becoming data-centric will improve data characteristics of findability, accessibility, interoperability, and re-usability (FAIR principles), thereby allowing data to be exported into any needed format with virtually free integration.\

Dr. Jans Aasman to present – Franz’s approach to Entity Event Data Modeling for Enterprise Knowledge Fabrics

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.