Franz Allegrograph

InBrief Daniel Howard – Senior Researcher www.franz.com 2201 Broadway, Suite 715, Oakland, CA 94612 Tel: +1 510 452 2000 Email: [email protected] Franz AllegroGraph

The company CREATIVITY SCALE Franz Inc. is a private, California-based company that originated with the initial Artificial Intelligence boom in 1984. In fact, it still provides its Lisp compiler to numerous Fortune 500 companies. The company started to develop AllegroGraph more than a decade ago at the request of U.S. DoD. In addition to AllegroGraph, Franz sells a variety of other Lisp- oriented products.

What is it? Franz AllegroGraph (henceforth Triple “ just AllegroGraph) is a semantic EXECUTION TECHNOLOGY attributes in AllegroGraph add graph database focused on The image in this Mutable Quadrant is derived from 13 high level a significant and complementary generating sophisticated metrics, the more the image covers a section the better. dimension to the RDF data model. Execution metrics relate to the company, Technology to the semantic knowledge graphs, product, Creativity to both technical and business innovation and It extends property graphs to support initially from your existing Scale covers the potential business and market impact. an entirely new array of use-cases and data. The graph database functionalities that were not possible itself is an RDF-based quad before, but most importantly enables store (in other words, a triple What does it do? implementation of fine grained security built directly into the storage layer.” store where all the triples are The basic idea behind AllegroGraph is that, it will a) Dr. Parsa Mirhaji, named) with property graph transform your existing enterprise data into triples, Director of Center for Health Data Innovations, Albert Einstein College support. Franz also provides Gruff, which can then be thought of as either entities or of Medicine and Montefiore Medical Center a browser-based visualisation and events; and b) store it all inside, effectively, one discovery engine which includes a enormous table comprised of all of your entities, visual graph query builder. events and sub-events, possibly including entire Analytics generated from AllegroGraph can taxonomies. This method of storage, makes it be exported back into the graph itself, to facilitate much easier and simpler to write many queries. continuous machine learning. Querying is done For example, picking out a single user or other in SPARQL, and the product additionally offers entity and obtaining a comprehensive, 360-degree ‘nDimensional’ indexing for complex values. For view of their relationships within the system example, in a weather application, you could query over is almost trivial, requiring only a single line of a combination of time, location, temperature, pressure SPARQL and essentially treating the graph as a and so on. AllegroGraph can associate probabilities key-value store. with relationships, which represent how likely a Franz offers multiple methods for deploying relationship is to be true, and graph algorithms and AllegroGraph across the enterprise, including social network analytics are provided out of the box. federated and distributed deployment Inferencing is supported, including both forward and models. It also provides a hybrid distributed/ backward chaining, as well as full PROLOG support for federated approach, as shown in Figure 1. logic-based reasoning. Integration with SOLR, Hadoop This methodology increases performance and and MongoDB is also provided. scalability by storing replicas of your unshardable The product is OLTP-enabled and fully ACID data (datasets that must be stored as a single compliant, with immediate consistency, as well piece, such as knowledge bases, terminology as supporting analytics. It is secure, and supports systems, and statistical systems) on each of your the requirements for various government security machines, then federating that data with the standards, including HIPAA. It is available both repositories located on that machine. This means on-premises and in the cloud. that you only need to load in your unshardable

“ We’re providing live feedback. As you’re typing, we’re providing question and suggestions for data once for each machine This is an event equipped with a probability that you live. AllegroGraph gives us during any given query. represents the likelihood of it occurring or of a performant way to be able to AllegroGraph utilises “multi- having occurred. This event is then treated like any work our way through the whole mode” artificial intelligence, other by the AI system, except that it only has a knowledge model and come up consisting of both machine certain likelihood of having happened. This means with suggestions to the user in real time. learning and a CEP (Complex Event that AllegroGraph’s AI can produce reasoning Wolters Kluwer” Processing) system developed in based on both known and unknown events. Prolog. The idea is to use multiple kinds Finally, AllegroGraph also features native, of AI to estimate the likelihood of future near real-time multi-master replication and events occurring based on currently observed management; multi-modal input from RDF, CSV, events stored in a graph. For example, if you are JSON, JSON Lines and JSON-LD files; built-in a police force, you might want to monitor the document storage (comparable to MongoDB) outgoing and incoming calls used by a criminal with support for graph algorithms and semantics; cell, estimate the probability they are about to natural language processing (NLP) and textual meet face to face, and send a notification when analysis, including entity extraction; and extensive that probability rises above a certain threshold. support for a variety of data science tools. It is also You could accomplish this with the rule displayed optimised for use with the newly released Intel in Figure 2. Optane line of memory and storage products. Although the structure of this rule is set in stone, the parameters (highlighted in yellow) Why should you care? are equipped with confidence intervals, which Compared to other offerings in the graph space, are calculated based on past observations and AllegroGraph stands out in two major ways. events. Moreover, instead of sending a notification, First of all, it is a distinctly flexible offering. For you can create a ‘possible event’ in your graph. example, it supports both transactional and analytics processing. Similarly, although it is an RDF graph it also supports property graphs and multi-modal ingestion. Secondly, it boasts a range of features which, if not unique, are at least rare. This includes probabilistic, multi-mode AI which goes beyond machine learning; natural language processing and textual analysis; a hybrid federated/distributed deployment methodology; ahead-of-the-curve integration with Intel Optane and some of the most advanced security capabilities (paid for by a US intelligence agency) of any product in this market.

Figure 1 – AllegroGraph’s hybrid distributed/federated deployment The Bottom Line AllegroGraph is a formidable graph offering with a lot of flexibility and a wide range of compelling features. If that flexibility, or any number of those features, appeal to you, we highly recommend it.

Figure 2 – Sending a notification using AI in AllegroGraph

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