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.
Last Updated: 23rd January 2019
Mutable Award: Gold 2018
Franz AllegroGraph (henceforth just AllegroGraph) is a semantic graph database focused on generating sophisticated semantic knowledge graphs, initially from your existing data. The graph database itself is an RDF-based quad store (in other words, a triple store where all the triples are named) with property graph support. Franz also provides Gruff, a browser-based visualisation and discovery engine which includes a visual graph query builder.
Analytics generated from AllegroGraph can be exported back into the graph itself, to facilitate continuous machine learning. Querying is done in SPARQL, and the product additionally offers ‘nDimensional’ indexing for complex values. For example, in a weather application, you could query over a combination of time, location, temperature, pressure and so on. AllegroGraph can associate probabilities with relationships, which represent how likely a relationship is to be true, and graph algorithms and social network analytics are provided out of the box. Inferencing is supported, including both forward and backward chaining, as well as full PROLOG support for logic-based reasoning. Integration with SOLR, Hadoop and MongoDB is also provided.
The product is OLTP-enabled and fully ACID compliant, with immediate consistency, as well as supporting analytics. It is secure, and supports the requirements for various government security standards, including HIPAA. It is available both on-premises and in the cloud.
“Triple attributes in AllegroGraph add a significant and complementary dimension to the RDF data model. It extends property graphs to support an entirely new array of use-cases and functionalities that were not possible before, but most importantly enables implementation of fine grained security built directly into the storage layer.”
Dr. Parsa Mirhaji, Director of Center for Health Data Innovations, Albert Einstein College of Medicine and Montefiore Medical Center
“We’re providing live feedback. As you’re typing, we’re providing question and suggestions for you live. AllegroGraph gives us a performant way to be able to work our way through the whole knowledge model and come up with suggestions to the user in real time.”
The basic idea behind AllegroGraph is that, it will a) transform your existing enterprise data into triples, which can then be thought of as either entities or events; and b) store it all inside, effectively, one enormous table comprised of all of your entities, events and sub-events, possibly including entire taxonomies. This method of storage, makes it much easier and simpler to write many queries. For example, picking out a single user or other entity and obtaining a comprehensive, 360-degree view of their relationships within the system is almost trivial, requiring only a single line of SPARQL and essentially treating the graph as a key-value store.
Franz offers multiple methods for deploying AllegroGraph across the enterprise, including federated and distributed deployment models. It also provides a hybrid distributed/federated approach, as shown in Figure 1. This methodology increases performance and scalability by storing replicas of your unshardable data (datasets that must be stored as a single piece, such as knowledge bases, terminology systems, and statistical systems) on each of your machines, then federating that data with the repositories located on that machine. This means that you only need to load in your unshardable data once for each machine during any given query.
AllegroGraph utilises “multi-mode” artificial intelligence, consisting of both machine learning and a CEP (Complex Event Processing) system developed in Prolog. The idea is to use multiple kinds of AI to estimate the likelihood of future events occurring based on currently observed events stored in a graph. For example, if you are a police force, you might want to monitor the outgoing and incoming calls used by a criminal cell, estimate the probability they are about to meet face to face, and send a notification when that probability rises above a certain threshold. You could accomplish this with the rule displayed in Figure 2.
Although the structure of this rule is set in stone, the parameters (highlighted in yellow) are equipped with confidence intervals, which are calculated based on past observations and events. Moreover, instead of sending a notification, you can create a ‘possible event’ in your graph. This is an event equipped with a probability that represents the likelihood of it occurring or of having occurred. This event is then treated like any other by the AI system, except that it only has a certain likelihood of having happened. This means that AllegroGraph’s AI can produce reasoning based on both known and unknown events.
Finally, AllegroGraph also features native, near real-time multi-master replication and management; multi-modal input from RDF, CSV, JSON, JSON Lines and JSON-LD files; built-in document storage (comparable to MongoDB) with support for graph algorithms and semantics; natural language processing (NLP) and textual analysis, including entity extraction; and extensive support for a variety of data science tools. It is also optimised for use with the newly released Intel Optane line of memory and storage products.
Compared to other offerings in the graph space, AllegroGraph stands out in two major ways. First of all, it is a distinctly flexible offering. For 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.
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.