Franz Inc

Figure 1 – AllegroGraph's hybrid distributed/federated deployment

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.

Figure 2 – Sending a notification using AI in AllegroGraph

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.

Fig 01 - An Entity-Event Knowledge Graph in Franz AllegroGraph

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 network comprised of all of your entities, events and sub-events, possibly including entire taxonomies. In other words, a graph. This method of storage makes it much easier and simpler to write complex queries. For example, picking out a single user or other entity and obtaining a comprehensive, 360º view of their relationships is almost trivial, requiring only a single line of SPARQL. Moreover, as of version 7.0 of AllegroGraph, you have the ability to create “Entity-Event Knowledge Graphs”, or EEKGs. This novel approach allows you to capture your core entities as well as related events and relevant knowledge bases within a hierarchical tree structure (see Figure 1). Notably, EEKGs can be built incrementally, starting with a simple model and extending as needed without altering what has come before. They also store provenance information and data lineage.

Franz offers multiple methods for deploying AllegroGraph across the enterprise, including federated, distributed and hybrid deployment models. The last of these uses a sharding approach, via the company’s patented FedShard technology, which works by storing replicas of your unshardable data (datasets that must be stored as a single piece) 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.

Fig 02 - Sending a notification using AI in AllegroGraph

AllegroGraph utilises “multimode” 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.

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), speech recognition, and textual analysis, including entity extraction; and extensive support for a variety of data science tools.

Compared to other offerings in the graph space, AllegroGraph stands out in three major ways. First of all, it is a distinctly flexible offering.

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, speech recognition and textual analysis, together with entity extraction; EEKGs, in general; and some of the most advanced security capabilities (paid for by a US intelligence agency) of any product in this market. In particular, the aforementioned speech recognition capabilities can be used to extract conceptual meaning from real speech – such as recorded conversations – then store that meaning in a graph and expose it for analysis.

Finally, the new hybrid federated/distributed deployment methodology in AllegroGraph 7 capability can deliver results for highly complex queries across distributed data sets and knowledge bases in real-time.

The Bottom Line

AllegroGraph is a formidable graph offering with a lot of flexibility, a wide range of compelling features and significant scalability. If that flexibility, distributed scale or any number of those features, appeal to you, we highly recommend it.