If you've never heard of MUMPS don't feel like the lone ranger. A colleague mentioned it to me and drove me to a bit of research. What I found is really astounding. MUMPS was born in 1966 to solve the problem of massive data flowing into multi-user systems in the healthcare industry. It predates RDBMS but has all the features of NoSQL including (in its modern form) massive parallel processing, horizontal scaling, and runs on commodity hardware.
The Intelligent Database Interface (IDI) is a cache-based interface that is designed to provide Artificial Intelligence systems with efficient access to one or more databases on one or more remote database management systems (DBMSs). It can be used to interface with a wide variety of different DBMSs with little or no modification since SQL is used to communicate with remote DBMSs and the implementation of the ID1 provides a high degree of portability. The query language of the ID1 is a restricted subset of function-free Horn clauses which is translated into SQL. Results from the ID1 are returned one tuple at a time and the ID1 manages a cache of result relations to improve efficiency. The ID1 is one of the key components of the Intelligent System Server (ISS) knowledge representation and reasoning system and is also being used to provide database services for the Unisys spoken language systems program.
Many dramatizations have depicted a fully automated home living environment, where actions and events are understood or even anticipated. While the realization of such environments requires innovations on many fronts, our current research focuses on the development of an active database subsystem to respond to events in a home. The architecture implements the well-known event-conditionaction (ECA) paradigm within an active data layer that is abstracted from the detection and processing of raw data sources. Our goals in this work are to develop and analyze the effectiveness of the active database system in a smart home system being developed as part of a larger collaborative effort. The knowledge encoded in the system is based on significant domain modeling, including analysis of inhabitant-device data collected from actual and simulated home environments, and standard knowledge acquisition techniques. The current system is composed of a Java framework for event management, an underlying data model to store transient and persistent data, and a JESS rule base to represent condition and actions. Interaction between the subsystems is maintained through an event manager responsible for interpreting the semantics of event execution and recognition.
The effective application of AI Technology and the development of future computing systems require the integration of AI and Database Technologies. The integration will benefit both AI and Databases and will substantially advance the state of computing. Information Systems are among the greatest potential beneficiaries of AI Technology. What if advanced reasoning capabilities could be added to any Information System? What if intelligent interfaces could replace unfriendly interfaces?