SimQL: an Interface for Integrating Access to Simulations into Information Systems

Gio Wiederhold, Hector Garcia-Molina

Stanford University, Computer Science Department.
Gates Computer Science Building 4A
Stanford CA 94305-9040
<gio, hector@cs.stanford.edu>

 

Abstract

We have performed proof-of-concept research on prediction software interfaces for information system. Such software can aid in the projection of effects of Command and Control (C2) decisions and allows alternate Courses-of-Action (CoAs) to be evaluated. Our objective is to significantly augment Command and Control decision services by making predictive results of simulations and similar software as accessible as databases and other information components. The motivating concept is that an interface language allows separation of customers and providers, and that the autonomy created allows progress to be made independently.

Motivation

C4I (C2, Communication, Computing, and Information) systems have grown in scope to include a variety of logistics, intelligence, tactical, and geographical databases, as well as message links, providing essential background for C2. However, the military commander primarily has to plan and schedule actions beyond the current point-in-time. Databases can make past and near-current data available, but cannot predict the future.

Diverse predictive tools come into play for projecting the effect in the future of decisions to be made now. These tools range from spreadsheets to war-gaming simulations. Major simulation programs are very costly and most are impossible to reuse in new settings [Zyda:97]. The most common tools used in practice is the spreadsheet, although its capabilities tend to be limited. They provide information which is complementary to the information about the past provided by databases, and help in selecting the best course-of-action [LindenG:92]. Quoting from "New World Vistas, Air and Space Power for the 21st Century" [McCall:96]: The two `Capabilities Requiring Military Investment in Information Technology' are:

  1. Highly robust real-time software modules for data fusion and integration;
  2. Integration of simulation software into military information systems.

Today rapid progress is being made in information fusion from heterogeneous resources such as databases, text, and semi-structured information bases [WiederholdG:97]. Much of this work is ready for transfer to practical settings. However, the results of predictive tools have been rarely addressed in terms of integration and fusion [Orsborn:94]. Because of this gap, for most C2 decisions the capabilities envisioned by C4I advocates remain disjoint. When predictive tools are hard to use the commanders will rely more on their intuition than on computing tools.

Our vision for true C4I systems is sketched in Figure 1. Note that there will be multiple potential futures, leading to requirements for use of SimQL that are more demanding than we encounter today. But new system concepts also provide an opportunity to integrate technology, that is now dispersed and hard to use, into a new model.

 

Figure 1: Model for needed C4I capabilities

Past Work

We were funded by DARPA DSO for a small investigation to define and demonstrate a simulation access language, `SimQL'. Such a language is NOT intended for writing new simulations, but for providing information systems access to the results of existing predictive tools, via a wrapper infrastructure. The tools we explored ranged from simple spreadsheets, to weather forecasting, to computational assessments of future resource availability. Within the limited demonstration, we were unable to access fully distributed simulations as performed in military training exercises (SIMNET [MillerT:95]), although we used such data in a related project [MalufWLP:97].

Concept

Technology has made great strides in accessing past information, stored in databases, object-bases, or the World-Wide Web. Access to information about current events is also improving dramatically. We now must expand the scope to include a peek into the future. Decision-making in planning, both in military and business environments, depends on knowing past, present, and future situations. For the latter we must access simulations. Many simulations are available from remote sites [FishwickH:98], they may also communicate with each other, as SimNet [Singhal:96], but are rarely accessible to be part of general information systems, so that we should handle both local and remote simulation services.

The concept of our simulation access language, SimQL, mirrors that of SQL for databases. Modern versions of SQL provide now also remote access [DateD:93]. Our projected ability to access simulations as part of an information system adds a significant new capability, by allowing simultaneous and seamless access to factual data and projections (e.g., logistics data with future deployment projections).

Figure 2: Simulation tool potential in JFACC

To enable rapid insertion of predictive services our SimQL interfaces can be built using emerging standard conventions for information systems. For instance, they might use a CORBA communication framework, and Java for client-based services. Such use of COTS technology will facilitate the integration of our SimQL interface to other systems that provide access to diverse non-predictive data resources.

To make the results obtained from a simulation clear and useful for the decision maker the interface should also use a simple model. Computer screens today focus on providing a desktop image, with cut and paste capability, while relational databases use tables to present their contents, and spreadsheets use a matrix.. However the objects to be described have a time dimension and also an uncertainty associated with them. We hence used a simple object model as the descriptive interface for SimQL.

Predictive Tools

To project the outcome of current decisions into the future, as every planner must, requires some form of simulation. In earlier generations, that simulation was done in the planner's mind, the planner would sketch reasonable scenarios and develop mentally alternate courses-of-action, focusing on those that had been worked out in earlier exercises. When the plans become complex, tools are needed for pruning, presentation, and assessment. Sand tables are still used for the training of military planners, but are increasingly being replaced by computer-based simulations. In business, spreadsheets are used frequently. Simple heuristics help the planners in pruning. For instance, military doctrine demands air superiority and that the forces and their armaments would be sufficient to overwhelm the enemy, while there would be enough uncommitted forces and logistical support to provide a reserve backup. While these rules still hold, the world is getting more complex, the resources are fewer, and more specialized, so that traditional heuristics are often barely relevant.

Rapid, ad hoc, access to information for planning, as exemplified by the use of DART during the preparations for Desert Storm, was extremely valuable, and superseded effectively many ponderous support planning systems that had been developed using older technology. However, DART could not execute arbitrary planning scenarios, and only one planning tool (originally developed for airport gate allocation by American Airlines) was adapted for use. Today, we have crucial simulations in all aspects of military and commercial planning. Logistics plans are developed by simulating alternate transport modalities, capacities, and risks. Production planners execute simulations to see how they can best exploit their resources. Financial planners use spreadsheets to work out alternate budgets. Most importantly, tactical plans are fully or partially simulated in all military exercises. The expectation, as cited in [McCall:96], is that this technology will transfer into military operations in the future. Even limited situational assessment of current status requires projection. Since C2 data are often out-of-date, commanders must routinely make undocumented projections even to judge the current readiness situation and obtain a complete tactical picture.

Lacking in today's practice is the ability to interoperate with even simple simulations at a direct functional level. Although interchange standards have been developed for the exchange of data objects within the SimNET initiative operated by the Defense Modeling and Simulation Office (DMSO), there is today no direct external access to its results, and no capability for its interoperation with existing military data and information systems. Access to predictive tools through SimQL will provide C2 applications with a powerful, generalized interface to their results.

Specifics

The prediction tools we focus on are pre-existing, and will be wrapped to provide robust access to the model parameters and execution results. Wrapping is needed to provide a compatible `machine-friendly' interface to exchange model information and respond to queries. We followed the model of SQL, which is not a language in which to write a database system - those may be written in C or Ada - but rather a language to select results for further use in information systems. Just as SQL provides access to a wealth of database technology and to database services that are often maintained by others, we expect that SimQL will provide access to the growing portfolio of simulation technology and predictive services maintained by others, and break the bottleneck which is now experienced when predictions are to be a part of larger systems that support planning for military, industry, or business.

There are two aspects to SQL which SimQL models:

  1. A Schema that describes to the invoking program, its programmers, and its customer the accessible content.
  2. A Query-language, which provides the actual access when the information resource is being used by the customer.

There are also some differences, of course, and some of them will require further investigation to assure effectiveness and seamless interoperation.

  1. Not all simulation information is described in the schema. Simulations are often controlled by hundreds of variables, and mapping all of them into a schema may be impractical. Only those variables that are needed for querying results and for controlling the simulation will be made externally accessible. (The rest will still be accessible to the tool developer.) Defining the appropriate schema will require the joint efforts of scientists and planners.
  2. Predictions always incorporate uncertainty. Thus, measures of uncertainty have to be reported with the results. In time, the information systems that process the results will have to take uncertainty explicitly into account, so that the decision-maker can weigh risks versus costs.
  3. Interoperation with past information is required. SimQL must integrate past, present, and simulated information, providing a continuous view. Furthermore, it must indicate what information is valid when. This is especially important since most databases are not fully up-to-date. The time-of-validity capability alone, while modest, can be of great value to decision-makers, and also help in validation of the reliability of the tools being accessed.
  4. Multiple courses-of-action (CoAs) must be supported. Multiple candidate alternatives may be valid simultaneously in some future domains. Thus, systems that access both databases and predictive information must deal with multiple courses-of-action.

Conclusion

We have investigated the potential of a major augmentation for future C4I systems. We have some early results, indicating that diverse predictive tools may be accessed in an integrated fashion. However, much work lies ahead.

 

References

[DateD:93] C.J. Date and Hugh Darwen: A Guide to the SQL Standard, 3rd ed; Addison Wesley, June 1993.

[FishwickH:98] Paul Fishwick and David Hill, eds: 1998 International Conference on Web-Based Modeling & Simulation; Society for Computer Simulation, Jan 1998, http://www.cis.ufl.edu/~fishwick/webconf.html.

[LindenG:92] Ted Linden and D. Gaw 1992: "JIGSAW: Preference-directed, Co-operative Scheduling," AAAI Spring Symposium: Practical Approaches to Scheduling and Planning, March 1992

[MalufWLP:97] David A. Maluf, Gio Wiederhold, Ted Linden, and Priya Panchapa-gesan: "Mediation to Implement Feedback in Training"; CrossTalk: Journal of Defense Software Engineering, Software Technology Support Center, Department of Defense, August 1997.

[McCall:96] Gene McCall (editor): New World Vistas, Air and Space Power for the 21st Century; Air Force Scientific Advisory Board, April 1996, Information Technology volume, pp. 9.

[MillerT:95] Duncan C. Miller and Jack A. Thorpe: "SIMNET: The Advent of Computer Networking"; Proceedings of the IEEE, August 1995, Vol.83 No.8, pages 1116-1123.

[Orsborn:94] Kjell Orsborn: "Applying Next Generation Object-Oriented DBMS for Finite Element Analysis"; ADB conference, Vadstena, Sweden, in Litwin, Risch Applications of Database', Lecture Notes In Computer Science, Springer, 1994.

[Singhal:96] Sandeep Singhal: Effective Remote Modeling in Large-Scale Distributed Interactive Simulation Environments; PhD Thesis, Stanford CSD, 1996.

[WiederholdG:97] Gio Wiederhold and Michael Genesereth: "The Conceptual Basis for Mediation Services"; IEEE Expert, Intelligent Systems and their Applications, Vol.12 No.5, Sep-Oct.1997.

[Zyda:97] Michael Zyda, chair: Modeling and Simulation, Linking Entertainment and Defense; Committee on Modeling and Simulation, National Academy Press, 1997.