Paper #221
Measuring System Interoperability (An i-Score Improvement) Thomas Ford
Scott Graham
[email protected]
[email protected]
John Colombi
David Jacques
[email protected]
[email protected]
Air Force Institute of Technology 2950 Hobson Way WPAFB, OH 45433
Abstract The key to measuring interoperability of systems lies in determining the basis of the measurement. Because of the difficulty in doing this, many extant interoperability measurement methods default to a model of interoperability levels into which systems are qualitatively binned vice measured. (LaVean 1980; DoD 1998; Clark & Jones 1999; Alberts & Hayes 2003; Tolk 2003a; Tolk 2003b; NATO 2003; Fewell, et al. 2004; Kingston, et al. 2005; Schade 2005; Legner & Wende 2006) An exception to this trend is the recent i-Score method (Ford, et al. 2007) which defined a set of discrete interoperability spins as the basis of an operational threadconstrained interoperability measurement. In this paper, an improvement to the i-Score method is proposed which uses biological classification techniques, applied to systems, to produce a higher-fidelity basis for interoperability measurement. Grounding the improved method are a survey of system classification, a framework for choosing system characters pertinent to interoperability measurement, and an application of the method. As not all information given in the original i-Score paper is duplicated herein, the two papers should be read side-by-side.
Introduction A brief Internet search on the term “interoperability” reveals that it is widely discussed, not only in academic circles, but in business and military circles as well. Among these discussions were many publications describing how to assess interoperability levels and make interoperability measurements. Useful models for determining the level of interoperability of technical systems (LaVean 1980), information systems (DoD 1998), organizations (Clark & Jones 1999; Fewell, et al. 2004), forces (Albert & Hayes 2003; Schade 2005), concepts & models (Tolk 2003a), coalitions (Tolk 2003b), NATO information systems (NATO 2003), organizational in-
teroperability agility (Kingston, et al. 2005), and businesses (Legner & Wende 2006), are most commonly found. A non-levelling method was proposed by Ford, et al. (2007) which was uniquely designed to analyze heterogeneous sets of systems. In all cases, however, the basis of the interoperability model or measure was tied to understanding the nature of the system, organization, concept, model, coalition, or business. It can be concluded that understanding the nature of systems is prerequisite to measuring their interoperability. One way to describe the nature of a set of systems is to first identify the systems’ characters (attributes or features) and then classify them according to those characters. (Ford, et al. 2008) If appropriate characters are chosen, the resulting system classification not only provides understanding about the systems themselves, but provides a basis for a variety of measurements, including interoperability. In this paper, a set of systems supporting an operational thread is considered. The nature of each system is described as a set of system character states, which set is called a system instantiation. The resemblance of each instantiation pair is measured using a distance metric, and the resulting set of resemblance coefficients is given as a resemblance matrix. The i-Score interoperability measurement method is then upgraded by replacing the original spin matrix with the resemblance matrix. Because the coefficients in the resemblance matrix represent exact measures of similarity between systems, based upon system characters pertinent to interoperability, the measure of interoperability obtained enjoys more fidelity and accuracy than that possible with the original spin-based method. The remaining sections of this paper include a survey of published system classifications (taxonomies) originating in both the system science and systems engineering disciplines; details of the improved i-Score interoperability measurement method; a preliminary framework for identifying system characters appropriate for interoperability measurement; and an application of the improved i-Score method.
Survey of System Classification In order to measure the interoperability of systems, an understanding of the types of systems involved is required. In fact, a generic taxonomy of the universe of systems is necessary as a starting point for developing the system interoperability character framework presented later in this paper. To this end, a summary of notable extant system taxonomies is given next, followed by a table (Table 1) which includes a more comprehensive list of taxonomies originating from the system science and systems engineering disciplines.
System Science System Taxonomies Kenneth Boulding classified systems as 1) frameworks, 2) clockworks, 3) thermostats, 4) cells, 5) plants, 6) animals, 7) human beings, 8) social organizations, and 9) transcendental systems (1956: 197-208), then later in life stated that systems were either physical, biological, or social. (1985: 31) Jordan defined eight cells (classifications) of systems by taking one property from each of the three principles—1) rate of change (structural/static or functional/dynamic), 2) purpose (purposive, non-purposive), and 3) connectivity (mechanistic/organismic). (1968: 44-65) Checkland took Jordan’s taxonomy as a foundation, merged some ideas from Boulding and created a systems typology with five types (natural, design physical, design abstract, human activity, and transcendental systems). (1981: 112) Wilson, a colleague of Checkland, adopted a revi-
sion of his typology, called a system classification, in which he gave four classes of systems (natural, designed, human activity, and social and cultural). (1990: 24-25)
Systems Engineering System Taxonomies Hall simply described a classification of natural vice man-made systems. (1962:63, 68) Shenhar, and Shenhar & Bonen classified systems “according to four levels of technological uncertainty (low, medium, high, and super-high tech), and three levels of system scope (assembly, system, and array).” (1995, 1997:137) Martin indirectly classified systems by classifying product types (hardware, software, personnel, facilities, data, materials, services, and techniques), relating them to systems by stating that systems are comprised of components, and components are comprised of one or more basic product types. (1997: 24) Maier separates systems-ofsystems from systems and classified them as virtual, voluntary, or directed. (1999:267-284) Conversely, Gideon et al., classified them by acquisition type (dedicated or virtual), operational type (chaotic, collaborative, or directed), and domain type (social, conceptual, or physical). (2005) Blanchard and Fabrycky defined systems as natural and man-made, physical and conceptual, static and dynamic, and closed and open. (2006: 6-8) Ford, et al. described a numerical taxonomy-type method of classifying a heterogeneous set of systems, constrained by a business process, according to system features. The resulting classification is appropriate as the basis of numerical measurements. (2008) Table 1: Historical Summary of Extant System Taxonomies Year Originator
SE/S S
Basis
Purpose of Taxonomy
1955 Von Bertalanffy
SS
open/closed
Launch GST
1956 Boulding
SS
complexity
An approach to GST
1957 Goode & Machol
SE
system inputs
Find solution to problems
1962 Hall
SE
none given
Partition subsystems/define “system”
1985 Boulding
SS
world corresponding
World modeling
1968 Jordan
SS
organizing principles
Furtherance of systems thinking
1971 Ackoff
SS
system concepts
Create system concept framework
1981 Checkland
SS
origin of system
Group by origin
1990 Wilson
SS
none given
Refine definition of “system”
1995, Shenhar & 1997 Bonen
SE
technical uncertainty & scope
Allocate appropriate SE methods
1997 Martin
SE
product type
Provide SE checklist
1999 Maier**
SE
operational & managerial independence of components
System-of-system architecting
2005 Gideon et al.**
SE
acquisition, operational, domain type
Aid in system-of-system understanding
2005 Kovacic
SE
complexity
Reduce set of systems into meaningful clusters
2006 Blanchard & Fabrycky
SE
Similarities & differences
Provide insight into wide range of systems
2007 Valdma
SS
information classes
Study of non-deterministic phenom.
**Provided a taxonomy of systems-of-systems vice systems
Improved i-Score Method In this paper, the original i-Score method is improved by replacing the spin matrix with a system resemblance matrix. Whereas the spin matrix described system interoperations with discrete values sij ∈ {−1, 0,1} , the resemblance matrix describes system interoperability with a continuous value of rij ∈ [ 01] ∩ which is methodically determined by an analysis of system interoperability functions. Although the spin matrix was well-defined, the resemblance matrix gives more fidelity and accuracy to the interoperability measure. A summary of the improved i-Score method is given next, beginning with definitions necessary to calculate inter-system resemblances. The original i-Score paper should be kept close at hand as its contents are not completely repeated here. Some variations in notation from the original i-Score paper are made for clarity. Refinements to the interoperability measurement model are also made for completeness and consistency with our system classification work. Let an operational thread be given as a graph Ω in which the vertices represent activities and the edges are the directed flows in the thread. Let S be the set of systems implementing the activities. If X is the set of system characters (i.e., features or attributes), pertinent to interoperability, which characterize S and C is the set of valid states of X , then X : S → C is a function which maps systems to their associated character states and σ j = X ( s j ) is a sequence of character states which instantiates s j and = Σ σ= X ( s j ) , j = 1 S . (Ford, et al. 2008) j The resemblance coefficient is the distance between each system instantiation pair in system character hyperspace. This resemblance is easily computed using the Czekanowski metric (1913) if absence/presence characters are used (i.e., C is constrained to be binary). The Czeka-
nowski metric is a variation of the Hamming distance which places double weight on shared presence character states and ignores shared absence states. The resemblance of all system pairs is given by an S × S matrix R which can be Table 2: Taxonomy of Systems symmetric or nonMan-Made or symmetric about the prinTaxa Example Natural ciple diagonal (arbitrarily set to one). (Ibid) Physical River, mountain, atmosphere Both The spin matrix deTechnological Computer, machine, furniture Man-made scribed in the original iBiological Plant, animal, human Natural Score paper is replaced Conceptual Political party, culture Man-made with R . Given a normalOrganizational Team, division, company Man-made ized multiplicity matrix Environmental Wind, e-mag interference Both U , previously called C , and using the original definition of the interoperability matrix M , we obtain M = mij where mij = uij rij , I=
i, j = 1 S
1 S S ∑ ∑ mij T =i 1 =j 1
.
Then the normalized i-Score for the operational thread is
.
The method for calculating the optimum i-Score is similar to that presented in the original paper. Whereas the original method called for upgrading spins to their theoretical operational and physical maximum, the new i-Score method requires that system instantiations be upgraded to possess maximum functionality. After upgrading, Ropt is calculated and used to determine = I opt − I . The interoperability gap is I gap The original i-Score measure was an integer ranging from −∞ to +∞ , but the new measure is a positive real number ranging from 0 to 1 which is more meaningful because a score of zero indicates no interoperability and a score of one indicates perfect interoperability. Because the accuracy of the new i-Score measure is largely dependent upon the completeness and the quality of the resemblance matrix from which it is derived, guidelines for its development are given next. I opt .
System Interoperability Character Framework Systems are instantiated by a set of system character states, but in order for a resemblance matrix to be used as the basis for an interoperability measurement, it must use the right set of system characters. For example, if only morphological characters are used, the final i-Score will be more a measure of how much the systems look like each other rather than how interoperable they are. Therefore, only interoperability-related functional system characters should be used to instantiate the systems. The cardinal rule to follow is that only functional system interoperability characters describing what systems do to each other should be used to instantiate systems. A system interoperability character framework, giving guidelines for selecting system interoperability characters, can be used to ensure that no important character is overlooked. A generic taxonomy of the universe of systems can be used a starting point in developing the system interoperability character framework. An appropriate taxonomy of systems is obtained by extracting from the system taxonomies surveyed early in this paper (Table 2). Guidance for choosing system interoperability characters for each type of system is found in succeeding para-
graphs and a combined framework is given in the Appendix. All characters given in the framework follow the simple grammar of “ s ′ s ′′ by m ” where s′, s′′ are sets of systems, is matter, energy, or information, and m represents a method for the interoperability function verb (if needed). Physical System Interoperability Characters: Nothing has been published specifically on the interoperability of physical systems with themselves or with other types of systems. Physical systems can interoperate with all other types of systems except conceptual and organizational, and generally, act to accommodate other systems or to transform their functions. Technical System Interoperability Characters: Technological systems are the most studied as far as interoperability is concerned and many interoperability levelling models have been published. (LaVean 1980; DoD 1998; Clark & Jones 1999; Alberts & Hayes 2003; NATO 2003; Tolk 2003a; Fewell, et al. 2004; Kingston, et al. 2005; Schade 2005; Legner & Wende 2006) Other non-levelling interoperability models (Mensh, Kite, & Darby 1989; Amanowicz & Gajewski 1996; Leite 1998) also lend a perspective on technological system interoperability. Technological systems can only interoperate with physical, technological, biological, and environmental systems. For example, a computer (technological system) communicates on Wi-Fi (electromagnetic emission) with another computer (another technological system). A human (biological system) can press keys (somaction) to provide input to that same computer. Or, a sonar system (technological) can emit sound (audition), which is reflected back by a submarine (technological system) which operates in the ocean (environmental system). Finally, a satellite (technological system) orbits (by gravitation) the earth (physical system). While these examples show the range of interoperations of technological systems, often, the most concerning interoperations are technical-to-technical system in nature and the interoperations are bi-directional communications. To capture the range of these communications, the hierarchical application of the OSI, TCP/IP, or physical/syntactic/semantic network model is useful. For example, the hierarchical series of characters given in Table 3 can be extracted from the sentence “the military aircraft can transmit target location data to other users on the Link-16 net.” Table 3: Hierarchical Series of System Interoperability Characters “can transmit/receive” “can transmit/receive electromagnetically” “can transmit/receive electromagnetically on L-band” “can transmit/receive electromagnetically on L-band, by frequency-hopping” “can transmit/receive electromagnetically on L-band, by frequency-hopping, on a Link-16 net” “can transmit/receive electromagnetically on L-band, by frequency-hopping, on a Link-16 net, by J-message ”
If these characters are absence/presence (binary) characters, then two possible system instantiations are σ ′ = {1,1,1,1,1,1} and σ ′′ = {1,1,1,1, 0, 0} . σ ′, σ ′′ have some measure of interoperability, but it is not perfect because σ ′′ does not understand J-messages passed on a Link-16 net. Biological System Interoperability Characters: Interestingly, at the highest level of abstraction, biological systems are instantiated similarly to technical systems. This is likely because humans designed technical systems according to human paradigms. For example, we move, so we design systems that can move, and we communicate, so we design systems which can also communicate. Other types of biological systems, such as plants and animals, function in similar ways to humans, only sometimes on a less advanced, or restricted scale, or by using only a subset of the methods available to humans. Biological systems are unique in that they are the only type of system which can interoperate with all other system types.
Conceptual System Interoperability Characters: Conceptual systems represent a body of philosophies, rules, processes, and related information. For example, an educational system includes a philosophy on how students are to be taught, sets of rules governing actions pertaining to teachers and administrators, and the locations and types of schools. Conceptual systems influence the actions of humans. They are not capable of interoperating with any other system type. Organizational System Interoperability Characters: Organizational systems are groups of people which often interoperate with and through their leader (a biological system). Any interoperation within the organization (i.e., group dynamics) is considered a biological-tobiological interoperation. Organizational systems perform three interoperability-related functions—receive direction from the leader, provide feedback to the leader, and implement the leader’s direction. Environmental System Interoperability Characters: Environmental systems transform the inputs and outputs of other types of systems. For example, wind changes the movement of an aircraft, electro-magnetic interference transforms the input to a satellite receiver, and temperature can degrade the performance of both biological and technological systems. For many operational threads, the set of systems implementing the thread need not contain environmental systems because the transformations made by environmental systems can be captured within the characters of other systems. For example, a technological system character “receives correct packet 90% of the time” accounts for the technological system’s receive functionality, but also instantiates the impact of environmental effect of electromagnetic interference.
An Application of the Improved i-Score Method The time-critical targeting example given in the original i-Score paper is re-addressed here. Let T remain unchanged from the original, let a normalized multiplicity matrix U be given in place of C , and let S be redefined as the set of systems. Let the set of system characters X be given as in Table 4 and constrain system character states C to be binary. Instantiate the four systems = Σ σ= i , i 1 S as given in Table 4 assuming that s1 , s2 , s4 are technical systems and s3 is a hybrid technological/biological (human) system. Then the i-Score is I = 0.5079 . The new i-Score is not comparable with the old due to differences in the scale of the measurement. If it is determined that within operational, physical, and fiscal constraints that the Shooter should have Tactical ISR sensor data, then Σ opt is obtained by changing Σ(4,13) from 0 to 1. Recalculating, the feasible, ideal interoperability is I opt = 0.5251 which results in an interoperability gap of I gap = 0.0172 .
Table 4: Time Critical Targeting Example Application = {Strategic ISR, Tactical ISR, Command Authority, Shooter} = { s1 , s2 , s3 , s4 } T = { s1 , s2 , s2 , s3 , s4 , s2 } X = {Communicate; S
Comm. by e-mag method; Comm. by e-mag method, by SATCOM method; Comm. by e-mag method, by SATCOM method, system control & feedback; Comm. by e-mag method, by SATCOM method, strategic sensor data; Comm. by e-mag method, on VHF; Comm. by e-mag method, on VHF, by secure method; Comm. by e-mag method, on VHF, by secure method, by frequency hopping; Comm. by e-mag method, on VHF, by secure method, by freq. hopping, by encrypted voice; Comm. by e-mag method, on UHF; Comm. by e-mag method, on UHF, by SATCOM method; Comm. by e-mag method, by SATCOM method, flight commands & feedback; Comm. by e-mag method, by SATCOM method, tactical sensor data; Comm. by e-mag method, on HF; Comm. by e-mag method, on UHF, by secure method} 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 0 1 1 1 1 0 0 Σ σ= C ={ 0,1 }; = i 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 0 0 0 1 0 0 0 1 1 0 1 1 3 1 3 1 2 7 1 2 4 11 0 2 7 1 6 4 33 2 0 1 23 23 1 3 4 815 0 1 1 2 16 45 7 ; R= 1 3 ;= U= M = uij rij 0 1 3 0 1 3 2 0 1 4 0 4 21 1 47 4 8 8 1 4 4 0 0 3 0 0 11 15 7 1 0 45 0
1 T
S
S
i-Score = I = = ∑ ∑ mij 0.5079 ; =i 1 =j 1
I opt = 0.5251 ;
I gap = 0.0172
Conclusion In this paper, an improvement was made to the previously published i-Score interoperability measurement method by replacing the discrete interoperability spin with the real-valued resemblance coefficient. An interoperability measure was obtained which has better fidelity and accuracy than the original. Guidelines for instantiating systems according to interoperability characters were given, as were instructions for calculating the resemblance matrix. While the method presented in this paper is complete, further research is needed to expand the preliminary system interoperability character framework presented. The ideal framework would include a complete set of interoperability function categories for each type of system and would also address any special considerations when instantiating systems-of-systems. Additionally, more applications of the improved i-Score method need to be made in order to determine its robustness and applicability. Interoperability lies at the root of many engineering problems such as interfaces (inter-system interoperability), reliability (component-to-component interoperability), maintainability (human-to-system interoperability), adaptability (interoperability agility), and usability (human-to-technological and technological-to-technological interoperability),
among others. A deeper understanding of interoperability as a foundational tenet of engineering is needed and this paper is a step in that direction.
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Biography Thomas C. Ford is a Systems Engineering Ph.D. candidate at the Air Force Institute of Technology (AFIT). His research focuses on interoperability measurement. He previously formulated architecture policy and guidance at Headquarters Air Force and oversaw the production and delivery of two Joint STARS surveillance aircraft as Deputy Chief of Joint STARS production. He is a member of INCOSE and Tau Beta Pi. He received the BS and BA degrees from Brigham Young University, Provo, UT and the MSE degree from Wright State University, Dayton, OH. Dr. John Colombi is member of the staff of Riverside Research Institute, serving an appointment as Adjunct Assistant Professor of Systems Engineering at the Air Force Institute of Technology (AFIT). He teaches graduate courses and leads research in support of the Systems Engineering program. Before joining RRI, Dr. Colombi served 21 years in the US Air Force leading various C4ISR systems integration and systems engineering activities. He developed information systems security at the National Security Agency (NSA) and researched communications networking at the Air Force Research Laboratory. Dr. Colombi is a member of INCOSE and IEEE. He earned his PhD at the Air Force Institute of Technology, Dayton, OH. David Jacques is an Assistant Professor of Aeronautical at the Air Force Institute of Technology (AFIT). His prior military assignments include tactical missile intelligence analysis, ballistic missile test and evaluation, and research and development for advanced munition concepts.
In the winter of 2002-2003, Dr. Jacques led the activation of the new Air Force Center for Systems Engineering initiated by then Secretary of the Air Force Dr. James Roche. Dr. Jacques’ research interests include multi-objective or constrained optimal design and cooperative behavior and control of autonomous vehicles. He is a member of INCOSE and is an Associate Fellow of AIAA. He earned his PhD degree from the Air Force Institute of Technology, Dayton, OH. Scott Graham is an Assistant Professor of Computer Engineering at the Air Force Institute of Technology (AFIT). He currently is the Chief of the AFIT Commander’s Action Group. Previously, he taught graduate courses in computer networking and led sponsored research in the area of mobile military networks. Prior to joining the AFIT faculty, Dr. Graham conducted software evaluation at the Air Force Operational Test and Evaluation Center (AFOTEC) and led testing of the Combat Talon II Mission Rehearsal Device at the Aeronautical Systems Center’s Training Systems Product Group. Dr. Graham is a member of IEEE and received the PhD degree from the University of Illinois Urbana-Champaign.
Appendix System Interoperability Character Framework Physical System Interoperability Character Framework the river accommodates the boat; s1 accommodates s2 the troposphere accommodates the aircraft; the river transforms the mountain by erosion; s1 transforms s2 by method m the ground slows the jeep by friction; Technological & Biological System Interoperability Character Framework radio #1 outputs human voice electromagnetis1 communicates by method m cally, on VHF to radio #2; with s2 Conceptual System Interoperability Character Framework s1 transforms s2 ’s by method m legal system requires citizen to pay taxes through executive enforcement Organizational System Interoperability Character Framework safety division receives manufacturing guides1 receives by method m lines by letter from division chief; from s2 s1 transmits by method m to safety division transmits feedback verbally to division chief; s2 s1
implements by method m
to s2 s1
safety division ensures manufacturing safety by verbalizing instructions to factory workers;
Environmental System Interoperability Character Framework solar-originating electromagnetic interference transforms s2 by method m disrupts TV station’s broadcasts by noise {vision, audition, gustation, olfaction, somaction, electromagnetic, chemical, gravitation}
