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Journal articles on the topic 'Adaptive software architecture'

To see the other types of publications on this topic, follow the link: Adaptive software architecture.

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Ahmadi, Reza, Oliver Marquardt, Marc Riedlinger, and Reinhard Reichel. "An Adaptive Software Architecture for Future CMS." SAE International Journal of Aerospace 8, no. 2 (September 15, 2015): 260–72. http://dx.doi.org/10.4271/2015-01-2545.

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2

Fitzpatrick, T., G. S. Blair, G. Coulson, N. Davies, and P. Robin. "Software architecture for adaptive distributed multimedia systems." IEE Proceedings - Software 145, no. 5 (1998): 163. http://dx.doi.org/10.1049/ip-sen:19982299.

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3

Tak, Sung Woo, and Eun Kyo Park. "Adaptive secure software architecture for electronic commerce." Software: Practice and Experience 33, no. 14 (2003): 1343–57. http://dx.doi.org/10.1002/spe.551.

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4

HUANG, Shuang-Xi. "Research on Generic Adaptive Software Architecture Style." Journal of Software 17, no. 6 (2006): 1338. http://dx.doi.org/10.1360/jos171338.

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5

JIANG, CHANG-HAI, HAI HU, KAI-YUAN CAI, DAZHI HUANG, and STEPHEN S. YAU. "AN INTELLIGENT CONTROL ARCHITECTURE FOR ADAPTIVE SERVICE-BASED SOFTWARE SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 19, no. 05 (August 2009): 653–78. http://dx.doi.org/10.1142/s0218194009004337.

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Service-oriented architecture (SOA) for distributed computing has become increasingly popular due to the big advantage that distributed applications can be rapidly synthesized with the needed services provided by various service providers through heterogeneous networks. Systems based on SOA are called Service-based Systems (SBS), and a special variety of SBS, namely the Adaptive Service-Based Systems (ASBS), is aimed to be adaptable to constantly changing user requirements, environments and resource constraints. An important and difficult issue is how to design and develop ASBS to satisfy multiple QoS requirements in an open dynamic environment. In this paper, inspired by the underlying principle of hierarchical intelligent control, a three-layer architecture for developing and deploying ASBS is presented to address this issue. Compared with existing architectures for SBS, the advantage of using our architecture is that it provides the flexibility for system designers to adopt different control based approaches to guarantee user requirements and satisfy resource constraints at different levels of the system. Moreover, our architecture enables the system to take hierarchical adaptation actions at runtime to avoid possible violation of user requirement or resource constraint. An example is given to illustrate how to adopt our architecture to guide the design of a simple ASBS, and preliminary experimental data are presented to demonstrate the feasibility of developing ASBS based on our architecture.

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6

Palopoli, L., T. Cucinotta, L. Marzario, and G. Lipari. "AQuoSA-adaptive quality of service architecture." Software: Practice and Experience 39, no. 1 (January 2009): 1–31. http://dx.doi.org/10.1002/spe.883.

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7

Lyle, Andrew C., and Michael D. Naish. "A Software Architecture for Adaptive Modular Sensing Systems." Sensors 10, no. 8 (August 10, 2010): 7514–60. http://dx.doi.org/10.3390/s100807514.

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8

Ye, Eun-Suk, Keun-Hyuk Yeom, and Mi-Kyeong Moon. "Product-Line Architecture Development for Self-Adaptive Software." KIPS Transactions:PartD 15D, no. 3 (June 30, 2008): 361–74. http://dx.doi.org/10.3745/kipstd.2008.15-d.3.361.

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9

CHANG, Zhi-Ming, Xin-Jun MAO, and Zhi-Chang QI. "Applying Bigraph Theory to Self-Adaptive Software Architecture." Chinese Journal of Computers 32, no. 1 (July 29, 2009): 97–106. http://dx.doi.org/10.3724/sp.j.1016.2009.00097.

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10

Oreizy, P., M. M. Gorlick, R. N. Taylor, D. Heimhigner, G. Johnson, N. Medvidovic, A. Quilici, D. S. Rosenblum, and A. L. Wolf. "An architecture-based approach to self-adaptive software." IEEE Intelligent Systems 14, no. 3 (May 1999): 54–62. http://dx.doi.org/10.1109/5254.769885.

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11

Yang, Shuo, Xinjun Mao, Sen Yang, and Zhe Liu. "Towards a hybrid software architecture and multi-agent approach for autonomous robot software." International Journal of Advanced Robotic Systems 14, no. 4 (July 1, 2017): 172988141771608. http://dx.doi.org/10.1177/1729881417716088.

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To support robust plan execution of autonomous robots in dynamic environments, autonomous robot software should include adaptive and reactive capabilities to cope with the dynamics and uncertainties of the evolving states of real-world environments. However, conventional software architectures such as sense-model-plan-act and behaviour-based paradigms are inadequate for meeting the requirements. A lack of sensing during acting in the sense-model-plan-act paradigm makes the software slow to react to run-time contingencies, whereas the behaviour-based architectures typically fall short in planning of long-range steps and making optimized plan adaptations. This article proposes a hybrid software architecture that maintains both adaptivity and reactivity of robot behaviours in dynamic environments. To implement this architecture, we further present the multi-agent development framework known as AutoRobot, which views the robot software as a multi-agent system in which diverse agent roles collaborate to achieve software functionalities. To demonstrate the applicability and validity of our concrete framework and software architecture, we conduct an experiment to implement a typical case, for example, a robot that autonomously picks up and drops off dishes for remote guests, which requires the robot to plan and navigate in a highly dynamic environment and can adapt its behaviours to unexpected situations.

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12

Pal, Partha, Paul Rubel, Michael Atighetchi, Franklin Webber, William H. Sanders, Mouna Seri, HariGovind Ramasamy, et al. "An architecture for adaptive intrusion-tolerant applications." Software: Practice and Experience 36, no. 11-12 (2006): 1331–54. http://dx.doi.org/10.1002/spe.747.

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13

Magableh, Basel, and Stephen Barrett. "Productivity Evaluation of Self-Adaptive Software Model Driven Architecture." International Journal of Information Technology and Web Engineering 6, no. 4 (October 2011): 1–19. http://dx.doi.org/10.4018/jitwe.2011100101.

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Anticipating context changes using a model-based approach requires a formal procedure for analysing and modelling context-dependent functionality and stable description of the architecture which supports dynamic decision-making and architecture evolution. This article demonstrates the capabilities of the context-oriented component-based application-model-driven architecture (COCA-MDA) to support the development of self-adaptive applications; the authors describe a state-of-the-art case study and evaluate the development effort involved in adopting the COCA-MDA in constructing the application. An intensive analysis of the application requirements simplified the process of modelling the application’s behavioural model; therefore, instead of modelling several variation models, the developers modelled an extra-functionality model. COCA-MDA reduces the development effort because it maintains a clear separation of concerns and employs a decomposition mechanism to produce a context-oriented component model which decouples the applications’ core functionality from the context-dependent functionality. Estimating the MDA approach’s productivity can help the software developers select the best MDA-based methodology from the available solutions. Thus, counting the source line of code is not adequate for evaluating the development effort of the MDA-based methodology. Quantifying the maintenance adjustment factor of the new, adapted, and reused code is a better estimate of the development effort of the MDA approaches.

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14

Hayes-Roth, B., K. Pfleger, P. Lalanda, P. Morignot, and M. Balabanovic. "A domain-specific software architecture for adaptive intelligent systems." IEEE Transactions on Software Engineering 21, no. 4 (April 1995): 288–301. http://dx.doi.org/10.1109/32.385968.

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15

Mohana Roopa, Y., M. Ramesh Babu, Jetti Kumar, and D. Kishore Babu. "Optimal component architecture using particle swarm optimization algorithm for self-adaptive software architecture." International Journal of Engineering & Technology 7, no. 1.6 (June 1, 2018): 23. http://dx.doi.org/10.14419/ijet.v7i1.6.11387.

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The component-based software engineering (CBSE) ensue the procedure of reconfiguration and reusability of components to reap the higher productivity. The context-aware structures are portion of CBSE, which observes the functionality of the system and adopt automatically according to the execution context. In this paper, we are focusing on the aware context guidelines that automatically adapt to the given context given by the customers and remodel the software architecture based totally on the requirements. The component repository turned into added, in which it carries the wide variety of reusable components. The fuzzy logic becomes carried out to the component evaluation in the component repository. The Particle Swarm Optimization (PSO) algorithm applied, to optimize component architecture. The Hospital management system is used to test the adaptability of the system.

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16

Krommer, Arnold R., and Christoph W. Ueberhuber. "Architecture adaptive algorithms." Parallel Computing 19, no. 4 (April 1993): 409–35. http://dx.doi.org/10.1016/0167-8191(93)90055-p.

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17

Muralidharan, Saurav, Amit Roy, Mary Hall, Michael Garland, and Piyush Rai. "Architecture-Adaptive Code Variant Tuning." ACM SIGPLAN Notices 51, no. 4 (June 9, 2016): 325–38. http://dx.doi.org/10.1145/2954679.2872411.

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18

Algabroun, Hatem. "Dynamic sampling rate algorithm (DSRA) implemented in self-adaptive software architecture: a way to reduce the energy consumption of wireless sensors through event-based sampling." Microsystem Technologies 26, no. 4 (September 21, 2019): 1067–74. http://dx.doi.org/10.1007/s00542-019-04631-9.

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Abstract With the recent digitalization trends in the industry, wireless sensors are, in particular, gaining a growing interest. This is due to the possibility of being installed in inaccessible locations for wired sensors. Although great success has already been achieved in this area, energy limitation remains a major obstacle for further advances. As such, it is important to optimize the sampling with a sufficient rate to catch important information without excessive energy consumption, and one way to achieve sufficient sampling is using adaptive sampling for sensors. As software plays an important role in the techniques of adaptive sampling, a reference framework for software architecture is important in order to facilitate their design, modeling, and implementation. This study proposes a software architecture, named Rainbow, as the reference architecture, also, it develops an algorithm for adaptive sampling. The algorithm was implemented in the Rainbow architecture and tested using two datasets; the results show the proper operation of the architecture as well as the algorithm. In conclusion, the Rainbow software architecture has the potential to be used as a framework for adaptive sampling algorithms, and the developed algorithm allows adaptive sampling based on the changes in the signal.

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19

Muqsith, Mohammed A., Hessam S. Sarjoughian, Dazhi Huang, and Stephen S. Yau. "Simulating adaptive service-oriented software systems." SIMULATION 87, no. 11 (October 11, 2010): 915–31. http://dx.doi.org/10.1177/0037549710382431.

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Simulation of dynamic service-based software systems is important for studying services that may change their composition and thus interactions at run-time. An approach based on Service Oriented Architecture-compliant DEVS (SOAD) and Dynamic Structure DEVS (DSDEVS) modeling approaches is developed to support structural changes in service model composition. To achieve this goal, a broker–executive model is devised based on the broker model defined for SOAD and the executive model defined for DSDEVS. The capability to simulate dynamic services is incorporated to the DEVS-Suite simulator. To demonstrate modeling of dynamic service-based software systems, a real voice communication system and a model of this system have been developed. The importance of enabling simulation-based design for adaptable systems is briefly discussed.

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20

KALYANARAMAN, SAI K., and MICHAEL S. BRAASCH. "GPS Adaptive Array Phase Compensation using a Software Radio Architecture." Navigation 57, no. 1 (March 2010): 53–68. http://dx.doi.org/10.1002/j.2161-4296.2010.tb01767.x.

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21

Rosenberg, J. L., S. A. Gruchevsky, and D. M. Piscitello. "Adaptive routing in Burroughs network architecture." ACM SIGCOMM Computer Communication Review 17, no. 5 (October 1987): 173–84. http://dx.doi.org/10.1145/55483.55500.

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22

Gorton, Ian, Yan Liu, and Nihar Trivedi. "An extensible and lightweight architecture for adaptive server applications." Software: Practice and Experience 38, no. 8 (July 10, 2008): 853–83. http://dx.doi.org/10.1002/spe.857.

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23

Srinivas, Shreya, Asif Qumer Gill, and Terry Roach. "Can Business Architecture Modeling be Adaptive?" IT Professional 23, no. 2 (March 1, 2021): 81–88. http://dx.doi.org/10.1109/mitp.2020.3043237.

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24

SHIN, CHULHO, SEONG-WON LEE, and JEAN-LUC GAUDIOT. "THE NEED FOR ADAPTIVE DYNAMIC THREAD SCHEDULING IN SIMULTANEOUS MULTITHREADING." Parallel Processing Letters 14, no. 03n04 (September 2004): 327–35. http://dx.doi.org/10.1142/s012962640400191x.

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Earlier studies on Simultaneous Multithreaded (SMT) architectures showed that performance of a realistic SMT architecture saturates early. This paper addresses our contention that a fixed hardware thread scheduling strategy cannot provide optimal results for various thread combinations. We propose an approach that partially schedules threads in the form of a detector thread at a nominal hardware and software cost. It offers the capability to adaptively switch thread scheduling policies depending on various situations. This article shows that there is much room for performance improvement for our adaptive dynamic thread scheduling approach. The results we have obtained by simulating a realistic SMT architecture show that no single fetch policy may outperform the rest more than 50% of the total time. We show that 27% is approximately the upper-bound of the performance improvement for SMT with eight contexts. This demonstrates that our approach may significantly improve performance with good low-throughput detection and fetch policy selection heuristics.

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25

Liang, Xun, Rong-Chang Chen, and Jian Yang. "An architecture-adaptive neural network online control system." Neural Computing and Applications 17, no. 4 (July 21, 2007): 413–23. http://dx.doi.org/10.1007/s00521-007-0137-3.

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26

Castelluccia, Claude, and Pars Mutaf. "An adaptive per-host IP paging architecture." ACM SIGCOMM Computer Communication Review 31, no. 5 (October 2001): 48–56. http://dx.doi.org/10.1145/1037107.1037114.

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27

Kim, Dongsun, and Sooyong Park. "Software Architecture-based Approach to Self-adaptive Function for Intelligent Robots." IFAC Proceedings Volumes 41, no. 2 (2008): 5297–302. http://dx.doi.org/10.3182/20080706-5-kr-1001.00891.

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28

Cámara, Javier, Pedro Correia, Rogério de Lemos, David Garlan, Pedro Gomes, Bradley Schmerl, and Rafael Ventura. "Incorporating architecture-based self-adaptation into an adaptive industrial software system." Journal of Systems and Software 122 (December 2016): 507–23. http://dx.doi.org/10.1016/j.jss.2015.09.021.

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29

Chen, Luxi, Linpeng Huang, Chen Li, and Xiwen Wu. "Self-adaptive architecture evolution with model checking: A software cybernetics approach." Journal of Systems and Software 124 (February 2017): 228–46. http://dx.doi.org/10.1016/j.jss.2016.03.010.

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30

Wen, Mei, Nan Wu, Hai-Yan Li, and Chun-Yuan Zhang. "Multiple-Morphs Adaptive Stream Architecture." Journal of Computer Science and Technology 20, no. 5 (September 2005): 635–46. http://dx.doi.org/10.1007/s11390-005-0635-7.

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31

Wang, Pengwei, Caihui Zhao, Yi Wei, Dong Wang, and Zhaohui Zhang. "An Adaptive Data Placement Architecture in Multicloud Environments." Scientific Programming 2020 (June 10, 2020): 1–12. http://dx.doi.org/10.1155/2020/1704258.

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Cloud service providers (CSPs) can offer infinite storage space with cheaper maintenance cost compared to the traditional storage mode. Users tend to store their data in geographical and diverse CSPs so as to avoid vendor lock-in. Static data placement has been widely studied in recent works. However, the data access pattern is often time-varying and users may pay more cost if static placement is adopted during the data lifetime. Therefore, it is a pending problem and challenge of how to dynamically store users’ data under time-varying data access pattern. To this end, we propose ADPA, an adaptive data placement architecture that can adjust the data placement scheme based on the time-varying data access pattern and subject for minimizing the total cost and maximizing the data availability. The proposed architecture includes two main components: data retrieval frequency prediction module based on LSTM and data placement optimization module based on Q-learning. The performance of ADPA is evaluated through several experimental scenarios using NASA-HTTP workload and cloud providers information.

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32

Alreshidi, Abdulrahman, and Aakash Ahmad. "Architecting Software for the Internet of Thing Based Systems." Future Internet 11, no. 7 (July 10, 2019): 153. http://dx.doi.org/10.3390/fi11070153.

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Context—Internet of Thing (IoT) based systems support any-time/place computations by interconnecting humans, systems, services, devices, and things that enabling autonomous systems to shape-up digitized societies. Software architecture, as the blue-print of software-intensive systems, abstracts the complexities of modeling, design, development, and evolution phases of a software to engineer complex IoT driven systems effectively and efficiently. Objectives and Method—Research and development efforts are required to exploit architectural principle and practices to design and develop IoT systems to go beyond the state-of-the-art for IoTs. The objectives of this research are to empirically investigate and systematically classify the state-of-the-art on architecting IoT based software. We have used the Evidence Based Software Engineering (EBSE) method to conduct a mapping study of the existing IoT solutions by investigating 88 qualitatively selected studies. Results and Implications—The results of the mapping study highlight various research themes that exploit software architecture models to develop IoT systems. The identified research themes include, but are not limited to, cloud-based software ecosystems, software defined networking, autonomous, and adaptive software and agent-based systems that IoTs drive. The mapping study suggests that futuristic research on architecting IoT software is focused on architectural languages and patterns that support reusability, automation, and human decision support to develop and dynamically adapt IoT software. The mapping study represents a concentrated knowledge regarding architectural principle and practices to facilitate knowledge transfer—benefiting researchers and practitioners—on the role of software architecture for IoT systems.

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33

Belalem, Ghalem, and Said Limam. "Fault Tolerant Architecture to Cloud Computing Using Adaptive Checkpoint." International Journal of Cloud Applications and Computing 1, no. 4 (October 2011): 60–69. http://dx.doi.org/10.4018/ijcac.2011100105.

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Cloud computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services. Failures of any type are common in current datacenters, partly due to the number of nodes. Fault tolerance has become a major task for computer engineers and software developers because the occurrence of faults increases the cost of using resources and to meet the user expectations, the most fundamental user expectation is, of course, that his or her application correctly finishes independent of faults in the node. This paper proposes a fault tolerant architecture to Cloud Computing that uses an adaptive Checkpoint mechanism to assure that a task running can correctly finish in spite of faults in the nodes in which it is running. The proposed fault tolerant architecture is simultaneously transparent and scalable.

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34

Kramer, Jeff, and Jeff Magee. "A Rigorous Architectural Approach to Adaptive Software Engineering." Journal of Computer Science and Technology 24, no. 2 (March 2009): 183–88. http://dx.doi.org/10.1007/s11390-009-9216-5.

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35

Cámara, Javier, Antónia Lopes, David Garlan, and Bradley Schmerl. "Adaptation impact and environment models for architecture-based self-adaptive systems." Science of Computer Programming 127 (October 2016): 50–75. http://dx.doi.org/10.1016/j.scico.2015.12.006.

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36

Al-Sharman, Mohammad K., Bara J. Emran, Mohammad A. Jaradat, Homayoun Najjaran, Raghad Al-Husari, and Yahya Zweiri. "Precision landing using an adaptive fuzzy multi-sensor data fusion architecture." Applied Soft Computing 69 (August 2018): 149–64. http://dx.doi.org/10.1016/j.asoc.2018.04.025.

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37

Yoder, Joseph W., Federico Balaguer, and Ralph Johnson. "Architecture and design of adaptive object-models." ACM SIGPLAN Notices 36, no. 12 (December 2001): 50–60. http://dx.doi.org/10.1145/583960.583966.

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38

Diguet, Jean-Philippe, Yvan Eustache, and Guy Gogniat. "Closed-loop--based self-adaptive Hardware/Software-Embedded systems." ACM Transactions on Embedded Computing Systems 10, no. 3 (April 2011): 1–28. http://dx.doi.org/10.1145/1952522.1952531.

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39

UM, Jae-Chul, Ki-Seong LEE, and Chan-Gun LEE. "Adaptive Weighting of Structural Dependency and Textual Similarity in Software Architecture Recovery." IEICE Transactions on Information and Systems E99.D, no. 3 (2016): 756–59. http://dx.doi.org/10.1587/transinf.2015edl8160.

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40

Hua Wang, Dingfei Ge, and Zhijun Zheng. "Aspect-oriented Self-adaptive Connector in Software Architecture of Cyber-Physical Systems." International Journal of Advancements in Computing Technology 5, no. 6 (March 31, 2013): 820–28. http://dx.doi.org/10.4156/ijact.vol5.issue6.96.

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41

Xiao, Liang, and Des Greer. "Adaptive Agent Model: Software Adaptivity using an Agent-oriented Model-Driven Architecture." Information and Software Technology 51, no. 1 (January 2009): 109–37. http://dx.doi.org/10.1016/j.infsof.2008.02.002.

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42

HAYSLIP, IRIS COX, JEFFREY P. ROSENKING, and JAMES EILBERT. "ADAPTIVE PLANNING FOR AIR COMBAT MANEUVERING." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 03 (September 1990): 447–71. http://dx.doi.org/10.1142/s0218001490000277.

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This paper describes Grumman’s Rapid Expert Assessment to Counter Threats (REACT) project, designed to aid pilots in air combat decision making. We present a hierarchical design for a planning system which addresses some of the real-time aspects of planning for threat response. This paper concentrates on the lowest level of this hierarchy which is responsible for planning combat maneuvers at low altitude over hilly terrain when the enemy is not in sight. REACT’s Lost Line of Sight module attempts to maximize the amount and depth of knowledge which can be utilized in the time available before the system must commit to its next action. It utilizes a hybrid architecture for planning decisions which incorporates multiple knowledge representations and planners based on artificial intelligence, neural networks, and decision theory. This architecture allows planning at different degrees of competence to be performed by concurrently operating planners with differing amounts of knowledge. We describe research on the planning issues in REACT as well as the associated knowledge representation and knowledge acquisition issues. In addition, we describe how work on developing terrain reasoning capability in REACT has suggested guidelines for knowledge base design and data management, system and language specifications, and planner architectures pertinent to real-time coupled systems.

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43

Al Faruque, Mohammad Abdullah, Thomas Ebi, and Jörg Henkel. "AdNoC: Runtime Adaptive Network-on-Chip Architecture." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 20, no. 2 (February 2012): 257–69. http://dx.doi.org/10.1109/tvlsi.2010.2094215.

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44

Popkov, S. I. "Features of the Software Implementation of the Digital Adaptive Platform for Learning Programming with the Teamwork Skills Forming Function." Моделирование и анализ данных 10, no. 3 (2020): 85–100. http://dx.doi.org/10.17759/mda.2020100307.

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A study of existing approaches to creating educational environments for learning programming is conducted. Their main shortcomings are analyzed. The concept of a digital adaptive platform for learning programming, taking into account the identified shortcomings, is proposed. The features of the software implementation are described in a form of links between platform components, operation concepts and the platform architecture overall. The features of the software architecture and implementation of the digital adaptive platform for learning programming with the teamwork skills forming function are demonstrated.

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45

Djemal, Ridha, Kais Belwafi, and Saleh Alshebeili. "An adaptive CFAR embedded system architecture for target detection." Design Automation for Embedded Systems 17, no. 1 (March 2013): 109–27. http://dx.doi.org/10.1007/s10617-013-9121-6.

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46

Dhodapkar, Ashutosh S., and James E. Smith. "Tuning adaptive microarchitectures." International Journal of Embedded Systems 2, no. 1/2 (2006): 39. http://dx.doi.org/10.1504/ijes.2006.010163.

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47

Zhou, Huiyang, Mark C. Toburen, Eric Rotenberg, and Thomas M. Conte. "Adaptive mode control." ACM Transactions on Embedded Computing Systems 2, no. 3 (August 2003): 347–72. http://dx.doi.org/10.1145/860176.860181.

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48

Wang, Rui, Shi Ying, Meiyan Li, and Shun Jia. "HSACMA: a hierarchical scalable adaptive cloud monitoring architecture." Software Quality Journal 28, no. 3 (August 24, 2020): 1379–410. http://dx.doi.org/10.1007/s11219-020-09524-z.

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49

IKEDA, Hiroki, Hidetoshi TAKESHITA, and Satoru OKAMOTO. "Future Service Adaptive Access/Aggregation Network Architecture." IEICE Transactions on Communications E95-B, no. 3 (2012): 696–705. http://dx.doi.org/10.1587/transcom.e95.b.696.

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50

Valle, Maurizio, and Francesco Diotalevi. "A dedicated very low power analog VLSI architecture for smart adaptive systems." Applied Soft Computing 4, no. 3 (August 2004): 206–26. http://dx.doi.org/10.1016/j.asoc.2004.03.002.

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