Charting the Landscape of Enterprise Architecture Complexity Cybernetics A systematic literature analysis Jiong Fu, Aimin Luo, Xueshan Luo and Junxian Liu
Abstract— Complexity Cybernetics relating to Enterprise Architecture (EA) is a hot topic recently. Although many researchers, organizations made much progress in the past years, common understanding in this domain is still limited. In this paper, a systematic literature analysis on complexity cybernetics in relation to EA was presented, in order to chart a landscape of state-of-the-art and to summarize some suggestions for future research. By analyzing the contexts of the 33 papers collected by the review method, different classifications were conducted. Besides, an extensive analysis, including distribution of papers over time, regional distribution, type of publication, and author-group distribution, was studied. In the end, some discussions and suggestions were given. Our work contributes to getting a better understanding towards the domain and to building a new groundwork future research.
I. I NTRODUCTION OWADAYS, relationships between business processes and IT systems are becoming more complicated and disordered, since both business processes and IT systems are various and complex. As a result, while the gap between business processes and IT gets bigger, it becomes difficult for Enterprise Architecture (EA) application to succeed. EA evolution and EA integration get relatively more complex as well. The original reason of these problems is that: EA complexity often gets out of control. Recently, complexity cybernetics of EA has been becoming quite hot. Many researchers, organizations conducted studies around the topic in the past years. Good cybernetic complexity of EA will benefit interactions between business processes and IT. Also, it will help improve the success rate of EA application, and help ensure the quality of evolution and integration of EA. Unfortunately, no common understanding of EA complexity cybernetics is developed, and this domain remains young and immature. This topic needs to be better defined and refocused. In order to move ahead, to get a better understanding of the domain and to build a new groundwork future research, this paper presents a systematic literature review of complexity cybernetics of Enterprise Architecture.
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Manuscript received January 10, 2016. Jiong Fu, Aimin Luo, Xueshan Luo and Junxian Liu are from Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, Hunan, P. R. China (email: {jiongfu, amluo, xsluo}@nudt.edu.cn,
[email protected]). This work was supported by the National Natural Science Foundation of China (71571189).
We aim to achieve the two goals: (I) Charting a landscape of state-of-the-art relating to complexity cybernetics of EA. (II) Providing some suggestion for future research. This paper is organized as follows. Section 2 presents a definition of EA complexity and related studies. Section 3 discusses our review method. Section 4 details the concrete research step-by-step. Some discussions as well as our contributions are provided in Section 5. Section 6 concludes the paper by noting future works. II. R ELATED W ORK Complexity science was proposed for the first time in 1960’s, but it made little progress until 1980’s, during which period John Holland, Stuart Kauffmann together with some other scientists made great contribution to the domain. After that, many new and great achievements emerged in some different but related domains, such as Software Engineering, Computing Science, Algorithm and Complex Network. As to the domain of EA, what is the complexity? The definition is not unified. However, two parallel threads of understandings are commonly available: (I) EA complexity is a kind of metric of all nodes and relationships in an Enterprise Architecture [1]; (II) the other one is a kind of metric of all flow paths in an Enterprise Architecture [2]. In the past years, some academic surveys on EA complexity are conducted. For example, A.W. Schneider [3] proposed a framework with simple notation of complexity for EA cybernetics by identifying eight aspects of complexity and by categorizing them (the eight aspects of complexity) into four independent categories. Recently, Schneider [4] focused on Application Landscape (AL) complexity cybernetics by analyzing the complexity metrics from the results of a literature survey. In some other surveys, EA complexity are connected with some other domains, e.g., M. Maurer [5] classified approaches towards complexity cybernetics in systems engineering. Similarly, P.N. Lowe [6] provided an introduction to relationships between (I) System of Systems (SoS), (II) complexity, and (III) modeling and simulation. However, a whole landscape or big picture of EA complexity cybernetics is still unavailable, while further improvement sounds necessary.
III. R EVIEW M ETHOD A systematic literature review has its own method framework and basic guidelines. Our literature review method is based on the guidelines of [7] and [8]. As shown in Figure 1, the adopted process of systematic literature review method includes 8 steps: • Purpose of the literature review; • Protocol and training; • Searching for the literature; • Practical screen; • Quality appraisal; • Data extraction; • Synthesis of studies; • and Writing the review. The rest of this section is organized as follows. First, Subsection A introduces our research questions. Then, Subsection B describes research tasks based on our research questions. Finally, Subsection C describes the detailed steps of search strategy Purpose of the literature review
Protocol and training
Searching for the literature
analyzing the distribution of papers, as to published time, their regional distribution, type of publication, and authorgroups distribution. To answer RQ3, we should analyze the weakness regarding the state-of-the-art of EA complexity cybernetics, and summarize the literature review with some directions of future research. Research Questions
Research Tasks Background
RQ1: How to classify the current research on EA complexity cybernetics?
Model
RQ2: What is the current state of EA complexity cybernetics?
RQ3: What can we learn for the current state to improve the future research?
Fig. 2.
Method
Distribution over time
Regional distribution
Type of publication
Authors groups distribution
Current state
Weakness
Direction
Research questions and corresponding research tasks
C. Search Strategy Data extraction
Quality appraisal
Synthesis of studies
Writing the review
Fig. 1.
Practical screen
8-step systematic literature review method, by Okoli[7]
The search strategy directly shapes the quality of finding papers. In our review, the search process includes six parts, shown in Figure 3, including: Define Review Protocol, Select Search Engines, Define Keywords, Keyword-based Search, Pick Relevant Papers and Collect Papers. Define Review Protocol
Select Search Engines
Define Keyword
Collect Papers
Pick Relevant Papers
Keyword-based Search
A. Research Questions Based on our research goals, our systematic literature review mainly targets at answering the following three research questions: • RQ1: How to classify the current research on EA complexity cybernetics? • RQ2: What is state-of-the-art of EA complexity cybernetics? • RQ3: What can we learn from state-of-the-art to improve the future research? B. Research Tasks To answer the three research questions above, we need to analyze and decide research tasks. Figure 2 shows the three research questions and the corresponding research tasks. To answer RQ1, we need to analyze the contexts of target publications to find the common standards (such as background, method, etc.) , and then to categorize target publications into different categories by the common standards. To answer RQ2, we chart a landscape of EA complexity cybernetics by performing an extensive analysis, including
Fig. 3.
Search process
Define Review Protocol: The review protocol is designed to service for answering the research questions, it can provide guidelines for defining keywords and choosing search engines to ensure the quality of target publications. Select Search Engines: the search engines and databases we used include IEEE XPlore, ACM Digital Library, Springer, Elsevier and Google Scholar. Define Keyword: we define two basic group keywords, and select one from each basic group to combine the final keyword for searching papers. One basic group includes ”Enterprise Architecture” and ”EA”. The other group includes ”Complexity Cybernetics”, ”Complexity Management”, ”Complexity” and ”Complex”. Keyword-based Search: based on the final keywords above, we select search engines one by one to search papers. The time range of publications is set between 1999 to 2015.
Pick Relevant Papers: the search model may get some irrelevant papers. In this step, we should pick relevant papers by analyzing the title and keywords of papers. Collect Papers: in this step, we download the selected papers and collect some relevant data, such as type of publications. Finally, based on the search process, we get 33 relevant papers. IV. A NALYSIS AND R ESULTS In this section, we performed an analysis of the 33 papers at the aspects which the research tasks requested, in order to answer the first and second research questions. Subsection A shows the context analysis and its results, including the analysis of backgrounds and methods in the papers, mainly to answer the first research question. In Subsection B, an extensive analysis is conducted to answer the second research question. A. Context Analysis To answer the first research question (RQ1), we categorize EA complexity cybernetics, by backgrounds and methods which the papers relate to, based on the research tasks. By scanning the 33 papers and analyzing their backgrounds, we can categorize these 33 papers into 11 subgroups. The result is shown in Table 1. From the Table 1 we find the backgrounds of complexity cybernetics are various and scattered. To get a normative classification, as shown in Table 2, these 11 sub-groups can be categorized into 3 categories: (I) First category is a high-level or enterprise-level one with the core to be Enterprise Architecture and Information System, including 5 groups, i.e., Enterprise Architecture, Information System, Information Technology, Product Architecture and Service Oriented Architecture; (II) Second category is a midlevel or system-level one mainly based on Software Engineering and System Engineering, including 5 groups, i.e., Software Architecture, Software System, System of Systems, System Engineering and Complex System; (III) The final category is a low-level one based on Network of Enterprise, including Network of Enterprise. From the Table 2 we can find that although the research on the complexity cybernetics in enterprise-level accounts for 60.6% within 33 papers, still a large part of research focus on the complexity cybernetics in system-level. Another kind of categorization is based on the method/approach used to study complexity: either quantitative one or qualitative one. By analyzing the methods-models used in the 33 papers, we get a basic categorization, as shown in Table 3. From the Table 3 we can find that more than half of publications employ qualitative methods. The representative papers using qualitative method to control EA complexity include [11], [22] and [28]. Dag I.K. Sjoberg et al. [11] presented a method to assess the cost of IT complexity. The steps included Identify the Affected Systems, Extract the Affected Cost Base, Estimate the Change of Complexity Per System and Total economic impact. The core steps, i.e., Extract the Affected Cost Base and Estimate
TABLE I 11
SUB - GROUPS OF THE
33
Background
PAPERS .
Literature
Enterprise Architecture Information System Information Technology Product Architecture Service Oriented Architecture Software Architecture Software System System of System System Engineering Complex System Network of Enterprise
[1],[3],[4],[17],[19],[31],[33],[34],[35] [9],[36] [10],[13],[18],[24],[28] [12] [20],[23],[26] [25],[27],[30] [11],[22] [6] [5],[16] [14],[21] [15],[29],[32] TABLE II
T HE NORMATIVE CLASSIFICATION . Classification Enterprise Architecture/Information System Software Engineering/System Engineering Network of Enterprise total
Number
Percent
20 10 3 33
60.6% 30.3% 9.1% 100%
the Change of Complexity Per System, were mainly based on exports’ experiences. H. Kandjani et al. [22] used Extended Axiomatic Design (EAD) theory to study the complexity of Global Software Development (GSD) projects. By using EAD theory, they presented the Generalised Enterprise Reference Architecture (GERA) modeling framework to reduce the complexity of enterprise models. Besides, they [35] also studied the complexity of disaster management based on the method. K. Solic et al. [28] applied the Simple Iterative Partitions (SIP), presented by Sessions [2], to reduce the complexity of IT System. The SIP process included partitioning of the system, simplification of the system and iteration between subsets of the system. Besides, representative papers, such as [1], [23] and [27], use quantitative method to manage EA complexity. A. Schuetz et al. [1] proposed a metric to quantify complexity in EA. They considered complexity as tuple of number and heterogeneity of the relations of an EA [1]. L. Liu et al. [23] presented a simulation-based approach to calculate the complexity in Service-oriented Computing. S. Sengupta et al. [27] presented the complexity metrics based on Component Architecture Graph (CAG) to calculate component architecture complexity. The metrics belonged to to different levels, i.e., component level metrics and architecture Level metrics. Due to the limited length of the paper, the formulas of these metrics are not listed. B. Extensive Analysis To answer the second research question (RQ2), we preformed an extensive analysis by analyzing the data discussed in research tasks, including distribution of papers over time, their regional distribution, type of publication, and authorgroup distribution. The number of EA complexity cybernetics publications
TABLE III T HE CLASSIFICATION OF 33 PAPERS BY DIFFERENT METHODS . Method
TABLE IV T YPE OF P UBLICATION D ISTRIBUTION .
Number
Percent
Representative
Type of publication
20 13
60.6% 39.4%
[11],[22],[28] [1],[23],[27]
Conferences Journals Books Reports total
Qualitative Quantitative
over year is shown in Figure 4. From the Figure 4 we can find that (1) 33 papers are all published during the period from 2002 to 2015; and (II) the publications from 2009 to 2014 are more than other period.
Griffith University TU Munchen BPPIMT
6
Percent
22 7 2 2 33
66.6% 21.2% 6.1% 6.1% 100%
TABLE V T HE T OP -3 P RODUCTIVE AUTHORS G ROUPS D ISTRIBUTION . Authors groups
7
Number
Publications
Number
[15],[21],[22],[32],[34] [1],[4],[5],[12] [20],[27]
5 4 2
5 4 3 2 1
Fig. 4.
2015
2014
2013
2012
2011
2010
2009
2008
2007
2005
2004
2002
0
Distribution of Papers: Over Time
In Figure 5, we show the regional distribution of the 33 papers. The 33 papers come from 11 countries, and the top-3 most productive countries are USA, Germany and Australia. More than half of all publications come from these three countries. Argentina, 1 USA, 7
Australia,, 6
UK, 1 Canada, 3
Sweden 1 Sweden, Norway, 1
China, 2
Netherlands ,2
Croatia,, 1 India, 2 Germany, 6
Fig. 5.
Regional Distribution
Table 4 shows the type of publication distribution. We can find a vast amount of papers are published in conference proceedings, and 7 papers are published in journals. Besides, 2 results are from books and another two are working reports. The top-4 most productive types of publication include: Hawaii International Conference on System Sciences (HICSS), ACM SIGSOFT Software Engineering (SIGSOFT),
Computer-Human Interaction for Management of Information Technology (CHIMIT) and Americas Conference on Information Systems (ACIS). HICSS contributed 3 papers and the other three types of publication contributed 2 papers to the total 33 papers, while others only contributed one. Besides, we analyzed the author-group distribution. Majority authors groups are universities, while only three papers’ authors come from industrial companies. As showed in Table 5, the top-3 productive organizations are Griffith University (in Australia), Technische Universitaet Munchen (in Germany) and B.P. Poddar Institute of Management and Technology (BPPIMT) (in India). To summarize, research of EA complexity cybernetics has been quite hot in recent years, and many academic and industrial organizations or research groups have paid lots of attention to this domain, especially some research organizations from Europe, Oceania and USA. V. D ISCUSSIONS In this section, we mainly answer the third research question (RQ3). With a detailed analysis of the research on EA complexity cybernetics above, there are some conclusions. • The research taxonomy of EA complexity cybernetics is not unified. As the domain of EA complexity cybernetics is still young, the research taxonomy is not unified, and a unified framework is needed to advance the future research. Although our work make a basic taxonomy of the current research by different backgrounds and methods the papers referred to, more efforts still need to be added to the research taxonomy. • The research groups is limited, while co-work between different research groups may be helpful. From the current state of EA complexity cybernetics, some research groups in Europe, Oceania and USA keep relative advantage compared to others in the field. Besides, high quality publications are yet few. Co-work between different research groups may be helpful.
Conceptual models and research based on conceptual models shape a new important trend. Although there are many researches on EA complexity, conceptual model of EA complexity is not yet well-unified. The definition and scope of EA complexity need to be ascertained and unified. Besides, research method and process based on conceptual model need to be focused in future research. •
VI. C ONCLUSIONS This paper presents a systematic literature analysis of EA complexity cybernetics. We conduct an analysis of 33 papers to discuss the research taxonomy, state-of-the-art, and future directions of EA complexity cybernetics. This research illustrates a basic landscape of EA complexity cybernetics. This sort of work will help us to better understand the current state-of-the-art of EA complexity cybernetics. Future related work could be conducted with a focus on conceptual models and research method (based on conceptual model) of EA complexity cybernetics. R EFERENCES [1] A. Schuetz, T. Widjaja and J. Kaiser, “Complexity in Enterprise Architectures - Conceptualization and Introduction of a Measure from a System Theoretic Perspective,” Proceedings of the 21st European Conference on Information Systems, Utrecht, The Netherlands, 2013. [2] R. Sessions, Simple Architecture for Complex Enterprises, Microsoft Press, 2008. [3] A.W. Schneider, M. Zec and F. Matthes, “Adopting Notions of Complexity for Enterprise Architecture Management,” Twentieth Americas Conference on Information Systems. Savannah, 2014. [4] A.W. Schneider, T. Reschenhofer, A. Schutz and F. Matthes,“Empirical Results for Application Landscape Complexity,” System Sciences (HICSS), 48th Hawaii International Conference on, pp. 4079-4088, 2015. [5] M. Maurer, R. Schneller and M. Omer, A survey on complexity management in systems engineering, 2014. [6] P.N. Lowe and M.W. Chen, System of Systems Complexity Modeling and Simulation Issues, Working Report, The Boeing Company, 2008. [7] B. Kitchenham, Procedures for Performing Systematic Reviews. keele 33, United Kingdom, 2004. [8] C. Okoli and K. Schabram, “A Guide to Conducting a Systematic Literature Review of Information Systems Research,” Social Science Electronic Publishing. 10(26), 2010. [9] B.L. Marcolin and A. Ross, “Complexities in IS sourcing: equifinality and relationship management,” Data Base for Advances in Information Systems, vol. 36, no. 4, pp. 29-46, 2005. [10] B. Lin, A.B. Brown and J.L. Hellerstein, “Towards an understanding of decision complexity in IT configuration,” Autonomic Computing, ICAC ’06. IEEE International Conference on, pp. 279-282, 2006. [11] Dag I.K. Sjoberg, E. Odberg and B. Warlo, “The challenge of assessing and controlling complexity in a large portfolio of software systems,” PROFES, June 21-23, Limerick, Ireland, 2010. [12] C. Daniilidis, D. Hellenbrand, W. Bauer and U. Lindemann, “Using structural complexity management for design process driven modularization,” IEEE International Conference on Industrial Engineering and Engineering Management, pp. 595-599, 2011. [13] G. Robiolo, “How Simple is It to Measure Software Size and Complexity for an IT Practitioner,” Empirical Software Engineering and Measurement , 2011 International Symposium on, pp. 40-48, 2011. [14] G.D. Snook, “A general theory of complex living systems: Exploring the demand side of dynamics,” Complexity, vol. 13, no. 6, pp. 12-20, 2007. [15] H. Kandjani, “Enterprise Architecture Cybernetics for Collaborative Networks: Reducing the Structural Complexity and Transaction Cost Via Virtual Brokerage,” Concurrent Engineering, pp. 1233-1239, 2012.
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