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This paper is available on arxiv under CC 4.0 license. Authors: (1) Ayei E. Ibor, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (2) Mark Hooper, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (3) Carsten Maple, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (4) Gregory Epiphaniou, WMG, University of Warwick, United Kingdom. Table of Links Abstract & Introduction Background Review of Literature Methodology Discussion of Findings Conclusion and Future Work & References 6. Conclusion and Future Work Trustworthy interoperability is a key enabler for identity management and widens the dimensions of e-government in developing countries. This research provided insights into the challenges, opportunities, and requirements of interoperability and highlighted the limitations of current interoperability solutions such as the Estonian X-Road and the eIDAS of the European Union. We found that each existing interoperability solution has its benefits and drawbacks, although both are targeted at a common goal. To achieve seamless communication of identification data, we proposed an approach for scoping requirements by first identifying the key processes to identity management in an interoperability context. Furthermore, we addressed the limitations of the current solutions using an architecture that considers the flexibility of identity verification and validation through an interoperable layer that is based on four interacting layers of data representation, semantics, binding, and security and privacy. For future work, an analysis of country-specific use cases for achieving interoperability in the African identity ecosystem will be performed. References African Union, 2020. The Digital Transformation Strategy for Africa (2020-30), Available at https://openresearch-repository.anu.edu.au/bitstream/1885/278587/1/GhaEcon_24.pdf, Accessed 20 May 2023. Alamillo, I., Mouille, S., Rock, A., Soumelidis, N., Tabor, M., Gorniak, S., 2023. Digital identity standards: Analysis of standardisation requirements in support of cybersecurity policy, Available at file:///C:/Users/aIbor/Downloads/Digital_Identity_Standards%20(1).pdf, Accessed 30 July 2023. Aldosary, M. and Alqahtani, N., 2021. A Survey on Federated Identity Management Systems Limitation and Solutions. International Journal of Network Security & Its Applications (IJNSA) Vol, 13. ANSSI, 2023. The EIDAS Regulation, Available at https://www.ssi.gouv.fr/en/regulation/digital-confidence/the-eidas-regulation/, Accessed 09 April 2023. Atick, J., 2016. Digital identity: the essential guide. In ID4Africa Identity Forum (Vol. 2016, pp. 1-3). Backhouse, J. and Halperin, R., 2009. Approaching interoperability for identity management systems. In The Future of Identity in the Information Society (pp. 245-268). Berlin, Heidelberg: Springer Berlin Heidelberg. Bakhtina, M., Matulevičius, R., Awad, A. and Kivimäki, P., 2022. Rebooting Trust Management in X-Road, Public Report. Nordic Institute for Interoperability Solutions (NIIS). Bhattarai, R., Pappel, I., Vainsalu, H., Yahia, S.B. and Draheim, D., 2019. The impact of the single digital gateway regulation from the citizens’ perspective. Procedia Computer Science, 164, pp.159-167. Bandura, R. and Ramanujam, S.R., 2021. Developing inclusive digital payment systems. Center for Strategic and International Studies, 21. Benaddi, H., Laaz, N., Bouhlal, A. and El Kettani, E., 2023. Data storage architecture for egovernment interoperability: Morocco case. Indonesian Journal of Electrical Engineering and Computer Science, 29(3), pp.1678-1686. Catuogno, L. and Galdi, C., 2014. Achieving interoperability between federated identity management systems: A case of study. Journal of High Speed Networks, 20(4), pp.209-221. Colbern, A. and Ramakrishnan, S.K., 2020. Citizenship reimagined: A new framework for state rights in the United States. Cambridge University Press. Cuijpers, C.M.K.C. and Schroers, J., 2014. eIDAS as guideline for the development of a pan European eID framework in FutureID. Domingo, E. and Teevan, C., 2022. Africa’s journey towards an integrated digital payments landscape and how the EU can support it. ECDPM brief, 23. European Commission, 2023a. X-Road Data Exchange Layer, Available at https://joinup.ec.europa.eu/collection/ict-security/solution/x-road-data-exchange-layer/about, Accessed 10 April 2023. European Commission, 2023b. Shaping Europe’s digital future, Available at https://digitalstrategy.ec.europa.eu/en/policies/discover-eidas, Accessed 20 April 2023. Fathalla, E.S., Azab, M., Xin, C. and Wu, H., 2023. PT-SSIM: A Proactive, Trustworthy SelfSovereign Identity Management System. IEEE Internet of Things Journal. Grassi, P.A., Garcia, M.E. and Fenton, J.L., 2017. Digital identity guidelines. NIST special publication, 800, pp.63-3. Gelb, A. and Metz, A.D., 2018. Identification revolution: Can digital ID be harnessed for development?. Brookings Institution Press. Halili, F. and Ramadani, E., 2018. Web services: a comparison of soap and rest services. Modern Applied Science, 12(3), p.175. Hoffmann, T. and Solarte-Vasquez, M.C., 2022. The estonian e-residency programme and its role beyond the country’s digital public sector ecosystem. Revista CES Derecho, 13(2), pp.184-204. Hölbl, M., Kežmah, B. and Kompara, M., 2023. eIDAS Interoperability and Cross-Border Compliance Issues. Mathematics, 11(2), p.430. Hummer, D. and Rebovich, D.J., 2023. Identity theft and financial loss. In Handbook on Crime and Technology (pp. 38-53). Edward Elgar Publishing. Irvin-Erickson, Y., 2023. How Does Immigration Status and Citizenship Affect Identity Theft Victimization Risk in the US? Insights from the 2018 National Crime Victimization Survey Identity Theft Supplement. Victims & Offenders, pp.1-24. Kaaniche, N., Laurent, M. and Belguith, S., 2020. Privacy enhancing technologies for solving the privacy-personalization paradox: Taxonomy and survey. Journal of Network and Computer Applications, 171, p.102807. Kanagwa, B., Nakatumba-Nabende, J., Mugwanya, R., Kigozi Kahiigi, E. and Ngabirano, S., 2018. Towards an interoperability e-government framework for Uganda. In e-Infrastructure and e-Services for Developing Countries: 9th International Conference, AFRICOMM 2017, Lagos, Nigeria, December 11-12, 2017, Proceedings 9 (pp. 16-28). Springer International Publishing. Kiourtis, A., Giannetsos, T., Menesidou, S.A., Mavrogiorgou, A., Symvoulidis, C., GRAZİANİ, A., KLEFTAKİS, S., MAVROGİORGOS, K., ZAFEİROPOULOS, N., GKOLİAS, C.A. and KYRİAZİS, D., 2023. Identity management standards: A literature review. Computers and Informatics, 3(1), pp.35-46. Krimmer, R., Dedovic, S., Schmidt, C. and Corici, A.A., 2021, August. Developing crossborder e-Governance: Exploring interoperability and cross-border integration. In International Conference on Electronic Participation (pp. 107-124). Cham: Springer International Publishing. Kotzé, P. and Alberts, R., 2017. Towards a Conceptual Model for an e-Government Interoperability Framework for South Africa. In ICEIS (3) (pp. 493-506). Lips, S., Bharosa, N. and Draheim, D., 2020. eIDAS implementation challenges: the case of Estonia and the Netherlands. In Electronic Governance and Open Society: Challenges in Eurasia: 7th International Conference, EGOSE 2020, St. Petersburg, Russia, November 18– 19, 2020, Proceedings 7 (pp. 75-89). Springer International Publishing. Luong, D.A. and Park, J.H., 2023. Privacy-Preserving Identity Management System on Blockchain Using Zk-SNARK. IEEE Access, 11, pp.1840-1853. Manda, M. I. and Backhouse, J., "Addressing trust, security and privacy concerns in egovernment integration, interoperability and information sharing through policy: a case of South Africa" (2016). CONF-IRM 2016 Proceedings. Paper 67. http://aisel.aisnet.org/confirm2016/6 Masiero, S., 2023. Digital identity as platform-mediated surveillance. Big Data & Society, 10(1), p.20539517221135176. McBride, K., Kütt, A., Ben Yahia, S. and Draheim, D., 2019, November. On positive feedback loops in digital government architecture. In Proceedings of the 11th International Conference on Management of Digital EcoSystems (pp. 174-180). Mocanu, S., Chiriac, A.M., Popa, C., Dobrescu, R. and Saru, D., 2019. Identification and trust techniques compatible with eIDAS regulation. In Security and Privacy in New Computing Environments: Second EAI International Conference, SPNCE 2019, Tianjin, China, April 13– 14, 2019, Proceedings 2 (pp. 656-665). Springer International Publishing. Pöhn, D. and Hommel, W., 2023. Towards an Improved Taxonomy of Attacks Related to Digital Identities and Identity Management Systems. Security and Communication Networks, 2023. Priisalu, J. and Ottis, R., 2017. Personal control of privacy and data: Estonian experience. Health and technology, 7(4), pp.441-451. Saputro, R., Pappel, I., Vainsalu, H., Lips, S. and Draheim, D., 2020, April. Prerequisites for the adoption of the X-Road interoperability and data exchange framework: a comparative study. In 2020 Seventh International Conference on eDemocracy & eGovernment (ICEDEG) (pp. 216-222). IEEE. Schmidt, C. and Krimmer, R., 2022. How to implement the European digital single market: identifying the catalyst for digital transformation. Journal of European Integration, 44(1), pp.59-80. Sedlmeir, J., Smethurst, R., Rieger, A. and Fridgen, G., 2021. Digital identities and verifiable credentials. Business & Information Systems Engineering, 63(5), pp.603-613. Sharif, A., Ranzi, M., Carbone, R., Sciarretta, G. and Ranise, S., 2022, August. SoK: A Survey on Technological Trends for (pre) Notified eIDAS Electronic Identity Schemes. In Proceedings of the 17th International Conference on Availability, Reliability and Security (pp. 1-10). Sharma, R. and Panigrahi, P.K., 2015. Developing a roadmap for planning and implementation of interoperability capability in e-government. Transforming Government: People, Process and Policy, 9(4), pp.426-447. Solvak, M., Unt, T., Rozgonjuk, D., Võrk, A., Veskimäe, M. and Vassil, K., 2019. Egovernance diffusion: Population level e-service adoption rates and usage patterns. Telematics and Informatics, 36, pp.39-54. Spiliotopoulos, T., Sheik, A.T., Gottardello, D. and Dover, R., 2023. Onboarding Citizens to Digital Identity Systems. arXiv preprint arXiv:2306.13511. Srinivas, J., Das, A.K. and Kumar, N., 2019. Government regulations in cyber security: Framework, standards and recommendations. Future generation computer systems, 92, pp.178-188. Tang, F., Ma, C. and Cheng, K., 2023. Privacy-preserving authentication scheme based on zero trust architecture. Digital Communications and Networks. The World Bank, 2023a. ID4D Global Dataset, Available at https://id4d.worldbank.org/global-dataset, Accessed: 17 July 2023. The World Bank, 2023b. Practitioner’s Guide: Interoperability, Available at https://id4d.worldbank.org/guide/interoperability, Accessed: 22 April 2023. Walters, R., 2023. Theft [Identity]. In Cybersecurity and Data Laws of the Commonwealth: International Trade, Investment and Arbitration (pp. 297-306). Singapore: Springer Nature Singapore. World Bank Group, 2016. ID4D Country Diagnostic: Nigeria, 2016, Available at https://documents1.worldbank.org/curated/en/136541489666581589/pdf/113567-REPLNigeria-ID4D-Diagnostics-Web.pdf, Accessed 20 May 2023. W3C Recommendation, 2022. Verifiable credentials data model v1.1, Available at https://www.w3.org/TR/vc-data-model/#dfn-credential, Accessed 04 April 2023. World Bank Group, 2018. Guidelines for ID4D Diagnostics, Available at https://documents1.worldbank.org/curated/en/370121518449921710/Guidelines-for-ID4DDiagnostics.pdf, Accessed: 22 April 2023. X-Road, 2020. X-Road: Message Transport Protocol, Available at https://www.xtee.ee/docs/live/xroad/pr-messtransp_x-road_message_transport_protocol.html, Accessed 12 April 2023. X-Road, 2023a. X-Road Security, Available at https://x-road.global/security, Accessed 03 April 2023. X-Road, 2023b. X-Road Technology Overview, Available at https://x-road.global/x-roadtechnology-overview, Accessed 10 March 2023. X-Road, 2023c. X-Road: Use Case Model for Member Communication, Available at https://docs.x-road.global/UseCases/uc-mess_x-road_member_communication_use_case_model.html#version-history, Accessed 12 April 2023. X-Road, 2023d. Technical Specification: X-Road Architecture, Available at https://docs.xroad.global/Architecture/arc-g_x-road_arhitecture.html#version-history, Accessed 12 April 2023. Yin, J., Xiao, Y., Pei, Q., Ju, Y., Liu, L., Xiao, M. and Wu, C., 2022. SmartDID: a novel privacy-preserving identity based on blockchain for IoT. IEEE Internet of Things Journal, 10(8), pp.6718-6732. This paper is available on under CC 4.0 license. Arxiv

Trustworthy Interoperability and Identity Management Systems: What Comes Next

As part of the series, this is my story. Tech Like Us I was born just outside of Mumbai, India. My family emigrated to the U.S. when I was 3 years old, and I grew up next to cornfields in a sleepy Ohio town. So while my ethnic background is Indian, I identify as an American from the Midwest. I refer to soda as “ ” and am prone to altitude sickness at the slightest sight of a hill. correctly pop I got interested in tech as a kid tinkering with all the electronic “toys” my dad would bring home like mobile phones the size of briefcases, hefty 10-pound camcorders, and computer towers that were as tall as me (and probably still are). I enjoyed ripping apart floppy disks to see what was inside and getting “under the hood” of our PCs to install some extra memory. And of course, I vividly remember the exciting early days of the Internet, including the anticipation of receiving a brightly colored in the mail. Once we were on the World Wide Web, creating webpages and learning how to code became my other favorite hobbies. AOL CD At the , I majored in Computer Engineering, which is an amalgam of Computer Science and Electrical Engineering. It blended software and hardware, along with theory and practice. I also minored in Mathematics to fully surrender to my nerdy tendencies. University of Michigan After undergrad, I stayed in the tech field but moved into a consulting role with . I visited clients all over the world, from Des Moines, IA to Anchorage, AK to Tokyo, Japan. At the end of my fourth year of consulting, I realized I wanted to equip my technical knowledge with a formal business education. Citrix Systems So I went to the University of Chicago’s where I concentrated in Finance and Entrepreneurship and worked in startups, both during and after. Business school was a fantastic experience for me. I had never even seen a balance sheet prior to grad school, but within those 2 years I pitched my own business plan to venture capitalists, took classes taught by Nobel Laureates, and met some of the most amazing over-achievers who are now some of my closest friends. Through that experience I also found my niche in tech startups and moved to the West Coast shortly after graduation. Booth School of Business Since moving out to the “Best Coast,” I’ve been working as a product manager at a broad range of tech companies. My first foray into the Silicon Valley scene was at a 7-person startup with Series A financing. Within a year, our company was acquired. After experiencing the ins and outs of an acquisition first-hand, I moved to a large company in , where I created a completely brand-new analytics-based product called . I am now in a lead product position at , where I’ve had opportunities to manage all of our product lines, develop strategies and goals across the business, build the product team, and see the company grow from 15 people to a team of 65 in the short time I’ve been here. Rackspace Cloud Intelligence PubNub I love the product manager role because it’s an exciting mix between the technical and business sides of product development. In this role, I’ve had opportunities to develop completely new tech products, drive conceptual ideas into real market offerings, create and influence company strategies, speak at industry events, and advise other startups and entrepreneurs. While I’m grateful for the enriching professional experiences I’ve had, looking back there were instances where being a woman in the field of tech made me feel like an outsider. For instance, it felt isolating to be one of only a handful of women in my major coursework in college and that trend persists in the professional realm. It was also frustrating when strangers spoke to me in a condescending manner or automatically assumed I was an assistant, instead of the conference speaker, product owner, or technical expert. But when you find yourself in those situations, stay confident and remember that you are an integral part to changing the makeup of the tech field! Your voice matters. Don’t shy away from having your opinions heard, don’t apologize for being awesome, and above all else — be yourself and let your contributions shine. I have also found such amazing people and camaraderie in the tech world, and know that you will too. I believe that those of us privileged enough to be a part of this field should tell our stories and mentor young women and men so that the future of our industry is more balanced. Finally, if I could go back in time to give my younger self some advice, I would tell her not to dwell on what other people may be thinking and not to sweat the small stuff. Instead, it’s more productive to take time to listen to yourself and recognize when you feel like trying something else, leaning in a new direction, or simply taking a break! Well, that’s my story… so far. I’m always learning every day, so all of this is (and will always be) a work in progress. I invite you to share this story along with yours! Write your own article. And to hear more stories about women and minorities working in tech, check out the documentary . Tech Like Us CODE: Debugging the Gender Gap

The New Normal — Engineer, Product Manager, Woman.

Who wants to get a nice and shiny infrastructure diagram?
We are!
Who wants to maintain it?
… Intro That’s right. You work at a company with a lot of infrastructure to manage. There are many services, complex networking, multi-cloud setups, and several CI/CD tools. You have a lot of code written. Maybe with comments. Perhaps even some documentation. Eventually, you start creating diagrams to show your infrastructure at a glance. Everybody loves diagrams! But as soon as you press ‘Save’ button the clock starts ticking, showing how outdated your diagram is. Unfortunately, no one loves outdated stuff. And no one has time to update it. You spent a lot of time being an infrastructure-as-a-code gitops hero. Everything is already here, in the code! There are plenty of tools that transform code into UML diagrams. Is there anything for infrastructure, please? And there is! Let’s take a closer look What’s in the review To make it more practical we are going to use a real repo. The repo has a minimalistic Dockerfile for a simple Spring Boot app. A pinch of Terraform, and a GitHub Actions workflow to glue everything together. It builds the app with Gradle, creates a Docker image, and deploys it to AWS Elastic Container Service. Tools under review Terraform graph
Inframap
Rover
NoReDraw
Others Terraform graph First, let's start with Terraform graph. Terraform provides an embedded option for making a diagram. It creates a Graphviz .dot file. To see a diagram as an image you have to install Graphviz. terraform graph | dot -Tpng > /tmp/graph.png This gives you next diagram Looks acceptable for an out-of-the-box solution. It’s hardly readable, has no coloring scheme and has no icons. I’m not sure if it provides a better sense of architecture than the actual code. Yet, it could be a nice addition to a pull request analysis by demonstrating the actual plan (specify -plan=plan.out). Inframap If the Terraform graph is too basic for your needs, let's move on to Inframap. This tool reads your tfstate or HCL to generate a graph specific to each provider. Instead of showing everything, the tool shows the most important resources. Developers of Inframap created a human-readable version of terraform graph. inframap generate . | dot -Tpng > inframap.png This gives the next diagram Well, that’s way more readable. If you want more app-centric representation that’s the way to go. It doesn't have too much detail (subnets, security groups, IAM roles, etc) and looks more concise. We have all main components like the load balancer, ECS service, and the cluster. A few nitpicks: I would put client instead of im_out. And make the ECS cluster a container (rectangle) with the service inside. Anyway, it’s pretty decent. Rover Next up is Rover, another Terraform visualizer. There are several options for how to run Rover. Unfortunately, I wasn’t able to make Rover run terraform under the hood. Image generation with the specified plan didn’t work for me either. So I had to run Rover in standalone mode and explicitly specify previously generated TF plan: terraform plan -out=plan.out
terraform show -json plan.out > plan.json

docker run --rm -it -p 9000:9000 -v $(pwd):/src im2nguyen/rover:latest -planJSONPath=/src/plan.json The UI on http://localhost:9000/ appeared to be pretty nice. It might be helpful to click around to get more details on what’s going on. Again, mostly for PRs and a general understanding of resources. This is how generated diagram looks like I'm pleased with the color legend and grouping. The information seems to be excessive for the needs of an architecture diagram. Again, it’s a terraform visualizer, it shows everything defined in the plan, with no surprises. Alright, you’ve got the idea of terraform visualizers. They try to represent the state of resources defined in .tf files. Is there anything else left overboard? Right, the applications. The whole point of infrastructure is to serve a set of applications. Without showing the apps it all doesn’t make a lot of sense. Inframap did a great job with its app-centric approach. Are Terraform files not enough for the big picture? Let’s fall down the rabbit hole. NoReDraw This tool takes a step further. Instead of dealing with Terraform, NoReDraw focuses on… everything. Well, currently Gradle, Docker, GitHub Action, and Terraform AWS resources are supported. And ‘supported’ is a bit of a strong word as the project is still in beta. NoReDraw takes a source folder or git repo URL as an input. Then parses and cross-matches everything it can. Want to add more? Implement your own provider as the tool is highly customizable. Don’t like how components are linked together? Create your own LinkStrategy. Another cool benefit is that you can generate a diagram in Plant UML or Mermaid. This gives you an option to embed the drawing directly into your docs as text. wget https://github.com/uladzimirFilipchanka/noredraw/raw/main/noredraw.sh && chmod +x noredraw.sh

./noredraw.sh \
--source . \
--export PNG \
--output noredraw.png Let’s see, from bottom to top. Gradle builds a Jar named arch-diagram-sample. Then it gets containerized by Docker and ends up being a Docker image. The image is assigned to the task which is deployed into Amazon ECS via a GitHub Actions workflow. All correct! The diagram definitely lacks in style, but if you plan to embed it you’ll have to suffer enjoy it anyway. At least some extra coloring wouldn’t hurt. The attributes in blocks nicely describe each artifact and are very handy. I really hope NoReDraw gets more traction in the near future. More providers and features can make it production-ready. Others Finally, let's look at some other tools and services. They didn’t make it to the shortlist but are worth mentioning. Pluralith This service helps to visualize and manage cloud infrastructure by parsing Terraform code. Pluralith goes beyond 'regular' visualizers and offers real-time updates. With nicely done CI/CD integrations (GitHub and GitLab as of now). Lucidscale The older sibling of Lucidchart. It’s a super-powerful tool for managing cloud infrastructure. Lucidscale offers automated and detailed visualizations but works another way. It connects to your cloud account and generates diagrams of the architecture. Currently, AWS, Azure, and GCP are supported. In this case, you don’t even need to adhere to infrastructure-as-a-code. Lucidscale provides all that you may or may not need. Including live sync, filtering, customization, and collaboration. Cloudcraft Like Lucidscale it connects to your cloud account. It offers deep integration with AWS (and now Azure), but focuses on simplicity.
Cloudcraft has a minimalistic and user-friendly interface. It allows you to quickly create cloud architecture diagrams. You might miss some minor features in Cloudcraft, but the beauty of diagrams compensates for it. Outro Yes, there are definitely more tools available on the market (let me know about your favorite ones in the comments). Maintaining up-to-date infrastructure diagrams can be a challenge. But with the right tools, it becomes much more manageable. The key is to find the one that best fits your needs and workflow. Who wants to get a nice and shiny infrastructure diagram? We are! Who wants to maintain it? … Who wants to get a nice and shiny infrastructure diagram? We are! Who wants to maintain it? … Intro That’s right. You work at a company with a lot of infrastructure to manage. There are many services, complex networking, multi-cloud setups, and several CI/CD tools. You have a lot of code written. Maybe with comments. Perhaps even some documentation. Eventually, you start creating diagrams to show your infrastructure at a glance. Everybody loves diagrams! But as soon as you press ‘Save’ button the clock starts ticking, showing how outdated your diagram is. Unfortunately, no one loves outdated stuff. And no one has time to update it. You spent a lot of time being an infrastructure-as-a-code gitops hero. Everything is already here, in the code! There are plenty of tools that transform code into UML diagrams. Is there anything for infrastructure, please? infrastructure-as-a-code And there is! Let’s take a closer look What’s in the review To make it more practical we are going to use a real repo. The repo has a minimalistic Dockerfile for a simple Spring Boot app. A pinch of Terraform, and a GitHub Actions workflow to glue everything together. It builds the app with Gradle, creates a Docker image, and deploys it to AWS Elastic Container Service. Tools under review Terraform graph Inframap Rover NoReDraw Others Terraform graph Terraform graph Inframap Inframap Rover Rover NoReDraw NoReDraw Others Terraform graph First, let's start with Terraform graph . Terraform provides an embedded option for making a diagram. It creates a Graphviz .dot file. To see a diagram as an image you have to install Graphviz . Terraform graph Graphviz terraform graph | dot -Tpng > /tmp/graph.png terraform graph | dot -Tpng > /tmp/graph.png This gives you next diagram Looks acceptable for an out-of-the-box solution. It’s hardly readable, has no coloring scheme and has no icons. I’m not sure if it provides a better sense of architecture than the actual code. Yet, it could be a nice addition to a pull request analysis by demonstrating the actual plan (specify -plan=plan.out ). -plan=plan.out Inframap If the Terraform graph is too basic for your needs, let's move on to Inframap . This tool reads your tfstate or HCL to generate a graph specific to each provider. Instead of showing everything, the tool shows the most important resources. Developers of Inframap created a human-readable version of terraform graph . Inframap terraform graph inframap generate . | dot -Tpng > inframap.png inframap generate . | dot -Tpng > inframap.png This gives the next diagram Well, that’s way more readable. If you want more app-centric representation that’s the way to go. It doesn't have too much detail (subnets, security groups, IAM roles, etc) and looks more concise. We have all main components like the load balancer, ECS service, and the cluster. A few nitpicks: I would put client instead of im_out . And make the ECS cluster a container (rectangle) with the service inside. Anyway, it’s pretty decent. client im_out Rover Next up is Rover , another Terraform visualizer. There are several options for how to run Rover. Unfortunately, I wasn’t able to make Rover run terraform under the hood. Image generation with the specified plan didn’t work for me either. So I had to run Rover in standalone mode and explicitly specify previously generated TF plan: Rover terraform plan -out=plan.out
terraform show -json plan.out > plan.json

docker run --rm -it -p 9000:9000 -v $(pwd):/src im2nguyen/rover:latest -planJSONPath=/src/plan.json terraform plan -out=plan.out
terraform show -json plan.out > plan.json

docker run --rm -it -p 9000:9000 -v $(pwd):/src im2nguyen/rover:latest -planJSONPath=/src/plan.json The UI on http://localhost:9000/ appeared to be pretty nice. It might be helpful to click around to get more details on what’s going on. Again, mostly for PRs and a general understanding of resources. http://localhost:9000/ This is how generated diagram looks like I'm pleased with the color legend and grouping. The information seems to be excessive for the needs of an architecture diagram. Again, it’s a terraform visualizer, it shows everything defined in the plan, with no surprises. Alright, you’ve got the idea of terraform visualizers. They try to represent the state of resources defined in .tf files. Is there anything else left overboard? Right, the applications. The whole point of infrastructure is to serve a set of applications. Without showing the apps it all doesn’t make a lot of sense. Inframap did a great job with its app-centric approach. Are Terraform files not enough for the big picture? Let’s fall down the rabbit hole. NoReDraw This tool takes a step further. Instead of dealing with Terraform, NoReDraw focuses on… everything. Well, currently Gradle, Docker, GitHub Action, and Terraform AWS resources are supported. And ‘supported’ is a bit of a strong word as the project is still in beta. NoReDraw NoReDraw takes a source folder or git repo URL as an input. Then parses and cross-matches everything it can. Want to add more? Implement your own provider as the tool is highly customizable. Don’t like how components are linked together? Create your own LinkStrategy . Another cool benefit is that you can generate a diagram in Plant UML or Mermaid . This gives you an option to embed the drawing directly into your docs as text. LinkStrategy Plant UML Mermaid wget https://github.com/uladzimirFilipchanka/noredraw/raw/main/noredraw.sh && chmod +x noredraw.sh

./noredraw.sh \
--source . \
--export PNG \
--output noredraw.png wget https://github.com/uladzimirFilipchanka/noredraw/raw/main/noredraw.sh && chmod +x noredraw.sh

./noredraw.sh \
--source . \
--export PNG \
--output noredraw.png Let’s see, from bottom to top. Gradle builds a Jar named arch-diagram-sample . Then it gets containerized by Docker and ends up being a Docker image. The image is assigned to the task which is deployed into Amazon ECS via a GitHub Actions workflow. All correct! arch-diagram-sample The diagram definitely lacks in style, but if you plan to embed it you’ll have to suffer enjoy it anyway. At least some extra coloring wouldn’t hurt. The attributes in blocks nicely describe each artifact and are very handy. I really hope NoReDraw gets more traction in the near future. More providers and features can make it production-ready. suffer Others Finally, let's look at some other tools and services. They didn’t make it to the shortlist but are worth mentioning. Pluralith Pluralith This service helps to visualize and manage cloud infrastructure by parsing Terraform code. Pluralith goes beyond 'regular' visualizers and offers real-time updates. With nicely done CI/CD integrations (GitHub and GitLab as of now). Lucidscale Lucidscale The older sibling of Lucidchart. It’s a super-powerful tool for managing cloud infrastructure. Lucidscale offers automated and detailed visualizations but works another way. It connects to your cloud account and generates diagrams of the architecture. Currently, AWS, Azure, and GCP are supported. In this case, you don’t even need to adhere to infrastructure-as-a-code. Lucidscale provides all that you may or may not need. Including live sync, filtering, customization, and collaboration. Cloudcraft Cloudcraft Like Lucidscale it connects to your cloud account. It offers deep integration with AWS (and now Azure), but focuses on simplicity.
Cloudcraft has a minimalistic and user-friendly interface. It allows you to quickly create cloud architecture diagrams. You might miss some minor features in Cloudcraft, but the beauty of diagrams compensates for it. Outro Outro Yes, there are definitely more tools available on the market (let me know about your favorite ones in the comments). Maintaining up-to-date infrastructure diagrams can be a challenge. But with the right tools, it becomes much more manageable. The key is to find the one that best fits your needs and workflow.

Say Goodbye to Outdated Diagrams: Automate Your Infrastructure Visualization

Evolution of Data Sharding Towards Automation and Flexibility in Apache Doris

অ্যাপাচি ডরিসে অটোমেশন এবং নমনীয়তার দিকে ডেটা ভাগ করার বিবর্তন

Entwicklung des Data Sharding hin zu Automatisierung und Flexibilität in Apache Doris

Evolución de la fragmentación de datos hacia la automatización y la flexibilidad en Apache Doris

Evolution du partage de données vers l'automatisation et la flexibilité dans Apache Doris

अपाचे डोरिस में स्वचालन और लचीलेपन की ओर डेटा शार्डिंग का विकास

Apache Doris における自動化と柔軟性に向けたデータ シャーディングの進化

Apache Doris의 자동화 및 유연성을 향한 데이터 샤딩의 진화

Evolução da fragmentação de dados em direção à automação e flexibilidade no Apache Doris

Эволюция сегментирования данных в сторону автоматизации и гибкости в Apache Doris

Apache Doris'te Veri Parçalamanın Otomasyona ve Esnekliğe Doğru Evrimi

Sự phát triển của việc phân chia dữ liệu theo hướng tự động hóa và linh hoạt trong Apache Doris

Apache Doris 中数据分片向自动化和灵活性的演变

Authors:
(1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Table of Links Abstract and 1. Introduction
Computational Approaches in Editorial Design
System
Experimentation and Discussion
Conclusion
Acknowledgments and References Abstract This paper proposes a generative approach for the automatic typesetting of books in desktop publishing. The presented system consists in a computer script that operates inside a widely used design software tool and implements a generative process based on several typographic rules, styles and principles which have been identified in the literature. The performance of the proposed system is tested through an experiment which included the evaluation of its outputs with people. The results reveal the ability of the system to consistently create varied book designs from the same input content as well as visually coherent book designs with different contents while complying with fundamental typographic principles. Keywords Design tools; Data-driven Design; Generative Design; Graphic design; Typography. 1. Introduction Typography is the art of giving our language a visual form. Thus, it is through typography that we materialise and store our knowledge and information [1]. Since the publication of the first typographic book, in the mid-fifteenth century, society has been looking for the most appropriate way to convey a message typographically. In the field of editorial design, this effort has focused on the search for the best principles for designing typographic compositions, such as books [2]. Advances in digital technologies have been changing the work process of graphic designers. The emergence of computational approaches in the design domain enabled designers to explore new perspectives, new conceptual and visual possibilities, and achieve new types of solutions. Furthermore, the emergence of computational approaches comes with a paradigm shift in the role of the designer, who begins to create processes that enable the creation of designs, instead of designing the final solution. In other words, the design concept is translated into a computer program that systematically explores various design possibilities from the original concept. That said, in the particular case of layout design, we consider that the potential of computational approaches is not yet being fully explored. In this work, we explore a computational generative approach for the automatic design of book layouts. The result is a computer system, which operates inside the Adobe InDesign environment, that automatically generates book designs from input content. Figure 1 shows different books created with the presented system. It starts by receiving the input content, namely text and images. Before generating compositions for the input content, the designer can specify restrictions on some of the visual characteristics of the output compositions, e.g. format, size, grid and font. Then, the system automatically typesets the content based on a set of typography rules and principles found in fundamental literature in the field. In the end, the system presents the generated composition to the user as an editable Adobe InDesign document. Overall, the system is capable of creating layout compositions that comply with specific fundamental typographic principles while matching the graphic preferences of the user. In addition, experiments conducted with the system demonstrate its ability to autonomously generate varied compositions with the same input content and also generate visually coherent compositions for different input contents. In addition, it creates functional layout designs in an almost unpredictable manner. This reveals the great potential of this approach to layout design, both in generating outputs that the designer uses as final solutions and in using the outputs as starting points for further explorations. This work is aligned with the experiments previously developed by Ferreira et al. [3] The remaining of the article is organised as follows: Section 2 overviews related works; Section 3 describes the system, namely the interaction process, the engine that runs the system, as well as its inputs and outputs; Section 4 presents an experiment conducted to validate the designs created with the system and discusses the results obtained; Lastly, Section 5 summarises the main contributions of this work and identifies future work. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Authors: Authors: (1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Table of Links Abstract and 1. Introduction Computational Approaches in Editorial Design System Experimentation and Discussion Conclusion Acknowledgments and References Abstract and 1. Introduction Abstract and 1. Introduction Computational Approaches in Editorial Design Computational Approaches in Editorial Design System System Experimentation and Discussion Experimentation and Discussion Conclusion Conclusion Acknowledgments and References Acknowledgments and References Abstract This paper proposes a generative approach for the automatic typesetting of books in desktop publishing. The presented system consists in a computer script that operates inside a widely used design software tool and implements a generative process based on several typographic rules, styles and principles which have been identified in the literature. The performance of the proposed system is tested through an experiment which included the evaluation of its outputs with people. The results reveal the ability of the system to consistently create varied book designs from the same input content as well as visually coherent book designs with different contents while complying with fundamental typographic principles. Keywords Keywords Design tools; Data-driven Design; Generative Design; Graphic design; Typography. 1. Introduction Typography is the art of giving our language a visual form. Thus, it is through typography that we materialise and store our knowledge and information [1]. Since the publication of the first typographic book, in the mid-fifteenth century, society has been looking for the most appropriate way to convey a message typographically. In the field of editorial design, this effort has focused on the search for the best principles for designing typographic compositions, such as books [2]. Advances in digital technologies have been changing the work process of graphic designers. The emergence of computational approaches in the design domain enabled designers to explore new perspectives, new conceptual and visual possibilities, and achieve new types of solutions. Furthermore, the emergence of computational approaches comes with a paradigm shift in the role of the designer, who begins to create processes that enable the creation of designs, instead of designing the final solution. In other words, the design concept is translated into a computer program that systematically explores various design possibilities from the original concept. That said, in the particular case of layout design, we consider that the potential of computational approaches is not yet being fully explored. In this work, we explore a computational generative approach for the automatic design of book layouts. The result is a computer system, which operates inside the Adobe InDesign environment, that automatically generates book designs from input content. Figure 1 shows different books created with the presented system. It starts by receiving the input content, namely text and images. Before generating compositions for the input content, the designer can specify restrictions on some of the visual characteristics of the output compositions, e.g. format, size, grid and font. Then, the system automatically typesets the content based on a set of typography rules and principles found in fundamental literature in the field. In the end, the system presents the generated composition to the user as an editable Adobe InDesign document. Overall, the system is capable of creating layout compositions that comply with specific fundamental typographic principles while matching the graphic preferences of the user. In addition, experiments conducted with the system demonstrate its ability to autonomously generate varied compositions with the same input content and also generate visually coherent compositions for different input contents. In addition, it creates functional layout designs in an almost unpredictable manner. This reveals the great potential of this approach to layout design, both in generating outputs that the designer uses as final solutions and in using the outputs as starting points for further explorations. This work is aligned with the experiments previously developed by Ferreira et al. [3] The remaining of the article is organised as follows: Section 2 overviews related works; Section 3 describes the system, namely the interaction process, the engine that runs the system, as well as its inputs and outputs; Section 4 presents an experiment conducted to validate the designs created with the system and discusses the results obtained; Lastly, Section 5 summarises the main contributions of this work and identifies future work. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Towards the Automation of Book Typesetting: Abstract and Introduction

Authors:
(1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Table of Links Abstract and 1. Introduction
Computational Approaches in Editorial Design
System
Experimentation and Discussion
Conclusion
Acknowledgments and References 5. Conclusion We have presented a novel approach to computationally design books. The presented system implements a generative design process which takes advantage of the scripting capabilities of Adobe InDesign to procedurally typeset books from content provided by the user. We have shown the ability of the system to (i) create book designs that consistently comply with a series of typographic rules, styles and principles identified in the literature; (ii) produce visually diversified books from the same input content; and (iii) produce visually coherent books with different contents. The work presented in the paper may challenge the typical roles of both the tool and the designer. First, by automatically creating and suggesting design alternatives, the tool ends up playing a more active role in the design process. Then, by modifying and developing custom tools, the designer is no longer a mere tool user and becomes the author of tools tailored to specific needs. We believe this shift can be fruitful since it enables the exploration and discovery of new technical and creative possibilities. This work can hopefully provide directions to further research on generative processes for supporting design exploration and finding unique designs. In the particular case of typography, generative approaches such as the one presented in the paper can be useful and reveal great potential, especially in the current print-on-demand market and digital publishing, where each publication may be unique. Our future work will move in the direction of employing Artificial Intelligence techniques, such as Evolutionary Computation and Machine Learning, to enable a deeper exploration of the vast space of book designs that can be achieved with the system and also to automatically suggest settings to designers according to their needs or goals. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Authors: Authors: (1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Table of Links Abstract and 1. Introduction Computational Approaches in Editorial Design System Experimentation and Discussion Conclusion Acknowledgments and References Abstract and 1. Introduction Abstract and 1. Introduction Computational Approaches in Editorial Design Computational Approaches in Editorial Design System System Experimentation and Discussion Experimentation and Discussion Conclusion Conclusion Acknowledgments and References Acknowledgments and References 5. Conclusion We have presented a novel approach to computationally design books. The presented system implements a generative design process which takes advantage of the scripting capabilities of Adobe InDesign to procedurally typeset books from content provided by the user. We have shown the ability of the system to (i) create book designs that consistently comply with a series of typographic rules, styles and principles identified in the literature; (ii) produce visually diversified books from the same input content; and (iii) produce visually coherent books with different contents. The work presented in the paper may challenge the typical roles of both the tool and the designer. First, by automatically creating and suggesting design alternatives, the tool ends up playing a more active role in the design process. Then, by modifying and developing custom tools, the designer is no longer a mere tool user and becomes the author of tools tailored to specific needs. We believe this shift can be fruitful since it enables the exploration and discovery of new technical and creative possibilities. This work can hopefully provide directions to further research on generative processes for supporting design exploration and finding unique designs. In the particular case of typography, generative approaches such as the one presented in the paper can be useful and reveal great potential, especially in the current print-on-demand market and digital publishing, where each publication may be unique. Our future work will move in the direction of employing Artificial Intelligence techniques, such as Evolutionary Computation and Machine Learning, to enable a deeper exploration of the vast space of book designs that can be achieved with the system and also to automatically suggest settings to designers according to their needs or goals. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Towards the Automation of Book Typesetting: Conclusion

Towards the Automation of Book Typesetting: Acknowledgments and References

Authors:
(1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author;
(4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Table of Links Abstract and 1. Introduction
Computational Approaches in Editorial Design
System
Experimentation and Discussion
Conclusion
Acknowledgments and References 2. Computational Approaches in Editorial Design Systematic approaches have been popular in layout design since the mid-twentieth century when some creative practitioners designed layouts based on grids and the variation of the visual features of typographic elements [4–6]. Some works have explored algorithmic approaches for book layout design. In the 1960s, Gerstner introduced a selective and combinatorial method for the design of graphics, including layouts [7]. Afterwards, he translated it into a logical language that computers could understand in Compendium for Literates: A system of Writing [8]. LeWitt compiled, in 1971, a set of formal instructions to design a conceptual art exhibition catalogue [9]. Already in the 1980s, Knuth and Plass [10] presented a dynamic programming algorithm to page breaks avoiding widows and orphans and employed it to typeset a two-column dictionary. Soon thereafter, Knuth introduced the parametric typeface design language, Metafont [11] and the TeX typesetting system [12], enabling anybody to produce a book using a structural markup language and a set of high-level commands. Cooper and her students, at the Visible Language Workshop, experimented with the generation of layouts, e.g. publications that resulted from the collaboration with IBM [13] and the cover for Design Quarterly 142 [14]. In the mid1990s, Maeda also designed a series of digital booklets, the Reactive Book series, where the graphics are controlled interactively by user input [15]. Nevertheless, in the last two decades, we observed the increasing employment of the use of computational graphic design approaches, especially because of the development of easier-to-use creative code environments [16,17]. A solid overview of the field is presented, for instance, by Reas et al. [6] or Richardson [18]. These approaches have been explored in several artistic and creative domains, including the generation of visual and communication artefacts such as poster designs (e.g. Rebelo et al. [19] or Guo et al. [20]), banners (e.g. Gatarski [21] or Yin et al. [22]), user interfaces (e.g. Quiroz et al. [23] or Amitani et al. [24]), visual identities (e.g. Levin [25] or Neue [26]), type designs (e.g. Ahn & Jin [27] or Martins et al. [28]), among others. In the context of book typesetting, two frameworks are stimulating the adoption of computational practices. The library Basil.js [29] provides friendly and accessible tools for scripting and automation in Adobe InDesign. Then, the opensource framework The Magic Book [30] facilitates the design, production, and self-publishing of books. Tailor-made procedural and template-based approaches are employed in the generation and definition of layouts, modification of layouts based on their contents and/or inter-relationships between the elements. LettError type design studio used random processes and parametric design methods to design several typographic artefacts such as calendars, type specimens and even their portfolio [31,32]. Oliveira [33] presented a recursive division method to place elements on both one and multiple-column grid layouts. Cleveland [34] proposed a method for generating style-based design layouts that explores the inter-relationships between text and graphics. Also, he presented a system to generate layouts employing these principles. LUST developed a set of scripts to layout and stylise the book “I Read Where I am” informed by its content [35]. Damera-Venkata et al. [36] presented a template-based probabilistic framework for generating document layouts for variable content. Ahmadullin and Damera-Venkata [37] also presented a probabilistic model for newspaper typeset that, based on given content, divides the available layout into regions and optimises the content to fit within these regions. Flipboard developed Duplo [38], a layout engine that creates news magazines adapted to its contents and based on a set of heuristics such as the amount and flux of text or the existence and position of images. We can also observe the use of Artificial Intelligence approaches for typesetting. Evolutionary computation and greedy approaches have been used to create layouts with varied purposes. For example, Geigel and Loui [39] evolved layouts for photo books by evaluating different aesthetic criteria. Goldenberg [40] employed an evolutionary approach to automatically generate page layouts, minimising the waste of space on the page. Gonzalvez et al. [41] used a greedy simulated annealing algorithm to create multi-column newspaper layouts. Purvis et al. [42] automatically evolved documents using a multi-objective optimisation approach, considering a set of layout constraints and aesthetic measures. Quiroz et al. [43] evolved brochure documents according to user preferences and design guidelines. Strecker and Hennig [44] proposed a grid-based method for newspaper layouts, minimising the wasted space and bearing in mind newspaper design aesthetic measures. Boll et al. [45] and Sandhaus et al. [46,47] evolved photo layouts based on rules of layout design and proposed a method to transform a blog into a photo book considering different aesthetic requirements. Önduygu [48] developed the system Gráphagos that generates compositions through the interactive evolution of specific features of visual elements. Klein [49] developed the tool Crossing, Mixing, Mutating to create variations in a template using genetic operators. Later, an updated version of this tool was released as an InDesign plug-in named Evolving Layout [50]. Lopes et al. [51] developed the system evoDesigner, which automatically creates and evolves designs in the InDesign environment. Recently, Machine Learning approaches have been employed in the layout design field taking into account the relation between elements on layout and the learning of specific typesetting and design styles, e.g. Zheng et al. [52], Li et al. [53], or Kikuchi et al. [54]. Our analysis of the related work unveils a series of computational approaches that reveal great potential for the support and automatisation of the creation of book layouts. However, as far as we know, the existing approaches on this context rarely enable the subsequent modification of the generated designs, even less in the natural working environment of the designer. This way, it is difficult to introduce such computational technologies in the design process in an easy and fluid fashion. Also, we denoted that most of these approaches do not present a multipurpose objective, being developed to generate specific designs. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Authors: Authors: (1) Sérgio M. Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (2) Tiago Martins, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (3) Diogo Ferreira, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal and a Corresponding author; (4) Artur Rebelo, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal. Table of Links Abstract and 1. Introduction Computational Approaches in Editorial Design System Experimentation and Discussion Conclusion Acknowledgments and References Abstract and 1. Introduction Abstract and 1. Introduction Computational Approaches in Editorial Design Computational Approaches in Editorial Design System System Experimentation and Discussion Experimentation and Discussion Conclusion Conclusion Acknowledgments and References Acknowledgments and References 2. Computational Approaches in Editorial Design Systematic approaches have been popular in layout design since the mid-twentieth century when some creative practitioners designed layouts based on grids and the variation of the visual features of typographic elements [4–6]. Some works have explored algorithmic approaches for book layout design. In the 1960s, Gerstner introduced a selective and combinatorial method for the design of graphics, including layouts [7]. Afterwards, he translated it into a logical language that computers could understand in Compendium for Literates: A system of Writing [8]. LeWitt compiled, in 1971, a set of formal instructions to design a conceptual art exhibition catalogue [9]. Already in the 1980s, Knuth and Plass [10] presented a dynamic programming algorithm to page breaks avoiding widows and orphans and employed it to typeset a two-column dictionary. Soon thereafter, Knuth introduced the parametric typeface design language, Metafont [11] and the TeX typesetting system [12], enabling anybody to produce a book using a structural markup language and a set of high-level commands. Cooper and her students, at the Visible Language Workshop, experimented with the generation of layouts, e.g. publications that resulted from the collaboration with IBM [13] and the cover for Design Quarterly 142 [14]. In the mid1990s, Maeda also designed a series of digital booklets, the Reactive Book series, where the graphics are controlled interactively by user input [15]. Nevertheless, in the last two decades, we observed the increasing employment of the use of computational graphic design approaches, especially because of the development of easier-to-use creative code environments [16,17]. A solid overview of the field is presented, for instance, by Reas et al. [6] or Richardson [18]. These approaches have been explored in several artistic and creative domains, including the generation of visual and communication artefacts such as poster designs (e.g. Rebelo et al. [19] or Guo et al. [20]), banners (e.g. Gatarski [21] or Yin et al. [22]), user interfaces (e.g. Quiroz et al. [23] or Amitani et al. [24]), visual identities (e.g. Levin [25] or Neue [26]), type designs (e.g. Ahn & Jin [27] or Martins et al. [28]), among others. In the context of book typesetting, two frameworks are stimulating the adoption of computational practices. The library Basil.js [29] provides friendly and accessible tools for scripting and automation in Adobe InDesign. Then, the opensource framework The Magic Book [30] facilitates the design, production, and self-publishing of books. Tailor-made procedural and template-based approaches are employed in the generation and definition of layouts, modification of layouts based on their contents and/or inter-relationships between the elements. LettError type design studio used random processes and parametric design methods to design several typographic artefacts such as calendars, type specimens and even their portfolio [31,32]. Oliveira [33] presented a recursive division method to place elements on both one and multiple-column grid layouts. Cleveland [34] proposed a method for generating style-based design layouts that explores the inter-relationships between text and graphics. Also, he presented a system to generate layouts employing these principles. LUST developed a set of scripts to layout and stylise the book “I Read Where I am” informed by its content [35]. Damera-Venkata et al. [36] presented a template-based probabilistic framework for generating document layouts for variable content. Ahmadullin and Damera-Venkata [37] also presented a probabilistic model for newspaper typeset that, based on given content, divides the available layout into regions and optimises the content to fit within these regions. Flipboard developed Duplo [38], a layout engine that creates news magazines adapted to its contents and based on a set of heuristics such as the amount and flux of text or the existence and position of images. We can also observe the use of Artificial Intelligence approaches for typesetting. Evolutionary computation and greedy approaches have been used to create layouts with varied purposes. For example, Geigel and Loui [39] evolved layouts for photo books by evaluating different aesthetic criteria. Goldenberg [40] employed an evolutionary approach to automatically generate page layouts, minimising the waste of space on the page. Gonzalvez et al. [41] used a greedy simulated annealing algorithm to create multi-column newspaper layouts. Purvis et al. [42] automatically evolved documents using a multi-objective optimisation approach, considering a set of layout constraints and aesthetic measures. Quiroz et al. [43] evolved brochure documents according to user preferences and design guidelines. Strecker and Hennig [44] proposed a grid-based method for newspaper layouts, minimising the wasted space and bearing in mind newspaper design aesthetic measures. Boll et al. [45] and Sandhaus et al. [46,47] evolved photo layouts based on rules of layout design and proposed a method to transform a blog into a photo book considering different aesthetic requirements. Önduygu [48] developed the system Gráphagos that generates compositions through the interactive evolution of specific features of visual elements. Klein [49] developed the tool Crossing, Mixing, Mutating to create variations in a template using genetic operators. Later, an updated version of this tool was released as an InDesign plug-in named Evolving Layout [50]. Lopes et al. [51] developed the system evoDesigner, which automatically creates and evolves designs in the InDesign environment. Recently, Machine Learning approaches have been employed in the layout design field taking into account the relation between elements on layout and the learning of specific typesetting and design styles, e.g. Zheng et al. [52], Li et al. [53], or Kikuchi et al. [54]. Our analysis of the related work unveils a series of computational approaches that reveal great potential for the support and automatisation of the creation of book layouts. However, as far as we know, the existing approaches on this context rarely enable the subsequent modification of the generated designs, even less in the natural working environment of the designer. This way, it is difficult to introduce such computational technologies in the design process in an easy and fluid fashion. Also, we denoted that most of these approaches do not present a multipurpose objective, being developed to generate specific designs. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

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Akto

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