Web 1.0

Miranda, Isaias and Costa (2014) define Web 1.0 as a content delivery platform where content was static and websites were mostly based on Hyper Text Markup Language (HTML). Often Web 1.0 is referred as “read-only” where website authors publish the information that they decide to share while end-users were allowed to view that information. Although end-user could view the content and reach out to author but there was no interactive link between author and end-user or content and end-user. In essence, Web 1.0 was meant for website owners and administrators to bring information that they want to share with public (2014).

Architecture of Web 1.0 is based on Uniform Resource Identifier (URI), Hyper Text Transfer Protocol (HTTP) and Hyper Text Markup Language (HTML) (Jacobs & Walsh, 2004). URI are used to identify the resources placed on World Wide Web. HTTP is the application layer protocol that enable interaction between the browser of end-user and server using messages like, GET, POST etc. with underlying transport protocols like, TCP, UDP etc. (2004). HTML is the standard markup language used for creating web pages with elements known as tags, such as heading, paragraph, table and so on (W3 Schools, 2018).

Harvard Business Review (2013) describe the era of big data from mid-2000s when internet based and social network firms began to collect and analyze for business intelligence. Digital universe has grown 50 times from 2010 to 2020 powered by human and machine generated data (InsideBigData, 2017). Due to wider use of web, 2.5 quintillion bytes are created everyday from social media, sensors, systems, mobile interactions and digital processes (IEEE Transmitter, 2016). Performance issues with the web technology evolution can be broken down as below:

Web 1.0 is the oldest development targeted only for human understanding thus very slow and require refresh with updated information (Sahu, Mohapatra & Balabantaray 2016). Two way or interactive communication was not part of Web 1.0 either (Nath, Dhar & Basishtha, 2014). Outdated network design and servers with static data caused performance issues, which were later resolved by caching at HTTP proxies (Arlitt, Cherkasova, Dilley, Friedrich & Jin, 2000).

Web 2.0 developed with frequent retrieve and update of persistent data that lead to higher database access, contention and reduced performance due to scalability issues (Ohara, Nagpurkar, Ueda & Ishizaki, 2009). Performance issues were mostly due to huge amount of content loaded by users through blogs, Wikis, and social networking communities (2009).

Web 3.0 evolved from information connection stage to connecting knowledge and semantically structuring documents (Algosaibi, Albahli & Melton, 2015). Web content become readable to machines as well with the usage of new languages, standards, technologies and data representation models, like machine process-able graph data model using resource descriptive language (RDF) or Web Ontology Language (OWL). However locating and extraction of RDF becomes a major performance bottleneck (Cure, Naacke, Randriamalala & Amann, 2015).

Web 4.0 transitioned the web functionality into cloud and allow user to create and control their data. Proliferation of wireless devices and IoT, like smart house devices, fitness watches, health monitor chips, and connected cars increased the data generation, which lead to the performance bottleneck of web performance (Ferrer-Roca, Tous & Milito, 2014; Nath, Dhar & Basishtha, 2014).

Web 5.0 is tagged as a decentralized evolution where machines or devices will explore other devices and create web (Alam, Cartledge & Nelson, 2014). Meta data like, feelings, emotions and effects along with AI capability will become part of large scale web data. However billions of inter-connected devices will generate large amount of data resulting in performance bottlenecks (2014).

Reference:

Ferrer-Roca, O., Tous, R., & Milito, R. (2014, October). Big and Small Data: The Fog. In Identification, Information and Knowledge in the Internet of Things (IIKI), 2014 International Conference on (pp. 260-261). IEEE.

Nath, K., Dhar, S., & Basishtha, S. (2014). Web 1.0 to Web 3.0-Evolution of the Web and its various challenges. In Optimization, Reliabilty, and Information Technology (ICROIT), 2014 International Conference on (pp. 86-89). IEEE.

Ohara, M., Nagpurkar, P., Ueda, Y., & Ishizaki, K. (2009). The data-centricity of web 2.0 workloads and its impact on server performance. In Performance Analysis of Systems and Software, 2009. ISPASS 2009. IEEE International Symposium on (pp. 133-142). IEEE.

Cure, O., Naacke, H., Randriamalala, T., & Amann, B. (2015, October). LiteMat: a scalable, cost-efficient inference encoding scheme for large RDF graphs. In Big Data (Big Data), 2015 IEEE International Conference on (pp. 1823-1830). IEEE.

Sahu, S. K., Mohapatra, D. P., & Balabantaray, R. C. (2016). Information retrieval in the context of checking semantic similarity in web: Vision of future web. Indian Journal of Science and Technology, 9(32).

Nath, K., Dhar, S., & Basishtha, S. (2014). Web 1.0 to Web 3.0-Evolution of the Web and its various challenges. In Optimization, Reliabilty, and Information Technology (ICROIT), 2014 International Conference on (pp. 86-89). IEEE.

Miranda, P., Isais, P. & Costa, C. (2014). E-learning and Web Generations: Towards Web 3.0 and E-learning 3.0. 2014 4th International Conference on Education, Research and Innovation, Vol 81, 92-103

Jacob, I., & Walsh, N. (2004). Architecture of the World Wide Web, Volume One. Retrieved from https://www.w3.org/TR/2004/REC-webarch-20041215/

IEEE Transmitter (2016). The ever growing world of big data. Retrieved from https://transmitter.ieee.org/ever-growing-world-big-data/

InsideBigData (2017). The exponential growth if data. Retrieved from https://insidebigdata.com/2017/02/16/the-exponential-growth-of-data/

W3 Schools (2018). HTML Introduction. Retrieved from https://www.w3schools.com/html/html_intro.asp