Web Science and Engineering

The main subject of the course is the Web and in particular Web Data. The course considers developments in the Web and the (big) data management challenges associated to it. In particular, the course considers the relationship between people and technology that come with the Web and Web-based information systems. The course considers the Web both from an engineering perspective as well as from an analytical perspective.

The course explains the concept of Web-based Information System and thus concentrates on a large class of modern information systems that use the web and web data in one way or another. The course gives an insight into the research area of Web Engineering, where methods and techniques for the design and development of web-based information systems are investigated.

The course outlines the developments related to Web Data, and its management, processing and retrieval. The course gives an overview of the research and practice concerning the Semantic Web, with its main languages, theory and applications and tools for describing semantics in machine-processable manner. It also considers the concepts behind Linked Open Data and the data processing pipelines to create and analyse Linked Open Data.

With the social-technical nature of the Web and its systems, the course pays attention to the interplay between people and systems. The course gives an overview of the research area of User Modeling, with its main approaches and techniques to represent and capture properties of users that provide a basis for user-adaptation and personalisation in web-based information systems. In relation to user modeling, the Social Web plays a major role, for example because data from the social web creates a great source of knowledge for user modelling. Therefore, the course also considers research in social web data analytics and data science techniques to extract user knowledge from social web data. The course also considers recent developments in the research area of Human Computation concerning the role of humans in the processing of (human-related) web data, for example using crowdsourcing to create or annotate web content.

As the web and its data are mirroring the world and the people in it, the course also takes a look at Web Science, as a branch of data science that considers the largest human-made artefact ever, i.e. the Web, and how that analytical research is addressing a whole new range of challenges. These challenges include studying how data analytics can be done by means of Web data, as well as studying how new systems can be created and engineered to make use of the Web and its properties.