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Aitor Apaolaza Llorente

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About me (CV)

I have a Ph.D. with three years of subsequent experience as a research associate at the University of Manchester. My main focus during my Ph.D. was on Web interaction analysis, building scalable tools to process, analyse, and derive insights from millions of low-level Web interaction events captured from thousands of users.

After my Ph.D. I joined the University of Manchester as a research associate, where I currently participate in projects that involve extensive data analysis, the design and implementation of analysis tools, and the evaluation of user interfaces. One such tool is WevQuery, an interactive Web application that provides users easy access to interaction data through the creation of event-based queries.

I am currently collaborating in a European funded project (9 partners from 6 countries), where I am responsible for capturing and processing interaction data to support personalisation and recommendation features, as well as the evaluation of user interaction with the platform.

Feel free to contact me at: aitor.apaolaza - AT -



I implemented a Web tool to allow non-technical partners to analyse user interaction patterns (WevQuery, best paper award at EICS). WevQuery supports the creation, scalable execution (MapReduce), and analysis of sequences of Web events.


As part of a European consortium with nine industry and academic partners from six different countries, I instrumented the MOVING information seeking platform to capture Web interaction events. MOVING allows researchers and industrial partners to explore multimedia sources using visualisations and data mining techniques. This interaction data is made available to stakeholders through WevQuery.


The IDInteraction project asks: can we exploit models of human behaviour to move away from direct, unambiguous user commands, towards seamless user-device interaction? It will investigate and develop the techniques to capture 'Indicative Usage Models (IUMs), behavioural patterns or cues that precede a particular activity, and translate these into software-based 'Indicative Usage Patterns' (IUPs), to drive interaction with an app. The project will focus on future television broadcasting, examining the extent to which it is possible to capture IUMs from device sensor and event data, and deploy these as IUPs to pull content (additional information or related activities) to a 'second screen companion app', which a viewer watches on a mobile device alongside a TV programme.


This research is the result of my PhD. The purpose of the research done during my PhD is capturing low-level Web interaction data from Web sites and applications to support longitudinal analysis. The main contribution is the analysis of this fine-grained interaction (mouse movements, window events) in a scalable way (it supports analysis of millions of interaction events, from thousands of users). To do this, I aggregate interaction data into micro behaviours, which consist of low-level aggregations of interaction data. In the context of my PhD, I used this technique to determine if and how users' Web interaction evolves, and how this evolution can be supported through a better design.



Open Innovation in the Big Data Era with the MOVING Platform: An Integrated Working and Training Approach for Data-savvy Information Professionals

Iacopo Vagliano, Franziska Guenther, Matthias Heinz, Aitor Apaolaza, Irina Bienia, Gert Breitfuss, Till Blume, Chrysa Collyda, Angela Fessl, Sebastian Gottfried, Peter Hasitschka, Jasmin Kellermann, Thomas Koehler, Annalouise Maas, Vasileios Mezaris, Ahmed Saleh, Andrzej Skulimowski, Stefan Thalmann, Markel Vigo, Alfred Wertner, Michael Wiese, Ansgar Scherp

IEEE Multimedia.


Best paper award WevQuery: Testing Hypotheses about Web Interaction Patterns

Aitor Apaolaza, Markel Vigo

The 9th ACM SIGCHI Symposium on Engineering Interactive Computing Systems EICS 2017.


ABC: Using Object Tracking to Automate Behavioural Coding

Aitor Apaolaza, Simon Harper, Caroline Jay

Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, CHI 2016.


Longitudinal analysis of low-level Web interaction through micro behaviours

Aitor Apaolaza, Simon Harper, Caroline Jay

Proceedings of the 26th ACM Conference on Hypertext & Social Media, HT 2015.


Understanding users in the wild

Aitor Apaolaza; Simon Harper, Caroline Jay

Proceedings of the 10th International Cross-Disciplinary Conference on Web Accessibility, W4A 2013.

Identifying emergent behaviours from longitudinal web use

Aitor Apaolaza

Proceedings of the adjunct publication of the 26th annual ACM symposium on User interface software and technology, UIST 2013.



More details about the tool, as well as instructions and sample data can be found in the project's repository . As this project is work in progress, there might be features that have not yet been properly described.


All the source code and processing pipelines developed and used in the IDinteraction project is available here.


The project UCIVIT is composed by three different modules. The capture tool provides an easy to deploy solution to capture Web interaction data remotely. The Analysis module contains a set of scripts employed to extract micro behaviours, and perform longitudinal analysis on them. The visualisation module provides an interactive visualisation Web application to ease the process of processing and analysing the data.

UCIVIT Capture tool provides the means to capture Web interaction data unobtrusively, in a scalable way. It has shown to be able to escalate to thousands of concurrent users, over periods longer than a year.

UCIVIT Analysis processes low-level interaction data in a scalable way. Making use of MongoDB and the MapReduce paradigm, data captured over extended periods of time using the UCIVIT-WebIntCap tool can be processed. Micro behaviours are small comparable units of user interaction. These units support scalable analysis of low-level interaction without disregarding its fine-grained nature.

UCIVIT Visualisation supports researchers' analysis of longitudinal interaction data. Distributions of interaction values at different stages of interaction are provided, as well as regression models through the use of linear mixed models.

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