Crowd- and nichesourcing for film and media scholars

[This post describes Aschwin Stacia‘s MSc. project and is based on his thesis]

There are many online and private film collections that lack structured annotations to facilitate retrieval. In his Master project work, Aschwin Stacia explored the effectiveness of a crowd-and nichesourced film tagging platform,  around a subset of the Eye Open Beelden film collection.

Specifically, the project aimed at soliciting annotations appropriate for various types of media scholars who each have their own information needs. Based on previous research and interviews, a framework categorizing these needs was developed. Based on this framework a data model was developed that matches the needs for provenance and trust of user-provided metadata.

Fimtagging screenshot
Screenshot of the FilmTagging tool, showing how users can annotate a video

A crowdsourcing and retrieval platform (FilmTagging) was developed based on this framework and data model. The frontend of the platform allows users to self-declare knowledge levels in different aspects of film and also annotate (describe) films. They can also use the provided tags and provenance information for retrieval and extract this data from the platform.

To test the effectiveness of platform Aschwin conducted an experiment in which 37 participants used the platform to make annotations (in total, 319 such annotations were made). The figure below shows the average self-reported knowledge levels.

Average self-reported knowledge levels on a 5-point scale. The topics are defined by the framework, based on previous research and interviews.
Average self-reported knowledge levels on a 5-point scale. The topics are defined by the framework, based on previous research and interviews.

The annotations and the platform were then positively evaluated by media scholars as it could provide them with annotations that directly lead to film fragments that are useful for their research activities.

Nevertheless, capturing every scholar’s specific information needs is hard since the needs vary heavily depending on the research questions these scholars have.

  • Read more details in Aschwin’s thesis [pdf].
  • Have a look at the software at , and maybe start your own Filmtagging instance
  • Test the annotation platform yourself at or watch the screencast below

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MSc. project: Requirements and design for a Business Intelligence system for SMEs

[This post was written by Arnold Kraakman and describes his MSc Thesis research] .

This master project is written as an advisory report for construction company and contractor K. Dekker B.V. and deals with Business Intelligence. Business Intelligence (BI) is a term that refers to information which can be used to make business decisions. The master thesis answers the question about what options are available for K. Dekker to implement BI within two years from the moment of writing. The research is done through semi-structured interviews and data mining. The interviews are used to gain a requirement list based on feedback the final users and with this list is a concept dashboard made, which could be used by K. Dekker. Having a BI dashboard is one of the solutions about what to do with their information to eventually implement Business Intelligence.

concept dashboard – project result in detail

Screenshot #1 shows an overview of the current running project, with the financial forecast. Most interviewees did not know which projects were currently running and done by K. Dekker B.V. Screenshot #2 shows the project characteristics and their financial result, this was the biggest must-have on the requirements list. A construction project has different characteristics, for example a bridge, made in Noord-Holland with a specific tender procedure and a specific contract form (for example: “design the whole project and build it as well” instead of only building it). Those characteristics could influence the final financial profit.

concept dashboard – project overview
concept dashboard – project overview

The thesis includes specific recommendations to K. Dekker to realize BI within two years from now on. This list is also generalized to Small and Medium-sized Enterprises (SMEs). These recommendations include that work instructions are made for ERP software therefore that everyone knows what and how information has to filled into the system. With incorrect entered data, the made decisions on this information could be incorrect as well. It is also recommended to make a project manager responsible for all the entered information. This will lead to better and more correct information and therefore the finally made business decisions are more reliable.

You can download the thesis here: arnold_kraakman_final_thesis

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