Detecting domestic violence.

Over 90% of the case data from police inquiries is stored as unstructured text in police databases. We use the combination of Formal Concept Analysis and Emergent Self Organizing Maps for exploring a dataset of unstructured police reports out of the Amsterdam-Amstelland police region in the Netherlands. In this paper, we specifically aim at making the reader familiar with how we used these two tools for browsing the dataset and how we discovered useful patterns for labelling cases as domestic or as non-domestic violence.Formal concept analysis (FCA); Emergent SOM; Domestic violence; Knowledge discovery in databases; Text mining; Exploratory data analysis;

"More of an art than a science": Supporting the creation of playlists and mixes

This paper presents an analysis of how people construct playlists and mixes. Interviews with practitioners and postings made to a web site are analyzed using a grounded theory approach to extract themes and categorizations. The information sought is often encapsulated as music information retrieval tasks, albeit not as the traditional "known item search" paradigm. The collated data is analyzed and trends identified and discussed in relation to music information retrieval algorithms that could help support such activity

Collaborative searching for video using the Físchlár system and a DiamondTouch table

Fischlar DT is one of a family of systems which support interactive searching and browsing through an archive of digital video information. Previous Fischlar systems have used a conventional screen, keyboard and mouse interface, but Fischlar-DT operates with using a horizontal, multiuser, touch sensitive tabletop known as a DiamondTouch. We present the Fischlar-DT system partly from a systems perspective, but mostly in terms of how its design and functionality supports collaborative searching. The contribution of the paper is thus the introduction of Fischlar-DT and a description of how design concerns for supporting collaborative search can be realised on a tabletop interface

Exploratory Analysis of Highly Heterogeneous Document Collections

We present an effective multifaceted system for exploratory analysis of highly heterogeneous document collections. Our system is based on intelligently tagging individual documents in a purely automated fashion and exploiting these tags in a powerful faceted browsing framework. Tagging strategies employed include both unsupervised and supervised approaches based on machine learning and natural language processing. As one of our key tagging strategies, we introduce the KERA algorithm (Keyword Extraction for Reports and Articles). KERA extracts topic-representative terms from individual documents in a purely unsupervised fashion and is revealed to be significantly more effective than state-of-the-art methods. Finally, we evaluate our system in its ability to help users locate documents pertaining to military critical technologies buried deep in a large heterogeneous sea of information.Comment: 9 pages; KDD 2013: 19th ACM SIGKDD Conference on Knowledge Discovery and Data Minin

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N