The success of electronic sports (eSports), where professional gamers participate in competitive leagues and tournaments, brings new challenges for the video game industry. Other than fun, games must be difficult and challenging for eSports professionals but still easy and enjoyable for amateurs. In this article, we consider Multi-player Online Battle Arena games (MOBA) and particularly, "Defense of the Ancients 2", commonly known simply as DOTA2. In this context, a challenge is to propose data analysis methods and metrics that help players to improve their skills. We design a data mining-based method that discovers strategic patterns from historical behavioral traces: Given a model encoding an expected way of playing (the norm), we are interested in patterns deviating from the norm that may explain a game outcome from which player can learn more efficient ways of playing. The method is formally introduced and shown to be adaptable to different scenarios. Finally, we provide an experimental evaluation over a dataset of 10 000 behavioral game traces.

Video game is a very lucrative industry, unleashed by the ubiquity of gaming devices, multi-player networks and live broadcasting platforms. Games generate large amounts of behavioural data which are valuable to face the new challenges of video game analytics such as detecting balance issues, bugs and cheaters. In electronic sports (e-sports), cyberathletes conceal their online training using different aliases or avatars (virtual identities), which allow them not being recognized by the opponents they may face in future competitions (with cash prices challenging already most of the traditional sports). It was recently suggested that behavioural data generated by the games allows predicting the avatar associated to a game play with high accuracy. However, when a player uses several avatars, accuracy drastically drops as prediction models cannot easily differentiate the player's different avatar aliases. Since mappings between players and avatars do not exist, we introduce the avatar aliases identification problem and propose an original approach for alias resolution based on supervised classification and Formal Concept Analysis. We thoroughly evaluate our method with the video game Starcraft 2 which has a very wide and active community with players from diverse cultures and nations. We show that under some circumstances, the avatars of a given player can easily be recognized as such. These results are valuable for e-sport structures (to help preparing tournaments), and game editors (detecting cheaters or usurpers).

In this work we present a novel technique for exhaustive bicluster enumeration using formal concept analysis (FCA). Particularly, we use pattern structures (an extension of FCA dealing with complex data) to mine similar row/column biclusters, a specialization of biclustering when attribute values have coherent variations. We show how biclustering can benefit from the FCA framework through its robust theoretical description and efficient algorithms. Finally, we evaluate our bicluster mining approach w.r.t. a standard biclustering technique showing very good results in terms of bicluster quality and performance.

Far from eliminating documents as some expected, the Internet has lead to a proliferation of digital documents, without a centralized control or indexing. Thus, identifying relevant documents becomes simultaneously more important and much harder, since what users require may be dispersed across many documents and many repositories. This paper describes Ontologic Anchoring, a technique to relate documents in domain ontologies, using named entity recognition (a natural-language processing approach) and semantic annotation to relate individual documents to elements in ontologies. This approach allows document retrieval using domain-level inferences, and integration of repositories with heterogeneous media, languages and structure. Ontological anchoring is a two-way street: ontologies allow semantic indexing of documents, and simultaneously new documents enrich ontologies. The approach is illustrated with an initial deployment for heritage documents in Spanish.