Section: New Results
As planned, our new results are splitted into our three sub-objectives as introduced below:
Mining for Knowledge Discovery in Information Systems
This year we get six main results: one related to how to integrate domain knowledge in a multi-view KDD process (cf. section 6.2.4 ), two on new KDD methods involving clustering (cf. sections 6.2.3 and 6.2.2 ), one on the construction of hierarchical structures of concepts in the field of e-tourism (cf. section 6.2.6 ), one on partitioning objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices (cf. section 6.2.1 ), and finally improvment of our work on critical edition of Sanskrit texts (cf. section 6.2.5 ).
Zhang based on his thesis (2010) has published this year his work on modeling and clustering users with evolving profiles in usage streams  . This paper will propose three models to summarize bi-streaming data, which are the batch model, the Evolving Objects (EO) model and the Dynamic Data Stream (DDS) model. Through creating, updating and deleting user proﬁles, the models summarize the behaviours of each user as an object. Based on these models, clustering algorithms are employed to identify the user groups. The proposed models are tested on a real-world data set showing that the DDS model can summarize the bi-streaming data eﬃciently and eﬀectively, providing better basis for clustering user proﬁles than the other two models.
The work described in 2011(see our AxIS annual report) on critical edition of Sanskrit texts and submitted as a paper at the Cicling 2012 conference has been accepted  .
A past work accepted in an international journal with A. Ciampi and colleagues  .
One article in an international conference on functional data analysis issued from a collaboration with F. Rossi  .
Information and Social Networks Mining for Supporting Information Retrieval
This year, we pursued two main works on clustering methods:
the detection of communities in a social network (graph extracted from relationnal data) (cf. section 6.3.1 ),
the improvment of our dynamic hard clustering method for relational data (cf. section 6.3.2 ).
Multidisciplinary Research For Supporting User Oriented Innovation
With the expansion of the innovation community beyond the firm's boundaries (the so-called "open innovation") a lot of changes have been introduced in design and evaluation processes : the users can become co-designers, HCI design and evaluation focus is no longer placed on usability only but also on the whole user experience, experimentations take place out of lab with large number of heterogeneous people instead of carefully controlled panels of users ... All these deep changes require improvements of existing practices, methods and tools for the design / evaluation of information systems as well as for usage analysis. This evolution calls also for a structured user centered methodology (methods and ICT tools) to deal with open innovation. Various different disciplines and trends are dedicated in understanding user behavior on Internet and with Digital Technologies, notably Human Computer Interaction community (HCI), CSCW, Workplace Studies, Distributed Cognition and Data Mining. Our contribution to open innovation research keeps its focus on usage analysis for design, evaluation and maintenance of information systems and our activities this year, as indicated in our roadmap presented at the Inria theme evaluation (2011) have been conducted both breadth wise and in depth with two main objectives :
The research was conducted along three focus:
Extension of usability methods and models (cf. section 6.4 ),
Designing and evaluating user experience in the context of a living lab process (cf. section 6.5 ),
FocusLab Platform (cf. section 6.6 ).
Let us note one research work related to Living labs done in 2011 and published in 2012  .