CAS-MINE: Providing personalized services in context-aware applications by means of generalized rules

Context-aware systems acquire and exploit information on the user context to tailor services to a particular user, place, time, and/or event. Hence, they allowservice providers to adapt their services to actual user needs, by offering personalized services depending on the current user context. Service providers are usually interested in profiling users both to increase client satisfaction and to broaden the set of offered services. Novel and efficient techniques are needed to tailor service supply to the user (or the user category) and to the situation inwhich he/she is involved. This paper presents the CAS-Mine framework to efficiently discover relevant relationships between user context data and currently asked services for both user and service profiling. CAS-Mine efficiently extracts generalized association rules, which provide a high-level abstraction of both user habits and service characteristics depending on the context. A lazy (analyst-provided) taxonomy evaluation performed on different attributes (e.g., a geographic hierarchy on spatial coordinates, a classification of provided services) drives the rule generalization process. Extracted rules are classified into groups according to their semantic meaning and ranked by means of quality indices, thus allowing a domain expert to focus on the most relevant patterns. Experiments performed on three context-aware datasets, obtained by logging user requests and context information for three real applications, show the effectiveness and the efficiency of the CAS-Mine framework in mining different valuable types of correlations between user habits, context information, and provided services

Extracting Correlated Patterns on Multicore Architectures

Part 1: Cross-Domain Conference and Workshop on Multidisciplinary Research and Practice for Information Systems (CD-ARES 2013)International audienceIn this paper, we present a new approach relevant to the discovery of correlated patterns, based on the use of multicore architectures. Our work rests on a full KDD system and allows one to extract Decision Correlation Rules based on the Chi-squared criterion that include a target column from any database. To achieve this objective, we use a levelwise algorithm as well as contingency vectors, an alternate and more powerful representation of contingency tables, in order to prune the search space. The goal is to parallelize the processing associated with the extraction of relevant rules. The parallelization invokes the PPL (Parallel Patterns Library), which allows a simultaneous access to the whole available cores / processors on modern computers. We finally present first results on the reached performance gains

One-stage surgery through posterior approach-for L5-S1 spondyloptosis

Grade 5 spondylolisthesis or spondyloptosis is a rare condition. Generally, the surgical management of spondyloptosis includes multi-staged procedures instead of one-staged procedures. One-stage treatment for spondyloptosis is very rare. A 15-year-old girl with L5-S1 spondyloptosis was admitted with severe low back pain. There was no history of trauma. The patient underwent L5 laminectomy, L5-S1 discectomy, resection of sacral dome, reduction, L3-L4-L5-S1 pedicular screw fixation, and interbody-posterolateral fusion through the posterior approach. The reduction was maintained with bilateral L5-S1 discectomy, resection of the sacral dome, and transpedicular instrumentation from L3 to S1. In this particular case, one-staged approach was adequate for the treatment of L5-S1 spondyloptosis. One-staged surgery using the posterior approach may be adequate for the treatment of L5-S1 spondyloptosis while avoiding the risks inherent in anterior approaches