Efficient Incremental Breadth-Depth XML Event Mining

Many applications log a large amount of events continuously. Extracting interesting knowledge from logged events is an emerging active research area in data mining. In this context, we propose an approach for mining frequent events and association rules from logged events in XML format. This approach is composed of two-main phases: I) constructing a novel tree structure called Frequency XML-based Tree (FXT), which contains the frequency of events to be mined; II) querying the constructed FXT using XQuery to discover frequent itemsets and association rules. The FXT is constructed with a single-pass over logged data. We implement the proposed algorithm and study various performance issues. The performance study shows that the algorithm is efficient, for both constructing the FXT and discovering association rules

Periodic control laws for bilinear quantum systems with discrete spectrum

We provide bounds on the error between dynamics of an infinite dimensional bilinear Schr\"odinger equation and of its finite dimensional Galerkin approximations. Standard averaging methods are used on the finite dimensional approximations to obtain constructive controllability results. As an illustration, the methods are applied on a model of a 2D rotating molecule.Comment: 6 pages, submitted to ACC 201

OLEMAR: An Online Environment for Mining Association Rules in Multidimensional Data

Data warehouses and OLAP (online analytical processing) provide tools to explore and navigate through data cubes in order to extract interesting information under different perspectives and levels of granularity. Nevertheless, OLAP techniques do not allow the identification of relationships, groupings, or exceptions that could hold in a data cube. To that end, we propose to enrich OLAP techniques with data mining facilities to benefit from the capabilities they offer. In this chapter, we propose an online environment for mining association rules in data cubes. Our environment called OLEMAR (online environment for mining association rules), is designed to extract associations from multidimensional data. It allows the extraction of inter-dimensional association rules from data cubes according to a sum-based aggregate measure, a more general indicator than aggregate values provided by the traditional COUNT measure. In our approach, OLAP users are able to drive a mining process guided by a meta-rule, which meets their analysis objectives. In addition, the environment is based on a formalization, which exploits aggregate measures to revisit the definition of the support and the confidence of discovered rules. This formalization also helps evaluate the interestingness of association rules according to two additional quality measures: lift and loevinger. Furthermore, in order to focus on the discovered associations and validate them, we provide a visual representation based on the graphic semiology principles. Such a representation consists in a graphic encoding of frequent patterns and association rules in the same multidimensional space as the one associated with the mined data cube. We have developed our approach as a component in a general online analysis platform called Miningcubes according to an Apriori-like algorithm, which helps extract inter-dimensional association rules directly from materialized multidimensional structures of data. In order to illustrate the effectiveness and the efficiency of our proposal, we analyze a real-life case study about breast cancer data and conduct performance experimentation of the mining process

The social driving forces of desertification in the high Algerian steppe plains

peer reviewedThe sedentarization of shepherds at the margins of the desert and the introduction of private land ownership in the high Algerian steppe plains has led to profound lifestyle transformations and increased desertification. Based on policy analysis, field observations and oral surveys with 188 household heads, this paper underlines the social drivers of desertification where short-term economic strategies and political misunderstanding are the key elements. Agro-forestry-pastoral practices are no longer in balance with the local environment. Assembling the puzzle pieces has underlined that political leaders and administrators are only partially familiar with the local geosystem, have implemented multiple unsuccessful measures, and have been unable to sufficiently learn (from their efforts) due to political instability. The paper shows that the sedentarization occurring in this fragile environment with low soil fertility creates rural systems that are less resilient to climate fluctuations, which severely impacts both the environment and the most vulnerable inhabitants.International Steppification Oberservatory1. No poverty10. Reduced inequalities13. Climate action15. Life on land11. Sustainable cities and communitie