Yoann Pitarch

Mining Sequential Patterns from MODIS Time Series for Cultivated Area Mapping

To predict and respond to famine and other forms of food insecurity, different early warning systems are using remote analyses of crop condition and agricultural production by using satellite-based information. To improve these predictions, a reliable estimation of the cultivated area at a national scale must be carried out. In this study, we developed a data mining methodology for extracting cultivated domain patterns based on their temporal behavior as captured in time-series of moderate resolution remote sensing MODIS images.

Summarizing multidimensional data streams: a hierarchy-graph-based approach

When dealing with potentially infinite data streams, storing the whole data stream history is unfeasible and providing a high-quality summary is required In this paper, we propose a summarization method for multidimensional data streams based on a graph structure and taking advantage of the data hierarchies The summarization method considers the data distribution and thus overcomes a major drawback of the Tilted Time Window common framework We adapt this structure for synthesizing frequent itemsets extracted on temporal windows Thanks to our approach, as users do not analyze any more numerous extraction results, the result processing is improved.

A conceptual model for handling personalized hierarchies in multidimensional databases

Hierarchies are extensively used in data warehouses, OLAP systems and more recently in data stream summarization systems. They indeed allow decision makers to consider information at multiple granularity levels and they enable efficient compression mechanisms. However, even if numerous models of hierarchies have been proposed, some hierarchies arising in real-world situations are still not manageable by the current systems. For instance, in medical applications, determining the normality of an arterial pressure measure is infeasible without considering the patients characteristics (e.g. age). In this paper, we thus propose to model such context-dependent hierarchies by introducing personalized hierarchies. Firstly, we motivate this new category by presenting the lacks of existing approaches and we propose a conceptual model for modeling personalized hierarchies. Finally, a first logical model for handling such context-dependent hierarchies is proposed.

A Hierarchy-Based Method for Synthesizing Frequent Itemsets Extracted from Temporal Windows

With the rapid development of information technology, many applications have to deal with potentially infinite data streams. In such a dynamic context, storing the whole data stream history is unfeasible and providing a high-quality summary is required for decision makers. A practical and consistent summarization method is the extraction of the frequent itemsets over temporal windows. Nevertheless, this method suffers from a critical drawback: results pile up quickly making the analysis either uncomfortable or impossible for users. In this paper, we propose to unify these results thanks to a synthesis method for multidimensional frequent itemsets based on a graph structure and taking advantage of the data hierarchies. We overcome a major drawback of the Tilted Time Window (TTW) standard framework by taking into account the data distribution. Experiments conducted on both synthetic and real datasets show that our approach can be applied to data streams.

Knowledge-Free Table Summarization

Considering relational tables as the object of analysis, methods to summarize them can help the analyst to have a starting point to explore the data. Typically, table summarization aims at producing an informative data summary through the use of metadata supplied by attribute taxonomies. Nevertheless, such a hierarchical knowledge is not always available or may even be inadequate when existing. To overcome these limitations, we propose a new framework, named cTabSum, to automatically generate attribute value taxonomies and directly perform table summarization based on its own content. Our innovative approach considers a relational table as input and proceeds in a two-step way. First, a taxonomy for each attribute is extracted. Second, a new table summarization algorithm exploits the automatic generated taxonomies. An information theory measure is used to guide the summarization process. Associated with the new algorithm we also develop a prototype. Interestingly, our prototype incorporates some additional features to help the user familiarizing with the data: i the resulting summarized table produced by cTabSum can be used as recommended starting point to browse the data; ii some very easy-to-understand charts allow to visualize how taxonomies have been so built; iii finally, standard OLAP operators, i.e. drill-down and roll-up, have been implemented to easily navigate within the data set. In addition we also supply an objective evaluation of our table summarization strategy over real data.

TIGER: Querying large tables through criteria extension

Sales on the Internet have increased significantly during the last decade, and so, it is crucial for companies to retain customers on their web site. Among all strategies towards this goal, providing customers with a flexible search tool is a crucial issue. In this paper, we propose an approach, called TIGER, for handling such flexibility automatically. More precisely, if the search criteria of a given query to a relational table or a Web catalog are too restrictive, our approach computes a new query combining extensions of the criteria. This new query maximizes the quality of the answer, while being as close as possible to the original query. Experiments show that our approach improves the quality of queries, in the sense explained just above.