On strongly chordal graphs that are not leaf powers

A common task in phylogenetics is to find an evolutionary tree representing proximity relationships between species. This motivates the notion of leaf powers: a graph G = (V, E) is a leaf power if there exist a tree T on leafset V and a threshold k such that uv is an edge if and only if the distance between u and v in T is at most k. Characterizing leaf powers is a challenging open problem, along with determining the complexity of their recognition. This is in part due to the fact that few graphs are known to not be leaf powers, as such graphs are difficult to construct. Recently, Nevries and Rosenke asked if leaf powers could be characterized by strong chordality and a finite set of forbidden subgraphs. In this paper, we provide a negative answer to this question, by exhibiting an infinite family \G of (minimal) strongly chordal graphs that are not leaf powers. During the process, we establish a connection between leaf powers, alternating cycles and quartet compatibility. We also show that deciding if a chordal graph is \G-free is NP-complete, which may provide insight on the complexity of the leaf power recognition problem

Segmentation-based modeling for advanced targeted marketing

Fingerhut Business Intelligence (BI) has a long and successful history of building statistical models to predict consumer behavior. The models constructed are typically segmentationbased models in which the target audience is split into subpopulations (i.e., customer segments) and individually tailored statistical models are then developed for each segment. Such models are commonly employed in the direct-mail industry; however, segmentation is often performed on an ad-hoc basis without directly considering how segmentation affects the accuracy of the resulting segment models. Fingerhut BI approached IBM Research with the problem of how to build segmentation-based models more effectively so as to maximize predictive accuracy. The IBM Advanced Targeted Marketing – Single Events ™ (IBM ATM-SE™) solution is the result of IBM Research and Fingerhut BI directing their efforts jointly towards solving this problem. This paper presents an evaluation of ATM-SE’s modeling capabilities using data from Fingerhut’s catalog mailings

Analyzing compliance of service-based business processes for root-cause analysis and prediction

Automatically monitoring and enforcing compliance of service-based business processes with laws, regulations, standards, contracts, or policies is a hot issue in both industry and research. Little attention has however been paid to the problem of understanding non-compliance and improving business practices to prevent non-compliance in the future, a task that typically still requires human interpretation and intervention. Building upon work on automated detection of non-compliant situations, in this paper we propose a technique for the root-cause analysis of encountered problems and for the prediction of likely compliance states of running processes that leverages (i) on event-based service infrastructures, in order to collect execution evidence, and (ii) on the concept of key compliance indicator, in order to focus the analysis on the right data. We validate our ideas and algorithms on real data from an internal process of a hospital. © 2010 Springer-Verlag