A Web2.0 Strategy for the Collaborative Analysis of Complex Bioimages

Loyek C, Kölling J, Langenkämper D, Niehaus K, Nattkemper TW. A Web2.0 Strategy for the Collaborative Analysis of Complex Bioimages. In: Gama J, Bradley E, Hollmén J, eds. Advances in Intelligent Data Analysis X: 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011. Proceedings. Lecture Notes in Computer Science. Vol 7014. Berlin, Heidelberg: Springer; 2011: 258-269

On the importance of nonlinear modeling in computer performance prediction

Computers are nonlinear dynamical systems that exhibit complex and sometimes even chaotic behavior. The models used in the computer systems community, however, are linear. This paper is an exploration of that disconnect: when linear models are adequate for predicting computer performance and when they are not. Specifically, we build linear and nonlinear models of the processor load of an Intel i7-based computer as it executes a range of different programs. We then use those models to predict the processor loads forward in time and compare those forecasts to the true continuations of the time seriesComment: Appeared in "Proceedings of the 12th International Symposium on Intelligent Data Analysis

Towards Meta-learning over Data Streams

Modern society produces vast streams of data. Many stream mining algorithms have been developed to capture general trends in these streams, and make predictions for future observations, but relatively little is known about which algorithms perform particularly well on which kinds of data. Moreover, it is possible that the characteristics of the data change over time, and thus that a different algorithm should be recommended at various points in time. Figure 1 illustrates this. As such, we are dealing with the Algorithm Selection Problem [9] in a data stream setting. Based on measurable meta-features from a window of observations from a data stream, a meta-algorithm is built that predicts the best classifier for the next window. Our results show that this meta-algorithm is competitive with state-of-the art data streaming ensembles, such as OzaBag [6], OzaBoost [6] and Leveraged Bagging [3]

Atlas.txt : Linking Geo-referenced Data to Text for NLG

Peer reviewedPreprin

How to Control Clustering Results?

One of the most important and challenging questions in the area of clustering is how to choose the best-fitting algorithm and parameterization to obtain an optimal clustering for the considered data. The clustering aggregation concept tries to bypass this problem by generating a set of separate, heterogeneous partitionings of the same data set, from which an aggregate clustering is derived. As of now, almost every existing aggregation approach combines given crisp clusterings on the basis of pair-wise similarities. In this paper, we regard an input set of soft clusterings and show that it contains additional information that is efficiently useable for the aggregation. Our approach introduces an expansion of mentioned pair-wise similarities, allowing control and adjustment of the aggregation process and its result. Our experiments show that our flexible approach offers adaptive results, improved identification of structures and high useability

Blogging birds : Generating narratives about reintroduced species to promote public engagement

Preprin