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Queries for data analysis

Queries for data analysis

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11th International Symposium on Intelligent data analysis, IDA 2012

作者： Siebes, Arno Algorithmic Data Analysis Group Universiteit Utrecht Netherlands

ISBN: (纸本)9783642341557

If we view data as a set of queries with an answer, what would a model be? In this paper we explore this question. The motivation is that there are more and more kinds of data that have to be analysed. data of such a diverse nature that it is not easy to define precisely what data analysis actually is. Since all these different types of data share one characteristic - they can be queried - it seems natural to base a notion of data analysis on this characteristic. The discussion in this paper is preliminary at best. There is no attempt made to connect the basic ideas to other - well known - foundations of data analysis. Rather, it just explores some simple consequences of its central tenet: data is a set of queries with their answer. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Query processing

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Sharing data with guaranteed privacy

Sharing data with guaranteed privacy

引用

European Conference on Machine Learning, ECML 1989

作者： Siebes, Arno Algorithmic Data Analysis Group Universiteit Utrecht Utrecht Netherlands

ISBN: (纸本)9783319417059

Big data is both a curse and a blessing. A blessing because the unprecedented amount of detailed data allows for research in, e.g., social sciences and health on scales that were until recently unimaginable. A curse, e.g., because of the risk that such - often very private - data leaks out though hacks or by other means causing almost unlimited harm to the individual. To neutralize the risks while maintaining the benefits, we should be able to randomize the data in such a way that the data at the individual level is random, while statistical models induced from the randomized data are indistinguishable from the same models induced from the original data. In this paper we first analyse the risks in sharing micro data - as statisticians tend to call it - even if it is anonymized, discretized, grouped, and perturbed. Next we quasi-formalize the kind of randomization we are after and argue why it is safe to share such data. Unfortunately, it is not clear that such randomizations of data sets exist. We briefly discuss why, if they exist at all, will be hard to find. Next I explain why I think they do exist and can be constructed by showing that the code tables computed by, e.g., Krimp are already close to what we would like to achieve. Thus making privacy safe sharing of micro-data possible. © Springer International Publishing Switzerland 2016.

关键词： Big data

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An experimental comparison of different inclusion relations in frequent tree mining

An experimental comparison of different inclusion relations ...

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作者： De Knijf, Jeroen Feelders, Ad Algorithmic Data Analysis Group Department of Information and Computing Sciences Universiteit Utrecht PO Box 80.089 3508 TB Utrecht Netherlands

In recent years a variety of mining algorithms, to derive all frequent subtrees from a database of labeled ordered rooted trees has been developed. These algorithms share properties such as enumeration strategies and pruning techniques. They differ however in the tree inclusion relation used and the way attribute values are dealt with. In this work we investigate the different approaches with respect to 'usefulness' of the derived patterns, in particular, the performance of classifiers that use the derived patterns as features. In order to find a good trade-off between expressiveness and runtime performance of the different approaches, we also take the complexity of the different classifiers into account, as well as the run time and memory usage of the different approaches. The experiments are performed on two real data sets, and two synthetic data sets. The results show that significant improvement in both predictive performance and computational efficiency can be gained by choosing the right tree mining approach.

关键词： Computational efficiency

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