Transactions of the Japanese Society for Artificial Intelligence Vol. 31(2016) No. 5

The potential desire of companies for creating values by combining data from different domains has been increased. In order to lead data-driven innovations, a market of data is expected to enhance this combination and data exchange through the communication among stakeholders. Innovators Marketplace on Data Jackets (IMDJ) is a gamified workshop for discovering the value of data by discussing the combination of Data Jackets, which supports creativity toward innovations and activates a market of data. A Data Jacket is meta-data, i.e., a summary of a dataset. Even if the data is not open, a Data Jacket enables participants to consider the latent value of datasets through creative communication. In this study, we discuss a system for structuring and reusing knowledge of data utilization, which are created in the workshops of IMDJ. By modeling and structuring knowledge not only with datasets, but also with solutions or requirements, it is expected to be possible to retrieve important information about solving problems. By implementing structured knowledge of data utilization using RDF (Resource Description Framework) and designing the interface for extracting accurate information for users, we propose the retrieval system named Data Jacket Store, and evaluate the performance.

In this paper, we propose a novel online ϵ-approximation algorithm, called LC-CloStream, for mining closed frequent itemsets embedded in a transactional stream. LC-CloStream is based on an incremental/cumulative intersection method and ϵ-elimination proposed by Lossy Counting algorithm. We show, LC-CloStream is essentially incomplete, but is still semi-complete for mining frequent closed itemsets in a stream. Moreover, we prove the completeness of extracting frequent itemsets and the ϵ-approximation for estimating the frequency. We also show several good performances of the experimental evaluation for LC-CloStream.