2 research outputs found

    An Efficient Content-based Time Series Retrieval System

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    A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, healthcare, and manufacturing. For example, users seeking to learn more about the source of a time series can submit the time series as a query to the CTSR system and retrieve a list of relevant time series with associated metadata. By analyzing the retrieved metadata, users can gather more information about the source of the time series. Because the CTSR system is required to work with time series data from diverse domains, it needs a high-capacity model to effectively measure the similarity between different time series. On top of that, the model within the CTSR system has to compute the similarity scores in an efficient manner as the users interact with the system in real-time. In this paper, we propose an effective and efficient CTSR model that outperforms alternative models, while still providing reasonable inference runtimes. To demonstrate the capability of the proposed method in solving business problems, we compare it against alternative models using our in-house transaction data. Our findings reveal that the proposed model is the most suitable solution compared to others for our transaction data problem

    Information Search, Integration, and Personalization [electronic resource] : 13th International Workshop, ISIP 2019, Heraklion, Greece, May 9–10, 2019, Revised Selected Papers /

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    This book constitutes the revised selected papers of the 13th International Workshop on Information Search, Integration and Personalization, ISIP 2019, held in Heraklion, Greece, in May 2019. The volume presents 11 revised full papers, which were carefully reviewed and selected from 16 papers submitted to these post-conference proceedings. The papers are organized in topical sections on linked data; data analytics; data integration; data mining applications. .This book constitutes the revised selected papers of the 13th International Workshop on Information Search, Integration and Personalization, ISIP 2019, held in Heraklion, Greece, in May 2019. The volume presents 11 revised full papers, which were carefully reviewed and selected from 16 papers submitted to these post-conference proceedings. The papers are organized in topical sections on linked data; data analytics; data integration; data mining applications.
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