Výsledky vyhledávání

Season- and Trend-aware Symbolic Approximation for Accurate and Efficient Time Series Matching

Autor: Wolfgang Lehner, Claudio Hartmann, Lars Kegel, Maik Thiele

Publikováno v: Datenbank-Spektrum. 21:225-236

Processing and analyzing time series datasets have become a central issue in many domains requiring data management systems to support time series as a native data type. A core access primitive of time series is matching, which requires efficient alg

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8895419a1c81c8f1d25614b2af584c4e
https://doi.org/10.1007/s13222-021-00389-5

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Large-Scale Time Series Analytics

Autor: Lars Kegel, Martin Hahmann, Wolfgang Lehner, Claudio Hartmann

Publikováno v: Datenbank-Spektrum. 19:17-29

More and more data is gathered every day and time series are a major part of it. Due to the usefulness of this type of data, it is analyzed in many application domains. While there already exists a broad variety of methods for this task, there is sti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2411c3a1767f6cc1da7531ac81bbd9ec
https://doi.org/10.1007/s13222-018-00304-5

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Feature-aware forecasting of large-scale time series data sets

Autor: Lars Kegel, Wolfgang Lehner, Claudio Hartmann

Publikováno v: it-Information Technology

The Internet of Things (IoT) sparks a revolution in time series forecasting. Traditional techniques forecast time series individually, which becomes unfeasible when the focus changes to thousands of time series exhibiting anomalies like noise and mis

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Big by blocks: modular analytics

Autor: Lars Kegel, Martin Hahmann, Wolfgang Lehner, Dirk Habich, Claudio Hartmann

Publikováno v: it - Information Technology. 58:176-185

Big Data and Big Data analytics have attracted major interest in research and industry and continue to do so. The high demand for capable and scalable analytics in combination with the ever increasing number and volume of application scenarios and da

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a77ae6a14f6c10cdeaa67c1111783aef
https://doi.org/10.1515/itit-2016-0003

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Generating What-If Scenarios for Time Series Data

Autor: Wolfgang Lehner, Lars Kegel, Martin Hahmann

Publikováno v: SSDBM
Proceedings of the 29th International Conference on Scientific and Statistical Database Management -SSDBM '17
Proceedings of the 29th International Conference on Scientific and Statistical Database Management-SSDBM 17
Proceedings of the 29th International Conference on Scientific and Statistical Database Management

Time series data has become a ubiquitous and important data source in many application domains. Most companies and organizations strongly rely on this data for critical tasks like decision-making, planning, predictions, and analytics in general. Whil

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e14ed6ae71b2aff958701ceae10f590
https://doi.org/10.1145/3085504.3085507

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Feature-based comparison and generation of time series

Autor: Martin Hahmann, Wolfgang Lehner, Lars Kegel

Publikováno v: Proceedings of the 30th International Conference on Scientific and Statistical Database Management
SSDBM
Proceedings of the 30th International Conference on Scientific and Statistical Database Management-SSDBM 18
Proceedings of the 30th International Conference on Scientific and Statistical Database Management -SSDBM '18

For more than three decades, researchers have been developping generation methods for the weather, energy, and economic domain. These methods provide generated datasets for reasons like system evaluation and data availability. However, despite the va

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1bf04c6937dfa6215be3fc3d9626ecac