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Titlebook: Database and Expert Systems Applications; 33rd International C Christine Strauss,Alfredo Cuzzocrea,Ismail Khalil Conference proceedings 202

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21#
發(fā)表于 2025-3-25 05:12:50 | 只看該作者
A Knowledge-Driven Business Process Analysis Methodologyformation system development. This paper proposes a knowledge framework, referred to as BPA Canvas, primarily conceived to guide business people in building a BPA knowledge base. The resulting knowledge base is organized into eight sections where only the last one, the BPA ontology, requires specialist competences.
22#
發(fā)表于 2025-3-25 07:57:25 | 只看該作者
Clustering-Based Cross-Sectional Regime Identification for?Financial Market Forecastingial system. A regime can be defined as a specific group of complex patterns that share common characteristics in a specific time interval. Regime switch, caused by external and/or internal drivers, refers to the changing behaviors exhibited by a system at a specific time point. The existing regime d
23#
發(fā)表于 2025-3-25 15:02:34 | 只看該作者
Alps: An Adaptive Load Partitioning Scaling Solution for?Stream Processing System on?Skewed Stream the impact of rate variation, but cannot maintain high performance with a low overhead when input stream is skewed. To solve this issue, we propose Alps, an Adaptive Load Partitioning Scaling system. Alps exploits adaptive partitioning scaling algorithm based on the willingness function to determin
24#
發(fā)表于 2025-3-25 18:10:09 | 只看該作者
25#
發(fā)表于 2025-3-25 21:02:53 | 只看該作者
InTrans: Fast Incremental Transformer for?Time Series Data Prediction Time-series forecasting has been extensively studied. Many existing forecasting models tend to perform well when predicting short sequence time-series. However, their performances greatly degrade when dealing with the long one. Recently, more dedicated research has been done for this direction, and
26#
發(fā)表于 2025-3-26 01:24:13 | 只看該作者
27#
發(fā)表于 2025-3-26 08:09:09 | 只看該作者
28#
發(fā)表于 2025-3-26 08:46:22 | 只看該作者
Sequence Recommendation Model with Double-Layer Attention Netopment of recurrent neural networks enables systems to better process sequence information to capture users’ long-term preferences. While it cannot effectively utilize both long-term and short-term preferences. In this paper, we propose a novel double-layer attention mechanism mode, which not only i
29#
發(fā)表于 2025-3-26 12:39:24 | 只看該作者
30#
發(fā)表于 2025-3-26 19:24:27 | 只看該作者
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