Spatio-temporal time series are widely used in real-world applications, including traffic prediction and weather forecasting. They are sequences of observations over extensive periods and multiple locations, naturally represented as multidimensional data. Forecasting is a central task in spatio-temporal analysis, and numerous deep learning methods have been developed to address it. However, as dataset sizes and model complexities continue to grow in practice, training deep learning models has become increasingly time- and resource-intensive. A promising solution to this challenge is dataset distillation, which synthesizes compact datasets that can effectively replace the original data for model training. Although successful in various domains, including time series analysis, existing dataset distillation methods compress only one dimension, making them less suitable for spatio-temporal datasets, where both spatial and temporal dimensions jointly contribute to the large data volume. To address this limitation, we propose STemDist, the first dataset distillation method specialized for spatio-temporal time series forecasting. A key idea of our solution is to compress both temporal and spatial dimensions in a balanced manner, reducing training time and memory. We further reduce the distillation cost by performing distillation at the cluster level rather than the individual location level, and we complement this coarse-grained approach with a subset-based granular distillation technique that enhances forecasting performance. On five real-world datasets, we show empirically that, compared to both general and time-series dataset distillation methods, datasets distilled by our STemDist method enable model training (1) faster (up to 6X) (2) more memory-efficient (up to 8X), and (3) more effective (with up to 12% lower prediction error).

Graphs are widely used to model complex systems across various domains, including social networks and biological systems. A key task in graph analysis is identifying recurring structural patterns, known as graphlets, which capture connectivity among a fixed-size subset of nodes. While graphlets have been extensively applied in tasks such as measuring graph similarity and identifying communities, conventional graphlets focus only on direct connections between nodes. This limitation overlooks potential insights from more distant relationships within the graph structure.

Changes in recent business environments have created the necessity for a more efficient and effective business process management. The workflow management system is software that assists in defining business processes as well as automatically controlling the execution of the processes. We propose a new approach to the automatic execution of business processes using event-condition-action (ECA) rules that can be automatically triggered by an active database. First of all, we propose the concept of blocks that can classify process flows into several patterns. A block is a minimal unit that can specify the behaviors represented in a process model. An algorithm is developed to detect blocks from a process definition network and transform it into a hierarchical tree model. The behaviors in each block type are modeled using ACTA formalism. This provides a theoretical basis from which ECA rules are identified. The proposed ECA rule-based approach shows that it is possible to execute the workflow using the active capability of database without users' intervention. The operation of the proposed methods is illustrated through an example process.

In this paper, an Integrated C-Object Tool, namely ICOT, is proposed for knowledge-based programming. A major drawback of current rule-based expert system languages is that they have difficulty in handling composite objects as a unit of inference. An object-oriented model is a powerful alternative to complement the drawback. Each of these alone cannot capture all the semantics of knowledge, particularly in complex engineering domains. For a knowledge-based approach to be effective, both the object-oriented paradigm and the rule-based mechanism may need to be integrated into one framework. The framework may also need to support manipulation of fuzzy knowledge to model the real world as close as possible. Three types of fuzzy information are identified, and a proper way of representing and inferencing them is developed. ICOT provides a new framework into which rule-based deduction, object-oriented modeling, and fuzzy inferencing are combined altogether. This can become especially useful for developing knowledge-based engineering applications.