Large Language Models (LLMs) hold promise in supporting creative writing, yet the role of proactive AI in collaborative writing remains underexplored due to concerns around human agency and disruption. To investigate effective strategies for proactive AI support, we conducted a Wizard-of-Oz study simulating two suggestion styles: intrusive suggestions (next-sentence completions) and non-intrusive suggestions (exploratory proposals), where participants completed two story outlining tasks under each style, receiving real-time proactive suggestions from a human wizard acting as the AI. Both quantitative and qualitative results show that proactive AI can enhance creativity and accelerate writing. However, we observed a trade-off between AI involvement and perceived human agency. This trade-off was moderated by how strongly AI stimulated users–greater inspiration led to stronger perceived agency even under high AI involvement. Based on wizards’ behavior, we offer guidance on suggestion style and timing to better balance creativity and agency for future proactive AI writing systems.

Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns.

Partial periodicity search, i.e., search for partial periodic patterns in time-series databases, is an interesting data mining problem. Previous studies on periodicity search mainly consider finding full periodic patterns, where every point in time contributes (precisely or approximately) to the periodicity. However, partial periodicity is very common in practice since it is more likely that only some of the time episodes may exhibit periodic patterns. We present several algorithms for efficient mining of partial periodic patterns, by exploring some interesting properties related to partial periodicity such as the Apriori property and the max-subpattern hit set property, and by shared mining of multiple periods. The max-subpattern hit set property is a vital new property which allows us to derive the counts of all frequent patterns from a relatively small subset of patterns existing in the time series. We show that mining partial periodicity needs only two scans over the time series database, even for mining multiple periods. The performance study shows our proposed methods are very efficient in mining long periodic patterns.