A geo-referenced time series is a crucial form of spatiotemporal data. Useful information that can empower the users to achieve economic development is hidden in this series. When confronted with this problem, researchers modeled this series as a transactional database and discovered various user interest-based patterns. Since transactional databases disregard the items’ sequential ordering information, existing studies are inadequate to find interesting patterns in the data of those applications, where the items’ sequential ordering needs to be considered. With this motivation, this paper first presents a new data transformation technique that converts geo-referenced time series data into a geo-referenced sequential database that preserves the items’ sequential occurrence information. Second, this paper presents a novel model of geo-referenced frequent sequential patterns that may exist in a database. Third, a novel neighborhood-aware exploration technique has been presented to effectively reduce the search space and the computational cost of finding the desired patterns. Finally, we present an efficient algorithm to find all desired patterns in a database. Experimental results demonstrate that the proposed algorithm is efficient. We demonstrate the usefulness of our patterns with a case study, which involves finding congestion patterns in road network data.

Large-scale online discussion platforms are receiving great attention as potential next-generation methods for smart democratic citizen platforms. One of the studies clarified the critical problem faced by human facilitators caused by the difficulty of facilitating large-scale online discussions. In this demonstration, we present our current implementation of D-agree, a crowd-scale discussion support system based on an automated facilitation agent. We conducted a large-scale social experiment with Nagoya local government. The results demonstrate that the agent worked well compared with human facilitators.

Autonomous decentralized control is a key concept for understanding the mechanism underlying the adaptive and versatile behavior of animals. Although the design methodology of decentralized control based on a dynamical system approach that can impart adaptability by using coupled oscillators has been proposed in previous studies, it cannot reproduce the versatility of animal behaviors comprehensively. Therefore, our objective is to understand behavioral versatility from the perspective of well-coordinated rhythmic and non-rhythmic movements. To this end, we focus on ophiuroids as simple “best” model living organisms that exhibit spontaneous role assignment of rhythmic and non-rhythmic arm movements, and we model such arm movements by using an active rotator model that can describe both oscillatory and excitatory properties. Simulation results show that the spontaneous role assignment of arm movements is successfully realized by using the proposed model, and the simulated locomotion is qualitatively equivalent to the locomotion of real ophiuroids. This fact can potentially facilitate a better understanding of the control mechanism responsible for behavioral versatility in animals.