Identifying the most interactive object in spatial databases

This paper investigates a new query, called an MIO query, that retrieves the Most Interactive Object in a given spatial dataset. Consider that an object consists of many spatial points. Given a distance threshold, we say that two objects interact with each other if they have a pair of points whose distance is within the threshold. An MIO query outputs the object that interacts with other objects the most, and it is useful for analytical applications e.g., neuroscience and trajectory databases. The MIO query processing problem is challenging: a nested loop algorithm is computationally inefficient and a theoretical algorithm is computationally efficient but incurs a quadratic space cost. Our solution efficiently processes MIO queries with a novel index, BIGrid (a hybrid index of compressed Bitset, Inverted list, and spatial Grid structures), with a practical memory cost. Furthermore, our solution is designed so that previous query results and multi-core environments can be exploited to accelerate query processing efficiency. Our experiments on synthetic and real datasets demonstrate the efficiency of our solution.Amagata D., Hara T.. Identifying the most interactive object in spatial databases. Proceedings - International Conference on Data Engineering 2019-April, 1286 (2019); https://doi.org/10.1109/ICDE.2019.00117

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Construction safety and digital design: a review

As digital technologies become widely used in designing buildings and infrastructure, questions arise about their impacts on construction safety. This review explores relationships between construction safety and digital design practices with the aim of fostering and directing further research. It surveys state-of-the-art research on databases, virtual reality, geographic information systems, 4D CAD, building information modeling and sensing technologies, finding various digital tools for addressing safety issues in the construction phase, but few tools to support design for construction safety. It also considers a literature on safety critical, digital and design practices that raises a general concern about ‘mindlessness’ in the use of technologies, and has implications for the emerging research agenda around construction safety and digital design. Bringing these strands of literature together suggests new kinds of interventions, such as the development of tools and processes for using digital models to promote mindfulness through multi-party collaboration on safet

Monitoring land use changes using geo-information : possibilities, methods and adapted techniques

Monitoring land use with geographical databases is widely used in decision-making. This report presents the possibilities, methods and adapted techniques using geo-information in monitoring land use changes. The municipality of Soest was chosen as study area and three national land use databases, viz. Top10Vector, CBS land use statistics and LGN, were used. The restrictions of geo-information for monitoring land use changes are indicated. New methods and adapted techniques improve the monitoring result considerably. Providers of geo-information, however, should coordinate on update frequencies, semantic content and spatial resolution to allow better possibilities of monitoring land use by combining data sets

Cumulative object categorization in clutter

In this paper we present an approach based on scene- or part-graphs for geometrically categorizing touching and occluded objects. We use additive RGBD feature descriptors and hashing of graph configuration parameters for describing the spatial arrangement of constituent parts. The presented experiments quantify that this method outperforms our earlier part-voting and sliding window classification. We evaluated our approach on cluttered scenes, and by using a 3D dataset containing over 15000 Kinect scans of over 100 objects which were grouped into general geometric categories. Additionally, color, geometric, and combined features were compared for categorization tasks