Modeling On-the-Spot Learning: Storage, Landmarks Weighting Heuristic and Annotation Algorithm
TL;DR: An OTSL model that includes the storage, retrieval and the landmark weighting heuristic is proposed that includes a weighting model to select the correct landmarks in the basic model and is extended to include other factors such as speed, direction, side of the road etc.
Abstract: Huge information is intrinsically associated with certain places in the globe such as historical, geographical, cultural and architectural specialties. The next generation systems require access of the site specific information where the user is roaming at the moment. The on-the-spot learning (OTSL) is a system that allows the users to learn about the location, landmarks, regions where he/she is walking through. In this paper, we have proposed an OTSL model that includes the storage, retrieval and the landmark weighting heuristic. Apart from learning about the individual landmarks, we have proposed two ways of storing the spatial learning objects. First, use the administrative hierarchy of the region to fetch the information. This can be easily done by the reverse-geocoding operation without actually storing the physical hierarchy. Second, spatial chunking, creates the region based on the groups of landmarks in order to define a learning region. A hybrid solution has also been considered to achieve the advantages of both the region based methods. We use a weighting model to select the correct landmarks in the basic model. We extend the core model to include other factors such as speed, direction, side of the road etc. A prototype has been implemented to show the feasibility of the proposed model.
Citations
More filters
[...]
TL;DR: The proposed method maintains a registry to keep the information about the visited users, their stay time and the travel distance from their home location, and considers the fact that the higher stay in a place is an implicit measure of the greater likings.
Abstract: Abstract Identifying the interesting places through GPS trajectory mining has been well studied based on the visitor’s frequency. However, the places popularity estimation based on the trajectory analysis has not been explored yet. The limitation in the majority of the traditional popularity estimation and place user-rating based methods is that all the participants are given the same importance. In reality, it heavily depends on the visitor’s category, for example, international visitors make distinct impact on popularity. The proposed method maintains a registry to keep the information about the visited users, their stay time and the travel distance from their home location. Depending on the travel nature the visitors are labeled as native, regional and tourist for each place in question. It considers the fact that the higher stay in a place is an implicit measure of the greater likings. Theweighted frequency is eventually fuzzified and applied rule based fuzzy inference system (FIS) to compute popularity of the places in terms of the ratings ∈ [0, 5]. We have evaluated the proposed method using a large real road GPS trajectory of 182 users for identifying the ratings for the collected 26807 point of interests (POI) in Beijing (China).
2 citations
Cites methods from "Modeling On-the-Spot Learning: Stor..."
[...]
References
More filters
[...]
TL;DR: The Cyberguide project is presented, in which the authors are building prototypes of a mobile context‐aware tour guide that is used to provide more of the kind of services that they come to expect from a real tour guide.
Abstract: Future computing environments will free the user from the constraints of the desktop. Applications for a mobile environment should take advantage of contextual information, such as position, to offer greater services to the user. In this paper, we present the Cyberguide project, in which we are building prototypes of a mobile context-aware tour guide. Knowledge of the user's current location, as well as a history of past locations, are used to provide more of the kind of services that we come to expect from a real tour guide. We describe the architecture and features of a variety of Cyberguide prototypes developed for indoor and outdoor use on a number of different hand-held platforms. We also discuss the general research issues that have emerged in our context-aware applications development in a mobile environment.
1,652 citations
[...]
TL;DR: An evaluation of GUIDE, an intelligent electronic tourist guide that combines mobile computing technologies with a wireless infrastructure to present city visitors with information tailored to both their personal and environmental contexts is presented.
Abstract: In this paper, we describe our experiences of developing and evaluating GUIDE, an intelligent electronic tourist guide. The GUIDE system has been built to overcome many of the limitations of the traditional information and navigation tools available to city visitors. For example, group-based tours are inherently inflexible with fixed starting times and fixed durations and (like most guidebooks) are constrained by the need to satisfy the interests of the majority rather than the specific interests of individuals. Following a period of requirements capture, involving experts in the field of tourism, we developed and installed a system for use by visitors to Lancaster. The system combines mobile computing technologies with a wireless infrastructure to present city visitors with information tailored to both their personal and environmental contexts. In this paper we present an evaluation of GUIDE, focusing on the quality of the visitor's experience when using the system.
1,119 citations
[...]
01 Jan 2007
TL;DR: In this article, the authors present an image-schemathematical account of spatial categories, including spatial information extraction for cognitive mapping with a mobile robot, as well as spatial semantic categories underlying the meaning of 'place'.
Abstract: Cultural Studies.- Progress on Yindjibarndi Ethnophysiography.- Study of Cultural Impacts on Location Judgments in Eastern China.- Cross-Cultural Similarities in Topological Reasoning.- Thalassographein: Representing Maritime Spaces in Ancient Greece.- Semantics.- From Top-Level to Domain Ontologies: Ecosystem Classifications as a Case Study.- Semantic Categories Underlying the Meaning of 'Place'.- Spatial Semantics in Difference Spaces.- Similarity.- Evaluation of a Semantic Similarity Measure for Natural Language Spatial Relations.- Affordance-Based Similarity Measurement for Entity Types.- An Image-Schematic Account of Spatial Categories.- Mapping and Representation.- Specifying Essential Features of Street Networks.- Spatial Information Extraction for Cognitive Mapping with a Mobile Robot.- Spatial Mapping and Map Exploitation: A Bio-inspired Engineering Perspective.- Scale-Dependent Simplification of 3D Building Models Based on Cell Decomposition and Primitive Instancing.- Perception and Cognition.- Degradation in Spatial Knowledge Acquisition When Using Automatic Navigation Systems.- Stories as Route Descriptions.- Three Sampling Methods for Visibility Measures of Landscape Perception.- Reasoning and Algorithms.- Reasoning on Spatial Semantic Integrity Constraints.- Spatial Reasoning with a Hole.- Geospatial Cluster Tessellation Through the Complete Order-k Voronoi Diagrams.- Drawing a Figure in a Two-Dimensional Plane for a Qualitative Representation.- Navigation and Landmarks.- Linguistic and Nonlinguistic Turn Direction Concepts.- A Uniform Handling of Different Landmark Types in Route Directions.- Effects of Geometry, Landmarks and Orientation Strategies in the 'Drop-Off' Orientation Task.- Uncertainty and Imperfection.- Data Quality Ontology: An Ontology for Imperfect Knowledge.- Triangulation of Gradient Polygons: A Spatial Data Model for Categorical Fields.- Relations in Mathematical Morphology with Applications to Graphs and Rough Sets.
522 citations
[...]
TL;DR: This paper proposes measures to formally specify the landmark saliency of a feature and describes how these measures are subject to hypothesis tests in order to define and extract landmarks from datasets.
Abstract: Navigation services communicate optimal routes to users by providing sequences of instructions for these routes. Each single instruction guides the wayfinder from one decision point to the next. The instructions are based on geometric data from the street network, which is typically the only dataset available. This paper addresses the question of enriching such wayfinding instructions with local landmarks. We propose measures to formally specify the landmark saliency of a feature. Values for these measures are subject to hypothesis tests in order to define and extract landmarks from datasets. The extracted landmarks are then integrated in the wayfinding instructions. A concrete example from the city of Vienna demonstrates the applicability and usefulness of the method.
502 citations
"Modeling On-the-Spot Learning: Stor..." refers methods in this paper
[...]
[...]
TL;DR: This paper proposes a data preprocessing model to add semantic information to trajectories in order to facilitate trajectory data analysis in different application domains and shows that the query complexity for the semantic analysis of trajectories will be significantly reduced.
Abstract: The collection of moving object data is becoming more and more common, and therefore there is an increasing need for the efficient analysis and knowledge extraction of these data in different application domains. Trajectory data are normally available as sample points, and do not carry semantic information, which is of fundamental importance for the comprehension of these data. Therefore, the analysis of trajectory data becomes expensive from a computational point of view and complex from a user's perspective. Enriching trajectories with semantic geographical information may simplify queries, analysis, and mining of moving object data. In this paper we propose a data preprocessing model to add semantic information to trajectories in order to facilitate trajectory data analysis in different application domains. The model is generic enough to represent the important parts of trajectories that are relevant to the application, not being restricted to one specific application. We present an algorithm to compute the important parts and show that the query complexity for the semantic analysis of trajectories will be significantly reduced with the proposed model.
407 citations
"Modeling On-the-Spot Learning: Stor..." refers background in this paper
[...]
Related Papers (5)
[...]
[...]
[...]
[...]
[...]