An Ontology-based Framework for Modeling Movement on a Smart Campus

Transcription

1 An Ontology-based Framework for Modeling Movement on a Smart Campus Junchuan Fan 1, Kathleen Stewart 1 1 Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA, 52242, USA {junchuan-fan, kathleen-stewart}@uiowa.edu 1. Introduction With the rapid development of information and communication technologies (ICT), teaching, learning, and facilities management across university campuses are evolving significantly, for example, through online education and mobile cloud education. Building a smart campus through the integration of ICT and geospatial technologies has attracted research interest in the GIScience community as well, for example, understanding diurnal and seasonal usage of campus buildings and spaces with the help of geospatial-enabled ICT (Sengupta et al. 2010). In this research, an ontology-based framework for representing, analyzing and visualizing human mobility as it relates to scheduled activities is presented. Movement by students on a university campus serves as the exemplar for designing the framework. This framework has three main elements. First, a scheduling ontology that models three different categories of scheduled activities (e.g., lectures, seminars) on campus. Scheduled activities play a key role in terms of shaping human movement patterns. The scheduling ontology models the core components (e.g., campus facility, student) in this domain as well as their interactions with scheduled activities. The second component is a Resource Description Framework (RDF)-based semantic data model that is built upon the ontology framework that integrates GeoSPARQL ontology and OWL-Time ontology, and the scheduling ontology. The underpinning ontology framework empowers the RDF data model with the ability to represent the spatial, temporal, and semantic aspects of domain classes. With the support of this ontology framework, a semantic reasoning engine is implemented to perform inferences about individual movements as well as aggregated dynamics of the campus. Using semantic web technologies, traditional campus information systems can be enriched with spatial and semantic information providing a means to help students and faculty plan their daily activities, and increasing possible opportunities for study and research collaborations. Facilities Management personnel can also use systems based on this approach to monitor the overall use and efficiency of campus buildings. 2. Background This research integrates semantic web technologies and time geography concepts to investigate human movement pattern associated with scheduled activities on a university campus. Using geospatial ontologies to capture the particular semantics of geospatial features and relations can help users retrieve geospatial information and services more effectively, especially as the traditional web of documents is turning into a web of data (Egenhofer 2002; Janowicz et al. 2012). Enriching spatiotemporal data with semantic information abstracts the data from geographic space to semantic space (Andrienko et al. 2013), providing the ability to investigate and discover more general patterns of geographic dynamic phenomenon. This is especially important in human movement

2 data analysis where general human behavior pattern is of particular interest to researchers As the era of cloud and mobile computing is underway, human movement patterns as well as behavioral habits are commonly tracked and significant changes may be occurring (Barnard et al. 2007). The human movement space has been expanded from a physical space to a virtual space to accommodate changes brought about by rapid progress in ICT( Yin et al. 2011). Equipped with location-aware handheld devices, humans are now acting as sensors (Goodchild 2007), being aware of the physical as well as social context in which they are situated (Zhang et al. 2011). Individuals alter their scheduled activities according to real world events as well as social activities (Neutens et al. 2010). Stewart et al. (2013) presents a formal model that goes beyond the conventional organization of scheduled activities, integrating both the spatial and temporal aspects of an individual s activities into an individual s schedule. 3. Campus scheduling ontology In the scheduling ontology, basic classes relating to the space-time dynamics of a domain are captured through three core classes, User, scheduledevent, and Building (Figure 1). Additional classes specific to the example domain of a campus are also included in this ontology. Properties shared by classes are represented by dashed lines (e.g., User hasschedule Schedule) Figure 1. Key classes in scheduling ontology User class is a high-level class in the ontology with at present two subclasses modeling campus users who will interact with the schedule, Faculty and Student. The attend property links a user with a scheduled event (e.g., lecture) while hasschedule property links a user with a Schedule instance (e.g., a student s weekly course schedule). The scheduledevent class represents a spatiotemporal entity that has start time, end time, location and anticipated participants. The scheduledevent class inherits hasbeginning and hasend from owltime:temporalentity class. Three subclasses of scheduledevent are modeled based on different spatiotemporal properties and their potential implications to a User s schedule, namely, Spatially Fixed Temporally Fixed event (SXTX) class, Spatially Fixed Temporally Flexible event (SXTF) class and Spatially Flexible Temporally Fixed event (SFTX) class. Different types of events have their own characteristics, impacting human movement differently.

3 The scheduling ontology draws on two predefined ontologies GeoSPARQL 1 and OWL-Time 2. All dimensions of course scheduling information (i.e., spatial, temporal, and semantic) can be represented through an RDF data model (represented as a triple store) based on the scheduling ontology. The RDF data model provides a structure for modeling the relationships between objects (i.e., users, courses, and buildings) explicitly by representing object relations as first class objects. The GeoSPARQL ontology extends the RDF data model for spatial dimensions, providing the RDF data model with the ability to represent and query geospatial data. The OWL-time ontology provides the RDF data model with temporal query capabilities. Spatial feature class, Building, isa subclass of geo:feature, a class defined in the GeoSPARQL ontology. The RDF data model augments the scheduling ontology with respect to representing the semantic relationships between different components in this domain. 4. Prototype implementation and case study Based on this ontology framework, a prototype system has been developed using the University of Iowa course information and a sample of students course schedules. The prototype system is developed using Protégé-OWL API 3, an open-source Java library for OWL and RDF data processing. A geospatial RDF triple store for all the scheduled courses on campus is generated through Protégé-OWL API. The Pellet reasoner API 4 is employed to supply reasoning functions over the OWL data model. Geospatial functions such as distance and buffering are implemented using Jena Spatial 5, an extension to Apache Jena ARQ. Individual students movements on campus are largely shaped by their course schedules. The spatiotemporal visualization of personal trajectories for individual students is initiated through a GeoSPARQL query that retrieves all the scheduled events in which an individual student participates from the geospatial RDF triple store, forming a personal Schedule for the student. Openlayers API is adopted to visualize the resulting Schedule based on the spatial and temporal information associated with the scheduledevents. The visualization of such trajectories maps the activity spaces of students on a campus (Figure 2(a)). Students can identify the areas that they move around most frequently based on a temporal aggregation of weekly or monthly trajectories. With this information, individuals can select places for choice activities such as studying or socializing more effectively. Visualization of the collective trajectories for a group of students (Figure 2(b)) reveals where are the busiest areas for groups of students, e.g., four buildings on the center of campus. From an academic standpoint, movement trajectories are helpful for arranging the space and time of courses more intelligently and directing resources, e.g., computer lab availability, or snow and ice removal in winter on highly accessed areas. Such a tool is also helpful for management of campus security or building energy efficiency

4 (a) (b) Figure 2. (a) Weekly trajectory for an individual student, (b) Collective trajectories for a group of 13 students The geospatial RDF triple store also offers us a basis for querying and visualizing dynamics for the whole campus at the granularity of buildings in real-time using GeoSPARQL queries. Thousands of students are moving from building to building on a campus and attending different scheduled events. Based on real-time course information for a semester, a spatiotemporal visualization of all buildings on campus can be generated, offering another important perspective to help manage and monitor campus dynamics. The campus perspective offers opportunities, for example, for managing parking and transportation (e.g., campus shuttles), based on locations and times of peak capacity. This work introduces an ontology-based framework for modeling, analysing, and visualizing human movement associated with scheduled activities through integrating semantic web technologies. We have selected scheduled courses on a university campus as our example scenario; however, this framework could be extended to other application domains where scheduled activities are common, e.g., transportation and logistic planning. Future efforts will incorporate road network information when generating individual trajectories and will investigate spatiotemporal interaction among multiple individual schedules. References Andrienko, N., Andrienko, G., and Fuchs, G. (2013). Towards Privacy-Preserving Semantic Mobility Analysis. In EuroVis Workshop on Visual Analytics (pp. 1923). The Eurographics Association. Barnard, L., Yi, J. S., Jacko, J. A., and Sears, A. (2007). Capturing the effects of context on human performance in mobile computing systems. Personal Ubiquitous Comput., 11(2): Egenhofer, M. J. (2002). Toward the semantic geospatial web. In Proceedings of the 10th ACM International Symposium on Advances in Geographic Information Systems, GIS 02, (pp. 1 4)

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