5,883 research outputs found
Semantic data mining and linked data for a recommender system in the AEC industry
Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations
Modeling Time-Series and Spatial Data for Recommendations and Other Applications
With the research directions described in this thesis, we seek to address the
critical challenges in designing recommender systems that can understand the
dynamics of continuous-time event sequences. We follow a ground-up approach,
i.e., first, we address the problems that may arise due to the poor quality of
CTES data being fed into a recommender system. Later, we handle the task of
designing accurate recommender systems. To improve the quality of the CTES
data, we address a fundamental problem of overcoming missing events in temporal
sequences. Moreover, to provide accurate sequence modeling frameworks, we
design solutions for points-of-interest recommendation, i.e., models that can
handle spatial mobility data of users to various POI check-ins and recommend
candidate locations for the next check-in. Lastly, we highlight that the
capabilities of the proposed models can have applications beyond recommender
systems, and we extend their abilities to design solutions for large-scale CTES
retrieval and human activity prediction. A significant part of this thesis uses
the idea of modeling the underlying distribution of CTES via neural marked
temporal point processes (MTPP). Traditional MTPP models are stochastic
processes that utilize a fixed formulation to capture the generative mechanism
of a sequence of discrete events localized in continuous time. In contrast,
neural MTPP combine the underlying ideas from the point process literature with
modern deep learning architectures. The ability of deep-learning models as
accurate function approximators has led to a significant gain in the predictive
prowess of neural MTPP models. In this thesis, we utilize and present several
neural network-based enhancements for the current MTPP frameworks for the
aforementioned real-world applications.Comment: Ph.D. Thesis (2022
GeoNotes: A Location-based Information System for Public Spaces
The basic idea behind location-based information systems is to connect information pieces to positions in outdoor or indoor space. Through position technologies such as Global Positioning System (GPS), GSM positioning, Wireless LAN positioning o
Service-oriented Context-aware Framework
Location- and context-aware services are emerging technologies in mobile and
desktop environments, however, most of them are difficult to use and do not
seem to be beneficial enough. Our research focuses on designing and creating a
service-oriented framework that helps location- and context-aware,
client-service type application development and use. Location information is
combined with other contexts such as the users' history, preferences and
disabilities. The framework also handles the spatial model of the environment
(e.g. map of a room or a building) as a context. The framework is built on a
semantic backend where the ontologies are represented using the OWL description
language. The use of ontologies enables the framework to run inference tasks
and to easily adapt to new context types. The framework contains a
compatibility layer for positioning devices, which hides the technical
differences of positioning technologies and enables the combination of location
data of various sources
- …