Bart Kuijpers
Biographic Data
| ID | 9964928 |
|---|---|
| NAME | Bart Kuijpers |
| GIVEN NAMES | Bart |
| FAMILY NAME | Kuijpers |
| SIGNATURE | KUIJPERS B |
| AFFILIATIONS | Hasselt University |
| ORCID | 0000-0001-5774-0948 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Temporal Paths in Real-World Sensor Networks
Sensor networks are used in an increasing number and variety of application areas, like traffic control or river monitoring. Sensors in these networks measure parameters of interest defined by domain experts and send these measurements to a central location for storage, viewing and analysis. Temporal graph data models, whose nodes contain time-series data reported by the sensors, have been proposed to model and analyze these networks in order to …
Analysing River Systems with Time Series Data Using Path Queries in Graph Databases
Transportation networks are used in many application areas, like traffic control or river monitoring. For this purpose, sensors are placed in strategic points in the network and they send their data to a central location for storage, viewing and analysis. Recent work proposed graph databases to represent transportation networks, since these networks can change over time, a temporal graph data model is required to keep track of these changes. In t…
Time-Series-Based Queries on Stable Transportation Networks Equipped with Sensors
In this paper, we propose a formalism to query transportation networks that are equipped with sensors that produce time-series data. The core of the proposed query mechanism is a logic-based language that is capable to return time, value, and time-series outputs, as well as Boolean queries. We can also use the language for node selection and path selection. Furthermore, we propose an implementation of this language in a graph database system and …
Visit Probability in Space–Time Prisms Based on Binomial Random Walk
Space–time prisms are used to model the uncertainty of space–time locations of moving objects between (for instance, GPS-measured) sample points. However, not all space–time points in a prism are equally likely and we propose a simple, formal model for the so-called “visit probability” of space–time points within prisms. The proposed mathematical framework is based on a binomial random walk within one- and two-dimensional space–time prisms. Witho…
No prominent works on this page.
Visit Probability in Space–Time Prisms Based on Binomial Random Walk
Space–time prisms are used to model the uncertainty of space–time locations of moving objects between (for instance, GPS-measured) sample points. However, not all space–time points in a prism are equally likely and we propose a simple, formal model for the so-called “visit probability” of space–time points within prisms. The proposed mathematical framework is based on a binomial random walk within one- and two-dimensional space–time prisms. Witho…
Time-Series-Based Queries on Stable Transportation Networks Equipped with Sensors
In this paper, we propose a formalism to query transportation networks that are equipped with sensors that produce time-series data. The core of the proposed query mechanism is a logic-based language that is capable to return time, value, and time-series outputs, as well as Boolean queries. We can also use the language for node selection and path selection. Furthermore, we propose an implementation of this language in a graph database system and …
Analysing River Systems with Time Series Data Using Path Queries in Graph Databases
Transportation networks are used in many application areas, like traffic control or river monitoring. For this purpose, sensors are placed in strategic points in the network and they send their data to a central location for storage, viewing and analysis. Recent work proposed graph databases to represent transportation networks, since these networks can change over time, a temporal graph data model is required to keep track of these changes. In t…
Temporal Paths in Real-World Sensor Networks
Sensor networks are used in an increasing number and variety of application areas, like traffic control or river monitoring. Sensors in these networks measure parameters of interest defined by domain experts and send these measurements to a central location for storage, viewing and analysis. Temporal graph data models, whose nodes contain time-series data reported by the sensors, have been proposed to model and analyze these networks in order to …
Computer Science (4 works) · Data Management and Algorithms (4 works) · Graph Theory and Algorithms (3 works) · Advanced Database Systems and Queries (2 works) · Artificial Intelligence (2 works) · Data mining (2 works) · Graph (2 works) · Real-time computing (2 works) · Theoretical Computer Science (2 works) · Binomial distribution (1 work)