# Special Issue on Advances in Database Engineered Applications
###### Abstract
They also compare the sectorial path prediction error at different angles from 0\({}^{\circ}\) to 180\({}^{\circ}\) and demonstrate the mathematical modeling of various operations involved in navigating the robot. The minimum deviation of 1.125 cm between the actual and predicted paths proves the effectiveness of the robot in a real-life environment. Contribution 3 by [PERSON] (University of West Bohemia, Pilsen) and [PERSON] (University of Defence, Brno) focuses on meshfree interpolation of multi-dimensional time-varying scattered data. Interpolating and approximating scattered scalar and vector data is fundamental to resolving numerous engineering challenges. These methodologies predominantly rely on establishing a triangulated structure within the data domain, typically constrained to 2D or 3D dimensions. Subsequently, an interpolation or approximation technique is employed to yield a smooth and coherent outcome. The above article introduces a meshless methodology founded upon radial basis functions. This approach exhibits a nearly dimensionless character, facilitating the interpolation of data evolving over time. Specifically, it enables the interpolation of dispersed spatio-temporally varying data, allowing for interpolation within the space-time domain devoid of the conventional \"time-frames\". Meshless methodologies tailored for scattered spatio-temporal data hold applicability across a spectrum of domains, encompassing the interpolation, approximation, and assessment of data originating from various sources, such as buoys, sensor networks, tsunami monitoring instruments, chemical and radiation detectors, vessel and submarine detection systems, weather forecasting models, as well as the compression and visualization of 3D vector fields, among others. Contribution 4 by [PERSON], [PERSON], and [PERSON] (University of Luebeck) uses machine learning and routing protocols to optimize distributed SPARQL queries. Due to increasing digitization, the amount of data on the Internet of Things (IoT) is constantly increasing. To process queries efficiently, strategies must be found to reduce the transmitted data as much as possible. SPARQL is particularly well-suited to the IoT environment because it can handle various data structures. However, due to the flexibility of data structures, more data have to be joined again during processing. Therefore, a good join order is crucial, as it significantly impacts the number of intermediate results. Also, computing the best linking order is NP-hard because the total number of possible linking orders increases exponentially with the number of inputs, plus there are different definitions of optimal join orders. Machine learning uses stochastic methods to achieve good results even with complex problems quickly. Other DBMSs also consider reducing network traffic but neglect the network topology. Network topology is crucial in IoT as devices are not evenly distributed. In this respect, the authors present new techniques for collaboration between routing, application, and machine learning. Their approach, which pushes the operators as close as possible to the data source, minimizes the generated network traffic by 10%. Additionally, the model can reduce the number of intermediate results by a factor of 100 in comparison to other state-of-the-art approaches. Contribution 5 by [PERSON], [PERSON], and [PERSON] (Newcastle University) examines specification mining over temporal data. Current specification mining algorithms for temporal data rely on exhaustive search approaches, which become detrimental in real data settings where a plethora of distinct temporal behaviors are recorded over prolonged observations. The above article proposes a novel algorithm, Bolt2, based on a refined heuristic search of our previous algorithm, Bolt. Their experiments show that the proposed approach not only surpasses exhaustive search methods in terms of running time but also guarantees a minimal description that captures the overall temporal behavior.