Trace the Untraceable: Online Image Search Tools for Researching Late Antique Art

In the context of digital humanities and access to cultural heritage online, this paper explores the discoverability of Late Antique material in some searchable museum collections and in some major archaeological and art historical image and object databases. It follows an exploratory approach by using simple keyword searches, such as ‘late antique’ or ‘byzantine’, and comparing the results with chronological searches when a date or period filter is available. Although Late Antique material often comprises a smaller number of objects compared to more popular periods like the Roman and the Renaissance, these are difficult to research due to inconsistent labelling practices and the frequent lack of a customizable date range filter. The ongoing debates on proper periodization and nomenclature also need to be taken into consideration.

CPRQ: Cost Prediction for Range Queries in Moving Object Databases

Predicting query cost plays an important role in moving object databases. Accurate predictions help database administrators effectively schedule workloads and achieve optimal resource allocation strategies. There are some works focusing on query cost prediction, but most of them employ analytical methods to obtain an index-based cost prediction model. The accuracy can be seriously challenged as the workload of the database management system becomes more and more complex. Differing from the previous work, this paper proposes a method called CPRQ (Cost Prediction of Range Query) which is based on machine-learning techniques. The proposed method contains four learning models: the polynomial regression model, the decision tree regression model, the random forest regression model, and the KNN (k-Nearest Neighbor) regression model. Using R-squared and MSE (Mean Squared Error) as measurements, we perform an extensive experimental evaluation. The results demonstrate that CPRQ achieves high accuracy and the random forest regression model obtains the best predictive performance (R-squared is 0.9695 and MSE is 0.154).

An Adaptive Parallel Method for Indexing Transportation Moving Objects

Transportation cyber-physical systems are constrained by spatiality and real-time because of their high level of heterogeneity. Therefore, applications like traffic control generally manage moving objects in a single-machine multithreaded manner, whereas suffering from frequent locking operations. To address this problem and improve the throughput of moving object databases, we propose a GPU-accelerated indexing method, based on a grid data structure, combined with quad-trees. We count object movements and decide whether a particular node should be split or be merged on the GPU. In this case, bottlenecked nodes can be translated to quad-tree without interfering with the CPU. Hence, waiting time of other threads caused by locking operations raised by object data updating can be reduced. The method is simple while more adaptive to scenarios where the distribution of moving objects is skewed. It also avoids shortcomings of existing methods with performance bottleneck on the hot area or spending plenty of calculation resources on structure balancing. Experiments suggest that our method shows higher throughput and lower response time than the existing indexing methods. The advantage is even more significant under the skewed distribution of moving objects.

Globular Gaussian Essential Part in Meager Bayesian Knowledge Structure for Nonlinear Deterioration

Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub) trajectories in the MOD. In order to find the most representative sub trajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative sub trajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.

Indexing Object Database using HC-Tree

One of the recent applications of object technology is in the area of databases. One of the stumbling blocks in the commercial development and deployment of object databases is the lack of an efficient indexing technique. The properties of object databases make the task of development of an indexing technique all the more difficult. This paper discusses the development of an indexing technique for object databases. A new indexing technique based on a new structure, HC-Tree has been proposed. Performance analysis has been conducted, and experimental and analytical results indicate that the HC-Tree is an efficient indexing structure for object databases. The performance of the HC-Tree has also been compared with that of the other popular existing techniques - CH-Tree, H-Tree and hcC-Tree.

Initial data preparation for 3D modelling of heritage building

Latterly problems of restoration of historic buildings have become especially important. Historical buildings require special attention while preserving their cultural and artistic values, so the three-dimensional digital model of the building can be used preparing their operational or renewal plans. The aim of the presented research is to analyze strengths, weaknesses, opportunities, and threats (SWOT) of preparation the initial data, heritage building 3D model development and parametric object databases creation. The object of the research is the Sapieha Palace built in Baroque style in 1689–1691 in the capital city of Lithuania. Applied research methods and tools – initial data collection by historical drawing analysis, manual measurement, photogrammetry methods, and “As-Built” 3D model creation. The results of the presented case study were analyzed by applying SWOT methodology as well as compared with the similar studies in the scientific literature. The research provides the basis for further construction heritage studies and analysis of possible object conversion issues.

A Survey of Parallel Indexing Techniques for Large-Scale Moving Object Databases

A moving object database is a database that tracks the movements of objects. As such, these databases have business intelligence applications in areas like trajectory-based advertising, disease control and prediction, hurricane path prediction, and drunk-driver detection. However, in order to extract knowledge from these objects, it is necessary to efficiently query these databases. To this end, databases incorporate special data structures called indexes. Multiple indexing techniques for moving object databases have been proposed. Nonetheless, indexing large sets of objects poses significant computational challenges. To cope with these challenges, some moving object indexes are designed to work with parallel architectures, such as multicore CPUs and GPUs (graphics processing units), which can execute multiple instructions simultaneously. This chapter discusses business intelligence applications of parallel moving object indexes, identifies issues and features of these techniques, surveys existing parallel indexes, and concludes with possible future research directions.