Object-oriented Database Research Articles

Database of virtual objects to be used in psychological research

Although many visual stimulus databases exist, to our knowledge, none includes 3D virtual objects, that can directly be used in virtual reality (VR). We present 121 objects that have been developed for scientific purposes. The objects were built in Maya, and their textures were created in Substance Painter. Then, the objects were exported to an FBX and OBJ format and rendered online using the Unreal Engine 4 application. Our goal was to develop the first set of high-quality virtual objects with standardized names, familiarity, and visual complexity. The objects were normed based on the input of 83 participants. This set of stimuli was created for use in VR settings and will facilitate research using VR methodology, which is increasingly employed in psychological research.

Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature Extraction

High-dimensional signals, such as image signals and audio signals, usually have a sparse or low-dimensional manifold structure, which can be projected into a low-dimensional subspace to improve the efficiency and effectiveness of data processing. In this paper, we propose a linear dimensionality reduction method—minimum eigenvector collaborative representation discriminant projection—to address high-dimensional feature extraction problems. On the one hand, unlike the existing collaborative representation method, we use the eigenvector corresponding to the smallest non-zero eigenvalue of the sample covariance matrix to reduce the error of collaborative representation. On the other hand, we maintain the collaborative representation relationship of samples in the projection subspace to enhance the discriminability of the extracted features. Also, the between-class scatter of the reconstructed samples is used to improve the robustness of the projection space. The experimental results on the COIL-20 image object database, ORL, and FERET face databases, as well as Isolet database demonstrate the effectiveness of the proposed method, especially in low dimensions and small training sample size.

Intelligent Indexing—Boosting Performance in Database Applications by Recognizing Index Patterns

An issue that most databases face is the static and manual character of indexing operations. This old-fashioned way of indexing database objects is proven to affect the database performance to some degree, creating downtime and a possible impact in the performance that is usually solved by manually running index rebuild or defrag operations. Many data mining algorithms can speed up by using appropriate index structures. Choosing the proper index largely depends on the type of query that the algorithm performs against the database. The statistical analyzers embedded in the Database Management System are neither always accurate enough to automatically determine when to use an index nor to change its inner structure. This paper provides an algorithm that targets those indexes that are causing performance issues on the databases and then performs an automatic operation (defrag, recreation, or modification) that can boost the overall performance of the Database System. The effectiveness of proposed algorithm has been evaluated with several experiments developed and show that this approach consistently leads to a better resulting index configuration. The downtime of having a damaged, fragmented, or inefficient index is reduced by increasing the chances for the optimizer to be using the proper index structure.

Using object-oriented databases in face recognition

The aim of the work is to develop an algorithm functioning by a face recognition system using object-oriented databases. The system provides automatic identification of the desired object or identifies someone using a digital photo or video frame from a video source. The technology includes comparing pre-scanned face elements from the resulting image with prototypes of faces stored in the database. Modern packages of object-oriented databases give the user the opportunity to create a new class with the specified attributes and methods, obtain classes that inherit attributes and methods from super classes, create instances of the class, each of which has a unique object identifier, extract these instances one by one or in groups, and also download and perform these procedures. Using a convolutional neural network in the algorithm allows the transition from specific features of the image to more abstract details.

Impairing Land Registry: Social, Demographic, and Economic Determinants of Forest Classification Errors

This paper investigates the social, demographic, and economic factors determining differences between forest identification based on remote sensing techniques and land registry. The Database of Topographic Objects and Sentinel-2 satellite imagery data from 2018 were used to train a forest detection supervised machine learning model. Results aggregated to communes (NUTS-5 units) were compared to data from land registry delivered in Local Data Bank by Statistics Poland. The differences identified between above mentioned sources were defined as errors of land registry. Then, geographically weighted regression was applied to explain spatially varying impact of investigated errors’ determinants: Urbanization processes, civic society development, education, land ownership, and culture and quality of spatial planning. The research area covers the entirety of Poland. It was confirmed that in less developed areas, local development policy stimulating urbanization processes does not respect land use planning principles, including the accuracy of land registry. A high education level of the society leads to protective measures before the further increase of the investigated forest cover’s overestimation of the land registry in substantially urbanized areas. Finally, higher coverage by valid local spatial development plans stimulate protection against forest classification errors in the land registry.

On-orbit recognition of resident space objects by using star trackers

The paper focuses on the opportunity to use star sensors to help space situational awareness and space surveillance. Catalogs of orbiting satellites around Earth are usually established on ground-based measurements that rely on optical or radar data provided by instruments on Earth. However, space-based observations offer new opportunities, as they are unrelated to the weather and the circadian rhythm to which the ground system is subjected. Consequently, space-based optical observation systems can provide data without atmospheric distortion from different observer-to-target distances, enhancing the possibility to detect and catalog resident space objects, i.e. satellites and space debris. This work deals with the feasibility study of an innovative strategy, which consists in the use of a star sensor with a dedicated algorithm to run directly on-board. This approach minimizes the impact on the platform so that, from a theoretical point of view, every satellite with a star sensor can be used as a space surveillance observer. The output of the algorithm consists of a database of pre-processed objects, which are then post-processed on ground. Numerical tests on synthetic images provided by a high-fidelity simulator demonstrate the applicability of the proposed approach. The study has been conducted within the framework of the Italian Space Agency project SPOT - Star sensor image Processing for orbiting Objects deTection.

Exploring a non-existent city via historical GIS system by the example of the Jewish district ‘Podzamcze’ in Lublin (Poland)

The historical geoportal of Lublin created by the “Grodzka Gate – NN Theatre” Centre is probably the first thematic portal using object-oriented spatiotemporal databases for presenting historical data in Poland. Authors discuss the theoretical and methodical basis of historical GIS system function and present the example of executed implementation – the interactive map of the non-existent Jewish district ‘Podzamcze’ in Lublin ( www.teatrnn.pl/miejsca/mapa/lublin-podzamcze-czwartek ). The application gives access to archival collection of documents as well as to the catalogue of events, places and people connected with the former Jewish district. Integration of genealogical databases, gazetteers, old maps/city plans and collections of archival documents with API Google Maps allows to discover the city independently and comparing historical events in historically accurate space along with the present one.

IMO

Due to the widespread use of GPS-enabled devices such as smartphones, the research field of moving objects databases has been quite active in the past decade. Human movements are recorded, managed and analyzed for a plethora of applications. In this demo, we introduce a toolbox named IMO to simulate and query infected moving objects. This is primarily motivated by COVID-19 virus pandemic recently. We model the spreading behavior to demonstrate when and where people are infected. The protection policy is simulated such that one can see the isolation and self protection effects such as human movement restriction and the wearing of masks. Optimization techniques are developed to enhance the performance, including data storage, data partition and index structures. This is not a standalone software but a toolbox embedded in SECONDO, an open source and extensible database system. To the best of our knowledge, simulating and querying infected objects are not supported in existing moving objects prototype systems. Demo attendees can conveniently pose their queries and adjust parameters in the interface and the system will visualize the result after only a short delay.

Landslides Triggered by the 3 August 2014 Ludian (China) Mw 6.2 Earthquake: An Updated Inventory and Analysis of Their Spatial Distribution

The 3 August 2014 Ludian, Yunnan, China Mw 6.2 (Ms 6.5) earthquake triggered a large number of coseismic landslides. Based on pre- and post-quake high-resolution optical satellite images, this study established a new, complete and objective database of these landslides with field investigations. The updated inventory shows that this earthquake triggered at least 12 817 landslides with a total occupation area of 16.33 km2, covering a nearly circular area about 600 km2, which all exceed those in our previous work and other relevant studies. In addition, we used this database to examine the correlations of the landslides with topographic, geologic, and seismic factors. Results show that the landslides occurred mostly at places with slope gradients 10°–40°, showing an increase tendency with steeper slopes. Affected by the propagation direction of the earthquake rupture, the eastward-facing slopes are more prone to landsliding. The differences between the landslide susceptibility in different strata indicate that lithology is also an important controlling factor. The landslide density of the places with peak ground acceleration (PGA) greater than 0.16g is obviously larger than those with PGA less than 0.16g. Meanwhile, the greater the distance from the epicenter, the lower the susceptibility of landslides is. This study suggests that when using satellite images to create coseismic landslide inventories, it should meet certain conditions, including high resolution, whole coverage, and timely data collection. The correct criteria of coseismic landslide inventorying also should be followed. Such inventories can provide a reliable basis for hazard assessment of earthquake-triggered landslides and other quantitative studies.

Enhancing spatial and temporal utilities in differentially private moving objects database release

The pervasive use of mobile technologies and GPS-equipped vehicles has resulted in a large number of moving objects databases. Privacy protection is one of the most significant challenges related to moving objects databases because of the legal requirements in many application domains. Over the last few years, several differentially private mechanisms have been proposed for moving objects databases. However, most of them aim to answer statistical queries and do not release a differentially private version of a moving objects database. In this paper, we present DP-MODR, a differentially private (DP) mechanism for synthetic moving objects database release (MODR). DP-MODR tries to efficiently and effectively release synthetic trajectories while preserving spatial and temporal utilities. In this way, the released differentially private moving objects database can be used for different purposes as well, including data analysis tasks. DP-MODR keeps some main spatial and temporal properties of original trajectories and defines a new differentially private tree structure to keep the most probable paths with different lengths and different starting points, which are then iteratively joined to generate synthetic trajectories in a bottom-up way. Also, we present an extension of DP-MODR to support moving objects databases whose locations are time-dependent. Extensive experiments on real moving objects datasets using multiple spatial and temporal evaluation measures show that DP-MODR enhances the utility of query answers and better preserves the main spatial and temporal properties of original trajectories in comparison with recent related work.

Using Remote Sensing Data to Identify Large Bottom Objects: The Case of World War II Shipwreck of General von Steuben

The seabed of the Baltic Sea is not yet fully searched for and investigated. In 2004 the crew of the Polish Navy hydrographic ship Arctowski discovered a new shipwreck that was not listed in the official underwater objects database nor was it marked on a chart. The identity of a new wreck is most frequently established based on artefacts found in the object by divers as a part of archaeological research, or through underwater inspection with remotely operated vehicle. The aim of this paper is to show how acoustic remote sensing data is used to identify large bottom object without having to go underwater. Bathymetric survey and sonar investigation were conducted over the study area. An appropriate methodology allowed for obtaining high-resolution imagery of the wreck. A review of literature concerning the end of World War II in the Baltic Sea was carried out. Moreover, the author presents a comparative analysis and evaluation of remote sensing data with archival photos, silhouette, and ship characteristics. The proposed approach led to the identification of a new Baltic Sea wreck as the General von Steuben, which was torpedoed in 1945 by soviet submarine. The author’s findings show that state of preservation of the shipwreck, quality data as well as historical records play a key role in establishing the wreck’s identity.

Unsupervised Satellite Image Time Series Clustering Using Object-Based Approaches and 3D Convolutional Autoencoder

Nowadays, satellite image time series (SITS) analysis has become an indispensable part of many research projects as the quantity of freely available remote sensed data increases every day. However, with the growing image resolution, pixel-level SITS analysis approaches have been replaced by more efficient ones leveraging object-based data representations. Unfortunately, the segmentation of a full time series may be a complicated task as some objects undergo important variations from one image to another and can also appear and disappear. In this paper, we propose an algorithm that performs both segmentation and clustering of SITS. It is achieved by using a compressed SITS representation obtained with a multi-view 3D convolutional autoencoder. First, a unique segmentation map is computed for the whole SITS. Then, the extracted spatio-temporal objects are clustered using their encoded descriptors. The proposed approach was evaluated on two real-life datasets and outperformed the state-of-the-art methods.

Pruning techniques for parallel processing of reverse top-k queries

In this paper, we address the problem of processing reverse top-k queries in a parallel setting. Given a database of objects, a set of user preferences, and a query object q, the reverse top-k query returns the subset of user preferences for which the query object belongs to the top-k results. Although recently the reverse top-k query operator has been studied extensively, its CPU-intensive nature results in prohibitively expensive processing cost, when applied on vast-sized data sets. This limitation motivates us to explore a scalable parallel processing solution, in order to enable reverse top-k processing over distributed large sets of input data in reasonable execution time. We present an algorithmic framework for the problem, in which different algorithms can be instantiated, targeting a generic parallel setting. We describe a parallel algorithm (DiPaRT) that exploits basic pruning properties and is provably correct, as an instantiation of the framework. Furthermore, we introduce novel pruning properties for the problem, and propose DiPaRT+ as another instance of the algorithmic framework, which offers improved efficiency and scales gracefully. All algorithms are implemented in MapReduce, and we provide a wide set of experiments that demonstrate the improved efficiency of DiPaRT+ using data sets that are four orders of magnitude larger than those handled by centralized approaches.

Fuzzy Object Oriented Database for Big Data

Due to the huge amount of uncertain data collection, mankind facing a colossal amount and fast data having such a composite configurations. The data can be transact on the web and manually exchanges, interpersonal organizations and through our everyday life exercises. A very helpful result of designing an appropriate Big Data in different zones, for example, medical services, human services, executives and administrators. Managing the Big Data efficiently, new investigation perspective has been secured at this point the viewpoints about huge information (Big Data) requires extra-long stretch innovative interests. The Fuzzy Logic s have been implemented here for Big Data because of their capacities to handle the vagueness and uncertainties in the information. A couple of imaginative approach for Big Data processing is presented previously. To abbreviate the current duties and extant a point of view of further enhancements. We survey the different examinations and concentrated on that there are constrained Fuzzy systems have been adopted in large information (Big Data preparing. We also examine the various advantages of Fuzzy Logic s in large information (Big Data) issues. Therefore in this paper a Fuzzy object-oriented database is designed for Big Data and perform some Fuzzy queries to check the performance of the designed Fuzzy object-oriented database. We focused on the continuously propelled augmentations of Fuzzy sets and their blends in with various contraptions could offer a novel promising planning condition.

Improving Project Finance Tools for the Spatial Development Strategy of Russia (Evidence from Priority Sectors)

The authors study total financial, organizational and management relations in implementing and developing project finance principles in priority sectors of the Russian economy. The aim of the work is to reveal the importance of the formation and further development of the project finance market to achieve the strategic objectives of the spatial development of Russia. The study employs the scientific methods and approaches of observation, comparison, analogy, analysis, generalization, system-structural approach to assessing the development of the research subject. The authors consider the basic provisions of the spatial development strategy of Russia from the perspective of the content of priority sectors of the economy. They update the list of priority sectors and assess their needs for financial support of competitive development in this context. The authors propose modern effective project finance tools to overcome the deficit of government sources in financing priority sectors, such as syndicated loans, concession bonds, bonds of the special-purpose vehicle in project finance (SPV), securitization, and risk management. The main barriers that hamper the development of the project finance institution in Russia are insufficient legal framework, imperfect bank reserve system for potential losses on loans, loan and equivalent debt, the lack of a comprehensive proposal for project finance, and the lack of a unified database of project finance transactions. As a result , the study made it possible to specify the areas for improving project finance tools. The authors highlight the role of the regulatory framework and the importance of its conceptual review to improve the conceptual framework, the formation of individual legislative acts related to project finance, not as a special form of lending, but as a separate funding institution. They concluded that a unified database of investment objects with project finance tools is necessary. Further study of the topic is associated with developing guidelines for structuring project finance transactions, using the proposed recommendations to improve project finance mechanism by development institutions, banking institutions, pension funds and insurance companies.

Plant, space and time - linked together in an integrative and scalable data management system for phenomic approaches in agronomic field trials

BackgroundTo ensure further genetic gain, genomic approaches in plant breeding rely on precise phenotypic data, describing plant structure, function and performance. A more precise characterization of the environment will allow a better dealing with genotype-by-environment-by-management interactions. Therefore, space and time dependencies of the crop production processes have to be considered. The use of novel sensor technologies has drastically increased the amount and diversity of phenotypic data from agronomic field trials. Existing data management systems either do not consider space and time, are not customizable to individual needs such as field trial handling, or have restricted availability. Hence, we propose an integrative data management and information system (DMIS) for handling of traditional and novel sensor-based phenotypic, environmental and management data. The DMIS must be customizable, applicable and scalable from individual users to organizations.ResultsKey element of the system is a dynamic PostgreSQL database with GIS-extension, capable of importing, storing and managing all types of data including images. The database references every structural database object and measurement in a threefold approach with semantic, spatial and temporal reference. Timestamps and geo-coordinates allow automated linking of all data. Traits can be precisely defined individually or uploaded as predefined lists. Filtering and selection routines allow compilation of all data for visualization via tables, charts or maps and for export and external statistical analysis. New possibilities of environmental information-based planning of field trials, weather-guided phenotyping and data analysis for outlier or hot-spot detection are demonstrated.ConclusionsThe DMIS supports users in handling experimental field trials with crop plants and modern phenotyping methods. It focuses on linking all space and time dependent processes of plant production. Weather, soil and management, as well as growth and yield formation of the plants can be depicted, thus allowing a more precise interpretation of the results in relation to environment and management. Breeders, extension specialists, official testing agencies and agricultural scientists are assisted in all steps of a typical workflow with planning, designing, conducting, controlling and analyzing field trials to generate new information for decision support in the crop improvement process.

THE DOCTRINE OF FORENSIC ACTIVITIES DIGITALIZATION AND THE PROBLEMS OF FORENSIC DIDACTICS

The global digitalization in all spheres of human activity has seriously affected the problems of using special knowledge when collecting and examining evidence in criminal, civil and administrative cases, and especially forensic activities. Purpose: to develop a methodology for forensic examination of criminally significant computer information in the context of forensic activities digitalization. Methods: methods of dialectical and formal logic, comparison, description, interpretation, special scientific methods of forensic expertology and the theory of information and computer support for forensic activities. Results: the author develops the fundamentals of the forensic activity digitalization theory, the subject and objects of the theory, the main directions of the theory development, including the study of the types and forms of digital footprints in various types of forensic examinations; solving problems of integration in the production of forensic examinations in the context of digitalization; solving the problems of digitalization, unification and statutory regulation of reference and information collections and databases of forensic objects.

АНАЛІЗ ГІДРОГЕОЛОГІЧНИХ І ГЕОТЕРМІЧНИХ ХАРАКТЕРИСТИК ГЕОТЕРМАЛЬНИХ ОБ’ЄКТІВ УКРАЇНИ

На основі матеріалів «Державного інформаційного геологічного фонду України» створено базу даних геотермальних об’єктів, в яку увійшло 655 об’єкта, які охоплюють 12 адміністративних областей України і близько 54 % існуючих родовищ вуглеводнів. Основними геотермічними і гідрогеологічними характеристиками об’єктів бази даних є: глибина залягання, літологія і склад порід, геологічний вік порід, товщина продуктивного горизонту, пластова температура і пластовий тиск, дебіт свердловини, зниження рівня води у водоносному горизонті під час випробування, мінералізація, газовий склад і вміст природного газу у підземних водах.
 Аналіз гідрогеологічних і геотермічних характеристик геотермальних об’єктів виконано на основі структурно-гідрогеологічного принципу, тобто усі об’єкти розглядались стосовно крупних гідрогеологічних структур з єдиними регіональними межами і умовами формування родовищ підземних вод. Було проаналізовано чотири гідрогеологічні структури: Закарпатський внутрішній прогин, Прикарпатський передгірний прогини, Дніпровсько-Донецький артезіанський басейн та Причорноморський артезіанський басейн. Для кожної структури побудовано діаграми розподілу пластових температур, глибин залягання і мінералізації підземних вод в геотермальних об'єктах бази даних.
 Аналіз показав, що умови формування геотермальних родовищ в першу чергу залежать від теплового фону земних надр, але на процеси формування родовищ дуже істотний вплив має глибина залягання кристалічного фундаменту, розташування областей живлення і розвантаження термоводоносних горизонтів, глибина сучасного водообміну, літологічний склад водовмісних порід.
 Створена база даних геотермальних об’єктів України може бути використана під час вибору першочергових проектів освоєння геотермальних ресурсів, а також для стратегічного планування геотермальної галузі в цілому. Бібл. 10, рис. 14.

Understanding the Role of Objects in Joint Attention Task Framework for Children With Autism

Joint Attention (JA) refers to the triadic relationship between two individuals with a common target of interest in their shared visual space. JA is one of the core deficits in children with autism spectrum disorder (ASD), which adversely affects their overall socio-communicative development. Researchers have focused on the JA skill training using various approaches where the mediator/facilitator provides the JA cues toward a target of interest. Research studies have used different objects as targets of interest in JA skill training. However, the role of the objects for JA skill training has often been neglected. Objects in our environment aid in mediating the social relationships between children and their social partners and establishing socio-communicative relations with the environment. In this study, we have designed a virtual environment with virtual objects to understand the role of objects in JA skill training for individuals with ASD. We have created a database of virtual objects and projected these objects in a computer-based JA task framework. Based on a survey, we selected objects that were preferred by these children, with the rest being comparatively less preferred. Our experimental study involves presenting the designed virtual objects in a randomized manner in the computer-based JA task trials. Results of this study with a group of 15 children with ASD were promising. The results of this study indicate differentiated implications of preferred objects presented as target and non-target on the task performance and looking pattern of these children.

Materialized view selection problem using genetic algorithm for manufacturing execution system

Nowadays, a lot of equipment and machines in factories generating a high volume of production data at high speed which are then stored in a data warehouse. These data are used for analytic reporting purposes, to assist the decision-maker in strategizing their business. To speed up the report generation queries, materialized views (MVs) are used. An MV is a database object that contains the results of a query. However, each MV has different query time and maintenance cost. Therefore, it is crucial to select the best set of MVs in order to generate the report while minimizing the incurred cost. Genetic Algorithm is proposed to find the best set of MVs with minimum cost and time, and at the same time maximizing the number MVs selected. Experimental results demonstrate that the proposed GA delivered better solutions compared to the conventional approach