Database Constraints Research Articles

Energy Efficiency Trends in Petroleum Extraction: A Bibliometric Study

This comprehensive bibliometric analysis investigates energy-saving strategies in petroleum extraction, shedding light on key research areas, trends, and collaborations. The analysis covers 98 research articles spanning from 2003 to 2024, sourced from the Web of Science (WOS) database and analyzed using the Bibliometrics R package v.4.1.3, including descriptive statistics, network analysis, and factorial analysis. Findings reveal significant contributions from China, Canada, Russia, and the USA, with notable collaborations and thematic clusters identified. Top journals, prolific authors, and leading institutions are highlighted, showcasing global efforts in advancing sustainability in the oil industry. Institutions like the University of Calgary and authors such as Gates ID, Ren SR, and Zhang L play significant roles in advancing knowledge in this domain. Keyword analysis underscores prevalent themes such as optimization, simulation, and energy efficiency. Technological innovations, process optimization, and organizational strategies emerge as crucial avenues for reducing electrical energy consumption in oil extraction operations. However, limitations include database constraints and language bias. Overall, this study offers valuable insights for researchers, policymakers, and industry stakeholders, informing future research directions and policy initiatives for enhancing energy efficiency and sustainability in petroleum extraction.

Subglacial valleys preserved in the highlands of south and east Greenland record restricted ice extent during past warmer climates

Abstract. The Greenland Ice Sheet is a key contributor to contemporary global sea level rise, but its long-term history and response to episodes of warming in Earth's geological past remain uncertain. The terrain covered by the ice sheet comprises ∼ 79 % of Greenland and ∼ 1.1 % of the Earth's land surface and contains geomorphological records that may provide valuable insights into past ice-sheet behaviour. Here we use ice surface morphology and radio-echo sounding data to identify ice-covered valleys within the highlands of southern and eastern Greenland and use numerical ice-sheet modelling to constrain the climatological and glaciological conditions responsible for valley incision. Our mapping reveals intricate subglacial valley networks with morphologies that are indicative of substantial glacial modification of an inherited fluvial landscape, yet many of these valleys are presently situated beneath cold-based, slow-moving (i.e. non-erosive) ice. We use the morphology of the valleys and our simple ice-sheet model experiments to infer that incision likely occurred beneath erosive mountain valley glaciers during one or more phases of Greenland's glacial history when ice was restricted to the southern and eastern highlands and when Greenland's contribution to barystatic sea level was up to +7 m relative to today. We infer that this valley incision primarily occurred prior to the growth of a continental-scale ice sheet, most likely during the late Miocene (ca. 7–5 Ma) and/or late Pliocene (ca. 3.6–2.6 Ma). Our findings therefore provide new data-based constraints on early Greenland Ice Sheet extent and dynamics that can serve as valuable boundary conditions in models of regional and global palaeoclimate during past warm periods that are important analogues for climate change in the 21st century and beyond.

Discovering Functional Dependencies through Hitting Set Enumeration

Functional dependencies (FDs) are among the most important integrity constraints in databases. They serve to normalize datasets and thus resolve redundancies, they contribute to query optimization, and they are frequently used to guide data cleaning efforts. Because the FDs of a particular dataset are usually unknown, automatic profiling algorithms are needed to discover them. These algorithms have made considerable advances in the past few years, but they still require a significant amount of time and memory to process datasets of practically relevant sizes. We present FDHits, a novel FD discovery algorithm that finds all valid, minimal FDs in a given relational dataset. FDHits is based on several discovery optimizations that include a hybrid validation approach, effective hitting set enumeration techniques, one-pass candidate validations, and parallelization. Our experiments show that FDHits, even without parallel execution, has a median speedup of 8.1 compared to state-of-the-art FD discovery algorithms while using significantly less memory. This allows the discovery of all FDs even on datasets that could not be processed by the current state-of-the-art.

Constraints on Phase Transitions in Neutron Star Matter

Recent inference results of the sound velocity in the cores of neutron stars are summarized. Implications for the equation of state and the phase structure of highly compressed baryonic matter are discussed. In view of the strong constraints imposed by the heaviest known pulsars, the equation of state must be very stiff in order to ensure the stability of these extreme objects. This required stiffness limits the possible appearance of phase transitions in neutron star cores. For example, a Bayes factor analysis quantifies strong evidence for squared sound velocities cs2>0.1 in the cores of 2.1 solar-mass and lighter neutron stars. Only weak first-order phase transitions with a small phase coexistence density range Δρ/ρ<0.2 (at the 68% level) in a Maxwell construction still turn out to be possible within neutron stars. The central baryon densities in even the heaviest neutron stars do not exceed five times the density of normal nuclear matter. In view of these data-based constraints, much discussed issues such as the quest for a phase transition towards restored chiral symmetry and the active degrees of freedom in cold and dense baryonic matter, are reexamined.

Temporal data modelling and integrity constraints in relational databases*

There are key issues in temporal relational data modelling such as representation of temporal data, temporal grouping identifiers, primary keys of temporal relations and temporal integrity constraints. Temporal grouping is a fundamental aspect of temporal data modelling and is the basis for the specification and enforcement of temporal integrity constraints. We extend and generalize traditional integrity constraints for the temporal domain by formally defining the temporal grouping identifier and show how it can replace a temporal primary key like a temporal atom, a temporal integrity constraint has a data and a temporal component. We also introduce the notion of consistent evolution of a temporal database that lays the foundation for an efficient implementation of temporal integrity constraints. We also provide clauses that need to be added to SQL for specifying the temporal integrity constraints.

On replacing standard system error messages for relational database constraints with context aware natural language ones

The goal of this paper is to provide a rigorous methodology for enhancing VBA software application users’ experience when faced with attempts to violate the underlying SQL Server database constraints. The original contribution is a pseudo-code algorithm for assisting developers in this process. We exemplify the results of using it with a full code example for a class taken from a genealogy database software application designed and developed using this methodology. Consequently, it replaces all context-independent and developer jargon standard system error messages with context aware and natural language ones. Moreover, it also synchronizes combo-box instances after each update or delete. We also show that the proposed algorithm works fine not only for other relational database management systems, but even with NoSQL platform backends, and that the modifications needed to adapt it to other similar SQL embedding frontend platforms are minimal. This algorithm main merit is that it may serve as the basis for developing a tool for performing this important task through automatic code generation.

On replacing standard system error messages for relational database constraints with context aware natural language ones

On replacing standard system error messages for relational database constraints with context aware natural language ones

Extended Property-level k-vertex Cardinality Constraints Model for Graph Databases

Graph databases are nowadays considered the most appropriate solution for highly connected domains. Nevertheless, the lack of a fixed schema perplexes the implementation of business rules and inhibits the usage of graph database technology in practical use cases. To tackle this challenge, we study cardinality constraints in graph databases, as their focus is on the essential component of the property graph data model - relationships between entities. This paper presents an abstract cardinality constraints model for enforcing cardinality constraints in graph databases, which represent complex graph patterns. We extend our initial k-vertex cardinality constraints model to allow the representation of cardinality constraints between a node and a subgraph with given node and/or edge properties. Second, we implement the proposed model as procedures deployed in the Neo4j Graph Database Management System (GDBMS) to prevent adding new edges that violate k-vertex cardinality constraints to the graph database. Finally, we study the performance of the implemented approach on synthetic and real datasets and analyze its performance compared to the initial model. Overall, the query execution time (QET) of the procedure increases exponentially on larger datasets. Still, the added node/edge property-level evaluation does not show a significant performance effect on the edge insertion process.

On Software Application Database Constraint-driven Design and Development

This paper presents a methodology driven by database constraints for designing and developing (database) software applications. Much needed and with excellent results, this paradigm guarantees the highest possible quality of the managed data. The proposed methodology is illustrated with an easy to understand, yet complex medium-sized genealogy software application driven by more than 200 database constraints, which fully meets such expectations.

COVID-19 prevalence and infection control measures at homeless shelters and hostels in high-income countries: a scoping review

BackgroundThe COVID-19 pandemic has disproportionately impacted homeless populations and service workers, especially within homeless shelter/hostel settings. To date, there have been few evidence syntheses examining outbreaks of COVID-19 among both homeless shelter residents and service workers and no critical review of infection control and prevention (IPAC) measures. This scoping review offers a much-needed synthesis of COVID-19 prevalence within homeless shelters and a review of pertinent IPAC measures. MethodsWe conducted a scoping review that aimed to synthesize academic and gray literature published from March 2020 to July 2021 pertaining to (1) the prevalence of COVID-19 among both residents and staff in homeless shelters and hostels in high-income countries and (2) COVID-19 IPAC strategies applied in these settings. Two reviewers independently screened the literature from several databases that included MEDLINE, PsycInfo, and the WHO’s COVID-19 Global Health Portal. The extracted data was mapped, categorized, and thematically discussed.ResultsThematic analysis of 77 academic and gray literature documents revealed four key themes: (1) the demographics of COVID-19 in homeless shelters, (2) asymptomatic spread, (3) pre-existing vulnerability of people experiencing homelessness and shelters, and (4) the inconsistency and ineffectiveness of IPAC implementation.ConclusionThis review offers a useful glimpse into the landscape of COVID-19 outbreaks in homeless shelters/hostels and the major contributing factors to these events. This review revealed that there is no clear indication of generally accepted IPAC standards for shelter residents and workers. This review also illustrated a great need for future research to establish IPAC best practices specifically for homeless shelter/hostel contexts. Finally, the findings from this review reaffirm that homelessness prevention is key to limiting disease outbreaks and the associated negative health outcomes in shelter populations. Limitations of this review included the temporal and database constraints of the search strategy, the exclusion of quality assessments of the literature, and the absence of investigation on the influence of emerging variants on public health policy.Systematic review registrationThis scoping review has not been registered on any database; the protocol is available on York University’s Institutional Repository https://dx.doi.org/10.25071/10315/38513.

Purging Compliance from Database Backups by Encryption

Data compliance laws establish rules intended to protect privacy. These define both retention durations (how long data must be kept) and purging deadlines (when the data must be destroyed in storage). To comply with the laws and to minimize liability, companies must destroy data that must be purged or is no longer needed. However, database backups generally cannot be edited to purge ``expired'' data and erasing the entire backup is impractical. To maintain compliance, data curators need a mechanism to support targeted destruction of data in backups. In this paper, we present a cryptographic erasure framework that can purge data from across database backups. We demonstrate how different purge policies can be defined through views and enforced without violating database constraints.

Frequent high minimum average utility sequence mining with constraints in dynamic databases using efficient pruning strategies

Frequent high minimum average utility sequence mining with constraints in dynamic databases using efficient pruning strategies

PatchIndex: exploiting approximate constraints in distributed databases

Cloud data warehouse systems lower the barrier to access data analytics. These applications often lack a database administrator and integrate data from various sources, potentially leading to data not satisfying strict constraints. Automatic schema optimization in self-managing databases is difficult in these environments without prior data cleaning steps. In this paper, we focus on constraint discovery as a subtask of schema optimization. Perfect constraints might not exist in these unclean datasets due to a small set of values violating the constraints. Therefore, we introduce the concept of a generic PatchIndex structure, which handles exceptions to given constraints and enables database systems to define these approximate constraints. We apply the concept to the environment of distributed databases, providing parallel index creation approaches and optimization techniques for parallel queries using PatchIndexes. Furthermore, we describe heuristics for automatic discovery of PatchIndex candidate columns and prove the performance benefit of using PatchIndexes in our evaluation.

IMPLEMENTING TOPOLOGICAL INTEGRITY CONSTRAINTS ON TEMPORAL DATABASES

Checking integrity constraints in real-time the database is an important field of investigation. In this paper, we implement topological integrity constraints depending on which user has chosen an integrity constraint on different times on many existing integrity constraints of temporal databases. We suggested using the Hamiltonian paths in directed graphs order to implement a test program on data simulated by a college's real data. The object needs to satisfy integrity constraints in temporal databases as students who have enrolled in the subjects. Moreover, the program also monitors the learning process and advises students to choose courses in the school's training program.

Applying k-vertex cardinality constraints on a Neo4j graph database

As with any other database solution, graph databases also need to be able to implement business rules related to a given application domain. At the moment, aside from integrity constraints, there is a limited number of mechanisms for business rules implementation in Graph Database Management Systems (GDBMSs). The underlying property graph data model does not include any formal notation on how to represent different constraints. Specifically, this paper discusses the problem of representing cardinality constraints in graph databases. We introduce the novel concept of k-vertex cardinality constraints, which enable us to specify the minimum and maximum number of edges between a vertex and a subgraph. We also propose an approach, which includes the representation of cardinality constraints through the property graph data model, and demonstrate its implementation through a series of stored procedures in Neo4j GDBMS. The proposed approach is then evaluated by performing experiments on synthetic and real datasets to test the influence of checking cardinality constraints on query execution times (QETs) when adding new edges. Additionally, a comparison is performed on synthetic datasets with varying outgoing vertex degrees in order to gain an insight into how increasing the vertex degree affects QETs. In general, the results obtained for each test scenario show that the implemented k-vertex cardinality constraints model does not significantly affect QETs. Also, the results indicate that the model is dependent on the order of the underlying k-vertex cardinality constraints and outgoing vertex degree in the dataset.

Approximate denial constraints

The problem of mining integrity constraints from data has been extensively studied over the past two decades for commonly used types of constraints, including the classic Functional Dependencies (FDs) and the more general Denial Constraints (DCs). In this paper, we investigate the problem of mining from data approximate DCs, that is, DCs that are "almost" satisfied. Approximation allows us to discover more accurate constraints in inconsistent databases and detect rules that are generally correct but may have a few exceptions. It also allows to avoid overfitting and obtain constraints that are more general, more natural, and less contrived. We introduce the algorithm ADCMiner for mining approximate DCs. An important feature of this algorithm is that it does not assume any specific approximation function for DCs, but rather allows for arbitrary approximation functions that satisfy some natural axioms that we define in the paper. We also show how our algorithm can be combined with sampling to return highly accurate results considerably faster.

Discovering Evolving Temporal Information: Theory and Application to Clinical Databases

Functional dependencies (FDs) allow us to represent database constraints, corresponding to requirements as “patients having the same symptoms undergo the same medical tests.” Some research efforts have focused on extending such dependencies to consider also temporal constraints such as “patients having the same symptoms undergo in the next period the same medical tests.” Temporal functional dependencies are able to represent such kind of temporal constraints in relational databases. Another extension for FDs allows one to represent approximate functional dependencies (AFDs), as “patients with the same symptomsgenerallyundergo the same medical tests.” It enables data to deviate from the defined constraints according to a user-defined percentage. Approximate temporal functional dependencies (ATFDs) merge the concepts of temporal functional dependency and of approximate functional dependency. Among the different kinds of ATFD, the Approximate Pure Temporally Evolving Functional Dependencies (textit{APE}-FDs for short) allow one to detect patterns on the evolution of data in the database and to discover dependencies as “For most patients with the same initial diagnosis, the same medical test is prescribed after the occurrence of same symptom.” Mining ATFDs from large databases may be computationally expensive. In this paper, we focus on textit{APE}-FDs and prove that, unfortunately, verifying a single textit{APE}-FD over a given database instance is in general NP-complete. In order to cope with this problem, we propose a framework for mining complex textit{APE}-FDs in real-world data collections. In the framework, we designed and applied sound and advanced model-checking techniques. To prove the feasibility of our proposal, we used real-world databases from two medical domains (namely, psychiatry and pharmacovigilance) and tested the running prototype we developed on such databases.

Anelastic and Electromechanical Properties of Doped and Reduced Ceria.

Room-temperature mechanical properties of thin films and ceramics of doped and undoped ceria are reviewed with an emphasis on the anelastic behavior of the material. Notably, the unrelaxed Young's modulus of Gd-doped ceria ceramics measured by ultrasonic pulse-echo techniques is >200 GPa, while the relaxed biaxial modulus, calculated from the stress/strain ratio of thin films, is ≈10 times smaller. Oxygen-deficient ceria exhibits a number of anelastic effects, such as hysteresis of the lattice parameter, strain-dependent Poisson's ratio, room-temperature creep, and nonclassical electrostriction. Methods of measuring these properties are discussed, as well as the applicability of Raman spectroscopy for evaluating strain in thin films of Gd-doped ceria. Special attention is paid to detection of the time dependence of anelastic effects. Both the practical advantages and disadvantages of anelasticity on the design and stability of microscopic devices dependent on ceria thin films are discussed, and methods of mitigating the latter are suggested, with the aim of providing a cautionary note for materials scientists and engineers designing devices containing thin films or bulk ceria, as well as providing data-based constraints for theoreticians who are involved in modeling of the unusual electrical and electromechanical properties of undoped and doped ceria.

Query Processing for Time Efficient Data Retrieval

<p class="TTPAbstract">In database management system (DBMS) retrieving data through structure query language is an essential aspect to find better execution plan for performance. In this paper, we incorporated database objects to optimize query execution time and its cost by vanishing poorly SQL statements. We proposed a method of evolving and inserting database constraints as database objects embedded with queries either to add them for the sake of transactions required by user to detect those queries for the betterment of performance. We took analysis on several databases while processing queries itself and assimilate real time database workload with the bunch of transactions are invoked in comparison with tuning approaches. These database objects are coded in procedural language environment pertaining rules to make it worth and are merged into queries offering improved execution plan.</p>

Improving Face Recognition Systems Using a New Image Enhancement Technique, Hybrid Features and the Convolutional Neural Network

The performance of most face recognition systems (FRSs) in unconstrained environments is widely noted to be sub-optimal. One reason for this poor performance may be the lack of highly effective image pre-processing approaches, which are typically required before the feature extraction and classification stages. Furthermore, it is noted that only minimal face recognition issues are typically considered in most FRSs, thus limiting the wide applicability of most FRSs in real-life scenarios. Therefore, it is envisaged that installing more effective pre-processing techniques, in addition to selecting the right features for classification, will significantly improve the performance of FRSs. Hence, in this paper, we propose an FRS, which comprises an effective image enhancement technique for face image pre-processing, alongside a new set of hybrid features. Our image enhancement technique adopts the use of a metaheuristic optimization algorithm for effective face image enhancement, irrespective of the conditions in the unconstrained environment. This results in adding more features to the face image so that there is an increase in recognition performance as compared with the original image. The new hybrid feature is introduced in our FRS to improve the classification performance of the state-of-the-art convolutional neural network architectures. Experiments on standard face databases have been carried out to confirm the improvement in the performance of the face recognition system that considers all the constraints in the face database.