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Database systems have consistently been prime targets for cyber-attacks and threats due to the critical nature of the data they store. Despite the increasing reliance on database management systems, this field continues to face numerous cyber-attacks. Database management systems serve as the foundation of any information system or application. Any cyber-attack can result in significant damage to the database system and loss of sensitive data. Consequently, cyber risk classifications and assessments play a crucial role in risk management and establish an essential framework for identifying and responding to cyber threats. Risk assessment aids in understanding the impact of cyber threats and developing appropriate security controls to mitigate risks. The primary objective of this study is to conduct a comprehensive analysis of cyber risks in database management systems, including classifying threats, vulnerabilities, impacts, and countermeasures. This classification helps to identify suitable security controls to mitigate cyber risks for each type of threat. Additionally, this research aims to explore technical countermeasures to protect database systems from cyber threats. This study employs the content analysis method to collect, analyze, and classify data in terms of types of threats, vulnerabilities, and countermeasures. The results indicate that SQL injection attacks and Denial of Service (DoS) attacks were the most prevalent technical threats in database systems, each accounting for 9% of incidents. Vulnerable audit trails, intrusion attempts, and ransomware attacks were classified as the second level of technical threats in database systems, comprising 7% and 5% of incidents, respectively. Furthermore, the findings reveal that insider threats were the most common non-technical threats in database systems, accounting for 5% of incidents. Moreover, the results indicate that weak authentication, unpatched databases, weak audit trails, and multiple usage of an account were the most common technical vulnerabilities in database systems, each accounting for 9% of vulnerabilities. Additionally, software bugs, insecure coding practices, weak security controls, insecure networks, password misuse, weak encryption practices, and weak data masking were classified as the second level of security vulnerabilities in database systems, each accounting for 4% of vulnerabilities. The findings from this work can assist organizations in understanding the types of cyber threats and developing robust strategies against cyber-attacks. [ABSTRACT FROM AUTHOR]

Copyright of Revista Cubana de Ciencias Informáticas is the property of Universidad de las Ciencias Informaticas (UCI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A blockchain is a distributed database that contains the records of transactions that are shared among all members of a community. Most members must confirm each and every transaction in order for a fraudulent transaction to fail to occur. As a rule, once a record is created and accepted by the blockchain, it cannot be altered or deleted by anyone. This study focuses on improving the investigation task in the database forensics field by utilizing blockchain technology. To this end, a novel conceptual framework is proposed for the forensic analysis of data from database systems engaging blockchain technology. This is the first time that blockchain technology is followed in database forensics for the purpose of tracing digital evidence. The design science research method was adopted to accomplish the objectives of the present study. The findings displayed that with the developed forensics framework, the data regarding database incidents could be gathered and analyzed in a more efficient manner. [ABSTRACT FROM AUTHOR]

Recently, numerous machine learning (ML) techniques have been applied to address database performance management problems, including cardinality estimation, cost modeling, optimal join order prediction, hint generation, etc. In this paper, we focus on query optimizer hints employed by users in their queries in order to mask some Query Optimizer deficiencies. We treat the query optimizer hints, bound to previous queries, as significant additional query metadata and learn to automatically predict which new queries will pose similar performance challenges and should therefore also be supported by query optimizer hints. To validate our approach, we have performed a number of experiments using real-life SQL workloads and we achieved promising results.

Database systems play a significant role in structuring, organizing, and managing data of organizations. In this regard, the key challenge is how to protect the confidentiality, integrity, and availability of database systems against attacks launched from within and outside an organization. To resolve this challenge, different database security techniques and mechanisms, which generally involve access control, database monitoring, data encryption, database backups, and strong passwords have been proposed. These techniques and mechanisms have been developed for certain purposes but fall short of many industrial expectations. This study used the design science research method to recommend a new Digital Forensic Readiness Framework, named DFRF, to secure database systems. DFRF involves risk assessments, data classification, database firewalls, data encryption, strong password policies, database monitoring and logging, data backups and recovery, incident response plans, forensic readiness, as well as education and awareness. The proposed framework not only identifies threats and responds to them more effectively than existing models, but also helps organizations stay fully compliant with regulatory requirements and improve their security. The design of the suggested framework was compared with existing models, confirming its superiority. [ABSTRACT FROM AUTHOR]

This article explores the impact of ChatGPT, an advanced language model, on technology and education. It compares the current revolution brought about by ChatGPT to previous technological revolutions and discusses the stages of grief experienced by individuals when faced with the obsolescence of old technologies. The author emphasizes the need for acceptance and adaptation in the face of technological advancements. The text also highlights the impact of ChatGPT on education and suggests strategies for incorporating it into teaching methodologies. It emphasizes the opportunities and possibilities that ChatGPT brings to education, while acknowledging the need for educators to accept and embrace this new technology. [Extracted from the article]

In data-driven systems, data exploration is imperative for making real-time decisions. However, big data are stored in massive databases that are difficult to retrieve. Approximate Query Processing (AQP) is a technique for providing approximate answers to aggregate queries based on a summary of the data (synopsis) that closely replicates the behavior of the actual data; this can be useful when an approximate answer to queries is acceptable in a fraction of the real execution time. This study explores the novel utilization of a Generative Adversarial Network (GAN) for the generation of tabular data that can be employed in AQP for synopsis construction. We thoroughly investigate the unique challenges posed by the synopsis construction process, including maintaining data distribution characteristics, handling bounded continuous and categorical data, and preserving semantic relationships, and we then introduce the advancement of tabular GAN architectures that overcome these challenges. Furthermore, we propose and validate a suite of statistical metrics tailored for assessing the reliability of GAN-generated synopses. Our findings demonstrate that advanced GAN variations exhibit a promising capacity to generate high-fidelity synopses, potentially transforming the efficiency and effectiveness of AQP in data-driven systems. [ABSTRACT FROM AUTHOR]

In data-driven systems, data exploration is imperative for making real-time decisions. However, big data are stored in massive databases that are difficult to retrieve. Approximate Query Processing (AQP) is a technique for providing approximate answers to aggregate queries based on a summary of the data (synopsis) that closely replicates the behavior of the actual data; this can be useful when an approximate answer to queries is acceptable in a fraction of the real execution time. This study explores the novel utilization of a Generative Adversarial Network (GAN) for the generation of tabular data that can be employed in AQP for synopsis construction. We thoroughly investigate the unique challenges posed by the synopsis construction process, including maintaining data distribution characteristics, handling bounded continuous and categorical data, and preserving semantic relationships, and we then introduce the advancement of tabular GAN architectures that overcome these challenges. Furthermore, we propose and validate a suite of statistical metrics tailored for assessing the reliability of GAN-generated synopses. Our findings demonstrate that advanced GAN variations exhibit a promising capacity to generate high-fidelity synopses, potentially transforming the efficiency and effectiveness of AQP in data-driven systems.

Graph serialization is a critical aspect of advancing graph-oriented systems and applications. Despite the importance of standardized serialization for property graphs, there is a lack of a universal format encompassing all essential features of graph database systems. This study introduces YARS-PG, a simple, extensible, and platform-independent serialization format tailored for property graphs. YARS-PG supports all the features permitted by current property graph-based database systems and is compatible with other graph-oriented databases and tools. We delineate the design requirements of YARS-PG by detailing both functional and non-functional aspects. Besides the basic features of property graph data, YARS-PG supports schema definition, metadata, metaproperties, variables, and graph definitions. Moreover, we discuss extensions of YARS-PG, demonstrating its flexibility through canonicalization techniques. Our comparative analyses with existing formats provide valuable insights, emphasizing the unique strengths that distinguish YARS-PG in the realm of graph data interchange. This paper serves as a definitive guide to YARS-PG, unraveling its complexities and showcasing its potential as a communication protocol, a data storage format, and a messaging specification.

This study proposes a conceptualization of a prototype And a possibility to converge classical database and fully quantum database. This study mostly identifies the gap between this classical and quantum database and proposes a prototype that can be implemented in future products. It is a way that can be used in future industrial product development on hybrid quantum computers. The existing concept used to consider oracle as a black box in this study opens up the possibility for the quantum industry to develop the QASAM module so that we can create a fully quantum database instead of using a classical database as BlackBox. As the Toffoli gate is basically an effective NAND gate it is possible to run any algorithm theoretically in quantum computers. So we will propose a logical design for memory management for the quantum database, security enhancement model, Quantum Recovery Manager & automatic storage management model, and more for the quantum database which will ensure the quantum advantages. In this study, we will also explain the Quantum Vector Database as well as the possibility of improvement in duality quantum computing. It opens up a new scope, possibilities, and research areas in a new approach for quantum databases and duality quantum computing. CCS Concepts: • Computer systems organization? Quantum computing; • Software and its engineering? Software infrastructure; • General and reference? Reference works; General literature; General literature; Reference works; Performance; • Information systems? DBMS engine architectures; Main memory engines; Key-value stores; Database utilities and tools; • Hardware? Quantum technologies;. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Generalized linear models are a popular analytics tool with interpretable results and broad applicability, but require iterative estimation procedures that impose data transfer and computational costs that can be problematic under some infrastructure constraints. We propose a doubly-sketched approximation of the iteratively re-weighted least squares algorithm to estimate generalized linear model parameters using a sequence of surrogate datasets. The procedure sketches once to reduce data transfer costs, and sketches again to reduce data computation costs, yielding wall-clock time savings. Regression coefficients and standard errors are produced, with comparison against literature methods. Asymptotic properties of the proposed procedure are shown, with empirical results from simulated and real-world datasets. The efficacy of the proposed method is investigated across a variety of commodity computational infrastructure configurations accessible to practitioners. A highlight of the present work is the estimation of a Poisson-log generalized linear model across almost 1.7 billion observations on a personal computer in 25 min. [ABSTRACT FROM AUTHOR]

Summary: Recently artificial intelligence techniques in the database community have become a driver for many database applications. The proposed solution adopting AI in the core database shows that incorporating AI improves the query processing and the self‐tuning of database systems. In traditional systems, self‐tuning database systems are commonly addressed with heuristics to suggest the physical structures (e.g., creation of indexes and materialized views) that enable the fastest execution of queries. However, existing designer tools do not explain/justify how the system behaves and the reasoning behind tuning activities. Moreover, these tools do not keep the database administrator (DBA) in the loop of the optimization process to trust some of the automatic tuning decisions. To address this problem, we introduce a framework called Explain‐Tun that enables to predict and explain self‐tuning actions with transparent strategy from historical data using two explicit models, that is, decision tree and random forests. First, we propose AI‐based DBMS to explain how to select physical structures and provide decision rules extracted by machine learning (ML) as a designed plug‐gable component. Second, a goal‐oriented model to keep DBA in the loop of the optimization process in order to manipulate ML models as CRUD entities. Finally, we evaluate our approach on three use cases, results show that bridging the DBA's experience and ML make sense in tuning database systems. [ABSTRACT FROM AUTHOR]

Cuban higher education demands the use of methodologies that encourage active participation of students in their training process and provide knowledge, skills, values and study habits that facilitate their insertion into the professional environment. This research aimed to describe the contributions of Project Based Learning in the subject Database Systems II in the third year of the Faculty of Educational Technology of the University of Computer Sciences. A descriptive research was carried out using the survey technique with a sample of 83 students. The main results were superior academic performance in terms of passes and grade quality, greater student satisfaction with their teaching process and a positive influence of some study habits initially diagnosed, which confirms the relevance of its use for train more prepared, independent and committed professionals. [ABSTRACT FROM AUTHOR]

Constructing energy-efficient database systems to reduce economic costs and environmental impact has been studied for 10 years. With the emergence of the big data age, along with the data-centric and data-intensive computing trend, the great amount of energy consumed by database systems has become a major concern in a society that pursues Green IT. However, to the best of our knowledge, despite the importance of this matter in Green IT, there have been few comprehensive or systematic studies conducted in this field. Therefore, the objective of this article is to present a literature survey with breadth and depth on existing energy management techniques for database systems. The existing literature is organized hierarchically with two major branches focusing separately on energy consumption models and energy-saving techniques. Under each branch, we first introduce some basic knowledge, then we classify, discuss, and compare existing research according to their core ideas, basic approaches, and main characteristics. Finally, based on these observations through our study, we identify multiple open issues and challenges, and provide insights for future research. It is our hope that our outcome of this work will help researchers develop more energy-efficient database systems. [ABSTRACT FROM AUTHOR]

High-performance computing (HPC) has become an indispensable tool for solving diverse problems in science and engineering. Harnessing the power of HPC is not just a matter of efficient computation, however; it also calls for the efficient management of vast quantities of scientific data. This presents daunting challenges: rapidly evolving storage technology has motivated a shift toward increasingly complex, heterogeneous storage architectures that are difficult to optimize, and scientific data management needs have become every bit as diverse as the application domains that drive them. There is a clear need for agile, adaptable storage solutions that can be customized for the task and platform at hand. This motivated the establishment of the Mochi composable data service project. The Mochi project provides a library of robust, reusable, modular, and connectable data management components and microservices along with a methodology for composing them into specialized distributed data services. Mochi enables rapid deployment of custom data services with a high degree of developer productivity while still effectively leveraging cutting-edge HPC hardware. This article explores how the principles of translational computer science have been applied in practice in Mochi to achieve these goals. [ABSTRACT FROM AUTHOR]

Emerging hardware devices (e.g., NVMe SSD, RISC-V, etc.) open new opportunities for improving the overall performance of computer systems. In addition, the applications try to fully utilize hardware resources to keep up with those improvements. However, these trends can cause significant file system overheads (i.e., fragmentation issues). In this paper, we first study the reason for the fragmentation issues on an F2FS file system and present a new tool, called FragTracer, which helps to analyze the ratio of fragmentation in real-time. For user-friendly usage, we designed FragTracer with three main modules, monitoring, pre-processing, and visualization, which automatically runs without any user intervention. We also optimized FragTracer in terms of performance to hide its overhead in tracking and analyzing fragmentation issues on-the-fly. We evaluated FragTracer with three real-world databases on the F2FS file system, so as to study the fragmentation characteristics caused by databases, and we compared the overhead of FragTracer. Our evaluation results clearly show that the overhead of FragTracer is negligible when running on commodity computing environments. [ABSTRACT FROM AUTHOR]

Background: Postnatal corticosteroids (PC) are widely used in very preterm infants. International reports and national multicenter trials describe a marked variability across countries and inter-sites, in the use of PC. Few information is available on therapeutic indications and prescription characteristics of PC. Aim: The main objective of this study was to describe the exposure to PC in a large cohort of preterm infants born at less than 32 weeks of gestation, according to the prescription data of 41 tertiary-care NICUs in France. Secondary objectives were to describe therapeutic indications, day of life (DOL) of the first exposure, route of administration, duration, cumulative dose for each drug, and differences in exposure rates across centers. Methods: We conducted a prospective observational cohort analysis from January 2017 to December 2021, in 41 French tertiary-care NICUs using the same computerized order-entry system. Results: In total, 13,913 infants [birth weight 1144.8 (±365.6) g] were included. Among them, 3633 (26.1%) were exposed to PC, 21.8% by systemic and 10.1% by inhaled route. Within the study population, 1,992 infants (14.3%) received the first corticosteroid treatment in the first week of life and 1641 (11.8%) after DOL 7. The more frequent indications were prevention and/or treatment of bronchopulmonary dysplasia, and arterial hypotension. Hydrocortisone was the more often prescribed molecule. For systemic PC the first exposure occurred in mean at DOL 9.4 (±13.5), mean duration of treatment was 10.3 (±14.3) days, and the cumulative dose (expressed as the equivalent dose of hydrocortisone) was in median [IQR] 9.0 [5.5-28.8] mg/kg. For inhaled PC, the first exposure occurred in mean at DOL 34.1 (±19.7), and mean duration of treatment 28.5 (±24.4) days. The exposure rate ranged from a minimum of 5% to a maximum of 56% among centers, and significantly increased over the study period (p < 0.0001). Conclusion: In this French cohort of very preterm infants, around one patient out to five was exposed to PC during hospital stay in the NICU. The exposure occurred early, starting from the first week of life. Exposure rate widely varied among centers. Pharmacoepidemiology studies are useful to increase knowledge on corticosteroid utilization patterns in preterm infants. [ABSTRACT FROM AUTHOR]

Background: Postnatal corticosteroids (PC) are widely used in very preterm infants. International reports and national multicenter trials describe a marked variability across countries and inter-sites, in the use of PC. Few information is available on therapeutic indications and prescription characteristics of PC.Aim: The main objective of this study was to describe the exposure to PC in a large cohort of preterm infants born at less than 32 weeks of gestation, according to the prescription data of 41 tertiary-care NICUs in France. Secondary objectives were to describe therapeutic indications, day of life (DOL) of the first exposure, route of administration, duration, cumulative dose for each drug, and differences in exposure rates across centers.Methods: We conducted a prospective observational cohort analysis from January 2017 to December 2021, in 41 French tertiary-care NICUs using the same computerized order-entry system.Results: In total, 13,913 infants [birth weight 1144.8 (±365.6) g] were included. Among them, 3633 (26.1%) were exposed to PC, 21.8% by systemic and 10.1% by inhaled route. Within the study population, 1,992 infants (14.3%) received the first corticosteroid treatment in the first week of life and 1641 (11.8%) after DOL 7. The more frequent indications were prevention and/or treatment of bronchopulmonary dysplasia, and arterial hypotension. Hydrocortisone was the more often prescribed molecule. For systemic PC the first exposure occurred in mean at DOL 9.4 (±13.5), mean duration of treatment was 10.3 (±14.3) days, and the cumulative dose (expressed as the equivalent dose of hydrocortisone) was in median [IQR] 9.0 [5.5–28.8] mg/kg. For inhaled PC, the first exposure occurred in mean at DOL 34.1 (±19.7), and mean duration of treatment 28.5 (±24.4) days. The exposure rate ranged from a minimum of 5% to a maximum of 56% among centers, and significantly increased over the study period (p < 0.0001).Conclusion: In this French cohort of very preterm infants, around one patient out to five was exposed to PC during hospital stay in the NICU. The exposure occurred early, starting from the first week of life. Exposure rate widely varied among centers. Pharmacoepidemiology studies are useful to increase knowledge on corticosteroid utilization patterns in preterm infants.

In crop and livestock management within the framework of precision agriculture, scenarios full of sensors and devices are deployed, involving the generation of a large volume of data. Some solutions require rapid data exchange for action or anomaly detection. However, the administration of this large amount of data, which in turn evolves over time, is highly complicated. Management systems add long-time delays to the spatio-temporal data injection and gathering. This paper proposes a novel spatio-temporal semantic data model for agriculture. To validate the model, data from real livestock and crop scenarios, retrieved from the AFarCloud smart farming platform, are modeled according to the proposal. Time-series Database (TSDB) engine InfluxDB is used to evaluate the model against data management. In addition, an architecture for the management of spatio-temporal semantic agricultural data in real-time is proposed. This architecture results in the DAM&DQ system responsible for data management as semantic middleware on the AFarCloud platform. The approach of this proposal is in line with the EU data-driven strategy. [ABSTRACT FROM AUTHOR]

To assess the current level of engagement between backyard poultry keepers and veterinarians in New Zealand; to understand the opportunities and barriers for improving access to poultry health care; and to gauge the interest of backyard poultry keepers in participating in a voluntary national poultry health information system. Backyard poultry were defined as any bird species kept for non-commercial purposes. Separate cross-sectional surveys were administered to backyard poultry keepers and veterinarians in New Zealand over 12-week periods starting 22 March 2021 and 03 May 2021 respectively. The veterinarian survey was advertised in the monthly update e-mail from the Veterinary Council of New Zealand, while the survey for backyard poultry keepers was advertised on various online platforms that focus on raising backyard poultry. Results for quantitative variables were reported as basic descriptive statistics, while qualitative free-text responses from open-ended questions were explored using thematic analysis. A total of 125 backyard poultry keepers and 35 veterinarians completed the survey. Almost half (56/125; 44.8%) of backyard poultry keepers reported that they had never taken their birds to a veterinarian, with common reasons being difficulty finding a veterinarian, cost of treatment, and perceptions that most visits result in the bird being euthanised. The majority (113/125; 90.4%) of backyard poultry keepers reported that a general internet search was their primary source for poultry health advice. However, it remains unclear if owners were satisfied with the advice found online, as many cited that having access to reliable health information would be an incentive for registering with a poultry health information system. Of the veterinarian responses, 29/35 (82.9%) reported treating an increasing number of poultry in the last 5 years, although many (27/35; 77.1%) suggested they would be hesitant to increase their poultry caseload due to concerns over their lack of knowledge and confidence in poultry medicine; a lack of clinic resources to treat poultry; concerns over the cost-effectiveness of treatments; and a general feeling of helplessness when treating poultry, with most consultations being for end-stage disease and euthanasia. The results of this study highlight opportunities for increased engagement between backyard poultry keepers and veterinarians, including making available accurate poultry health information and providing veterinarians with improved training in poultry medicine. The results also support the development of a poultry health information system in New Zealand to further enhance health and welfare in backyard poultry populations. Abbreviations: MPI: Ministry for Primary Industries [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of security measures in database systems, focusing on authentication, access control, encryption, auditing, intrusion detection, and privacy-enhancing techniques. It aims to provide valuable insights into the latest advancements and best practices in securing databases. The review examines the challenges, vulnerabilities, and mitigation strategies associated with database security. It explores various authentication methods, access control models, encryption techniques, auditing and monitoring approaches, intrusion detection systems, and data leakage prevention mechanisms. The paper also discusses the impact of emerging trends such as cloud computing, big data, and the Internet of Things on database security. By synthesizing existing research, this review aims to contribute to the advancement of database security and assist organizations in protecting their sensitive data. [ABSTRACT FROM AUTHOR]

In recent times, the production of multidimensional data in various domains and their storage in array databases has witnessed a sharp increase; this rapid growth in data volumes necessitates compression in array databases. However, existing compression schemes used in array databases are general-purpose and not designed specifically for the databases. They could degrade query performance with complex analytical tasks, which incur huge computing costs. Thus, a compression scheme that considers the workflow of array databases is required. This study presents a compression scheme, SEACOW, for storing and querying multidimensional array data. The scheme is specially designed to be efficient for both dimension-based and value-based exploration. It considers data access patterns for exploration queries and embeds a synopsis, which can be utilized as an index, in the compressed array. In addition, we implement an array storage system, namely MSDB, to perform experiments. We evaluate query performance on real scientific datasets and compared it with those of existing compression schemes. Finally, our experiments demonstrate that SEACOW provides high compression rates compared to existing compression schemes, and the synopsis improves analytical query processing performance.

Biological data have gained wider recognition during the last few years, although managing and processing these data in an efficient way remains a challenge in many areas. Increasingly, more DNA sequence databases can be accessed; however, most algorithms on these sequences are performed outside of the database with different bioinformatics software. In this article, we propose a novel approach for the comparative analysis of sequences, thereby defining heuristic pairwise alignment inside the database environment. This method takes advantage of the benefits provided by the database management system and presents a way to exploit similarities in data sets to quicken the alignment algorithm. We work with the column-oriented MonetDB, and we further discuss the key benefits of this database system in relation to our proposed heuristic approach. [ABSTRACT FROM AUTHOR]

The computing continuum enables new opportunities for managing big data pipelines concerning efficient management of heterogeneous and untrustworthy resources. We discuss the big data pipelines lifecycle on the computing continuum and its associated challenges, and we outline a future research agenda in this area. [ABSTRACT FROM AUTHOR]

Query execution techniques in database systems constantly adapt to novel hardware features to achieve high query performance, in particular for analytical queries. In recent years, vectorization based on the Single Instruction Multiple Data parallel paradigm has been established as a state-of-the-art approach to increase single-query performance. However, since concurrent analytical queries running in parallel often access the same columns and perform a same set of vectorized operations, data accesses and computations among different queries may be executed redundantly. Various techniques have already been proposed to avoid such redundancy, ranging from concurrent scans via the construction of materialized views to applying multiple query optimization techniques. Continuing this line of research, we investigate the opportunity of sharing vector registers for concurrently running queries in analytical scenarios in this paper. In particular, our novel sharing approach relies on processing data elements of different queries together within a single vector register. As we are going to show, sharing vector registers to optimize the execution of concurrent analytical queries can be very beneficial in single-threaded as well as multi-thread environments. Therefore, we demonstrate the feasibility and applicability of such a novel work sharing strategy and thus open up a wide spectrum of future research opportunities. [ABSTRACT FROM AUTHOR]

This article proposes an architecture for vaccination information validation and tracking with a fog and cloud-based blockchain system, providing a privacy-aware and scalable approach for interoperable and effective data management. It evaluates the scalability of the underlying blockchain system by means of simulation. [ABSTRACT FROM AUTHOR]

Due to recent explosion of data volume and velocity, a new array of lightweight key-value stores have emerged to serve as alternatives to traditional databases. The majority of these storage engines, however, sacrifice their read performance in order to cope with write throughput by avoiding random disk access when writing a record in favor of fast sequential accesses. But, the boundary between sequential versus random access is becoming blurred with the advent of solid-state drives (SSDs). In this work, we propose our new key-value store, LogStore, optimized for hybrid storage architectures. Additionally, introduce a novel cost-based data staging model based on log-structured storage, in which recent changes are first stored on SSDs, and pushed to HDD as it ages, while minimizing the read/write amplification for merging data from SSDs and HDDs. Furthermore, we take a holistic approach in improving both the read and write performance by dynamically optimizing the data layout, such as deferring and reversing the compaction process, and developing an access strategy to leverage the strengths of each available medium in our storage hierarchy. Lastly, in our extensive evaluation, we demonstrate that LogStore achieves up to 6x improvement in throughput/latency over LevelDB, a state-of-the-art key-value store. [ABSTRACT FROM AUTHOR]

The Middle Eastern region has witnessed many cyber-attacks in recent years, especially in Saudi Arabia. Saudi Arabian organisations face problems anticipating, detecting, mitigating, or preventing cyber-attacks despite policies and regulations. The reasons for this have not been investigated adequately. This research aims to study the methods used to address cyber security issues in the private sector. A survey of IT managers of private organisations yielded 230 usable responses. The data were analysed for descriptive statistics and frequency estimations of responses, and the results are presented in this paper. Poor awareness of cyber security issues is reflected in the survey responses. The expenditure on cyber security, especially by large firms, was inadequate. There was a greater tendency to outsource many aspects of cyber security without concern about the risks. A very small percentage of IT managers considered the certainty of a cyber threat within the next year. It is important from the point of proactive strategies to prevent attacks. The findings highlight a lack of required knowledge and skills in performing their expected roles well. Additionally, many weaknesses have been detected in cyber security management in Saudi private organisations, and there is room to improve the quality of computer security systems. The published literature largely supported this. The findings from this study have implications for the stakeholders, especially IT managers working in the private sector of Saudi Arabia. The learnings from this study may be used to address the vulnerabilities identified. The findings clearly show the need to train IT managers of Saudi private organisations. [ABSTRACT FROM AUTHOR]

Databases are an integral part of almost every application nowadays. For example, applications using Internet of Things (IoT) sensory data, such as in Industry 4.0, are a classic example of an organized storage system. Due to its enormous size, it may be stored in the cloud. This paper presents the authors' proposition for cloudcentric sensory measurements and measurements acquisition. Then, it focuses on evaluating industrial cloud storage engines for sensory functions, experimenting with three open-source types of distributed Database Management Systems (DBMS); MongoDB and PostgreSQL, with two forms of PostgreSQL schemes (Javascript Object Notation (JSON)-based and relational), against their respective horizontal scaling strategies. Several experimental cases have been performed to measure database queries' response time, achieved throughput, and corresponding failures. Three distinct scenarios have been thoroughly tested, the most common but widely used: (i) data insertions, (ii) select/find queries, and (iii) queries related to aggregate correlation functions. The experimental results concluded that PostgreSQL with JSON achieves a 5–57% better response than MongoDB for the insert queries (cases of native, two, and four shards implementations), while, on the contrary, MongoDB achieved 56–91% higher throughput than PostgreSQL for the same set up. Furthermore, for the data insertion experimental cases of six and eight shards, MongoDB performed 13–20% more than Postgres in response time, achieving × 2 times higher throughput. Relational PostgreSQL was × 2 times faster than MongoDB in its standalone implementation for selection queries. At the same time, MongoDB achieved 19–31% faster responses and 44–63% higher throughput than PostgreSQL in the four tested sharding subcases (two, four, six, eight shards), accordingly. Finally, the relational PostgreSQL outperformed MongoDB and PostgreSQL JSON significantly in all correlation function experiments, with performance improvements from MongoDB, closing the gap with PostgreSQL towards minimizing response time to 26% and 3% for six and eight shards, respectively, and achieving significant gains towards average achieved throughput. [ABSTRACT FROM AUTHOR]

As one of the most challenging problems in data management, query processing and optimization are always widely concerned by researchers. However, it is very difficult for traditional techniques to meet the diverse requirements of modern database system due to the needs of hand-tuning for specific workloads and datasets. Inspired by advances in applying artificial intelligence (AI) to multi-field researches, recently, the AI powered new techniques for query processing and optimization have been proposed and made significant success. In view of these researches, this paper first presents the main tasks of the AI powered new techniques of query processing and optimization, and analyzes the differences between the new tasks and traditional AI tasks. Second, the recent research progress is reviewed, and the main advantages and application bottlenecks are summarized. Third, this paper discusses the main challenges of the AI powered new techniques of query processing and optimization. Finally, the future research directions are prospected.

Bounded treewidth is one of the most cited combinatorial invariants in the literature. It was also applied for solving several counting problems efficiently. A canonical counting problem is #Sat, which asks to count the satisfying assignments of a Boolean formula. Recent work shows that benchmarking instances for #Sat often have reasonably small treewidth. This paper deals with counting problems for instances of small treewidth. We introduce a general framework to solve counting questions based on state-of-the-art database management systems (DBMSs). Our framework takes explicitly advantage of small treewidth by solving instances using dynamic programming (DP) on tree decompositions (TD). Therefore, we implement the concept of DP into a DBMS (PostgreSQL), since DP algorithms are already often given in terms of table manipulations in theory. This allows for elegant specifications of DP algorithms and the use of SQL to manipulate records and tables, which gives us a natural approach to bring DP algorithms into practice. To the best of our knowledge, we present the first approach to employ a DBMS for algorithms on TDs. A key advantage of our approach is that DBMSs naturally allow for dealing with huge tables with a limited amount of main memory (RAM). [ABSTRACT FROM AUTHOR]

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In crop and livestock management within the framework of precision agriculture, scenarios full of sensors and devices are deployed, involving the generation of a large volume of data. Some solutions require rapid data exchange for action or anomaly detection. However, the administration of this large amount of data, which in turn evolves over time, is highly complicated. Management systems add long-time delays to the spatio-temporal data injection and gathering. This paper proposes a novel spatio-temporal semantic data model for agriculture. To validate the model, data from real livestock and crop scenarios, retrieved from the AFarCloud smart farming platform, are modeled according to the proposal. Time-series Database (TSDB) engine InfluxDB is used to evaluate the model against data management. In addition, an architecture for the management of spatio-temporal semantic agricultural data in real-time is proposed. This architecture results in the DAM&DQ system responsible for data management as semantic middleware on the AFarCloud platform. The approach of this proposal is in line with the EU data-driven strategy.