Bibliografie tematiche / Factorized Databases

Letteratura scientifica selezionata sul tema "Factorized Databases"

Autore: Grafiati

Pubblicato: 15 giugno 2024

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Articoli di riviste sul tema "Factorized Databases":

Olteanu, Dan, e Maximilian Schleich. "Factorized Databases". ACM SIGMOD Record 45, n. 2 (28 settembre 2016): 5–16. http://dx.doi.org/10.1145/3003665.3003667.

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Olteanu, Dan. "Report on the Workshop on Factorized Databases". ACM SIGMOD Record 52, n. 2 (10 agosto 2023): 53–56. http://dx.doi.org/10.1145/3615952.3615967.

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Abstract (sommario):

The workshop took place in Zurich and online from August 2 to 4, 2022. It was attended by researchers from 17 academic institutions and industry labs, including Microsoft Gray Systems Lab, Omics Data Automation, Oracle Labs Zurich, RelationalAI, and TigerGraph. It featured 18 talks and plenty of opportunities for discussions. The vast majority of participants attended in person.

Ruiz, A., P. E. Lopez-de-Teruel e M. C. Garrido. "Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians". Journal of Artificial Intelligence Research 9 (1 ottobre 1998): 167–217. http://dx.doi.org/10.1613/jair.533.

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This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of finite mixture models, conjugate families and factorization. Both the joint probability density of the variables and the likelihood function of the (objective or subjective) observation are approximated by a special mixture model, in such a way that any desired conditional distribution can be directly obtained without numerical integration. We have developed an extended version of the expectation maximization (EM) algorithm to estimate the parameters of mixture models from uncertain training examples (indirect observations). As a consequence, any piece of exact or uncertain information about both input and output values is consistently handled in the inference and learning stages. This ability, extremely useful in certain situations, is not found in most alternative methods. The proposed framework is formally justified from standard probabilistic principles and illustrative examples are provided in the fields of nonparametric pattern classification, nonlinear regression and pattern completion. Finally, experiments on a real application and comparative results over standard databases provide empirical evidence of the utility of the method in a wide range of applications.

Huang, Zezhou, Rathijit Sen, Jiaxiang Liu e Eugene Wu. "JoinBoost: Grow Trees over Normalized Data Using Only SQL". Proceedings of the VLDB Endowment 16, n. 11 (luglio 2023): 3071–84. http://dx.doi.org/10.14778/3611479.3611509.

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Although dominant for tabular data, ML libraries that train tree models over normalized databases (e.g., LightGBM, XGBoost) require the data to be denormalized as a single table, materialized, and exported. This process is not scalable, slow, and poses security risks. In-DB ML aims to train models within DBMSes to avoid data movement and provide data governance. Rather than modify a DBMS to support In-DB ML, is it possible to offer competitive tree training performance to specialized ML libraries...with only SQL? We present JoinBoost, a Python library that rewrites tree training algorithms over normalized databases into pure SQL. It is portable to any DBMS, offers performance competitive with specialized ML libraries, and scales with the underlying DBMS capabilities. JoinBoost extends prior work from both algorithmic and systems perspectives. Algorithmically, we support factorized gradient boosting, by updating the Y variable to the residual in the non-materialized join result. Although this view update problem is generally ambiguous, we identify addition-to-multiplication preserving , the key property of variance semi-ring to support rmse the most widely used criterion. System-wise, we identify residual updates as a performance bottleneck. Such overhead can be natively minimized on columnar DBMSes by creating a new column of residual values and adding it as a projection. We validate this with two implementations on DuckDB, with no or minimal modifications to its internals for portability. Our experiment shows that JoinBoost is 3× (1.1×) faster for random forests (gradient boosting) compared to LightGBM, and over an order of magnitude faster than state-of-the-art In-DB ML systems. Further, JoinBoost scales well beyond LightGBM in terms of the # features, DB size (TPC-DS SF=1000), and join graph complexity (galaxy schemas).

Bakibayev, Nurzhan, Tomáš Kočiský, Dan Olteanu e Jakub Závodný. "Aggregation and ordering in factorised databases". Proceedings of the VLDB Endowment 6, n. 14 (settembre 2013): 1990–2001. http://dx.doi.org/10.14778/2556549.2556579.

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Bakibayev, Nurzhan, Dan Olteanu e Jakub Závodný. "Demonstration of the FDB query engine for factorised databases". Proceedings of the VLDB Endowment 5, n. 12 (agosto 2012): 1950–53. http://dx.doi.org/10.14778/2367502.2367545.

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Hutchinson, Harry. "Smarter Factories". Mechanical Engineering 123, n. 03 (1 marzo 2001): 60–62. http://dx.doi.org/10.1115/1.2001-mar-2.

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This article highlights about getting more out of information technology, from online services to digital databases, in order to make plants more flexible, to improve product development, or to let people at far-flung sites cooperate more closely. There are industry-led groups namely the Consortium for Advanced Manufacturing-International, which has an office in Bedford, TX, and Intelligent Manufacturing Systems, headquartered in Tokyo. These organizations address concerns ranging from budgeting to the best practices for designing a plant floor. The Consortium for Advanced Manufacturing and Intelligent Manufacturing Systems support a research initiative called Next Generation Manufacturing Systems (NIST). NIST also operates the Manufacturing Engineering Laboratory, where a variety of projects are being run. In another part of NIST—the physics lab, to be specific—Marc Desrosiers, a research chemist, has led an effort to cut the time and cost of certifying the calibration of irradiation equipment, which can be used to cure materials and coatings, or to kill bacteria on products ranging from medical devices to hamburger.

Beck, Thomas J. "China and the Modern World, Imperial China and the West, Part I, 1815‐1881". Charleston Advisor 24, n. 4 (1 aprile 2023): 16–19. http://dx.doi.org/10.5260/chara.24.4.16.

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China and the Modern World, Imperial China and the West, one of several Gale Primary Sources databases, consists of two parts and this database is the first, covering 1815‐1881. The documents are drawn from the FO 17 and FO 605 series of British Foreign Office files held at the UK National Archives. The database contains correspondence relating to China from missionaries, interpreters, the factories of Canton (modern Guangzhou), interpreters, envoys, legations, consulates, and missions sent to China from Britain. Included are materials on war, diplomacy, international law, rebellions, trade, piracy, treaty ports, cross-cultural communication, and emigration. Also included are materials regarding the internal politics and relationship between China and Britain, as well as the relationships between other Western powers involved in East Asia. While this database is relatively easy to navigate, its search functions, though understandable, frequently do not provide results that are apparently relevant to the query submitted. Nevertheless, it provides an impressive collection of primary sources on the history of China in this time period, especially with regard to its relationship with Britain and the West. The database can be subscribed to or purchased. The starting purchase price ranges from $5,405 (with an annual hosting fee starting at $49) to $7,721 (with an annual hosting fee of $69). Libraries and other institutions may or may not find this pricing reasonable, depending on their budgets and need for materials on Chinese and British history in this time period. The licensing agreement for this database is too long and detailed, but has average provisions and presents little reason for concern.

Hatou, Kenji, Toshinobu Takeuchi e Yasushi Hashimoto. "Image Database for the Remote Control of Plant Factories in the Internet Age". IFAC Proceedings Volumes 33, n. 19 (luglio 2000): 309–14. http://dx.doi.org/10.1016/s1474-6670(17)40929-3.

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Reining, Nine, e Simone Kauffeld. "Empirical Findings on Learning Success and Competence Development at Learning Factories: A Scoping Review". Education Sciences 12, n. 11 (29 ottobre 2022): 769. http://dx.doi.org/10.3390/educsci12110769.

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To meet the changing competence requirements for employees in engineering professions, education and training need to adapt accordingly. Learning factories offer various possibilities to design or integrate practice-oriented learning into training measures. Whether this approach in fact facilitates learning and competence development is rarely investigated. For this reason, the objective of this scoping review is to analyze and summarize the existing empirical findings on learning success and competence development in learning factories regarding their evaluation methods and results. Following standardized guidelines (PRISMA, JBI) for scoping reviews, 12 databases were researched. The literature screening led to the identification of 24 publications included in the final analysis. The results indicate that a variety of evaluation methods are used to assess learning and competences at learning factories and that criteria of all four competence facets (professional, methodological, social, and self-competence) can be enhanced at learning factories in general. As many of the identified studies show potential for improvement regarding the quality of the used methods and analysis of results, further studies on these topics are needed. Evaluations should be integrated into all training measures at learning factories to ensure learning success and competence development and to be able to readjust design, structure, and didactics where necessary.

Più fonti

Tesi sul tema "Factorized Databases":

Crosetti, Nicolas. "Enrichir et résoudre des programmes linéaires avec des requêtes conjonctives". Electronic Thesis or Diss., Université de Lille (2022-....), 2023. http://www.theses.fr/2023ULILB003.

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L'optimisation mathématique et la gestion des données sont deux domaines majeurs de l'informatique qui sont largement étudiés par des communautés essentiellement distinctes.Cependant, les problèmes d'optimisation complexes dépendent souvent de grands jeux de données qui peuvent être difficiles à gérer,alors que la gestion de grandes quantités de données n'est utile que dans la mesure où l'on analyse ces données pour en extraire des connaissancesafin de résoudre un problème pratique, de sorte que ces domaines sont souvent entremêlés en pratique.Cette thèse se place à la croisée de ces deux domaines en étudiant les programmes linéaires qui raisonnent sur les réponses de requêtes de bases de données.La première contribution de cette thèse est la définition de ce que nous appelons le langage des programmes linéaires avec requêtes conjonctives (que nous noterons LP(CQ)).Il s'agit d'un langage de modélisation de programmes linéaires avec des constructions permettant d'exprimer des contraintes et sommes linéairesqui raisonnent sur les ensembles de réponses de requêtes de bases de données sous forme conjonctive.Nous décrivons ensuite la sémantique naturelle du langage en montrant comment de tels modèles peuvent être interprétés,en conjonction avec une base de données, en de vrais programmes linéairesqui peuvent ensuite être résolus par tout solveur de programmes linéaires standard et nous discutons de la difficulté de résoudre les modèles LP(CQ).Motivés par la difficulté de résoudre les modèles LP(CQ) en général, nous introduisons ensuiteun processus basé sur ce que nous appelons l'interprétation T-factorisée pour résoudre de tels modèles plus efficacement.Cette approche est basée sur des techniques classiques en théorie des bases de donnéespour exploiter la structure des requêtes en utilisant des décompositions arborescentes de petite largeur.L'interprétation T-factorisée produit un programme linéaire qui a la même valeur optimale que la sémantique naturelle du modèle mais moins de variableset qui peut donc être utilisé pour résoudre le modèle plus efficacement.La troisième contribution est une généralisation du résultat précédent au cadre des bases de données factorisées.Nous introduisons une structure de données spécifique pour coder succinctement les relations sous forme de circuit.Nous définissons ensuite l'interprétation dite C-factorisée qui exploite le caractère succinct de ces circuitspour produire un programme linéaire qui a la même valeur optimale que la sémantique naturelle du modèle mais avec moins de variablesde manière similaire à l'interprétation T-factorisée.Enfin, nous montrons que nous pouvons explicitement compiler les ensembles de réponses de requêtes conjonctives admettant une décomposition de petite largeuren circuits succincts, ce qui nous permet de récapturer l'interprétation T-factorisée
Mathematical optimization and data management are two major fields of computer science that are widely studied by mostly separate communities.However complex optimization problems often depend on large datasets that may be cumbersome to manage,while managing large amounts of data is only useful insofar as one analyzes this data to extract some knowledgein order to solve some practical problem, so these fields are often actually intertwined in practice.This thesis places itself at the crossroads between these two fields by studying linear programs that reason about the answers of database queries.The first contribution of this thesis is the definition of the so-called language of linear programs with conjunctive queries, or LP(CQ) for short.It is a language to model linear programs with constructs that allow one to express linear constraints and linear sumsthat reason over the answer sets of database queries in the form of conjunctive queries.We then describe the natural semantics of the languageby showing how such models can be interpreted, in conjunction with a database, into actual linear programsthat can then be solved by any standard linear program solver and discuss the hardness of solving LP(CQ) models.Motivated by the hardness of solving LP(CQ) models in general, we then introducea process based on the so-called T-factorized interpretation to solve such models more efficiently.This approach is based on classical techniques from database theoryto exploit the structure of the queries using hypertree decompositions of small width.The T-factorized interpretation yields a linear programthat has the same optimal value as the natural semantics of the model but fewer variableswhich can thus be used to solve the model more efficiently.The third contribution is a generalization of the previous result to the framework of factorized databases.We introduce a specific circuit data-structure to succintly encode relations.We the define the so-called C-factorized interpretation that leverages the succintness of these circuitsto yield a linear program that has the same optimal value as the natural semantics of the model but fewer variablessimilarly to the T-factorized interpretation.Finally we show that we can explicitly compile the answer sets of conjunctive queries with small fractional hypertreewidthinto succinct circuits, thus allowing us to recapture the T-factorized interpretation

Capitoli di libri sul tema "Factorized Databases":

Krompaß, Denis, Maximilian Nickel e Volker Tresp. "Querying Factorized Probabilistic Triple Databases". In The Semantic Web – ISWC 2014, 114–29. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11915-1_8.

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Schilling, Nicolas, Martin Wistuba, Lucas Drumond e Lars Schmidt-Thieme. "Hyperparameter Optimization with Factorized Multilayer Perceptrons". In Machine Learning and Knowledge Discovery in Databases, 87–103. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23525-7_6.

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Rügamer, David, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. Müller e Clemens Stachl. "Factorized Structured Regression for Large-Scale Varying Coefficient Models". In Machine Learning and Knowledge Discovery in Databases, 20–35. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26419-1_2.

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Li, Boyu, Ting Guo, Xingquan Zhu, Yang Wang e Fang Chen. "ConGCN: Factorized Graph Convolutional Networks for Consensus Recommendation". In Machine Learning and Knowledge Discovery in Databases: Research Track, 369–86. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43421-1_22.

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Pajarinen, Joni, Jaakko Peltonen, Ari Hottinen e Mikko A. Uusitalo. "Efficient Planning in Large POMDPs through Policy Graph Based Factorized Approximations". In Machine Learning and Knowledge Discovery in Databases, 1–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15939-8_1.

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Hoos, Eva, Pascal Hirmer e Bernhard Mitschang. "Context-Aware Decision Information Packages: An Approach to Human-Centric Smart Factories". In Advances in Databases and Information Systems, 42–56. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66917-5_4.

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Harding, Bruce A. "Database Exchange in the CAD/CAM/CIM Arena". In CAD/CAM Robotics and Factories of the Future ’90, 61–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-84338-9_7.

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Lau, K. W. N., e M. Ramulu. "A Databased Time and Cost Estimation Algorithm for Piece Part Design and Manufacturing". In CAD/CAM Robotics and Factories of the Future ’90, 458–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-84338-9_64.

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Atti di convegni sul tema "Factorized Databases":

Witeck, Gabriela R., e Anabela C. Alves. "Worldwide Lean Learning Factories". In ASME 2023 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/imece2023-112983.

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Abstract Lean production, also known as the Toyota Production System, is a management model that is being actively taught in academia through active learning methodologies. One such methodology is the use of learning factories to teach Lean concepts. Many Higher Education Institutions are promoting and installing learning factories to teach lean concepts effectively. This paper’s objectives are to discuss lean learning factories (LFF): characterization; importance for managers and organizations; lessons learned; lean learning aspects and general outcomes for life-long learning. To achieve this, the authors developed A literature review was conducted on lean learning factories, utilizing scientific articles obtained from the Scopus database. The review encompassed the period from 1990 to 2023. This literature review resulted in 95 papers, being the first articles relating to lean and learning factories published in 2006. The main findings revealed that the LLF can be found worldwide, and these initiatives were taken by universities and some involved organizations. Findings evidenced that setting up and installing one such LLF is not trivial and needs resources, but they contribute to the effectiveness of lean learning. The significance of these factories lies in their aim to replicate intricate real-world settings as accurately as possible, facilitating the best training in lean methodologies that align with the fundamental principles of Industry 4.0 and Industry 5.0.

Fodor, Kristian, e Zoltán Balogh. "Automation of the Collection and Processing of Physiological Functions from Wearable Sensors". In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001473.

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The current period is evidence of the massive replacement of human strength by the results of the mental cognitive work of researchers. To cope with this situation, it is necessary to examine the impact of these technologies on the quality of life in the work and home environment when introducing new technologies. The main purpose of this work will be the collection of physiological functions such as heart rate in real-time from wearable sensors by creating an application that automatically collects sensor data. The work also proposes a mobile application that will be able to read the sensor data from the database and display it. The data from the sensors will be later used in a complex system and its proposal will also be described in this paper. Such a system could be used in many industries, like the automotive industry, industrial factories, smart homes and even education.

Arisoy, Erhan Batuhan, Guannan Ren, Erva Ulu, Nurcan Gecer Ulu e Suraj Musuvathy. "A Data-Driven Approach to Predict Hand Positions for Two-Hand Grasps of Industrial Objects". In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60095.

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The wide spread use of 3D acquisition devices with high-performance processing tools has facilitated rapid generation of digital twin models for large production plants and factories for optimizing work cell layouts and improving human operator effectiveness, safety and ergonomics. Although recent advances in digital simulation tools have enabled users to analyze the workspace using virtual human and environment models, these tools are still highly dependent on user input to configure the simulation environment such as how humans are picking and moving different objects during manufacturing. As a step towards, alleviating user involvement in such analysis, we introduce a data-driven approach for estimating natural grasp point locations on objects that human interact with in industrial applications. Proposed system takes a CAD model as input and outputs a list of candidate natural grasping point locations. We start with generation of a crowdsourced grasping database that consists of CAD models and corresponding grasping point locations that are labeled as natural or not. Next, we employ a Bayesian network classifier to learn a mapping between object geometry and natural grasping locations using a set of geometrical features. Then, for a novel object, we create a list of candidate grasping positions and select a subset of these possible locations as natural grasping contacts using our machine learning model. We evaluate the advantages and limitations of our method by investigating the ergonomics of resulting grasp postures.

Lee, Chun-Tse, Cheng-Han Tsai e Jen-Yuan (James) Chang. "A CAD-Free Random Bin Picking System for Fast Changeover on Multiple Objects". In ASME 2020 29th Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/isps2020-1911.

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Abstract Random bin picking (RBP) has been a popular research topic due to the demand of industry 4.0, techniques like object detection, picking strategies and robot motion planning are more and more important. Much of the existing research uses the CAD model of the workpiece as the database. However, building CAD models is time-consuming and not all objects have CAD models. In this paper, a CAD-free random bin picking system is proposed to pick miscellaneous objects. By using Mask-RCNN instance segmentation, the object’s category and pickable area can be determined within a 2D image captured from RGB-D camera. Then, the pixels of pickable area can be converted into point clouds for picking tasks with the depth data of RGB-D camera. Compared with traditional RBP systems, a system with the Mask-RCNN doesn’t need to create CAD models, and it only requires fewer images of stacked objects (less than 50) and heuristic picking points labelling as the training data. Thus, the RBP systems which proposed in this paper can lowers the barriers to introduce the random bin picking system into factories. Through this scheme, a fast changeover for different objects could be made within 10 hours. The experiment results show that this system could pick two different objects with high success rate and acceptable cycle time. This system provides a useful and efficient solution for the industrial automation implementations that require bin picking.

Imran, Huma, Mohamed Salama e Colin Turner. "Digitization, Cybersecurity and Risk Management in the Oil and Gas Sector in the post COVID world: A Systematic Review". In AHFE 2023 Hawaii Edition. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004253.

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Digitization and cybersecurity are intertwined. The surge in digitization has made the cyber threat landscape complex and real, allowing cybercriminals to target nations' critical infrastructure, especially oil and gas. Recent high-profile incidents demonstrate the quickly evolving and intensifying threat landscape. Oil and gas producers must protect themselves from these stealthy adversaries, which necessitates a thorough understanding of the relationship between digitization, risk management, and cybersecurity. Academics and industry practitioners have been digitizing the oil and gas industry for decades to make it more cost-effective and resilient. However, managerial progress is slow and traditional methods are still used. Cybersecurity was largely neglected during digitization. This paper chronicles the growth of smart factories and cyber risk management practices through different digitization strategies, and identifies gaps in digitization and risk management. A systematic literature review of the petroleum industry was conducted using four databases (AAPG, SCOPUS, ProQuest and Web of Science). 56 papers were shortlisted after a comprehensive research. The paper examines the publication, source and goal of digitization models and their use in the oil and gas industry. It also integrates digitization, cybersecurity and risk management in the petroleum sector. In the past, cyber-attacks were responded to after they occurred, and digitization has outpaced cybersecurity management. . This poses a real challenge in the world of oil and gas sector, where billions of dollars are at stake due to this knowledge gap. With the passage of time cyber security has become an integral part of the digitization programs, and recently, the proposed models have integrated cybersecurity as an essential element of digitization frameworks, however much needs to be explored and studied at project level as these models require further validation and updating.

Rapporti di organizzazioni sul tema "Factorized Databases":

Hazarika, Darshana. Mudige Arun Kumar. World Wide Journals, febbraio 2023. http://dx.doi.org/10.36106/ijar/8406946.

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Any personal injury, sickness, or death caused by an employment accident is referred to as an occupational injury.These hazards can be categorized as physical (accidents and injuries),biological(diseases like inuenza, HIV, Anthrax etc.),psychological(stress, anxiety, depression) , chemical hazards (exposure to acids and pesticides), and environmental hazards(unsafe drinking water, inadequate sanitation, hazardous healthcare waste, etc).Occupational injuries are more widespread among workers in numerous sectors, factories, and health-care organizations and they constitute a critical worldwide health issue affecting the majority of people in various occupations. As a result, the current review sought to evaluate the prevalence, pattern, and risk factors of occupational hazards experienced by workers in a variety of situations.Searches for relevant articles were conducted in four electronic databases using a broad range of search terms.