Search

BACKGROUND: The American College of Surgeons requires continuous process improvement review to maintain trauma center verification. Paper-based systems to monitor and track performance improvement are commonly used but can be inadequate to monitor concurrent ongoing improvement effectively. OBJECTIVE: To describe the implementation of an electronic process to capture and monitor performance improvement in near real time. METHODS: In 2020, a Midwestern U.S. Level I adult trauma center and a Level II pediatric trauma center's trauma programs transitioned from a paper to an electronic file-sharing system for performance improvement. We converted our primary, secondary, and tertiary review documentation into a single electronic performance improvement file stored on the institution's virtual hard drive, accessible to designated staff, allowing continuous real-time updates. RESULTS: The electronic file-sharing and monitoring process reinvigorated the team and enhanced performance improvement efforts, leading to increased efficiency through documentation and effective loop closure. Real-time monitoring allowed the trauma program to identify opportunities for improvement and enact timely action plans, including targeted performance improvement projects, department education, and ongoing training. CONCLUSION: We found that implementing an electronic file-sharing system enhanced the trauma team's ability to monitor and trend performance improvement in real time. [ABSTRACT FROM AUTHOR]

Big data refers to extremely large data generated at high volume, velocity, variety, and veracity. The nurse scientist is uniquely positioned to leverage big data to suggest novel hypotheses on patient care and the healthcare system. The purpose of this paper is to provide an introductory guide to understanding the use and capability of big data for nurse scientists. Herein, we discuss the practical, ethical, social, and educational implications of using big data in nursing research. Some practical challenges with the use of big data include data accessibility, data quality, missing data, variable data standards, fragmentation of health data, and software considerations. Opposing ethical positions arise with the use of big data, and arguments for and against the use of big data are underpinned by concerns about confidentiality, anonymity, and autonomy. The use of big data has health equity dimensions and addressing equity in data is an ethical imperative. There is a need to incorporate competencies needed to leverage big data for nursing research into advanced nursing educational curricula. Nursing science has a great opportunity to evolve and embrace the potential of big data. Nurse scientists should not be spectators but collaborators and drivers of policy change to better leverage and harness the potential of big data. [ABSTRACT FROM AUTHOR]

Data culture/s as a research topic has begun to attract attention from a wide range of disciplines, albeit with inconsistent application of definitions, dimensions, and applications. This work builds on a call to investigate data culture/s within the information studies domain as a topic related to, but distinct from, information culture. The purpose of this study is to explore what is known about data culture/s in greater depth. We apply a retroductive approach to select and consider likely dimensions, inputs, and aspects of data culture/s in order to further map this construct to the literature, and thereby highlight gaps and opportunities to add to this body of knowledge. The initial candidate dimensions explored below include data‐related skills and attitudes, data sharing, data use/reuse, data ethics and governance, and a specific focus on Indigenous perspectives to provide insights on why and how a group may contest the emergent dominant discourse of data culture/s. Our conclusion highlights areas needing further research to fully define and examine the dimensions, inputs, and aspects of data culture/s, and calls for greater understanding and engagement with data culture/s from the information studies community. [ABSTRACT FROM AUTHOR]

Exam period can be a stressful time for students, and having examples of past papers to help prepare for the tests can be extremely helpful. It is possible that past exams are already shared on your campus--by professors in their specific courses, via student unions or groups, or between individual students. In this article, we will go over the workflows and infrastructure to support the systematic collection, provision of access to, and repository management of past exam papers. We will discuss platform-agnostic considerations of opt-in versus opt-out submission, access restriction, discovery, retention schedules, and more. Finally, we will share the University of Toronto setup, including a dedicated instance of DSpace, batch metadata creation and ingest scripts, and our submission and retention workflows that take into account the varying needs of stakeholders across our three campuses. [ABSTRACT FROM AUTHOR]

Why you should read this article: • To avoid bias in analysis, interpretation and generalisability of findings that can occur from missing data when using self-administered questionnaires • To develop strategies to prevent missing data in self-administered questionnaires • To understand the methodological and statistical considerations underlying the use of prevention strategies in quantitative studies Background: Self-administered questionnaires are efficient and low-cost ways of collecting data with wide cohorts. Nonetheless, their use in studies can result in a high occurrence of missing data, which can affect the statistical power, representativeness and generalisability of the findings. Imputation methods have been considered efficient statistical techniques for managing missing data. However, they have also been associated with limits, such as the risk of under-estimation of the effect, lower statistical power and decrease of correlation among variables. Recent studies have highlighted the importance of using prevention strategies to avoid missing data before the data are analysed. Aim: To identify strategies for preventing the occurrence of missing data and to discuss their effects, as well as their methodological and statistical considerations. Discussion: The article discusses prevention strategies related to the administration format and follow-up and reminders. Strategies such as the use of electronic tablets, email and telephone reminders are associated with lower rates of missing data in self-administered questionnaires. However, methodological and statistical limits, including the absence of a comparison group and statistical validation of the reported results, limits the capacity to establish robust consensus. Conclusion: Prevention strategies represent relevant and feasible avenues for handling missing data in a wide range of clinical, nursing and epidemiological research. More projects based on robust design are needed to ensure accurate and reliable data are collected from patients, families, communities and clinicians. Implications for practice: It is important for clinicians and nurses to understand the phenomenon of missing data and the strategies available to prevent missing data, to collect data representing the patients' and families' perspectives and experiences. [ABSTRACT FROM AUTHOR]

The data paper, an emerging scholarly genre, describes research data sets and is intended to bridge the gap between the publication of research data and scientific articles. Research examining how data papers report data events, such as data transactions and manipulations, is limited. The research reported on in this article addresses this limitation and investigated how data events are inscribed in data papers. A content analysis was conducted examining the full texts of 82 data papers, drawn from the curated list of data papers connected to the Global Biodiversity Information Facility. Data events recorded for each paper were organized into a set of 17 categories. Many of these categories are described together in the same sentence, which indicates the messiness of data events in the laboratory space. The findings challenge the degrees to which data papers are a distinct genre compared to research articles and they describe data‐centric research processes in a through way. This article also discusses how our results could inform a better data publication ecosystem in the future. [ABSTRACT FROM AUTHOR]

In this study, we explore paper characteristics that facilitate the knowledge flow from science to technology by using the patent-to-paper citation data. The linear growth trajectory of the number of patent citations to a scientific paper over time is used to measure the dynamism of its utilization for technology applications. The citation data used were obtained from the USPTO database based on two 5-year citation windows, 2001–2005 and 2009–2013. The former included patent citations to the publications in the Thomson Reuters Web of Science in 1998, and the latter included those in 2006. Only the publications in the top ten most frequently cited subject categories in the Web of Science were selected. By using growth modeling, we have found that the mean slope of the trajectory is significant. Moreover, the paper citation count, the ranking factor of the journal in which the paper was published, whether the paper is an industrial publication, and whether it is a review article have been identified to exert significant effects on the growth of the citation of scientific literature by patented inventions. Some policy implications are discussed. [ABSTRACT FROM AUTHOR]

Introduction: In the big data era, where corporations commodify health data, non-fungible tokens (NFTs) present a transformative avenue for patient empowerment and control. NFTs are unique digital assets on the blockchain, representing ownership of digital objects, including health data. By minting their data as NFTs, patients can track access, monetize its use, and build secure, private health information systems. However, research on NFTs in healthcare is in its infancy, warranting a comprehensive review. Methods: This study conducted a systematic literature review and thematic analysis of NFTs in healthcare to identify use cases, design models, and key challenges. Five multidisciplinary research databases (Scopus, Web of Science, Google Scholar, IEEE Explore, Elsevier Science Direct) were searched. The approach involved four stages: paper collection, inclusion/exclusion criteria application, screening, full-text reading, and quality assessment. A classification and coding framework was employed. Thematic analysis followed six steps: data familiarization, initial code generation, theme searching, theme review, theme definition/naming, and report production. Results: Analysis of 19 selected papers revealed three primary use cases: patientcentric data management, supply chain management for data provenance, and digital twin development. Notably, most solutions were prototypes or frameworks without real-world implementations. Four overarching themes emerged: data governance (ownership, tracking, privacy), data monetization (commercialization, incentivization, sharing), data protection, and data storage. The focus lies on user-controlled, private, and secure health data solutions. Additionally, data commodification is explored, with mechanisms proposed to incentivize data maintenance and sharing. NFTs are also suggested for tracking medical products in supply chains, ensuring data integrity and provenance. Ethereum and similar platforms dominate NFT minting, while compact NFT storage options are being explored for faster data access. Conclusion: NFTs offer significant potential for secure, traceable, decentralized healthcare data exchange systems. However, challenges exist, including dependence on blockchain, interoperability issues, and associated costs. The review identified research gaps, such as developing dual ownership models and data pricing strategies. Building an open standard for interoperability and adoption is crucial. The scalability, security, and privacy of NFT-backed healthcare applications require further investigation. Thus, this study proposes a research agenda for adopting NFTs in healthcare, focusing on governance, storage models, and perceptions. [ABSTRACT FROM AUTHOR]

The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboration between different sites or data transfer to a third party, precautions are required to safeguard patient privacy. Safety measures are required to prevent inadvertent access to and transfer of identifiable information. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI Ethical and Legal standing committee with the mandate to guide the medical imaging community in terms of best practices in data management, access to health care data, de-identification, and accountability practices. Part 2 of this article will inform CAR members on the practical aspects of medical imaging de-identification, strengths and limitations of de-identification approaches, list of de-identification software and tools available, and perspectives on future directions. [ABSTRACT FROM AUTHOR]

Many long-term geomagnetic observation results recorded on photographic bromide paper have not yet been fully digitized. To that end, we developed a method to automatically convert photographic records to one-minute digital data. We applied our method to the observation records of Kakioka Magnetic Observatory and confirmed that the resolution of time and amplitude could be greatly improved by numerical conversion compared with conventional data conversion by hand scaling. Our results suggest that highly precise digitization of analog magnetograms is possible. [ABSTRACT FROM AUTHOR]

Big data analytics in medicine is driving significant change, as it offers vital information for improving functions, developing cutting-edge solutions and overcoming inefficiencies. With the right archiving and analysis tools, all players in the healthcare system, from hospitals to patients and from medical personnel to the pharmaceutical industry, can yield numerous benefits. Therefore, to analyze and interpret these analytics effectively, so that they can be useful for the advancement of scientific knowledge, we require information sharing, specific skills, training, integration between all system players, unique infrastructures and security. All these characteristics will make it possible to establish and harmonize real big data biobanks, for which it will be appropriate to consider new forms of governance compared to those traditionally conceived for large-sample biobanks. [ABSTRACT FROM AUTHOR]

Bibliometric studies have gained popularity since they are able to define the characteristics of articles on specific subjects. The aim of this study was to analyze the characteristics of 100 most-cited papers related to septic arthritis of the native joints. Web of Science database was analyzed and 100 most-cited articles about septic arthritis were determined. The characteristics of the articles including publication year, country, journal, study type, and sponsorship were recorded and investigated for any possible relationship with citation numbers. The visualization of the most commonly used keywords was made by software. The highest citation number and density were 309 and 21.6, respectively. The highest contribution was from the USA with 44 articles. The most common study type was case series with 26 articles. Annals of the Rheumatic Diseases journal had the highest number of articles with 8 papers. Fifteen studies were funded. The average citation density of review articles was significantly higher than in clinical and basic science studies (p < 0.001). Citation density was positively correlated with publication year and institution number, while it was negatively correlated with the level of evidence. This study summarizes the general characteristics and research trends of the 100 most influential septic arthritis papers. Citation density and level of evidence performance were better in more recent articles. Additionally, citation density was higher in papers that included contributions from multiple institutions and papers with a high level of evidence. However, a high level of evidence is lacking indicating the need for better study design in future research. [ABSTRACT FROM AUTHOR]

Particularly, the relational database management systems and the big data systems (e.g., Key-Value stores, Document stores, Graph stores and Graph Computation Systems, Spark, MapReduce/Hadoop, or Data Stream Processing Systems) have evolved with novel additions and extensions. The paper by Harish Kumar Harihara Subramanian et al. on supporting DBMS GPU-based operations with out-of-the-box libraries describes the ongoing research in the database community regarding the use of GPUs for query processing. Data management systems have evolved in terms of functionality, performance characteristics, complexity, and variety during the last 40 years. [Extracted from the article]

There has been growing attention to multi-class classification problems, particularly those challenges of imbalanced class distributions. To address these challenges, various strategies, including data-level re-sampling treatment and ensemble methods, have been introduced to bolster the performance of predictive models and Artificial Intelligence (AI) algorithms in scenarios where excessive level of imbalance is present. While most research and algorithm development have been focused on binary classification problems, in health informatics there is an increased interest in the field to address the problem of multi-class classification in imbalanced datasets. Multi-class imbalance problems bring forth more complex challenges, as a delicate approach is required to generate synthetic data and simultaneously maintain the relationship between the multiple classes. The aim of this review paper is to examine over-sampling methods tailored for medical and other datasets with multi-class imbalance. Out of 2,076 peer-reviewed papers identified through searches, 197 eligible papers were chosen and thoroughly reviewed for inclusion, narrowing to 37 studies being selected for in-depth analysis. These studies are categorised into four categories: metric, adaptive, structure-based, and hybrid approaches. The most significant finding is the emerging trend toward hybrid resampling methods that combine the strengths of various techniques to effectively address the problem of imbalanced data. This paper provides an extensive analysis of each selected study, discusses their findings, and outlines directions for future research. [ABSTRACT FROM AUTHOR]

Formally established in the fall of 1947, the Laboratory of Archaeology at the University of Georgia is an archaeological research and collection repository. It is considered one of the premier institutions for curation of archaeological collections from the American Southeast. For over 70 years, the Laboratory has served as a repository for objects and associated records generated from archaeological projects and research undertaken by faculty, students, CRM professionals, and state and federal agencies. The Laboratory curates over 20,000 cubic feet of artifacts as well as paper and digital archives. In addition, the Laboratory houses the Georgia Archaeological Site File and manages data from more than 59,000 archaeological sites, including over 11,500 archaeological reports. In this paper, we explore implementation procedures for bringing legacy collections up to modern curation standards. We also outline how we migrate the data on paper records into the digital realm, articulating them within a comprehensive framework. [ABSTRACT FROM AUTHOR]

This paper outlines an initial analysis of 20 years of data held on an electronic bridge management database for approximately 3500 arch bridges across Northern Ireland (NI) by the Department for Infrastructure. Arch bridges represent the largest group of bridge types, making up nearly 56% of the total bridge stock in NI. This initial analysis aims to identify trends that might help inform maintenance decisions in the future. Consideration of the Bridge Condition Indicator (BCI) average value for the overall arch bridge stock indicates the potential for regional variations in the overall condition and the potential for human bias in inspections. The paper presents the most prevalent structural elements and associated defects recorded in the inspections of arch bridges. This indicated a link to scour and undermining for the worst-conditioned arch bridges. An Analysis of Variance (ANOVA) analysis identified function, number of spans, and deck width as significant factors during the various deterioration stages in a bridge's lifecycle. [ABSTRACT FROM AUTHOR]

Background: Through a nationally representative household survey in Afghanistan, we conducted an operational study in two relatively secure provinces comparing effectiveness of computer-aided personal interviewing (CAPI) with paper-and-pencil interviewing (PAPI). Methods: In Panjshir and Parwan provinces, household survey data were collected using paper questionnaires in 15 clusters, and OpenDataKit (ODK) software on electronic tablets in 15 other clusters. Added value was evaluated from three perspectives: efficient implementation, data quality, and acceptability. Efficiency was measured through financial expenditures and time stamped data. Data quality was measured by examining completeness. Acceptability was studied through focus group discussions with survey staff. Results: Survey costs were 68% more expensive in CAPI clusters compared to PAPI clusters, due primarily to the upfront one-time investment for survey programming. Enumerators spent significantly less time administering surveys in CAPI cluster households (248 min survey time) compared to PAPI (289 min), for an average savings of 41 min per household (95% CI 25–55). CAPI offered a savings of 87 days for data management over PAPI. Among 49 tracer variables (meaning responses were required from all respondents), small differences were observed between PAPI and CAPI. 2.2% of the cleaned dataset's tracer data points were missing in CAPI surveys (1216/ 56,073 data points), compared to 3.2% in PAPI surveys (1953/ 60,675 data points). In pre-cleaned datasets, 3.9% of tracer data points were missing in CAPI surveys (2151/ 55,092 data points) compared to 3.2% in PAPI surveys (1924/ 60,113 data points). Enumerators from Panjsher and Parwan preferred CAPI over PAPI due to time savings, user-friendliness, improved data security, and less conspicuity when traveling; however approximately half of enumerators trained from all 34 provinces reported feeling unsafe due to Taliban presence. Community and household respondent skepticism could be resolved by enumerator reassurance. Enumerators shared that in the future, they prefer collecting data using CAPI when possible. Conclusions: CAPI offers clear gains in efficiency over PAPI for data collection and management time, although costs are relatively comparable even without the programming investment. However, serious field staff concerns around Taliban threats and general insecurity mean that CAPI should only be conducted in relatively secure areas. [ABSTRACT FROM AUTHOR]

In the paper by Dwivedi et al., a compact size wideband circularly polarized 2-port MIMO antenna is designed for 5G with (<= - 10 dB) impedance bandwidth of 900 MHz in the frequency band 3.3-4.2 GHz and 100% 3-dB axial ratio bandwidth. 5G wireless standards will play a vital role in ensuring security, enabling automation on many fronts such as connected vehicles, and facilitating data rates that are orders of magnitude greater than 4G LTE and thereby enabling rapid access to information. [Extracted from the article]

We document the growth in published papers on behavioral genetics for 5-year intervals from 1960 through 2014. We used 1861 papers published in Behavior Genetics to train our search strategy which, when applied to Ovid PsychINFO, selected more than 45,000 publications. Five trends stand out: (1) the number of behavioral genetic publications has grown enormously; nearly 20,000 papers were published in 2010-2014. (2) The number of human quantitative genetic (QG) publications (e.g., twin and adoption studies) has steadily increased with more than 3000 papers published in 2010-2014. (3) The number of human molecular genetic (MG) publications increased substantially from about 2000 in 2000-2004 to 5000 in 2005-2009 to 9000 in 2010-2014. (4) Nonhuman publications yielded similar trends. (5) Although there has been exponential growth in MG publications, both human and nonhuman QG publications continue to grow. A searchable resource of this corpus of behavioral genetic papers is freely available online at and will be updated annually. [ABSTRACT FROM AUTHOR]

Newly, big data utilization has been widely interested in a wide variety of industrial fields. Big data is the art of extracting value from volume sets of structured and unstructured data, beyond the capabilities of traditional database management tools, and analyzing the results. Big data is often characterized by the (3V) volume, velocity, and variety. Using the R language, a big data analysis tool, you can express various analysis results through various visualization functions using pre-processed unstructured data. The data used in this research was a comparative analysis of 104 papers from January to May 2021 and 108 papers from September 2022 to January 2023 among the papers published in the Korea Institute of Information and Communication Engineering. The analysis showed that the highest frequency was Data (2,038). Therefore, we discuss the limitations and practical implications of the study based on the analysis results. [ABSTRACT FROM AUTHOR]

Simple Summary: Large datasets concerning childhood cancers are rare. Therefore, it is important to fully exploit all available data, which are distributed over several resources, including biomaterials, images, clinical trials, and registries. With privacy-preserving record linkage (PPRL), datasets can be merged, without disclosing the patients' identities. Although PPRL is already implemented or described in various settings, use case descriptions are fragmented and incomplete. The present paper gives an overview of current and future use cases of PPRL in childhood cancer. We screened the literature, projects, and trial protocols, analysed a hypothetical patient journey, and discussed use cases with experts. All the identified use cases were structured along six key dimensions. We conclude that PPRL is a key concept in childhood cancer. Therefore, PPRL strategies should already be considered when starting research projects, to avoid distributed data silos, to maximise the knowledge derived from collected data, and, ultimately, to improve outcomes for children with cancer. Large datasets in paediatric oncology are inherently rare. Therefore, it is paramount to fully exploit all available data, which are distributed over several resources, including biomaterials, images, clinical trials, and registries. With privacy-preserving record linkage (PPRL), personalised or pseudonymised datasets can be merged, without disclosing the patients' identities. Although PPRL is implemented in various settings, use case descriptions are currently fragmented and incomplete. The present paper provides a comprehensive overview of current and future use cases for PPRL in paediatric oncology. We analysed the literature, projects, and trial protocols, identified use cases along a hypothetical patient journey, and discussed use cases with paediatric oncology experts. To structure PPRL use cases, we defined six key dimensions: distributed personalised records, pseudonymisation, distributed pseudonymised records, record linkage, linked data, and data analysis. Selected use cases were described (a) per dimension and (b) on a multi-dimensional level. While focusing on paediatric oncology, most aspects are also applicable to other (particularly rare) diseases. We conclude that PPRL is a key concept in paediatric oncology. Therefore, PPRL strategies should already be considered when starting research projects, to avoid distributed data silos, to maximise the knowledge derived from collected data, and, ultimately, to improve outcomes for children with cancer. [ABSTRACT FROM AUTHOR]

Graph theory is a well-established theory with many methods used in mathematics to study graph structures. In the field of medicine, electronic health records (EHR) are commonly used to store and analyze patient data. Consequently, it seems straight-forward to perform research on modeling EHR data as graphs. This systematic literature review aims to investigate the frontiers of the current research in the field of graphs representing and processing patient data. We want to show, which areas of research in this context need further investigation. The databases MEDLINE, Web of Science, IEEE Xplore and ACM digital library were queried by using the search terms health record, graph and related terms. Based on the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA) statement guidelines the articles were screened and evaluated using full-text analysis. Eleven out of 383 articles found in systematic literature review were finally included for analysis in this literature review. Most of them use graphs to represent temporal relations, often representing the connection among laboratory data points. Only two papers report that the graph data were further processed by comparing the patient graphs using similarity measurements. Graphs representing individual patients are hardly used in research context, only eleven papers considered such kind of graphs in their investigations. The potential of graph theoretical algorithms, which are already well established, could help increasing this research field, but currently there are too few papers to estimate how this area of research will develop. Altogether, the use of such patient graphs could be a promising technique to develop decision support systems for diagnosis, medication or therapy of patients using similarity measurements or different kinds of analysis. [ABSTRACT FROM AUTHOR]

Digital health technologies (DHTs) at the intersection of health, medical informatics, and business aim to enhance patient care through personalised digital approaches. Ensuring the efficacy and reliability of these innovations demands rigorous clinical validation. A PubMed literature review (January 2006 to July 2023) identified 1250 papers, highlighting growing academic interest. A focused narrative review (January 2018 to July 2023) delved into challenges, highlighting issues such as diverse regulatory landscapes, adoption issues in complex healthcare systems, and a plethora of evaluation frameworks lacking pragmatic guidance. Existing frameworks often omit crucial criteria, neglect empirical evidence, and clinical effectiveness is rarely included as a criterion for DHT quality. The paper underscores the urgency of addressing challenges in accreditation, adoption, business models, and integration to safeguard the quality, efficacy, and safety of DHTs. A pivotal illustration of collaborative efforts to address these challenges is exemplified by the Digital Health Validation Center, dedicated to generating clinical evidence of innovative healthcare technologies and facilitating seamless technology transfer. In conclusion, it is necessary to harmonise evaluation approaches and frameworks, improve regulatory clarity, and commit to collaboration to integrate rigorous clinical validation and empirical evidence throughout the DHT life cycle. [ABSTRACT FROM AUTHOR]

In a wide area environment, leveraging a unified interface for the management of diverse databases is appealing. Nonetheless, variations in access and operation across heterogeneous databases pose challenges in abstracting a unified access model while preserving specific database operations. Simultaneously, intricate deployment and network conditions in wide-area environments create obstacles for forwarding database requests and achieving high-performance access. To address these challenges, this paper introduces a technology for unified access to heterogeneous databases in wide-area environments, termed Global Virtual Data Space SQL (GvdsSQL). Initially, this paper implements a unified data access mechanism for heterogeneous databases through metadata extraction, abstracts the unified access model, and accomplishes identification and forwarding of fundamental database operations. Secondly, the paper introduces a mechanism for expanding database operations through code generation. This mechanism achieves compatibility for special database operations by injecting rules to generate code. Lastly, this paper implements a multilevel caching mechanism for query results in wide-area databases utilizing semantic analysis. Through intelligent analysis of operation statements, it achieves precise management of cache items, enhancing wide-area access performance. The performance is improved by approximately 35% and 240% compared to similar methods. [ABSTRACT FROM AUTHOR]

Any kind of datasets which are so large and complex which becomes difficult to process them using traditional data processing applications is considered as Big Data. While handling huge dataset different challenges may be faced by the user. One can get additional large data from analysis of single large set of related data as compared to separate smaller dataset with the same amount of data. For example correlations to be found to "prevent diseases, spot business trends, combat crime and so on." It is difficult to work with Big Data using traditional database management systems and visualization packages and desktop statistics requiring instead "massively parallel software running on hundreds, or even thousands of servers". Data sets with sizes beyond the capability of usually used software tools to capture, manage, and process data within a tolerable elapsed time, are included in Big Data. Big Data "size" is a constantly increasing, as of its ranging from a few dozen terabytes to many petabytes of data. So, Big Data is a collection of techniques and technologies that need new forms of integration to uncover large hidden values from large datasets that are complex, diverse and of a massive scale. Big Data environment is used to arrange and analyse the various types of data. Big Data is data which is so large in volume, so various in variety or moving with high velocity is called Big Data. Acquiring and analysing Big Data is a challenging task as it includes large distributed file systems which should be flexible, fault tolerant and scalable. Different technologies used by big data application to handle the huge amount of data are Hadoop, Map Reduce, etc. In this paper, firstly the definition of big data is presented.. Following that, the architecture of different technologies which are used for handling Big Data is defined. Finally, applications of Big Data system is represented. [ABSTRACT FROM AUTHOR]

In VANET environment the data of consecutive vehicle position truly are discrete, so the key to the moving vehicle modeling is to efficiently reduces update frequency of the position data to progress the Major Attributes regarding Security like communication and database management load Privacy, authentication and confidentiality for all applications of VANET. Geograhically VANET might be of two types either Fully connected or sparsly connected. Its type depends on day time or saturation rate of market devices. Many solutions of security Problems in VANET exist but its implementation is very costly for both user and for Service Provider. [ABSTRACT FROM AUTHOR]