Articles tagged with Big Data
Comprehensive exploration of living organisms, biological systems, and life processes across all scales from molecules to ecosystems. Encompasses cutting-edge research in biology, genetics, molecular biology, ecology, biochemistry, microbiology, botany, zoology, evolutionary biology, genomics, and biotechnology. Investigates cellular mechanisms, organism development, genetic inheritance, biodiversity conservation, metabolic processes, protein synthesis, DNA sequencing, CRISPR gene editing, stem cell research, and the fundamental principles governing all forms of life on Earth.
Comprehensive medical research, clinical studies, and healthcare sciences focused on disease prevention, diagnosis, and treatment. Encompasses clinical medicine, public health, pharmacology, epidemiology, medical specialties, disease mechanisms, therapeutic interventions, healthcare innovation, precision medicine, telemedicine, medical devices, drug development, clinical trials, patient care, mental health, nutrition science, health policy, and the application of medical science to improve human health, wellbeing, and quality of life across diverse populations.
Comprehensive investigation of human society, behavior, relationships, and social structures through systematic research and analysis. Encompasses psychology, sociology, anthropology, economics, political science, linguistics, education, demography, communications, and social research methodologies. Examines human cognition, social interactions, cultural phenomena, economic systems, political institutions, language and communication, educational processes, population dynamics, and the complex social, cultural, economic, and political forces shaping human societies, communities, and civilizations throughout history and across the contemporary world.
Fundamental study of the non-living natural world, matter, energy, and physical phenomena governing the universe. Encompasses physics, chemistry, earth sciences, atmospheric sciences, oceanography, materials science, and the investigation of physical laws, chemical reactions, geological processes, climate systems, and planetary dynamics. Explores everything from subatomic particles and quantum mechanics to planetary systems and cosmic phenomena, including energy transformations, molecular interactions, elemental properties, weather patterns, tectonic activity, and the fundamental forces and principles underlying the physical nature of reality.
Practical application of scientific knowledge and engineering principles to solve real-world problems and develop innovative technologies. Encompasses all engineering disciplines, technology development, computer science, artificial intelligence, environmental sciences, agriculture, materials applications, energy systems, and industrial innovation. Bridges theoretical research with tangible solutions for infrastructure, manufacturing, computing, communications, transportation, construction, sustainable development, and emerging technologies that advance human capabilities, improve quality of life, and address societal challenges through scientific innovation and technological progress.
Study of the practice, culture, infrastructure, and social dimensions of science itself. Addresses how science is conducted, organized, communicated, and integrated into society. Encompasses research funding mechanisms, scientific publishing systems, peer review processes, academic ethics, science policy, research institutions, scientific collaboration networks, science education, career development, research programs, scientific methods, science communication, and the sociology of scientific discovery. Examines the human, institutional, and cultural aspects of scientific enterprise, knowledge production, and the translation of research into societal benefit.
Comprehensive study of the universe beyond Earth, encompassing celestial objects, cosmic phenomena, and space exploration. Includes astronomy, astrophysics, planetary science, cosmology, space physics, astrobiology, and space technology. Investigates stars, galaxies, planets, moons, asteroids, comets, black holes, nebulae, exoplanets, dark matter, dark energy, cosmic microwave background, stellar evolution, planetary formation, space weather, solar system dynamics, the search for extraterrestrial life, and humanity's efforts to explore, understand, and unlock the mysteries of the cosmos through observation, theory, and space missions.
Comprehensive examination of tools, techniques, methodologies, and approaches used across scientific disciplines to conduct research, collect data, and analyze results. Encompasses experimental procedures, analytical methods, measurement techniques, instrumentation, imaging technologies, spectroscopic methods, laboratory protocols, observational studies, statistical analysis, computational methods, data visualization, quality control, and methodological innovations. Addresses the practical techniques and theoretical frameworks enabling scientists to investigate phenomena, test hypotheses, gather evidence, ensure reproducibility, and generate reliable knowledge through systematic, rigorous investigation across all areas of scientific inquiry.
Study of abstract structures, patterns, quantities, relationships, and logical reasoning through pure and applied mathematical disciplines. Encompasses algebra, calculus, geometry, topology, number theory, analysis, discrete mathematics, mathematical logic, set theory, probability, statistics, and computational mathematics. Investigates mathematical structures, theorems, proofs, algorithms, functions, equations, and the rigorous logical frameworks underlying quantitative reasoning. Provides the foundational language and tools for all scientific fields, enabling precise description of natural phenomena, modeling of complex systems, and the development of technologies across physics, engineering, computer science, economics, and all quantitative sciences.
A mobile contact tracing app can reduce transmission at any stage of the epidemic, helping to bring the pandemic under control. The app uses low-energy Bluetooth to log close proximity contacts and alerts users if they become infected, supporting health services and reducing serious social impacts.
A coronavirus mobile app could significantly help contain the spread of the virus, according to Oxford University experts. The team recommends deploying the app as part of an integrated control strategy that identifies infected people and their recent contacts using digital technology.
By using approximations instead of explicit kernels, researchers have accelerated machine learning speeds and improved AI's ability to handle large datasets. The new approach uses statistics to derive a nearly accurate kernel that can be computed much faster than traditional methods.
A new study by Michigan State University quantifies soil and landscape features and spatial and temporal yield variations in response to climate variability. The research identifies areas within individual fields where yield is unstable, with over one-quarter of corn and soybean cropland in the Midwest experiencing this issue.
Researchers using big data analytics have identified over three quarters of Spanish-founded colonial settlements in the former Inca Empire, providing new insights into social life and population history. The use of high-resolution satellite imagery also raises concerns about individual privacy and national security.
A new study highlights the need to balance research benefits with patient privacy concerns as big data is increasingly used in medical care. Researchers suggest improved education and legislation are necessary to protect consumers' sensitive health data.
RUDN University mathematicians developed a model to optimize data center efficiency using Markov chains. Their method reduces server overheating and improves server capacity utilization, resulting in significant cost savings.
The BC2 Basel Computational Biology Conference aims to bridge the gap between Big Data and clinical applications, featuring renowned experts from precision oncology to infectious diseases. Key takeaways include the importance of single-cell data in cancer research and biological big data analysis methods.
KAUST researchers develop a universal framework for querying big data, allowing researchers to focus on advancing the query engine rather than coding for specific platforms. The approach uses sparse-matrix algebra and achieves performance comparable to existing specialized approaches.
The Northeast Big Data Innovation Hub has been awarded a $4 million grant from the NSF to build cross-sector partnerships, spur economic development, and accelerate big data innovation. The hub will focus on mission-driven projects that coordinate and stimulate translational data science.
Recent Chinese AI research has made significant breakthroughs in big data analysis, with a focus on developing algorithms to detect abnormal data regions and repair historical correlations. Researchers have also proposed using Doppler measurements to improve target tracking performance in noisy environments.
Researchers developed a series of models that strongly predict how terrain slope affects human travel rates, accounting for variability in movement. The study used crowdsourced fitness-tracking data from nearly 30,000 people, resulting in more advanced models than previous ones.
Researchers connected big data marketing tools to land conservation efforts, identifying landowners more likely to install riparian buffers. The study found that using microtargeting techniques could increase the impact of conservation programs and reduce outreach costs.
A team of international scientists used big data analysis to study how neurons communicate with each other in the brain, identifying patterns related to memory and discovering major proteins responsible for changes observed in neurons.
A study of over 8 million albums from 1956 to 2015 reveals that new musical styles emerge as a result of counter-signaling from outsider groups. This challenges traditional theories on the evolution of fashion and trends in music, highlighting the role of elite competition in driving innovation.
Developed with NSF funding, SETA (Scalable Event Trend Analytics) is an open-source software that analyzes high-volume data streams in real time to provide actionable insights. It helps organizations make data-driven decisions quickly, enabling applications like autonomous vehicle networks and healthcare.
Researchers urge scientists to utilize massive open-access data resources to address global biodiversity issues and forecast plant life's impact on a human-dominated planet. Big data can help make timely diagnoses and prescribe treatment plans for the planet.
A research team led by Heng Huang aims to create a framework for secure and efficient multi-site collaborative big brain data mining. The project addresses computational challenges in analyzing complex brain disorders and genomics data.
Researchers are using big data and machine learning techniques to optimize sports performance, game-day decision making, and even predict the end of daily fantasy sports. The special issue explores various applications of big data in sports analytics, from pacing strategies in long-distance running to business-side insights.
A new study by MIT researchers finds that compiling massive, anonymized datasets about people's movement patterns can make it easier to discern users' identities. The study shows how merging different types of location-stamped data can lead to a high matchability success rate, increasing the possibility of deanonymizing real user data.
Archaeologists used machine learning techniques to classify and predict the technological elements of ancient hunter-gatherer groups in Patagonia. The study identified two distinct 'landscapes' of technology, one for pedestrian groups and another for nautical societies, shedding light on their mobility patterns and interactions.
A RIT researcher is developing new signal processing solutions to improve data analysis efficiency and reliability. The project aims to reduce the impact of faulty measurements in complex sensing systems by creating algorithms that can detect and mitigate corrupted data.
By eliminating redundant data, researchers have developed a technique that reduces the amount of information needed for accurate predictions. This approach has been successfully applied to various applications, including soil quality prediction, healthcare, and environmental studies.
The University of Pittsburgh has received a $1.2 million NSF grant to analyze electronic anesthesia records and prevent postoperative complications and death using machine learning and Big Data analysis. Dr. Heng Huang will develop a new deep learning algorithm to predict surgical outcomes based on historical patient data.
Researchers at Cold Spring Harbor Laboratory have developed a new approach called Density Estimation using Field Theory (DEFT) to analyze small datasets, inspired by theoretical physics. The method fixes shortcomings of common statistical methods, providing more certainty in conclusions.
The article discusses the complexity of data ethics, emphasizing personal data ownership, consent, trustworthiness, and privacy. Researchers aim to strike a balance between harnessing data's potential benefits and mitigating its risks.
A synergistic approach to big data in science emphasizes the importance of collaborative research efforts and open science. Ecologists must work together to answer complex, globally relevant questions that cross disciplines and require extrapolating findings from one location to another.
The article highlights the need for harnessing technology to analyze healthcare data and generate new evidence, which can be combined with published reviews to improve health outcomes. Michigan Medicine's Knowledge Grid platform is taking the lead in transforming biomedical knowledge into computable forms that can inform medical practice.
The review identified gaps in using technologies, with a lack of information on extreme temperatures and flooding. Despite limitations, big data and ICT hold promise for potential solutions to harness diverse and chaotic data in disasters.
The ZPID Twin Conference brought together over 150 participants to discuss Big Data in Psychology and Research Synthesis. Keynote speakers highlighted the importance of psychology's theoretical strength in addressing big data challenges, while also emphasizing the need for innovative methods and tools for research synthesis.
PlinyCompute is a system designed for developing high-performance big data codes, offering faster implementation of complex object manipulation and library-style computations compared to Spark. The platform was developed by Rice University's DARPA-funded Pliny Project team, which aims to create sophisticated programming tools using mac...
The consulting firm is founded by four computer science experts from Saarland University to provide sound advice on data analysis. Data Science Consulting focuses on collecting, cleaning and merging data, as well as removing errors, maintaining data, setting up a scalable architecture and defining critical characteristics for analysis.
The FraudBuster approach uses proactive risk prediction at the underwriting stage to identify unprofitable drivers who are likely fraudulent risks. This novel method can help insurers reduce fraud in high-risk markets, such as the automobile insurance market.
A study published in EPJ Data Science found that fiction books, especially those with high initial sales numbers, are more likely to become bestsellers. Non-fiction titles, however, are more likely to retain their bestseller status once achieved.
Portland State University has received a $300,000 grant to bring eight college students from across the U.S. to work on real-world projects using computational modeling and big data to improve Portland's quality of life. The program aims to provide valuable mentoring and research experience to undergraduate students.
GW and UGA researchers develop GlyGen portal to integrate glycan data with gene and protein data, enabling more effective analysis. The project simplifies the process of understanding glycans' roles in diseases like cancer.
Researchers have turned salmon migration patterns into sound using sonification, enabling untrained listeners to interpret large amounts of complex data. The approach has shown promise in helping scientists feel less overwhelmed by interpreting big data, leading them to spend more time exploring the experience.
A team from Lehigh University's Industrial and Systems Engineering department participated in a workshop on big data optimization algorithms, theory, applications and systems. Researchers presented their work on Interior Point Methods, machine learning methodologies, and other topics relevant to big data analytics.
Researchers employed computational approaches to estimate the fitness landscape of gp160, a polyprotein that comprises HIV's spike. The inferred landscape was validated through comparisons with diverse experimental measurements.
Experiments suggest that farsightedness is negatively associated with risk-taking and positively associated with choosing increased future rewards. Analysis of over 90 million tweets reveals a connection between future thinking and decision-making.
A new algorithm, VarQuest, has been developed to identify naturally occurring antibiotics, revealing an order of magnitude more compounds than previous studies. This breakthrough could lead to the discovery of new medicines and help combat rising antibiotic resistance.
A new collection of articles explores the role and risks of bots in influencing public opinion and political elections. Researchers examine approaches to detect and control bots, their impact on recent elections, and potential methods for automation identification of bot activity.
Researchers developed a tool to help users cloak their identity and avoid certain types of inferences drawn about them based on their social media behavior. The 'cloaking device' can significantly reduce the predictive value of online information used for targeting.
A critical review of existing literature reveals that social media big data can be used to monitor and intervene on behalf of people with drug addiction and abuse problems. Researchers have developed an evidence-based framework to inform future social media-based substance use prevention and recovery programs.
The UW Department of Astronomy is joining the ZTF team to develop new methods for identifying celestial objects in the night sky. The ZTF's massive real-time data stream will impact studies of stars, our solar system, and the evolution of our universe.
A North Carolina State University-led study found that six imperatives facilitate collaboration in novel partnerships between government, academia, and industry. These include mutual benefit, trust, common understanding, access and agility, incentives, and mission criticality.
The Data Science Institute developed a novel statistical method to measure predictivity in big data analysis. The approach allows researchers to compare their predictions to a theoretical baseline, enhancing accuracy. The team will help the New York City Department of Transportation assess complex social problems using big data sets.
The traditional 24 Solar Terms are being upgraded with new, geographically correlated models using big meteorological data. The updated system reveals inconsistent timing and spatial inhomogeneity between the old and new systems.
Researchers found that big data analytics can amplify existing policing practices, leading to more marginalization and distrust. The influx of personal data enables law enforcement to surveil communities more easily, but also raises concerns about the use of objective crime data and predictive algorithms.
Researchers have developed a new algorithm that allows AI to collect error reports and correct them immediately, without affecting existing skills. This enables robots to learn from their mistakes and spread new knowledge amongst themselves.
A groundbreaking study applies big data analysis to mineralogy, predicting the existence of 1,500 missing minerals and new deposits. The technique enables scientists to represent data from multiple variables on thousands of minerals in a single graph, revealing patterns of occurrence and distribution.
A new special issue of Big Data highlights the risks associated with big data, including discrimination, lack of diversity, and bias. The issue discusses strategies for making decision-making 'discrimination-aware' and emphasizes the importance of considering ethical issues in model development.
Researchers from Penn State's IST have developed a method to identify bow echoes in radar images, a phenomenon associated with fierce winds. The algorithm can automatically detect bow echoes as they begin to form, providing instant notifications for severe weather alerts.
Researchers at Tsinghua University outline recent advances on nonparametric Bayesian methods, regularized Bayesian inference, scalable algorithms, and system implementation to tackle the challenges of Big Data. They also discuss connections with deep learning and highlight the need for human expertise in devising appropriate features a...
Researchers found that simple 'single-show models' can have high predictive accuracy in predicting presidential election outcomes based on television viewership data. This approach may offer a more accurate predictive tool compared to poll-data-driven models.
Industry experts examine the advantages of shifting data analytics to the cloud, including cost savings and improved management of analytics workloads. The panel discussion highlights the potential for companies to gain by moving their data analytics activities to the cloud, including more efficient use of IT resources.
Researchers developed an adaptive control approach based on online learning to correct dynamics errors in real-time, improving robustness of motion systems. The DOOMED algorithm updates a correction model until correct acceleration is achieved, minimizing error between desired and actual accelerations.
A new KIT Motion-Language Dataset has been created to support the development of robot activities based on natural language input. The dataset, which includes over 4,000 motions and 6,200 annotations in natural language, aims to unify and standardize research linking human motion and natural language.
Xia emphasizes that big data is about more than just numbers and requires new mathematical methods to analyze. Researchers in mathematics, signal processing, and computer science must develop these new tools to unlock the full potential of big data.
Data Civilizer aggregates scattered data from various files, creating unified datasets for analysis. The system identifies commonalities between columns and traverses a map to find related data, enabling users to compose queries and save results.