Articles tagged with Big Data
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.
Computing science researchers at the University of Alberta have developed a technique to automate geotagging for news articles and other online documents. The model integrates two competing hypotheses: inheritance and near-location, achieving high accuracy in matching named entities to geographical locations.
A Princeton-led team has created a new measure called the influence score, which can effectively differentiate between noisy and predictive variables in big data. This approach significantly improves prediction rates in various fields, including breast cancer diagnosis, terrorism, and financial markets.
Researchers identified six core emotional storylines: rags to riches, richness to rags, man in a hole, icarus, Cinderella, and Oedipus. These findings may help create compelling stories and teach common sense to AI systems.
The NIH-led initiative reviews the use of big data in infectious disease surveillance, combining traditional and non-traditional data sources to provide more accurate and timely information. This approach aims to forecast outbreak sizes and trajectories, enabling better responses to emerging threats.
New research using big data analysis has found that people's collective behaviour is more predictable than thought, with strong periodic patterns influenced by the weather and seasons. Historical news and social media data revealed cycles in leisure and work activities, diet, diseases, and mental health.
Researchers developed a way to integrate multiple big data sets from biology to understand cellular processes, discovering new regularities and biological consistencies. The study found pause sites dictate protein structure and folding, providing insights into cancer biology.
Interdisciplinary research highlights changing scientific landscape, where large data sets and computational methods encourage an iterative approach. The authors note that despite new technology, the reinvigorated approaches are rooted in centuries-old debates over iterative and hypothesis-driven science.
A novel tensor mining tool enables automated modeling in big data applications, facilitating the analysis of complex multiaspect data. This innovation addresses the challenge of extracting knowledge from massive amounts of data represented as tensors.
A new data-cleaning tool called ActiveClean analyzes a user's prediction model to identify mistakes and update the model as it works. By minimizing human error, ActiveClean improves model accuracy and reduces statistical biases, making it an essential tool for building better prediction models.
Researchers are uniting to tackle the complex challenge of understanding brain function through large-scale computational modeling. This approach aims to improve our knowledge of brain function by creating realistic models based on biological data.
A Bristol student, Paul Harris, has achieved a world record in 5G wireless spectrum efficiency using Massive MIMO. He set a new record of 145.6 bit/s/Hz with his research team, demonstrating the potential for this technology to deliver ultra-fast data rates to high densities of smartphones and tablets.
Researchers have identified comprehensibility as a key goal in model development, considering stakeholders' understanding of the modeling process. The article provides a holistic framework for comprehensibility in data science projects, prioritizing human needs and understanding.
Researchers developed a novel visualization tool to explore dynamic Bitcoin transactional data, revealing behavioral patterns and potential money laundering activities. The top-down approach enables drill-down analysis of individual transactions.
Insilico Medicine scientists will present advances in deep learning for biomarker development and drug discovery at the ISFA-Columbia University Actuarial Science Workshop. The workshop aims to integrate deep learning with actuarial science to assess risk in finance, insurance, and other industries.
The Global Names project enables the rapid indexing of content using scientific names, improving the discovery of small data. The study found that name-matching was improved to almost 85% with simplified or canonical versions of names.
A novel type 2 diabetes risk model has been developed to better understand disease progression, revealing people on atypical trajectories face significantly increased or decreased risks of developing T2D. The new model has overcome challenges associated with estimating T2D onset based on comorbid conditions.
The Instituto de Astrofísica de Canarias will participate in the SUNDIAL network, training young researchers in astronomy and computer science to understand galaxy formation and evolution. The network aims to detect ultradiffuse galaxies and apply research to society in medical imaging and remote sensing.
A new doctoral training program at the University of Missouri aims to create a unique type of data scientist by combining expertise from life sciences, medicine, and computing. The six-student program will focus on massive and complex data analytics for one health, using Big Data practices to improve medical discoveries.
A new simulator is being developed to help individuals and families select the most suitable health insurance plans based on realistic cost estimates and potential healthcare outcomes. The simulator combines buyer-specific information with bespoke databases, producing transparent output that enables informed decision-making.
Researchers at University of Houston are developing a new framework to protect big data processing and minimize risks of data breaches on the cloud. The project aims to address security concerns in large-scale data analytics, with potential commercial value in the growing $125 billion market.
A Penn State psychologist argues that big data can enhance our understanding of human development by aggregating empirical work from multiple investigators. This approach could also enable personalized medicine and wearable data-collection with more accurate results.
University of Illinois researchers have achieved record-breaking speeds for fiber-optic data transmission, reaching 57 Gbps at room temperature and 50 Gbps at higher temperatures. This technology could enable faster data transfer and use of large data streams in applications such as virtual reality.
The symposium highlights opportunities to utilize federal big data initiatives in dental research, such as BD2K, to advance research and practice. Panelists will discuss the potential of linked data for dental providers.
RevEx performs faceted searches and analyzes text and data across multiple domains to reveal important findings. Its applications range from investigating medical services to visualizing humanitarian data on a country-by-country basis.
Researchers developed Eyebrowse, a system allowing users to share self-selected aspects of their online activity with friends and the public. Users can add whitelisted sites, track friend visits, and view community browsing history, providing insights for academics and companies targeting consumers.
A new study proposes using data mining tools to identify patterns in QS data that can inform users' decisions on diet and exercise. This approach has the potential to reveal ways to improve personal well-being without compromising data privacy.
A national patient-powered registry, MyLymeData, has enrolled over 3,000 patients to accelerate research for chronic Lyme disease. Big data tools can help identify treatment responses and subgroup analysis, leading to a better understanding of the disease.
A new study identified distinct patient diagnoses and emergency department usage patterns linked to high risk of ED readmission. The researchers used electronic healthcare records data from over one million patients to build predictive models for risk of 72-hour ED readmission.
Big data analytics is revolutionizing healthcare by developing predictive models for disease onset, such as type 2 diabetes, and providing personalized insights through wearable devices. The new approaches aim to improve diagnosis accuracy and patient outcomes.
Recent epigenetics research highlights molecular mechanisms influencing gene expression through socioenvironmental factors. Big data raises concerns over immortalized participant data, privacy, and anonymity, prompting recommendations for strengthening ethical consent practices.
Scientists used a database of 418 plant species to analyze patterns in growth, reproduction, and survival. They found that two characteristics - growth rate and reproduction strategy - can predict population growth and response to disturbances.
University of Texas at Arlington engineer Jeff Lei has received a $375,000 grant to develop combinatorial testing techniques for big data software. This will enable developers to efficiently test large datasets, reducing the likelihood of bugs and improving software reliability.
Researchers used TV viewing data from 99 programs with predictive accuracies over 59% and three that predicted outcomes above 79%. The model forecasts election outcomes at the state and county levels, providing insights into key drivers of election results. The study's findings suggest a potential application to future elections.
The partnership aims to equip researchers with ethical guidelines for using big data and real-time analytics. The three-day module will provide training on the ethics of big data and data linkage, utilizing IBM's research into big data ethics.
Researchers developed a system to analyze and improve running motions using big data and artificial intelligence, identifying key differences between high-rank marathon runners and beginners. The technology, called 'skill grouping,' converts movements into objective scores, enabling the development of healthcare tools and assisting tra...
A study published in Science China: Information Sciences found that crowdsourcing can help improve label quality and reduce costs. Researchers analyzed worker performance and provided criteria for evaluating their qualities, enabling the elimination of low-quality workers from the crowd.
The University of Oklahoma is collaborating with the National Science Foundation's South Big Data Regional Innovation Hub to tackle regional challenges using big data analysis. The hub aims to develop new approaches for integrating data from disparate sources, enabling effective decision-making in areas like healthcare, coastal hazards...
The South Big Data Regional Innovation Hub aims to address regional challenges through big data analysis, serving 16 southern states. The hub will develop innovative public-private partnerships, leveraging Georgia Tech's national data repositories and Southern Crossroads network.
The Northeast Big Data Innovation Hub aims to address health, energy, finance, urbanization, natural science, and education challenges using data analytics and collaboration between experts. The hub will focus on extracting insights from large datasets to bring about tangible results.
A team of researchers at Clemson University is working on simplifying collaboration and improving efficiency in the handling of large data sets, also known as Big Data. They are studying ways to bridge the gap between technology experts and scientists, enabling better communication and workflow optimization.
The Kavli HUMAN Project aggregates 20 years of measurements from 10,000 individuals to study the dynamic interplay between biology, behavior, and environment. The project reveals new insights into factors increasing risk for cognitive decline and its impact on daily life, healthcare utilization, and end-of-life decisions.
The use of big data and analytics is crucial for managing chronic diseases in Singapore, where 48% of the disease burden is related to chronic illnesses. Data analytics can help identify patterns and correlations that improve healthcare outcomes and reduce costs.
The partnership aims to improve genomic research efficiency, increase output, and reduce costs. Gene technology will be utilized to create a better life for humanity, with the goal of advancing life sciences research.
The number of statistics graduates has increased by 300% since the 1990s, with bachelor's degrees growing 17% from 2013 to 2014. Demand for statisticians is expected to outpace growth, with a potential shortage of 140,000 workers with deep analytical skills by 2018.
Wladek Minor is rescuing vital submicroscopic data with a $20,000 grant, making it accessible for scientists and industry. His online search engine simplifies data access, speeding up research and potentially leading to new treatments.
A UTSA computer science professor has received a Department of Army grant to create an image searching algorithm for combing through large amounts of surveillance videos. The goal is to identify individuals more quickly, especially in crowded and hectic situations like the Boston Marathon bombing.
The BigStorage project aims to develop new approaches for handling Big Data, leveraging theoretical research, complex infrastructures, and software packages. The European consortium will focus on high-performance computing and storage technologies to address the needs of climate research, medicine, and environmental sciences.
The Alan Turing Institute has made significant progress in its first few days of operations, with the appointment of Professor Andrew Blake as its first Director. The Institute will promote the development and use of advanced mathematics, computer science, algorithms, and big data for human benefit.
Scientists from InSilico Medicine have developed an approach to screen and rank geroprotective drugs using big data analysis, identifying compounds with potential geroprotective properties. The Geroscope software was applied to gene expression data derived from stem cells to select five drugs that displayed geroprotective action.
Researchers at Case Western Reserve University are developing a platform to collect, analyze disparate clinical information from multiple sources, ensuring comparability and reproducibility. The goal is to integrate datasets for enhanced approaches to care and treatment of conditions like epilepsy and lung cancer.
The Person-Event Data Environment (PDE) database captures financial, health, medical and other data for every soldier from entry to separation. The database includes information on soldiers' military experience, contractors and dependents, representing a heterogeneous population with various socioeconomic backgrounds.