Hot "Big Data" is a global set off a smart advertising revolution. Those pervasive advertising is no longer the big 4A advertising company by art director or creative division of the hand, but from the automatic generation of ...

Is big data just for big business? For several years, larger corporations have been gathering informative insights from this next-generation approach to data analysis. But now small businesses are finding ways to leverage this comprehensive data analysis for marketing insights.

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Big  Data seems to be the phrase on everybody’s lips these days, and without a doubt, it has revolutionized the business world. Companies are getting valuable insights reading into mountains of data collected from all kinds of sources. Using Big Data simply makes sense, but the very prospect can be intimidating, particularly when it comes to small businesses and startups. One recent survey shows that 35 percent of startups are not even considering the possibility of leveraging Big Data or taking into account how much data is being generated every year – such a decision could be disastrous for the future success of a company.

Small businesses may feel that the ability to leverage Big Data is simply outside of their grasp, but Big Data solutions have become much more affordable in recent years. In addition, they are easier to use and oftentimes automated. In other words, small businesses have every reason to get into the Big Data game now as long as they know how best to use it. Here are just a few ways small companies can truly make Big Data work toward further success.

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Big data and business intelligence (BI) used to be only for enterprise companies. Now, however, thanks to the software as a service (SaaS) revolution, even small businesses can afford to track and tap into a wealth of information.

However, becoming a data-driven small business is not easy. Because you are dealing with complex troves of records that have multiple sources and are therefore highly unlikely to be structured uniformly, it can be difficult to process it all and interpret it into insights your business can actually use.

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(EMAILWIRE.COM, October 31, 2024 ) The global Big Data and Data Engineering Services market is experiencing significant growth, driven by the increasing volume of unstructured data and the need for advanced analytics. Key factors driving this growth include the rise of IoT devices, social media,...

In order to further improve the public health assurance system and the infectious diseases early warning system to give play to their positive roles and enhance their collaborative capacity, this paper, based on the big and thick data analytics technology, designs a 'rolling-type' data synergy model. This model covers districts and counties, municipalities, provinces, and the country. It forms a data blockchain for the public health assurance system and enables high sharing of data from existing system platforms such as the infectious diseases early warning system, the hospital medical record management system, the public health data management system, and the health big and thick data management system. Additionally, it realises prevention, control and early warning by utilising data mining and synergy technologies, and ideally solves problems of traditional public health assurance system platforms such as excessive pressure on the 'central node', poor data tamper-proofing capacity, low transmission efficiency of big and thick data, bad timeliness of emergency response, and so on. The realisation of this technology can greatly improve the application and analytics of big and thick data and further enhance the public health assurance capacity.

Emergency disaster management presents substantial risks and obstacles to shield tunnel building projects, particularly in the event of water leakage accidents. Contemporary water leak detection is critical for guaranteeing safety by reducing the likelihood of disasters and the severity of any resulting damages. However, it can be difficult. Deep learning models can analyse images taken inside the tunnel to look for signs of water damage. This study introduces a unique strategy that employs deep learning techniques, generative adversarial networks (GAN) with long short-term memory (LSTM) for water leakage detection i shield tunnel construction (WLD-STC) to conduct classification and prediction tasks on the massive image dataset. The results demonstrate that for identifying and analysing water leakage episodes during shield tunnel construction, the WLD-STC strategy using LSTM-based GAN networks outperformed other methods, particularly on huge data.

In the context of smart city development, the managed pressure drilling (MPD) drilling process faces many uncertainties, but the characteristics of the process are complex and require accurate wellbore pressure control. However, this process runs the risk of introducing un-modelled dynamics into the system. To this problem, this paper employs neural network control techniques to construct a dual-network system for throttle pressure control, the design encompasses both the controller and identifier components. The radial basis function (RBF) network and proportional features are connected in parallel in the controller structure, and the RBF network learning algorithm is used to train the identifier structure. The simulation results show that the actual wellbore pressure can quickly track the reference pressure value when the pressure setpoint changes. In addition, the controller based on neural network realises effective control, which enables the system to track the input target quickly and achieve stable convergence.

Research on the quality evaluation of teaching reforms plays an important role in promoting improvements in teaching quality. Therefore, an evaluation method of teaching reform quality in colleges and universities based on big data analysis is proposed. A multivariate logistic model is used to select the evaluation indicators for the quality evaluation of teaching reforms in universities. And clustering and cleaning of the evaluation indicator data are performed through big data analysis. The evaluation indicator data is used as input vectors, and the results of the teaching reform quality evaluation are used as output vectors. A support vector machine model based on the whale algorithm is built to obtain the relevant evaluation results. Experimental results show that the proposed method achieves a minimum recall rate of 98.7% for evaluation indicator data, the minimum data processing time of 96.3 ms, the accuracy rate consistently above 97.1%.

Big data management is a reality for an increasing number of organizations in many areas and represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial to facilitate the management of big data in any kind of organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management can be supported by these three dimensions: technology, people and processes. Hence, this article discusses these dimensions: the technological dimension that is related to storage, analytics and visualization of big data; the human aspects of big data; and, in addition, the process management dimension that involves in a technological and business approach the aspects of big data management.

Aim/Purpose: Board of Directors seek to use their big data as a competitive advantage. Still, scholars note the complexities of corporate governance in practice related to information security risk management (ISRM) effectiveness. Background: While the interest in ISRM and its relationship to organizational success has grown, the scholarly literature is unclear about the effects of Chief Technology Officers (CTOs) leadership styles, the alignment of the governance of big data, and ISRM effectiveness in organizations in the West-ern United States. Methodology: The research method selected for this study was a quantitative, correlational research design. Data from 139 participant survey responses from Chief Technology Officers (CTOs) in the Western United States were analyzed using 3 regression models to test for mediation following Baron and Kenny’s methodology. Contribution: Previous scholarship has established the importance of leadership styles, big data governance, and ISRM effectiveness, but not in a combined understanding of the relationship between all three variables. The researchers’ primary objective was to contribute valuable knowledge to the practical field of computer science by empirically validating the relationships between the CTOs leadership styles, the alignment of the governance of big data, and ISRM effectiveness. Findings: The results of the first regression model between CTOs leadership styles and ISRM effectiveness were statistically significant. The second regression model results between CTOs leadership styles and the alignment of the governance of big data were not statistically significant. The results of the third regression model between CTOs leadership styles, the alignment of the governance of big data, and ISRM effectiveness were statistically significant. The alignment of the governance of big data was a significant predictor in the model. At the same time, the predictive strength of all 3 CTOs leadership styles was diminished between the first regression model and the third regression model. The regression models indicated that the alignment of the governance of big data was a partial mediator of the relationship between CTOs leadership styles and ISRM effectiveness. Recommendations for Practitioners: With big data growing at an exponential rate, this research may be useful in helping other practitioners think about how to test mediation with other interconnected variables related to the alignment of the governance of big data. Overall, the alignment of governance of big data being a partial mediator of the relationship between CTOs leadership styles and ISRM effectiveness suggests the significant role that the alignment of the governance of big data plays within an organization. Recommendations for Researchers: While this exact study has not been previously conducted with these three variables with CTOs in the Western United States, overall, these results are in agreement with the literature that information security governance does not significantly mediate the relationship between IT leadership styles and ISRM. However, some of the overall findings did vary from the literature, including the predictive relationship between transactional leadership and ISRM effectiveness. With the finding of partial mediation indicated in this study, this also suggests that the alignment of the governance of big data provides a partial intervention between CTOs leadership styles and ISRM effectiveness. Impact on Society: Big data breaches are increasing year after year, exposing sensitive information that can lead to harm to citizens. This study supports the broader scholarly consensus that to achieve ISRM effectiveness, better alignment of governance policies is essential. This research highlights the importance of higher-level governance as it relates to ISRM effectiveness, implying that ineffective governance could negatively impact both leadership and ISRM effectiveness, which could potentially cause reputational harm. Future Research: This study raised questions about CTO leadership styles, the specific governance structures involved related to the alignment of big data and ISRM effectiveness. While the research around these variables independently is mature, there is an overall lack of mediation studies as it relates to the impact of the alignment of the governance of big data. With the lack of alignment around a universal framework, evolving frameworks could be tested in future research to see if similar results are obtained.

Aim/Purpose: The aim of this research paper is to suggest a comprehensive model that incorporates the technology acceptance model with the task-technology fit model, information quality, security, trust, and managerial support to investigate the intended usage of big data analytics (BDA) in banks and insurance companies. Background: The emergence of the concept of “big data,” prompted by the widespread use of connected devices and social media, has been pointed out by many professionals and financial institutions in particular, which makes it necessary to assess the determinants that have an impact on behavioral intention to use big data analytics in banks and insurance companies. Methodology: The integrated model was empirically assessed using self-administered questionnaires from 181 prospective big data analytics users in Moroccan banks and insurance firms and examined using partial least square (PLS) structural equation modeling. The results cover sample characteristics, an analysis of the validity and reliability of measurement models’ variables, an evaluation of the proposed hypotheses, and a discussion of the findings. Contribution: The paper makes a noteworthy contribution to the BDA adoption literature within the finance sector. It stands out by ingeniously amalgamating the Technology Acceptance Model (TAM) with Task-Technology Fit (TTF) while underscoring the critical significance of information quality, trust, and managerial support, due to their profound relevance and importance in the finance domain. Thus showing BDA has potential applications beyond the finance sector. Findings: The findings showed that TTF and trust’s impact on the intention to use is considerable. Information quality positively impacted perceived usefulness and ease of use, which in turn affected the intention to use. Moreover, managerial support moderates the correlation between perceived usefulness and the intention to use, whereas security did not affect the intention to use and managerial support did not moderate the influence of perceived ease of use. Recommendations for Practitioners: The results suggest that financial institutions can improve their adoption decisions for big data analytics (BDA) by understanding how users perceive it. Users are predisposed to use BDA if they presume it fits well with their tasks and is easy to use. The research also emphasizes the importance of relevant information quality, managerial support, and collaboration across departments to fully leverage the potential of BDA. Recommendation for Researchers: Further study may be done on other business sectors to confirm its generalizability and the same research design can be employed to assess BDA adoption in organizations that are in the advanced stage of big data utilization. Impact on Society: The study’s findings can enable stakeholders of financial institutions that are at the primary stage of big data exploitation to understand how users perceive BDA technologies and the way their perception can influence their intention toward their use. Future Research: Future research is expected to conduct a comparison of the moderating effect of managerial support on users with technical expertise versus those without; in addition, international studies across developed countries are required to build a solid understanding of users’ perceptions towards BDA.

Aim/Purpose: In this study, the research proposes and experiments with a new model of collecting, storing, and analyzing big data on customer feedback in the tourism industry. The research focused on the Vietnam market. Background: Big Data describes large databases that have been “silently” built by businesses, which include product information, customer information, customer feedback, etc. This information is valuable, and the volume increases rapidly over time, but businesses often pay little attention or store it discretely, not centrally, thereby wasting an extremely large resource and partly causing limitations for business analysis as well as data. Methodology: The study conducted an experiment by collecting customer feedback data in the field of tourism, especially tourism in Vietnam, from 2007 to 2022. After that, the research proceeded to store and mine latent topics based on the data collected using the Topic Model. The study applied cloud computing technology to build a collection and storage model to solve difficulties, including scalability, system stability, and system cost optimization, as well as ease of access to technology. Contribution: The research has four main contributions: (1) Building a model for Big Data collection, storage, and analysis; (2) Experimenting with the solution by collecting customer feedback data from huge platforms such as Booking.com, Agoda.com, and Phuot.vn based on cloud computing, focusing mainly on tourism Vietnam; (3) A Data Lake that stores customer feedback and discussion in the field of tourism was built, supporting researchers in the field of natural language processing; (4) Experimental research on the latent topic mining model from the collected Big Data based on the topic model. Findings: Experimental results show that the Data Lake has helped users easily extract information, thereby supporting administrators in making quick and timely decisions. Next, PySpark big data processing technology and cloud computing help speed up processing, save costs, and make model building easier when moving to SaaS. Finally, the topic model helps identify customer discussion trends and identify latent topics that customers are interested in so business owners have a better picture of their potential customers and business. Recommendations for Practitioners: Empirical results show that facilities are the factor that customers in the Vietnamese market complain about the most in the tourism/hospitality sector. This information also recommends that practitioners reduce their expectations about facilities because the overall level of physical facilities in the Vietnamese market is still weak and cannot be compared with other countries in the world. However, this is also information to support administrators in planning to upgrade facilities in the long term. Recommendation for Researchers: The value of Data Lake has been proven by research. The study also formed a model for big data collection, storage, and analysis. Researchers can use the same model for other fields or use the model and algorithm proposed by this study to collect and store big data in other platforms and areas. Impact on Society: Collecting, storing, and analyzing big data in the tourism sector helps government strategists to identify tourism trends and communication crises. Based on that information, government managers will be able to make decisions and strategies to develop regional tourism, propose price levels, and support innovative programs. That is the great social value that this research brings. Future Research: With each different platform or website, the study had to build a query scenario and choose a different technology approach, which limits the ability of the solution’s scalability to multiple platforms. Research will continue to build and standardize query scenarios and processing technologies to make scalability to other platforms easier.

Aim/Purpose: The use of digital technology, such as an electronic records management system (ERMS), has prompted widespread changes across organizations. The organization needs to support its operations with an automation system to improve production performance. This study investigates ERMS’s potentiality to enhance organizational performance in the oil and gas industry. Background: Oil and gas organizations generate enormous electronic records that lead to difficulties in managing them without any system or digitalization procedure. The need to use a system to manage big data and records affects information security and creates several problems. This study supports decision-makers in oil and gas organizations to use ERMS to enhance organizational performance. Methodology: We used a quantitative method by integrating the typical partial least squares (SEM-PLS) approach, including measurement items, respondents’ demographics, sampling and collection of data, and data analysis. The SEM-PLS approach uses a measurement and structural model assessment to analyze data. Contribution: This study contributes significantly to theory and practice by providing advancements in identity theory in the context of big data management and electronic records management. This study is a foundation for further research on the role of ERMS in operations performance and Big Data Management (BDM). This research makes a theoretical contribution by studying a theory-driven framework that may serve as an essential lens to evaluate the role of ERMS in performance and increase its potentiality in the future. This research also evaluated the combined impacts of general technology acceptance theory elements and identity theory in the context of ERMS to support data management. Findings: This study provides an empirically tested model that helps organizations to adopt ERMS based on the influence of big data management. The current study’s findings looked at the concerns of oil and gas organizations about integrating new technologies to support organizational performance. The results demonstrated that individual characteristics of users in oil and gas organizations, in conjunction with administrative features, are robust predictors of ERMS. The results show that ERMS potentiality significantly influences the organizational performance of oil and gas organizations. The research results fit the big ideas about how big data management and ERMS affect respondents to adopt new technologies. Recommendations for Practitioners: This study contributes significantly to the theory and practice of ERMS potentiality and BDM by developing and validating a new framework for adopting ERMS to support the performance and production of oil and gas organizations. The current study adds a new framework to identity theory in the context of ERMS and BDM. It increases the perceived benefits of using ERMS in protecting the credibility and authenticity of electronic records in oil and gas organizations. Recommendation for Researchers: This study serves as a foundation for future research into the function and influence of big data management on ERMS that support the organizational performance. Researchers can examine the framework of this study in other nations in the future, and they will be able to analyze this research framework to compare various results in other countries and expand ERMS generalizability and efficacy. Impact on Society: ERMS and its impact on BDM is still a developing field, and readers of this article can assist in gaining a better understanding of the literature’s dissemination of ERMS adoption in the oil and gas industry. This study presents an experimentally validated model of ERMS adoption with the effect of BDM in the oil and gas industry. Future Research: In the future, researchers may be able to examine the impact of BDM and user technology fit as critical factors in adopting ERMS by using different theories or locations. Furthermore, researchers may include the moderating impact of demographical parameters such as age, gender, wealth, and experience into this study model to make it even more robust and comprehensive. In addition, future research may examine the significant direct correlations between human traits, organizational features, and individual perceptions of BDM that are directly related to ERMS potentiality and operational performance in the future.

Aim/Purpose: While existing literature has extensively explored factors influencing the success of big data projects and proposed big data maturity models, no study has harnessed machine learning to predict project success and identify the critical features contributing significantly to that success. The purpose of this paper is to offer fresh insights into the realm of big data projects by leveraging machine-learning algorithms. Background: Previously, we introduced the Global Big Data Maturity Model (GBDMM), which encompassed various domains inspired by the success factors of big data projects. In this paper, we transformed these maturity domains into a survey and collected feedback from 90 big data experts across the Middle East, Gulf, Africa, and Turkey regions regarding their own projects. This approach aims to gather firsthand insights from practitioners and experts in the field. Methodology: To analyze the feedback obtained from the survey, we applied several algorithms suitable for small datasets and categorical features. Our approach included cross-validation and feature selection techniques to mitigate overfitting and enhance model performance. Notably, the best-performing algorithms in our study were the Decision Tree (achieving an F1 score of 67%) and the Cat Boost classifier (also achieving an F1 score of 67%). Contribution: This research makes a significant contribution to the field of big data projects. By utilizing machine-learning techniques, we predict the success or failure of such projects and identify the key features that significantly contribute to their success. This provides companies with a valuable model for predicting their own big data project outcomes. Findings: Our analysis revealed that the domains of strategy and data have the most influential impact on the success of big data projects. Therefore, companies should prioritize these domains when undertaking such projects. Furthermore, we now have an initial model capable of predicting project success or failure, which can be invaluable for companies. Recommendations for Practitioners: Based on our findings, we recommend that practitioners concentrate on developing robust strategies and prioritize data management to enhance the outcomes of their big data projects. Additionally, practitioners can leverage machine-learning techniques to predict the success rate of these projects. Recommendation for Researchers: For further research in this field, we suggest exploring additional algorithms and techniques and refining existing models to enhance the accuracy and reliability of predicting the success of big data projects. Researchers may also investigate further into the interplay between strategy, data, and the success of such projects. Impact on Society: By improving the success rate of big data projects, our findings enable organizations to create more efficient and impactful data-driven solutions across various sectors. This, in turn, facilitates informed decision-making, effective resource allocation, improved operational efficiency, and overall performance enhancement. Future Research: In the future, gathering additional feedback from a broader range of big data experts will be valuable and help refine the prediction algorithm. Conducting longitudinal studies to analyze the long-term success and outcomes of Big Data projects would be beneficial. Furthermore, exploring the applicability of our model across different regions and industries will provide further insights into the field.

Policy-makers and science and industry representatives are discussing how to make large amounts of Earth observation data accessible to a wider user community. To explore this idea, some 250 science, industry and policy-making representatives and national delegates from Europe, the US, Australia, China and Africa met at ESA’s ESRIN centre in Frascati, Italy last week for ESA’s first ‘Big Data from Space’ event.

Representatives from ESA and NASA opened the event together with the European Commission. European Commission Directorates-General for Enterprise and Industry, Research and Innovation and Communications Networks, Content and Technology, along with representatives from the European Environment Agency, the National Oceanic and Atmospheric Administration and the Open Geospatial Consortium acted as session chairs. 

Javier de la Torre, representing the EU BON partner Vizzuality gave a presentation 'Global Deforestation through Timeme: Big Data Meets Scalable Visualizations,' which included some of the work Vizzuality is doing toward the EUBON project.

The event concluded with a strong call by all parties for the ability to handle and use big Earth observing data. This could potentially open new opportunities for research and international cooperation schemes such as programmatic and industrial coordination.

Taxonomic descriptions, introduced by Linnaeus in 1735, are designed to allow scientists to tell one species from another. Now there is a new futuristic method for describing new species that goes far beyond the tradition. The new approach combines several techniques, including next generation molecular methods, barcoding, and novel computing and imaging technologies, that will test the model for big data collection, storage and management in biology. The study has just been published in the Biodiversity Data Journal.

While 13,494 new animal species were discovered by taxonomists in 2012, animal diversity on the planet continues to decline with unprecedented speed. Concerned with the rapid disappearance rates scientists have been forced towards a so called 'turbo taxonomy' approach, where rapid species description is needed to manage conservation.

While acknowledging the necessity of fast descriptions, the authors of the new study present the other 'extreme' for taxonomic description: "a new species of the future". An international team of scientists from Bulgaria, Croatia, China, UK, Denmark, France, Italy, Greece and Germany illustrated a holistic approach to the description of the new cave dwelling centipede species Eupolybothrus cavernicolus, recently discovered in a remote karst region of Croatia. The project was a collaboration between GigaScience, China National GeneBank, BGI-Shenzhen and Pensoft Publishers.

Eupolybothrus cavernicolus has become the first eukaryotic species for which, in addition to the traditional morphological description, scientists have provided a transcriptomic profile, DNA barcoding data, detailed anatomical X-ray microtomography (micro-CT), and a movie of the living specimen to document important traits of its behaviour. By employing micro-CT scanning in a new species, for the first time a high-resolution morphological and anatomical dataset is created - the 'cybertype' giving everyone virtual access to the specimen.

This, most data-rich species description, represents also the first biodiversity project that joins the ISA (Investigation-Study-Assay) Commons, that is an approach created by the genomic and molecular biology communities to store and describe different data types collected in the course of a multidisciplinary study.

"Communicating the results of next generation sequencing effectively requires the next generation of data publishing" says Prof. Lyubomir Penev, Managing director of Pensoft Publishers. "It is not sufficient just to collect 'big' data. The real challenge comes at the point when data should be managed, stored, handled, peer-reviewed, published and distributed in a way that allows for re-use in the coming big data world", concluded Prof. Penev.

"Next generation sequencing is moving beyond piecing together a species genetic blueprint to areas such as biodiversity research, with mass collections of species in "metabarcoding" surveys bringing genomics, monitoring of ecosystems and species-discovery closer together. This example attempts to integrate data from these different sources, and through curation in BGI and GigaScience's GigaDB database to make it interoperable and much more usable," says Dr Scott Edmunds from BGI and Executive Editor of GigaScience.

Additional information:

Pensoft and the Natural History Museum London have received financial support by the EU FP7 projects ViBRANT and pro-iBiosphere. The China National GeneBank (CNGB) and GigaScience teams have received support from the BGI. The DNA barcodes were obtained through the International Barcode of Life Project supported by grants from NSERC and from the government of Canada through Genome Canada and the Ontario Genomics Institute.

Original Sources:

Stoev P, Komerički A, Akkari N, Shanlin Liu, Xin Zhou, Weigand AM, Hostens J, Hunter CI, Edmunds SC, Porco D, Zapparoli M, Georgiev T, Mietchen D, Roberts D, Faulwetter S, Smith V, Penev L (2013) Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data. Biodiversity Data Journal 1: e1013. DOI: 10.3897/BDJ.1.e1013

Edmunds SC, Hunter CI, Smith V, Stoev P, Penev L (2013) Biodiversity research in the "big data" era: GigaScience and Pensoft work together to publish the most data-rich species description. GigaScience 2:14 doi:10.1186/2047-217X-2-14

Watch the 3D cybertype video: http://www.youtube.com/watch?v=vqPuwKG8hE4&feature=em-upload_owner

The ESWC 2013 takes place from May 26th, 2013 to May 30th, 2013 in Montpellier, France.
The ESWC is a major venue for discussing the latest scientific results and technology innovations around semantic technologies. Building on its past success, ESWC is seeking to broaden its focus to span other relevant research areas in which Web semantics plays an important role.
Event web site: ESWC 2013

The European Space Agency in Frascati is organising a "Big data from Space" event to address the barriers that hamper an effective use of large volumes of Earth observation data. The event will focus on issues associated with the organisation and delivery of large volumes of  contemporary and historical Earth observations, either space-based or from ground (including ubiquitous information-sensing mobile devices, aerial sensory technologies, wireless sensor networks).

The aim of the event is to inform and guide prospective applicants preparing project proposals, to facilitate sharing of ideas and experiences. It will give participants the chance to network and to find partners for their projects.

ICT-16 is part of Horizon 2020's ICT work programme 2014-2015

The activities supported under this topic contribute to the Big Data challenge by addressing the fundamental research problems related to the scalability and responsiveness of analytics capabilities (such as privacy-aware machine learning, language understanding, data mining and visualization). Special focus is on industry-validated, user-defined challenges like predictions, and rigorous processes for monitoring and measurement.

The current outline of the draft agenda can be found HERE

Registration

The registration to the event is now open and is on the first-come-first-serve basis. Click HERE to register. The closing date is 8 January 2015.

After the registration you will have the possibility to upload a presentation for the ICT-16 networking and partner finding session which will take place in the afternoon (max 3 slides for 3 minutes presentation).

In parallel to the ICT-16 networking session a workshop on multilingual data value chains will be organised. Therefore, you should pay attention to which part of the event you enrol.

Please note that bilateral meetings with EC Project Officers to discuss proposal ideas (proposal clinics) will *not* be possible, in compliance with H2020 regulations.

More information available here.

Management tools are creating major gains for contractors.

In just a few years, digital technologies have allowed faster mobilization in response to humanitarian crises, better documentation of war crimes in conflict zones like Syria and Yemen, and more accessible platforms for organizing peaceful demonstrations around the world.

Big data allows retailers to analyze vast sets of customer information including purchase history, demographics, browsing behaviour, and social media interactions. This enables highly targeted marketing campaigns, product recommendations, and loyalty programs.

EC-Council, the world's leading cybersecurity credentialing body, will be launching an Autonomous, Big Data Cyber Engine for Skill Measurement innovation that is set to revolutionize cyber skill learning, practice, and assessment on October 19, 2020

Here is a Storify round up of the SpotOn London session: Tackling the terabyte: how should

Dr. Andrii Gakhov, author of the book Probabilistic Data Structures and Algorithms for Big Data Applications talks about probabilistic data structures and their application to the big data domain with host Robert Blumen.

What is Big Data, really, and why does it matter? A conversation with experts Jean-Pierre Dijks and Andrew Bond.

What new challenges does Big Data present for Architects? What do architects need to do to prepare themselves and their organizations?

Which stakeholders are driving the adoption of Big Data strategies in organizations, and why?

What distinguishes an architecture that is ready for Big Data from one that is not? What are some of the typical mistakes organizations make as they take their first steps toward big data?

Big data experts discuss how Oracle Database fits into a Big Data Architecture, and share insight on a persistent problem in technology adoption.

There's big, and then there's BIG. Experts discuss the challenges of keeping up with coming data explosion.

This 3D printing podcast asks: What is the best way to protect my 3D Printing Data? This is the type of work the 3D Printing Association deals with every day.               

Big data - Overview and requirements for data preservation

Big data driven networking - functional architecture

Big data - Deep packet inspection mechanism for big data in network

2019 - Big Data - Concept and application for telecommunications

FIGI - DFS - Big data machine learning consumer protection and privacy

Data centre infrastructure management system based on big data and artificial intelligence technology

Security requirements and framework for big data analytics in mobile Internet services

This Warsaw-based start-up was founded in 2020, and aims to address the ‘performance bottleneck’ of high-volume data processing.

Read more: Oxla: Reducing the big costs of big data processing

Ready to turn big data into big business insights? Look to the cloud.

The post Why it's hard to say "big data" without saying "cloud" appeared first on The Data Roundtable.

Sydney, Australia (SPX) Nov 04, 2024
In a recent study led by Professor Xin Li and Dr. Yanlong Guo of the Institute of Tibetan Plateau Research, Chinese Academy of Sciences, researchers analyze how scientific research is evolving through the power of big data and artificial intelligence (AI). The paper discusses how the traditional "correlation supersedes causation" model is being increasingly challenged by new "data-intensive scie

Yeswanth Surampudi's expertise in data engineering has been transformative. His work building scalable pipelines, refining data processes, and managing cloud migrations highlights his proficiency in Big Data