Big data is becoming a very important concept nowadays as it can handle data in different formats and structures, velocity, and huge volume. NOSQL databases are used for handling the data with these characteristics as traditional database can't be used in managing this type of data. NoSQL database design is based on horizontal scalability with the concept of BASE which supports eventual consistence and data is considered in a soft state and basically available. Although NoSQL has a lot to offer when used in big data it is still not mature enough and faces some challenges including low join performance, concurrency control and recovery. Not only this but also it is very challenging for organizations to know which NoSQL data model to use and how does it fit with its organizational needs. This paper mainly displays the different NOSQL data models and the opportunities and challenges alongside with some techniques for handling these challenges.
In this paper, we aim to introduce a survey on the applications of deep learning for breast cancer detection and diagnosis to provide an overview of the progress in this field. In the survey, we firstly provide an overview on deep learning and the popular architectures used for breast cancer detection and diagnosis. Especially we present four popular deep learning architectures, including convolutional neural networks, fully convolutional networks, auto encoders, and deep belief networks in the survey. Secondly, we provide a survey on the studies exploiting deep learning for breast cancer detection and diagnosis.
Electricity load demand converts from time to time frequently in a day. Encountering time-varying demand particularly in peak times is considered a big challenge that faces electric utilities. Persistent growth in peak load increases the prospect of power failure and increases the electricity equipping marginal cost. Therefore, balancing production and consumption of electricity or addressing peak load has become a key attention of utilities. Most previous works and researches were focused on applying Shave/Shift peak load to solve energy scarcity. In this study, we introduce four significant technologies and techniques for achieving peak load shaving, namely “Internet of Things (IoT) in Energy System”, “On-site Generation systems (Renewable Energy Resources)”, “Demand Side Management (DSM)” applications of control center and “Energy Storage Systems (ESSs)”. The impact of these four major methods for peak load shaving to the grid has been discussed in detail. Finally, we suggest a conceptual framework as guiding tool for illustrating the presented technologies of Shave/Shift peak load in energy systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.