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Subject: Big data

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    Conference Object
    Perceptions, Expectations and Implementations of Big Data in Public Sector : Kamuda Buÿük Veri: Alglar, Beklentiler Ve Uygulamalar
    (Institute of Electrical and Electronics Engineers Inc., 2018) Dogdu,E.; Ozbayoglu,M.; Yazici,A.; Karakaya,Z.
    Big Data is one of the most commonly encountered buzzwords among IT professionals nowadays. Technological advancements in data acquisition, storage, telecommunications, embedded systems and sensor technologies resulted in huge inflows of streaming data coming from variety of sources, ranging from financial streaming data to social media tweets, or wearable health gadgets to drone flight logs. The processing and analysis of such data is a difficult task, but as appointed by many IT experts, it is crucial to have a Big Data Implementation plan in today's challenging industry standards. In this study, we performed a survey among IT professionals working in the public sector and tried to address some of their implementation issues and their perception of Big Data today and their expectations about how the industry will evolve. The results indicate that most of the public sector professionals are aware of the current Big Data requirements, embrace the Big Data challenge and are optimistic about the future. © 2018 IEEE.
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    Article
    Citation - WoS: 197
    Citation - Scopus: 296
    Co-Lstm: Convolutional Lstm Model for Sentiment Analysis in Social Big Data
    (Elsevier Sci Ltd, 2021) Behera, Ranjan Kumar; Jena, Monalisa; Rath, Santanu Kumar; Misra, Sanjay
    Analysis of consumer reviews posted on social media is found to be essential for several business applications. Consumer reviews posted in social media are increasing at an exponential rate both in terms of number and relevance, which leads to big data. In this paper, a hybrid approach of two deep learning architectures namely Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) (RNN with memory) is suggested for sentiment classification of reviews posted at diverse domains. Deep convolutional networks have been highly effective in local feature selection, while recurrent networks (LSTM) often yield good results in the sequential analysis of a long text. The proposed Co-LSTM model is mainly aimed at two objectives in sentiment analysis. First, it is highly adaptable in examining big social data, keeping scalability in mind, and secondly, unlike the conventional machine learning approaches, it is free from any particular domain. The experiment has been carried out on four review datasets from diverse domains to train the model which can handle all kinds of dependencies that usually arises in a post. The experimental results show that the proposed ensemble model outperforms other machine learning approaches in terms of accuracy and other parameters.