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Annals of Data Science OnlineFirst articles


A Novel Multiview Topic Model to Compute Correlation of Heterogeneous Data

With fast development of Internet technologies and sensor techniques, it is much easier to acquire data from different sources in different dates and times. However, how to compute the correlation of those heterogeneous data is a big challenge for …

17.02.2018 Open Access

Assessing Survival Time of Women with Cervical Cancer Using Various Parametric Frailty Models: A Case Study at Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia

Cervical cancer is one of the leading causes of death in the world and represents a tremendous burden on patients, families and societies. It is estimated that over one million women worldwide currently have cervical cancer; most of them have not …


Build a Tourism-Specific Sentiment Lexicon Via Word2vec

Online travel and online travel culture developed fast in China recently years while useful knowledge still hidden under a large number of tourism reviews. Therefore, we need effective sentiment analysis methods to mine useful knowledge which can …


User Data Can Tell Defaulters in P2P Lending

Online peer-to-peer (P2P) lending service is a new type of financial platforms that enables individuals borrow and lend money directly from one to another. As P2P lending service is rapidly developing, a number of rating systems of borrowers’ …


Enhancing Situation Awareness Using Semantic Web Technologies and Complex Event Processing

Data fusion techniques combine raw data of multiple sources and collect associated data to achieve more specific inferences than what could be attained with a single source. Situational awareness is one of the levels of the JDL, a matured …

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Annals of Data Science (AODS) is a new academic journal focusing on Big Data analytics and applications. It not only promotes how to use interdisciplinary techniques, including statistics, artificial intelligence and optimization, to process Big Data and conduct data mining, but also how to use the knowledge gleaned from Big Data for real-life applications. AODS accepts high-quality contributions on the foundations of data science, technical papers on various challenging problems in Big Data and meaningful case studies concerning business analytics in the context of Big Data.

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