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Diseases are affected and altered by different diets in multiple ways. Although diet is an important factor, there is a lack of reliable information related to disease and diet associations. The associations can only be known by reading biomedical research papers as no such dataset is readily available. Manual extraction of such associations is a time-consuming process, so in this paper, we have developed Disease Diet Associations Curator and Explorer (DIDACE) for automatically curating and further exploring disease-diet association database. A two-phase approach has been followed which includes curation of medical literature in the first phase so as to quantify the strength of association of different diseases and diets. In the second phase, generated database is further analyzed to predict the nature (harmful or helpful) of unknown associations. This is done by performing sentiment analysis and machine learning using curated database. The database, thus generated, comprises both nature and strength of Disease-Diet associations. Such databases might prove to be a useful resource for medical and health informatics researchers for understanding complex interdependencies of different foods and diseases. [ABSTRACT FROM AUTHOR]

Driven by the core technologies, i.e., sensor-based autonomous data acquisition and the cloud-based big data analysis, IoT automates the actuation of data-driven intelligent actions on the connected objects. This automation enables numerous useful real-life use-cases, such as smart transport, smart living, smart cities, and so on. However, recent industry surveys reflect that data-related challenges are responsible for slower growth of IoT in recent years. For this reason, this article presents a systematic and comprehensive survey on IoT Big Data (IoTBD) with the aim to identify the uncharted challenges for IoTBD. This article analyzes the state-of-the-art academic works in IoT and big data management across various domains and proposes a taxonomy for IoTBD management. Then, the survey explores the IoT portfolio of major cloud vendors and provides a classification of vendor services for the integration of IoT and IoTBD on their cloud platforms. After that, the survey identifies the IoTBD challenges in terms of 13 V's challenges and envisions IoTBD as "Big Data 2.0." Then the survey provides comprehensive analysis of recent works that address IoTBD challenges by highlighting their strengths and weaknesses to assess the recent trends and future research directions. Finally, the survey concludes with discussion on open research issues for IoTBD. [ABSTRACT FROM AUTHOR]