The primary purpose of this paper is to provide an in-depth analysis of different platforms available for performing big data analytics. This paper surveys different hardware platforms available for big data analytics and assesses the advantages and drawbacks of each of these platforms based on various metrics such as scalability, data I/O rate, fault tolerance, real-time processing, data size supported and iterative task support. In addition to the hardware, a detailed description of the software frameworks used within each of these platforms is also discussed along with their strengths and drawbacks. Some of the critical characteristics described here can potentially aid the readers in making an informed decision about the right choice of platforms depending on their computational needs. Using a star ratings table, a rigorous qualitative comparison between different platforms is also discussed for each of the six characteristics that are critical for the algorithms of big data analytics. In order to provide more insights into the effectiveness of each of the platform in the context of big data analytics, specific implementation level details of the widely used k-means clustering algorithm on various platforms are also described in the form pseudocode.

Fig. 1. Shaded relief of the Caucasus Mountains created with a neural network trained with a manual relief shading of Switzerland.

Aims and Objectives: The purpose of this study was to determine the association of BMI-for-age with dental caries and socioeconomic status. Method: A random sample of 2033 school going children aged 6-15 years were selected from ten different schools located in the south of Bangalore city. Height and weight of each child was recorded to obtain BMI-for-age. The socioeconomic status (SES) was assessed based on educational status, profession and annual income of parents. Dental caries was recorded according to WHO criteria. A diet recording sheet was given to each child to record his/her dietary intake of the four basic food groups and snacks for 5 consecutive days including one weekend day. The data obtained was subjected to statistical analysis. Results: The results showed that a higher number of children who were overweight and at a risk of overweight were seen in the upper SES and both showed a higher mean dietary intake of all the four food groups and snacks. The mean deft score was significantly higher in underweight children. A significantly higher mean DMFT score was observed in children at risk of overweight and overweight children. Conclusions: Children from the upper classes consumed more food, including snacks and were either at a risk of overweight or overweight. They had more caries in their permanent dentition. Underweight children were seen in the lower class. Although their intake of snacks was less, they had higher caries in their primary dentition.

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Automatic detection of tweets that provide Location-specific information will be extremely useful in conveying geo-location based knowledge to the users. However, there is a significant challenge in retrieving such tweets due to the sparsity of geo-tag information, the short textual nature of tweets, and the lack of pre-defined set of topics. In this paper, we develop a novel framework to identify and summarize tweets that are specific to a location. First, we propose a weighting scheme called Location Centric Word Co-occurrence (LCWC) that uses the content of the tweets and the network information of the twitterers to identify tweets that are location-specific. We evaluate the proposed model using a set of annotated tweets and compare the performance with other weighting schemes studied in the literature. This paper reports three key findings: (a) top trending tweets from a location are poor descriptors of location-specific tweets, (b) ranking tweets purely based on users' geo-location cannot ascertain the location specificity of tweets, and (c) users' network information plays an important role in determining the location-specific characteristics of the tweets. Finally, we train a topic model based on Latent Dirichlet Allocation (LDA) using a large collection of local news database and tweet-based Urls to predict the topics from the location-specific tweets and present them using an interactive web-based interface.