Big Data Analytics 7th International Conference, BDA 2019, Ahmedabad, India, December 17–20, 2019, Proceedings

The 25 papers presented in this volume were carefully reviewed and selected from 53 submissions. The papers are organized in topical sections named: big data analytics: vision and perspectives; search and information extraction; predictive analytics in medical and agricultural domains; graph analytics; pattern mining; and machine learning.

Big Data Analytics: Vision and Perspectives.- Transforming Sensing Data into Smart Data for Smart Sustainable Cities.- Deep Learning Models for Medical Image Analysis: Challenges and Future Directions.- Recent Advances and Challenges in design of Non-Goal Oriented Dialogue System.- Data Cube is Dead, Long Life to Data Cube  in the Age of Web Data.- Search and Information Extraction.- Improving Result Diversity using Query Term Proximity in Exploratory Search.- Segment-search vs Knowledge Graphs: Making a Keyword Search  Engine for Web Documents.- Pairing Users in Social Media via Processing Meta-data from Conversational Files.- Large-Scale Information Extraction from Emails with Data Constraints.- Comparative Analysis of Rule-based, Dictionary-based and Hybrid Stemmers for Gujarati Language.- Predictive Analytics in Medical and Agricultural Domains.- Artificial Intelligence and Bayesian Knowledge Network in Health Care – Smartphone Apps for diagnosis and differentiation of anemias with higher accuracy at Resource Constrained Point-of-Care settings.- Analyzing Domain Knowledge for Big Data Analysis: A Case Study with Urban Tree Type Classification.- Market Intelligence for Agricultural Commodities using Forecasting and Deep Learning Techniques.- Graph Analytics.- TKG: Efficient Mining of Top-K Frequent Subgraphs.- Why Multilayer Networks Instead  Of Simple Graphs? Modeling  Effectiveness  And Analysis Flexibility & Efficiency!.- Gossip Based Distributed Real Time Task Scheduling with Guaranteed Performance on Heterogeneous Networks.- Data-Driven Optimization of Public Transit Schedule.- Pattern Mining.- Discovering Spatial High Utility Frequent Itemsets in Spatiotemporal Databases.- Efficient Algorithms For Flock Detection in Large Spatio-Temporal Data.- Local Temporal  Compression for (Globally) Evolving Spatial Surfaces.- An Explicit Relationship between Sequential Patterns and their Concise Representations.- Machine Learning.- A novel approach to identify the determinants of online review helpfulness and predict the helpfulness score across product categories.- Analysis and Recognition of Hand-drawn Images with Effective Data Handling.- Real Time Static Gesture Detection Using Deep Learning.- Interpreting Context of Images using Scene Graphs.- Deep Learning in the Domain of  Near-Duplicate Document Detection.