This book constitutes the refereed proceedings of the 7th International Conference on Big Data analytics, BDA 2019, held in Ahmedabad, India, in December 2019.
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.
