User mobility represents the movement of either individual or group. In smart cities, detection and prediction of mobility patterns are required for numerous applications like resource distribution, traffic management, and user behavioral analysis. With the increase in the number of smart vehicles, urban mobility detection and prediction have become a critical problem for study. Bike-sharing ecosystems (BSS) form an integral part of such ecosystems, as it supports the green revolution, ease of access, and solves traffic problems. However, recent schemes have suggested that BSS are challenged by issues of high density, mobility complexity of bikes (stations), large commute cost, uneven distribution, and route imbalances. To address the critical issues, the article proposes a hybrid scheme that combines rebalancing using clustering that addresses the mobility complexity. Once rebalancing is done, we address the uneven distribution among clusters using prediction models. This article is presented a comparative analysis of algorithms like fuzzy C-means clustering, linear regression, decision tree, and random forest classifiers for predictive analysis performed on weather data and nonweather data. The presented results indicate the viability of the proposed model in real-world scenarios.

Recently, there is an exponential influx of textual data in big data applications, which necessitates the requirement of text mining tools for analysis of data. In Text Mining applications (TM), Text Summarization (TS) has emerged as an emergent field in Natural Language Processing (NLP). Mostly, in TS, abstractive approaches are presented which build complex models, and thus, a shift is envisioned towards graph-based extractive text summarization models. Such models allow review and feedback analysis of a service or product, and have the benefits of being less complex, flexible, and require low computational resources. This makes them an effective fit for modern text mining based big data and Internet-of-Things (IoT) applications. Thus, in the proposed work, we present a scheme, GETS, which exploits a graph-based model to establish relations between words and sentences based on statistical operations. In the scheme, a post processing phase is presented which uses sentence clustering based on graph preparation. To make the scheme scalable fit for real world applications, we use the Apache Spark environment for parallel execution of graph-based operations. In experimental setup, the Recall-oriented Understudying Gisting Evaluation (ROUGE) parameters is used to evaluate the proposed graph based model with a comparative analysis with ROUGE 1,2,L measures. Comparative analysis is done based on clustered and non-clustered approaches. The obtained results renders the scheme effective as a backend of Artificial Intelligence (AI) models in crowdsourcing applications and decision-analytics models.