Wireless body area networks (WBANs) have emerged as a promising solution for addressing challenges faced by elderly individuals, limited medical facilities, and various chronic medical conditions. WBANs consist of wearable sensing and computing devices interconnected through wireless communication channels, enabling the collection and transmission of vital physiological data. However, the energy constraints of the battery-powered sensor nodes in WBANs pose a significant challenge to ensuring long-term operational efficiency. Two-hop routing protocols have been suggested to extend the stability period and maximize the network’s lifetime. These protocols select appropriate parent nodes or forwarders with a maximum of two hops to relay data from sensor nodes to the sink. While numerous energy-efficient routing solutions have been proposed for WBANs, reliability has often been overlooked. Our paper introduces an energy-efficient routing protocol called a Hybrid Clustering Approach for Extending WBAN Lifetime (HCEL) to address these limitations. HCEL leverages a utility function to select parent nodes based on residual energy (RE), proximity to the sink node, and the received signal strength indicator (RSSI). The parent node selection process also incorporates an energy threshold value and a constrained number of serving nodes. The main goal is to extend the overall lifetime of all nodes within the network. Through extensive simulations, the study shows that HCEL outperforms both Stable Increased Throughput Multihop Protocol for Link Efficiency (SIMPLE) and Energy-Efficient Reliable Routing Scheme (ERRS) protocols in several key performance metrics. The specific findings of our article highlight the superior performance of HCEL in terms of increased network stability, extended network lifetime, reduced energy consumption, improved data throughput, minimized delays, and improved link reliability.

Visual data exploration is ubiquitous in nearly every industry and organization to support discovering data-driven actionable insights. However, unlocking those insights requires analysts to manually construct a prohibitively large number of aggregate queries and visually explore their results looking for those valuable and insightful visualizations. Such challenge naturally motivated the development of novel solutions that automate the visual explorations process, and recommend to analysts those particular queries that best visualize their data and reveal interesting actionable insights. In such automated solutions, there is a clear need for providing analysts with a diversified and concise set of recommended visualizations, which cover and represent a large combinatorial high-dimensional space of possible visualizations. However, directly incorporating existing diversification methods leads to a "process-first-diversify-next" approach, in which all possible data visualizations are generated first through executing a large number of aggregate queries. To address this challenge and minimize the incurred query processing costs, in this work we propose novel optimization techniques for the efficient diversification of recommended insightful visualizations. The key idea underlying our proposed techniques is to identify and eliminate the processing of a large number of low-utility insignificant visualizations. Meanwhile, for the potentially high-utility insightful visualizations, shared multi-query optimization techniques are proposed for further reduction in data processing cost. Our extensive experimental evaluation on real datasets demonstrates the performance gains provided by our proposed techniques, in terms of minimizing the query processing cost (i.e., efficiency), as well as maximizing the quality of recommendations (i.e., effectiveness).

The need for efficient and effective data exploration has resulted in several solutions that automatically recommend interesting visualizations. The main idea underlying those solutions is to automatically generate all possible views of data, and recommend the top-k interesting views. However, those solutions assume that the analyst is able to formulate a well-defined query that selects a subset of data, which contains insights. Meanwhile, in reality, it is typically a challenging task to pose an exploratory query, which can immediately reveal some insights. To address that challenge, in this work we propose utilizing query refinement as one technique that allows to automatically adjust the analyst's input query to discover such valuable insights. However, a naive query refinement, in addition to generating a prohibitively large search space, also raises other problems such as deviating from the user's preference and recommending statistically insignificant views. In this paper, we address those problems and propose a novel suit of schemes, which efficiently navigate the refined queries search space to recommend the top-k insights that meet all of the analyst's pre-specified criteria.

Analyzing crowdfunding data has been the focus of many research efforts, where analysts typically explore this data to identify the main factors and characteristics of the lending process as well as to discover unique patterns and anomalies in loan distributions. However, the manual exploration and visualization of such data is clearly an ad hoc, time-consuming, and labor-intensive process. Hence, in this work, we propose LoanVis, which is an automated solution for discovering and recommending those valuable and insightful visualizations. LoanVis is a data-driven system that utilizes objective metrics to quantify the “interestingness” of a visualization and employs such metrics in the recommendation process. We demonstrate the effectiveness of LoanVis in analyzing and exploring different aspects of the Kiva crowdfunding dataset.