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Depending on the Internet as the main source of information regarding all aspects of our life is becoming a trend. People seek relevant information, suggestions, and recommendations in an overloaded online world and through social ties regarding their daily activities, including places to visit and restaurants to try new food. The wide variety of choices that are available online causes information overloading, which thereby complicates the selection process. Traditional recommender systems are mostly dependent on a conventional model that is based on user-item-rating interaction without considering contextual information. We claim that new generations of recommendation systems able to exploit context in an innovative and efficient way is important and may statistically yield more significant rating predictions. However, only few research works have focused on how to effectively and efficiently exploit context metadata in Deep Learning (DL)-based recommendations. The main reason lies, perhaps most significantly, in the fact that most current DL algorithms are not intrinsically designed to incorporate contextual tags. In this paper, we provide a significant contribution for filling this gap by designing a hybrid algorithm that retrofits and repurposes a prefiltering contextual incorporation method and feeds the new dimension to a DL-based neural collaborative filtering method, thus preserving and recovering the benefits of both without their limitations. The paper also reports quantitative results that show that our method outperforms the baselines by statistically significant margins. INDEX TERMS Deep learning, recommender systems, collaborative filtering, context awareness, apache spark.

The widespread adoption of ubiquitous IoT edge devices and modern telemetry has generated an unprecedented avalanche of spatially-tagged datasets, which if could interactively be explored, would offer relevant insights into interesting natural phenomena. Online application of spatial queries is expensive, a problem that is further inflated by the fact that we, more than often, do not have access to a full dataset population in nonstationary settings. As a way of coping up, sampling stands out as a natural solution for approximating estimators such as averages and totals of some interesting correlated parameters. In any sampling design, representativeness remains the main issue upon which a method is regarded good or bad. In a loose way, in a spatial context, this means fairly sampling quantities in a way that preserves spatial characteristics so as to provide more accurate approximates for spatial query responses. Current big data management systems either do not offer over-the-counter spatialaware online sampling solutions or, at best, rely on randomness, which causes too many imponderables for an overall estimation. We herein have designed a QoS-spatial-aware online sampling method that outperforms vanilla baselines by statically significant magnitudes. Our method sits atop Apache Spark Structured Streaming's codebase and have been tested against a benchmark that is consisting of millions-records of spatially-augmented dataset.

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The high abundance of IoT devices have caused an unprecedented accumulation of avalanches of georeferenced IoT spatial data that if could be analyzed correctly would unleash important information. This can feed decision support systems for better decision making and strategic planning regarding important aspects of our lives that depend heavily on LBSs. Several spatial data management systems for IoT data in Cloud has recently gained momentum. However, the literature is still missing a comprehensive survey that conceptualize a convenient framework that classify those frameworks under appropriate categories. In this survey paper, we focus on the management of big geospatial data that are generated by IoT data sources. We also define a conceptual framework and match the woks of the recent literature with it. We then identify future research frontiers in the field depending on the surveyed works.