Dynamic Identification of Participatory Mobile Health Communities

Aljawarneh, Isam Mashhour; Bellavista, Paolo; Rolt, Carlos Roberto De

doi:10.1007/978-3-319-67636-4_22

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…Also, analyzing patient's health condition at the time of attack for checking severity degree. Second, system can identify communities in the surroundings of the patient (this needs a community detection algorithm [2]) and selects the best volunteer who is the nearest and capable of providing first-aid assistance. Here election is made based on distance to the patient location, social relativeness between patient and volunteer, the real ability of volunteer to provide first-aid (i.e., being trained enough or not), and all those depend on many factors (among which the degree of severity of the patient health condition has a highest priority).…”

Section: Highly Dynamic Environments As a Motivating Scenariomentioning

confidence: 99%

“…In the last decade or so, the proliferation of ubiquitous positioning devices, and a massive spread of the Internet of Things (IoT) paradigm have caused an accumulation of an unprecedented huge mass of datasets, forming a phenomenon referred to as big data. Today, all kinds of businesses are data-driven, with data being mostly geocoded and real-time [1] , making timely analysis a priority, and thus promoting the emergence of Geographic Information Systems (GISs), with wide spectrum of applications, including participatory healthcare [2] , neurology analytics [3] , medical pathology imaging [4] , and city planning [5]. IoT is loosely defined as a network of interconnected computing devices that may constitute home electronic appliances (e.g., security systems and cameras), connected vehicles, and sensor-enabled positioning devices (and actuators) which communicate endlessly and transfer data in real-time [6].…”

Section: Introductionmentioning

confidence: 99%

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Big Spatial Data Management for the Internet of Things: A Survey

et al. 2020

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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.

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Section: Highly Dynamic Environments As a Motivating Scenariomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Big Spatial Data Management for the Internet of Things: A Survey

et al. 2020

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“…Large amounts of geo-referenced data streams are generated daily from IoT devices in high-traffic dynamic smart cities [ 1 ]. The data arrive in streams, characterized as highly skewed in data patterns and distributions [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…In summary, the following are our contributions in this paper (1) we designed an accuracy/latency aware data stream processing system, ApproxSSPS, for statistics computations and aggregation queries of mobility data in dynamic smart cities. ApproxSSPS contains accuracy and latency controllers for maintaining system stability during transient peaks in data arrival rates; (2) we compared ApproxSSPS with a similar baseline system from the recent literature, specifically, we compared it with the work by [8]; (3) we implemented ApproxSSPE on Spark Structured Streaming and evaluated it using real geo-referenced big mobility data streams.…”

Section: Introductionmentioning

confidence: 99%

QoS-Aware Approximate Query Processing for Smart Cities Spatial Data Streams

Jawarneh

Bellavista

Corradi

et al. 2021

Sensors

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Large amounts of georeferenced data streams arrive daily to stream processing systems. This is attributable to the overabundance of affordable IoT devices. In addition, interested practitioners desire to exploit Internet of Things (IoT) data streams for strategic decision-making purposes. However, mobility data are highly skewed and their arrival rates fluctuate. This nature poses an extra challenge on data stream processing systems, which are required in order to achieve pre-specified latency and accuracy goals. In this paper, we propose ApproxSSPS, which is a system for approximate processing of geo-referenced mobility data, at scale with quality of service guarantees. We focus on stateful aggregations (e.g., means, counts) and top-N queries. ApproxSSPS features a controller that interactively learns the latency statistics and calculates proper sampling rates to meet latency or/and accuracy targets. An overarching trait of ApproxSSPS is its ability to strike a plausible balance between latency and accuracy targets. We evaluate ApproxSSPS on Apache Spark Structured Streaming with real mobility data. We also compared ApproxSSPS against a state-of-the-art online adaptive processing system. Our extensive experiments prove that ApproxSSPS can fulfill latency and accuracy targets with varying sets of parameter configurations and load intensities (i.e., transient peaks in data loads versus slow arriving streams). Moreover, our results show that ApproxSSPS outperforms the baseline counterpart by significant magnitudes. In short, ApproxSSPS is a novel spatial data stream processing system that can deliver real accurate results in a timely manner, by dynamically specifying the limits on data samples.

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