Abstract:Citizen engagement is one of the key factors for smart city initiatives to remain sustainable over time. This in turn entails providing citizens and other relevant stakeholders with the latest data and tools that enable them to derive insights that add value to their day-to-day life. The massive volume of data being constantly produced in these smart city environments makes satisfying this requirement particularly challenging. This paper introduces Explora, a generic framework for serving interactive low-laten… Show more
“…However, the system employs sampling on the original polygons representing the study area, which could reduce the efficiency in cases of very big data streams. Also, EXPLORA [24] has been designed to support AQP and geo-visualization of spatiotemporal data at scale with QoS guarantees. It hosts a sampling method that selects online representative samples of arriving data, which aims to speed up processing while keeping accuracy in check.…”
The unprecedented availability of sensor networks and GPS-enabled devices has caused the accumulation of voluminous georeferenced data streams. These data streams offer an opportunity to derive valuable insights and facilitate decision making for urban planning. However, processing and managing such data is challenging, given the size and multidimensionality of these data. Therefore, there is a growing interest in spatial approximate query processing depending on stratified-like sampling methods. However, in these solutions, as the number of strata increases, response time grows, thus counteracting the benefits of sampling. In this paper, we originally show the design and realization of a novel online geospatial approximate processing solution called GeoRAP. GeoRAP employs a front-stage filter based on the Ramer–Douglas–Peucker line simplification algorithm to reduce the size of study area coverage; thereafter, it employs a spatial stratified-like sampling method that minimizes the number of strata, thus increasing throughput and minimizing response time, while keeping the accuracy loss in check. Our method is applicable for various online and batch geospatial processing workloads, including complex geo-statistics, aggregation queries, and the generation of region-based aggregate geo-maps such as choropleth maps and heatmaps. We have extensively tested the performance of our prototyped solution with real-world big spatial data, and this paper shows that GeoRAP can outperform state-of-the-art baselines by an order of magnitude in terms of throughput while statistically obtaining results with good accuracy.
“…However, the system employs sampling on the original polygons representing the study area, which could reduce the efficiency in cases of very big data streams. Also, EXPLORA [24] has been designed to support AQP and geo-visualization of spatiotemporal data at scale with QoS guarantees. It hosts a sampling method that selects online representative samples of arriving data, which aims to speed up processing while keeping accuracy in check.…”
The unprecedented availability of sensor networks and GPS-enabled devices has caused the accumulation of voluminous georeferenced data streams. These data streams offer an opportunity to derive valuable insights and facilitate decision making for urban planning. However, processing and managing such data is challenging, given the size and multidimensionality of these data. Therefore, there is a growing interest in spatial approximate query processing depending on stratified-like sampling methods. However, in these solutions, as the number of strata increases, response time grows, thus counteracting the benefits of sampling. In this paper, we originally show the design and realization of a novel online geospatial approximate processing solution called GeoRAP. GeoRAP employs a front-stage filter based on the Ramer–Douglas–Peucker line simplification algorithm to reduce the size of study area coverage; thereafter, it employs a spatial stratified-like sampling method that minimizes the number of strata, thus increasing throughput and minimizing response time, while keeping the accuracy loss in check. Our method is applicable for various online and batch geospatial processing workloads, including complex geo-statistics, aggregation queries, and the generation of region-based aggregate geo-maps such as choropleth maps and heatmaps. We have extensively tested the performance of our prototyped solution with real-world big spatial data, and this paper shows that GeoRAP can outperform state-of-the-art baselines by an order of magnitude in terms of throughput while statistically obtaining results with good accuracy.
“…In addition to urban vehicle emission pollution prediction [20][21][22][23][24][25]. Visualization and interactive exploratory data analysis is another challenge that is linked to such scenarios comprising heterogeneous smart city data [26].…”
Numerous real-life smart city application scenarios require joint analytics on unified views of georeferenced mobility data with environment contextual data including pollution and meteorological data. particularly, future urban planning requires restricting vehicle access to specific areas of a city to reduce the adverse effect of their engine combustion emissions on the health of dwellers and cyclers. Current editions of big spatial data management systems do not come with over-the-counter support for similar scenarios. To close this gap, in this paper, we show the design and prototyping of a novel system we term as EMDI for the enrichment of human and vehicle mobility data with pollution information, thus enabling integrated analytics on a unified view. Our system supports a variety of queries including single geo-statistics, such as ‘mean’, and Top-N queries, in addition to geo-visualization on the combined view. We have tested our system with real big georeferenced mobility and environmental data coming from the city of Bologna in Italy. Our testing results show that our system can be efficiently utilized for advanced combined pollution-mobility analytics at a scale with QoS guarantees. Specifically, a reduction in latency that equals roughly 65%, on average, is obtained by using EMDI as opposed to the plain baseline, we also obtain statistically significant accuracy results for Top-N queries ranging roughly from 0.84 to 1 for both Spearman and Pearson correlation coefficients depending on the geo-encoding configurations, in addition to significant single geo-statistics accuracy values expressed using Mean Absolute Percentage Error on the range from 0.00392 to 0.000195.
“… Fig. 7 VR content prefetching architecture: inspired by the Explora framework by Ordonez et al [ 43 ] …”
Section: Architecture and Proof-of-concept Implementationmentioning
confidence: 99%
“…This is the core component of the system. In devising the functional submodules of this server, we have drawn inspiration from the data processing pipeline presented by Ordonez et al in [ 43 ], which decouples stream data ingestion/preprocessing from data storage and content retrieval. The prefetch server features three main modules: ( 1 ) the prefetching component , ( 2 ) the content buffers , and ( 3 ) the retrieval component .…”
Section: Architecture and Proof-of-concept Implementationmentioning
Despite the growing popularity of immersive video applications during the last few years, the stringent low latency requirements of this kind of services remain a major challenge for the existing network infrastructure. Edge-assisted solutions compensate for network latency by relying on cache-enabled edge servers to bring frequently accessed video content closer to the client. However, these approaches often require historical request traces from previous watching sessions or adopt passive caching strategies subject to the
cold-start
problem and prone to playout freezes. This paper introduces
Explora-VR
, a novel edge-assisted content prefetching method for tile-based 360
video streaming. This method leverages the client’s rate adaptation heuristic to preemptively retrieve the content that the viewer will most likely watch in the upcoming segments, and loads it into a nearby edge server. At the same time,
Explora-VR
incrementally builds a dynamic collective buffer for serving the requests from active streaming sessions based on the estimated popularity of video tiles per segment. An evaluation of the proposed method was conducted on head movement traces collected from 48 unique users while watching three different 360
videos. Results show that
Explora-VR
is able to serve over 98% of the client requests from the cache-enabled edge server, leading to an average increase of 2.5
and 1.4
in the client’s perceived throughput, compared to a conventional client-server setup and a
least recently used
caching policy, respectively. This enables
Explora-VR
to serve higher quality video content while providing a freeze-free playback experience and effectively reducing network traffic to the content server.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
