Spatio-Temporal Data Streams

Galić, Zdravko

doi:10.1007/978-1-4939-6575-5

Cited by 17 publications

(9 citation statements)

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“…Analysis of trajectory data leads to extraction curial information which helps the researchers to find solutions for many challenges such as traffic congestion [1]. One of the most important analysis tools is clustering; clustering aims to aggregate data in clusters such that the similarity among cluster members is high and the similarity of members belonging to different clusters is very low [2,3]. Clustering of stream data is more complex than classical data, since clustering stream data faces a set of challenges: (i) single pass processing due to continuous arriving of data, (ii) unbounded size of data stream and limited memory space and time, and (iii) evolving data where the model underlying the data stream may change over time.…”

Section: Introductionmentioning

confidence: 99%

“…Clustering of stream data is more complex than classical data, since clustering stream data faces a set of challenges: (i) single pass processing due to continuous arriving of data, (ii) unbounded size of data stream and limited memory space and time, and (iii) evolving data where the model underlying the data stream may change over time. Thus the clustering algorithm should be able to detect such changes [3,4]. Many algorithms of data stream clustering depend on object based paradigm which consists of two phases: online phase and offline phase.…”

Section: Introductionmentioning

confidence: 99%

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CC_TRS: Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life

Riyadh

Mustapha

Sulaiman

et al. 2017

Mathematical Problems in Engineering

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The rapid spreading of positioning devices leads to the generation of massive spatiotemporal trajectories data. In some scenarios, spatiotemporal data are received in stream manner. Clustering of stream data is beneficial for different applications such as traffic management and weather forecasting. In this article, an algorithm for Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life is proposed. The algorithm consists of two phases. There is the online phase where temporal micro clusters are used to store summarized spatiotemporal information for each group of similar segments. The clustering task in online phase is based on temporal micro cluster lifetime instead of time window technique which divides stream data into time bins and clusters each bin separately. For offline phase, a density based clustering approach is used to generate macro clusters depending on temporal micro clusters. The evaluation of the proposed algorithm on real data sets shows the efficiency and the effectiveness of the proposed algorithm and proved it is efficient alternative to time window technique.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CC_TRS: Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life

Riyadh

Mustapha

Sulaiman

et al. 2017

Mathematical Problems in Engineering

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“…DSEs provide high throughput query processing for data streams by buffering and processing streams in main memory. Stream queries are implemented as scalable, one-pass (Muthukrishnan, 2005) stream operators; they can be parallelized automatically (Galić, 2016). Today, open source and commercial DSEs such as Apache Spark(Apache Spark, 2017) achieve query performance over streams with a throughput of >1 Million updates/s per CPU core (Carbone et al, 2015).…”

Section: Monitoring Earthquakes In Southern Californiamentioning

confidence: 99%

Real-Time Earthquake Monitoring With Spatio-Temporal Fields

Whittier¹,

Nittel²,

Subasinghe³

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:With live streaming sensors and sensor networks, increasingly large numbers of individual sensors are deployed in physical space. Sensor data streams are a fundamentally novel mechanism to deliver observations to information systems. They enable us to represent spatio-temporal continuous phenomena such as radiation accidents, toxic plumes, or earthquakes almost as instantaneously as they happen in the real world. Sensor data streams discretely sample an earthquake, while the earthquake is continuous over space and time. Programmers attempting to integrate many streams to analyze earthquake activity and scope need to write code to integrate potentially very large sets of asynchronously sampled, concurrent streams in tedious application code. In previous work, we proposed the field stream data model (Liang et al., 2016) for data stream engines. Abstracting the stream of an individual sensor as a temporal field, the field represents the Earth's movement at the sensor position as continuous. This simplifies analysis across many sensors significantly. In this paper, we undertake a feasibility study of using the field stream model and the open source Data Stream Engine (DSE) Apache Spark(Apache Spark, 2017) to implement a real-time earthquake event detection with a subset of the 250 GPS sensor data streams of the Southern California Integrated GPS Network (SCIGN). The field-based real-time stream queries compute maximum displacement values over the latest query window of each stream, and related spatially neighboring streams to identify earthquake events and their extent. Further, we correlated the detected events with an USGS earthquake event feed. The query results are visualized in real-time.

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Section: Introductionmentioning

confidence: 99%

“…In other words, what served in the past and still serves for scenarios in non-real time, does not fit well with scenarios that handle data streams. State-of-the-art research in analytics platforms for stream computing, which facilitate measuring and quantifying location-and context-related aspects based on real-time streamed data and take it into account in the application, are still in their infancy, especially in the support for the spatial and spatio-temporal dimensions of data streams [30].In this article, we present an analytics platform for defining and computing spatio-temporal, context-aware metrics. The proposed concept of metrics is central to allow application developers to define data requirements that capture relevant spatio-temporal aspects of an observed phenomenon, collect the required (client-generated) data, and execute the associated function to process streams of collected data.…”

mentioning

confidence: 99%

An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics

Rodríguez-Pupo

Granell

Casteleyn

2019

IJGI

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In large-scale context-aware applications, a central design concern is capturing, managing and acting upon location and context data. The ability to understand the collected data and define meaningful contextual events, based on one or more incoming (contextual) data streams, both for a single and multiple users, is hereby critical for applications to exhibit location-and context-aware behaviour. In this article, we describe a context-aware, data-intensive metrics platform -focusing primarily on its geospatial support-that allows exactly this: to define and execute metrics, which capture meaningful spatio-temporal and contextual events relevant for the application realm. The platform (1) supports metrics definition and execution; (2) provides facilities for real-time, in-application actions upon metrics execution results; (3) allows post-hoc analysis and visualisation of collected data and results. It hereby offers contextual and geospatial data management and analytics as a service, and allow context-aware application developers to focus on their core application logic. We explain the core platform and its ecosystem of supporting applications and tools, elaborate the most important conceptual features, and discuss implementation realised through a distributed, micro-service based cloud architecture. Finally, we highlight possible application fields, and present a real-world case study in the realm of psychological health. IntroductionNotwithstanding the early promise of location-and context-aware applications (see e.g., [1] for a survey of early systems), only in the last decade have we witnessed the required technological and infrastructural enablers to truly unleash their potential [2,3]. For the technological enablers, the increasing availability of a variety of context-capturing machinery, in which embedded sensors, local processing and communication capabilities are combined, allows for large-scale, high-volume capturing and streaming of a broad variety of context data. Examples notably include sensor-packed smart vehicles, mobile hand-held devices (e.g., smart phones, tables) and smart, wearable devices (e.g., smart watches and bracelets, sport trackers, smart clothing), which can effectively collect an individual's real-time location, along with other relevant contextual information (e.g., [4]). A second technological milestone is the rapid evolution and proliferation of powerful mobile hand-held computing devices, a condition sine qua non to run full-fledged, context-aware applications [5].On the other hand, infrastructure-related enablers are now in place: fully rolled-out communication networks (e.g., 3G and 4G) with resulting ubiquitous internet access, and commercially available, economised cloud-based storage and computing infrastructures [6], provide unprecedented means to build the next-generation of context-aware applications and services, based on multi-user, real-time and high-frequency input streams; real-time data handling, processing and analytics; and real-time, location-and context-based r...

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Spatio-Temporal Data Streams

Cited by 17 publications

References 0 publications

CC_TRS: Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life

CC_TRS: Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life

Real-Time Earthquake Monitoring With Spatio-Temporal Fields

An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics

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