Generating Linked Data in Real-time from Sensor Data Streams

2015 ◽  
pp. 103-126
Author(s):  
Nikolaos Konstantinou ◽  
Dimitrios-Emmanuel Spanos
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Linked Data ◽  
Sensor Data

scholarly journals REAL-TIME EARTHQUAKE MONITORING WITH SPATIO-TEMPORAL FIELDS

2017 ◽  
Vol IV-4/W2 ◽  
pp. 215-222 ◽  
Author(s):  
J. C. Whittier ◽  
S. Nittel ◽  
I. Subasinghe
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Data Stream ◽  
Apache Spark ◽  
Sensor Data ◽  
Live Streaming ◽  
Earthquake Activity ◽  
Maximum Displacement ◽  
Earthquake Event ◽  
Spatio Temporal

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.

Real-Time Monitoring of Mobile Biological Sensor Data-Streams: Architecture and Cost-Model

2008 ◽  
Author(s):  
Alfredo Goni ◽  
Jimena Rodriguez ◽  
Alfredo Burgos ◽  
Arantza Illarramendi ◽  
Lacramioara Dranca
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Cost Model ◽  
Sensor Data ◽  
Real Time Monitoring ◽  
Biological Sensor

scholarly journals INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS

2015 ◽  
Vol II-4/W2 ◽  
pp. 67-71 ◽  
Author(s):  
A. Bhushan ◽  
M. H. Sharker ◽  
H. A. Karimi
Keyword(s):  
Principal Component Analysis ◽  
Real Time ◽  
Outlier Detection ◽  
Data Streams ◽  
Credit Card ◽  
Principal Component ◽  
Component Analysis ◽  
Spatiotemporal Data ◽  
Sensor Data ◽  
Detection Methods

In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

Real-time sensor data streams

2015 ◽  
Vol 7 (2) ◽  
pp. 22-28 ◽  
Author(s):  
Silvia Nittel
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Sensor Data

scholarly journals Adaptive Segmentation of Streaming Sensor Data on Edge Devices

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6884
Author(s):  
Roman Dębski ◽  
Rafał Dreżewski
Keyword(s):  
Real Time ◽  
Data Streams ◽  
State Of The Art ◽  
Sensor Data ◽  
Approximation Accuracy ◽  
Look Ahead ◽  
Approximation Errors ◽  
Segmentation Quality ◽  
Streaming Algorithm ◽  
Signal Approximation

Sensor data streams often represent signals/trajectories which are twice differentiable (e.g., to give a continuous velocity and acceleration), and this property must be reflected in their segmentation. An adaptive streaming algorithm for this problem is presented. It is based on the greedy look-ahead strategy and is built on the concept of a cubic splinelet. A characteristic feature of the proposed algorithm is the real-time simultaneous segmentation, smoothing, and compression of data streams. The segmentation quality is measured in terms of the signal approximation accuracy and the corresponding compression ratio. The numerical results show the relatively high compression ratios (from 135 to 208, i.e., compressed stream sizes up to 208 times smaller) combined with the approximation errors comparable to those obtained from the state-of-the-art global reference algorithm. The proposed algorithm can be applied to various domains, including online compression and/or smoothing of data streams coming from sensors, real-time IoT analytics, and embedded time-series databases.

scholarly journals Pemantauan Kualitas Udara Terintegrasi dengan Semantic Web Of Thing

2020 ◽  
Vol 4 (2) ◽  
pp. 153
Author(s):  
M. Udin Harun Al Rasyid ◽  
Rengga Asmara ◽  
Hendi Yanuar Setianto
Keyword(s):  
Social Media ◽  
Semantic Web ◽  
Real Time ◽  
Linked Data ◽  
Structured Data ◽  
Sensor Data ◽  
Sensor Web ◽  
Semantic Sensor Web ◽  
Living Organisms ◽  
Set Up

Abstrak: Udara merupakan salah satu sumber daya alam yang paling penting bagi keberadaan makhluk hidup di bumi ini. Semua organisme hidup membutuhkan kualitas udara yang baik bebas dari gas berbahaya untuk melanjutkan hidup mereka. Beberapa organisasi telah membuat sistem monitoring dengan struktur data yang berbeda tanpa adanya standar penyamaan. Di sisi lain, manusia masih membutuhkan waktu untuk menafsirkan data-data sensor untuk mendapatkan informasi. Linked Data merupakan metode untuk merepresentasikan dan menghubungkan data terstruktur pada web. Data terstruktur tersebut diintegrasikan dengan Semantic Sensor Web (SSW) yang dipublikasikan pada beberapa format sehingga mudah dibaca mesin dan dapat dihubungkan ke data terstruktur lainnya. Kemudian, untuk menyajikan data yang aktual, sistem monitoring didesain untuk menerima data secara terus-menerus, diquery secara real-time dan dibagikan melalui sosial media.   Kata kunci: Linked Data, Pemantauan Kualitas Udara, Semantic Web, Sosial Media.   Abstract: Air is one of the most essential natural resources for the existence and survival of the entire life on this planet. all living organisms need good quality of air which is free of harmful gases to continue their life. Some organizations have set up monitoring systems with different data structures without an equalization standard. On the other hand, humans still need time to interpret sensor data to get information. Linked Data is a method for representing and connecting structured data on the web. The structured data is integrated with the Semantic Sensor Web (SSW) which is published in several formats so that it is easy to read and can be connected to other structured data. Then, to present the actual data, the monitoring system is designed to receive data continuously, queried in real time and shared through social media   Keywords: Air Quality Monitoring, Linked Data, Semantic Web, Social Media

Architecture, cost-model and customization of real-time monitoring systems based on mobile biological sensor data-streams

2009 ◽  
Vol 96 (2) ◽  
pp. 141-157 ◽  
Author(s):  
Alfredo Goñi ◽  
Alfredo Burgos ◽  
Lacramioara Dranca ◽  
Jimena Rodríguez ◽  
Arantza Illarramendi ◽  
...  
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Cost Model ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Real Time Monitoring ◽  
Biological Sensor
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