Sensor Data Stream Selection and Aggregation for the Ex Post Discovery of Impact Factors on Process Outcomes

2021 ◽  
pp. 29-37
Author(s):  
Matthias Ehrendorfer ◽  
Juergen Mangler ◽  
Stefanie Rinderle-Ma
Keyword(s):  
Data Stream ◽  
Sensor Data ◽  
Impact Factors ◽  
Ex Post ◽  
Process Outcomes

Research on Sequence Query Processing Techniques over Data Streams

2013 ◽  
Vol 284-287 ◽  
pp. 3507-3511 ◽  
Author(s):  
Edgar Chia Han Lin
Keyword(s):  
Query Processing ◽  
Data Streams ◽  
Management System ◽  
Data Stream ◽  
Sensor Data ◽  
Data Management System ◽  
Content Filtering ◽  
Data Stream Management ◽  
Stream Management ◽  
Great Progress

Due to the great progress of computer technology and mature development of network, more and more data are generated and distributed through the network, which is called data streams. During the last couple of years, a number of researchers have paid their attention to data stream management, which is different from the conventional database management. At present, the new type of data management system, called data stream management system (DSMS), has become one of the most popular research areas in data engineering field. Lots of research projects have made great progress in this area. Since the current DSMS does not support queries on sequence data, this project will study the issues related to two types of data. First, we will focus on the content filtering on single-attribute streams, such as sensor data. Second, we will focus on multi-attribute streams, such as video films. We will discuss the related issues such as how to build an efficient index for all queries of different streams and the corresponding query processing mechanisms.

scholarly journals Correction to: Semantic annotation of summarized sensor data stream for effective query processing

2018 ◽  
Vol 76 (6) ◽  
pp. 4040-4040
Author(s):  
Shobharani Pacha ◽  
Suresh Ramalingam Murugan ◽  
R. Sethukarasi
Keyword(s):  
Query Processing ◽  
Data Stream ◽  
Semantic Annotation ◽  
Sensor Data

Sensor Data Hiding Based on Image Watermarking Using Interpolation Technique over Inter-Packet Delays

2014 ◽  
Vol 573 ◽  
pp. 543-548 ◽  
Author(s):  
K.P. Ramya ◽  
R. Chithra Devi ◽  
M.K. Revathi ◽  
P. Annapandi
Keyword(s):  
Data Stream ◽  
Image Watermarking ◽  
Location Based Services ◽  
Sensor Data ◽  
Storage Efficiency ◽  
Interpolation Technique ◽  
Packet Delays ◽  
Watermarking Scheme ◽  
Secure Transmission ◽  
Efficient Transmission

Large number of application areas, like location-based services, transaction logs, sensor networks are qualified by uninterrupted data stream from many. Sensor data handling of continuous data needs to cover various issues, admitting the storage efficiency, processing throughput, bandwidth conception and secure transmission. This paper addresses the challenges by providing secure and efficient transmission of sensor data by embedding it over the inter-packet delays (IPDs). The embedding of sensor data within a host medium makes this technique reminiscent of watermarking. Interpolation technique is used to hide the sensor data into an image which is send to another node. By enforcing linear enlargement to interpolation-errors, a extremely effective reversible watermarking scheme is achieved, which can ensure high image quality without sacrificing embedding capacity. Time-Based flow watermarking technique is proposed, that avoids data degradation due to traditional watermarking. Sensor data is extracted effectively based on the inter-packet delays that minimizes the probability of decoding error. The outcome of the observation depicts that this system is scalable and highly resilient in 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.

Smart Sensor Data Stream Delivery Technologies

2017 ◽  
pp. 97-122
Author(s):  
Tomoya Kawakami ◽  
Yoshimasa Ishi ◽  
Tomoki Yoshihisa ◽  
Yuuichi Teranishi
Keyword(s):  
Data Stream ◽  
Sensor Data ◽  
Smart Sensor
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