scholarly journals DRSP : Dimension Reduction for Similarity Matching and Pruning of Time Series Data Streams

2013 ◽  
Vol 3 (6) ◽  
pp. 107-126
Author(s):  
Vishwanath R H ◽  
Samartha T V ◽  
Srikantaiah K C ◽  
Venugopal K R ◽  
Patnaik L M
Keyword(s):  
Time Series ◽  
Dimension Reduction ◽  
Data Streams ◽  
Time Series Data ◽  
Series Data ◽  
Similarity Matching

Sensors to Events: Semantic Modeling and Recognition of Events from Data Streams

2016 ◽  
Vol 10 (04) ◽  
pp. 461-501 ◽  
Author(s):  
Om Prasad Patri ◽  
Anand V. Panangadan ◽  
Vikrambhai S. Sorathia ◽  
Viktor K. Prasanna
Keyword(s):  
Time Series ◽  
Real World ◽  
Data Streams ◽  
Time Series Data ◽  
Semantic Representation ◽  
Expressive Power ◽  
Sensor Data ◽  
Series Data ◽  
Formal Approach ◽  
Semantic Computing

Detecting and responding to real-world events is an integral part of any enterprise or organization, but Semantic Computing has been largely underutilized for complex event processing (CEP) applications. A primary reason for this gap is the difference in the level of abstraction between the high-level semantic models for events and the low-level raw data values received from sensor data streams. In this work, we investigate the need for Semantic Computing in various aspects of CEP, and intend to bridge this gap by utilizing recent advances in time series analytics and machine learning. We build upon the Process-oriented Event Model, which provides a formal approach to model real-world objects and events, and specifies the process of moving from sensors to events. We extend this model to facilitate Semantic Computing and time series data mining directly over the sensor data, which provides the advantage of automatically learning the required background knowledge without domain expertise. We illustrate the expressive power of our model in case studies from diverse applications, with particular emphasis on non-intrusive load monitoring in smart energy grids. We also demonstrate that this powerful semantic representation is still highly accurate and performs at par with existing approaches for event detection and classification.

Living on the Edge

2021 ◽  
Vol 2 (3) ◽  
pp. 1-31
Author(s):  
Thilina Buddhika ◽  
Matthew Malensek ◽  
Shrideep Pallickara ◽  
Sangmi Lee Pallickara
Keyword(s):  
Time Series ◽  
Data Streams ◽  
Time Series Data ◽  
Data Transfer ◽  
Holistic Approach ◽  
Series Data ◽  
Real World Data ◽  
Compact Representations ◽  
Performance Benchmarks ◽  
Effective Use

Voluminous time-series data streams produced in continuous sensing environments impose challenges pertaining to ingestion, storage, and analytics. In this study, we present a holistic approach based on data sketching to address these issues. We propose a hyper-sketching algorithm that combines discretization and frequency-based sketching to produce compact representations of the multi-feature, time-series data streams. We generate an ensemble of data sketches to make effective use of capabilities at the resource-constrained edge devices, the links over which data are transmitted, and the server pool where this data must be stored. The data sketches can be queried to construct datasets that are amenable to processing using popular analytical engines. We include several performance benchmarks using real-world data from different domains to profile the suitability of our design decisions. The proposed methodology can achieve up to ∼ 13 × and ∼ 2, 207 × reduction in data transfer and energy consumption at edge devices. We observe up to a ∼ 50% improvement in analytical job completion times in addition to the significant improvements in disk and network I/O.

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