DRSP : Dimension Reduction For Similarity Matching And Pruning Of Time Series Data Streams

Similarity matching and join of time series data streams has gained a lot of relevance in today's world that has large streaming data. This process finds wide scale application in the areas of location tracking, sensor networks, object positioning and monitoring to name a few. However, as the size of the data stream increases, the cost involved to retain all the data in order to aid the process of similarity matching also increases. We develop a novel framework to addresses the following objectives. Firstly, Dimension reduction is performed in the preprocessing stage, where large stream data is segmented and reduced into a compact representation such that it retains all the crucial information by a technique called Multi-level Segment Means (MSM). This reduces the space complexity associated with the storage of large time-series data streams. Secondly, it incorporates effective Similarity Matching technique to analyze if the new data objects are symmetric to the existing data stream. And finally, the Pruning Technique that filters out the pseudo data object pairs and join only the relevant pairs. The computational cost for MSM is O(l*ni) and the cost for pruning is O(DRF*wsize*d), where DRF is the Dimension Reduction Factor. We have performed exhaustive experimental trials to show that the proposed framework is both efficient and competent in comparison with earlier works.Comment: 20 pages,8 figures, 6 Table

iTeleScope: Intelligent Video Telemetry and Classification in Real-Time using Software Defined Networking

Video continues to dominate network traffic, yet operators today have poor visibility into the number, duration, and resolutions of the video streams traversing their domain. Current approaches are inaccurate, expensive, or unscalable, as they rely on statistical sampling, middle-box hardware, or packet inspection software. We present {\em iTelescope}, the first intelligent, inexpensive, and scalable SDN-based solution for identifying and classifying video flows in real-time. Our solution is novel in combining dynamic flow rules with telemetry and machine learning, and is built on commodity OpenFlow switches and open-source software. We develop a fully functional system, train it in the lab using multiple machine learning algorithms, and validate its performance to show over 95\% accuracy in identifying and classifying video streams from many providers including Youtube and Netflix. Lastly, we conduct tests to demonstrate its scalability to tens of thousands of concurrent streams, and deploy it live on a campus network serving several hundred real users. Our system gives unprecedented fine-grained real-time visibility of video streaming performance to operators of enterprise and carrier networks at very low cost.Comment: 12 pages, 16 figure

Techno-economic energy models for low carbon business parks

To mitigate climate change, global greenhouse gas emissions need to be reduced substantially. Industry and energy sector together are responsible for a major share of those emissions. Hence the development of low carbon business parks by maximising energy efficiency and changing to collective, renewable energy systems at local level holds a high reduction potential. Yet, there is no uniform approach to determine the optimal combination and operation of energy technologies composing such energy systems. However, techno-economic energy models, custom tailored for business parks, can offer a solution, as they identify the configuration and operation that provide an optimal trade-off between economic and environmental performances. However, models specifically developed for industrial park energy systems are not detected in literature, so identifying an existing model that can be adapted is an essential step. In this paper, energy model classifications are scanned for adequate model characteristics and accordingly, a confined number of models are selected and described. Subsequently, main model features are compared, a practical typology is proposed and applicability towards modelling industrial park energy systems is evaluated. Energy system evolution models offer the most perspective to compose a holistic, but simplified model, whereas advanced energy system integration models can adequately be employed to assess energy integration for business clusters up to entire industrial sites. Energy system simulation models, however, provide deeper insight in the system’s operation