Implications of Z-normalization in the matrix profile

Companies are increasingly measuring their products and services, resulting in a rising amount of available time series data, making techniques to extract usable information needed. One state-of-the-art technique for time series is the Matrix Profile, which has been used for various applications including motif/discord discovery, visualizations and semantic segmentation. Internally, the Matrix Profile utilizes the z-normalized Euclidean distance to compare the shape of subsequences between two series. However, when comparing subsequences that are relatively flat and contain noise, the resulting distance is high despite the visual similarity of these subsequences. This property violates some of the assumptions made by Matrix Profile based techniques, resulting in worse performance when series contain flat and noisy subsequences. By studying the properties of the z-normalized Euclidean distance, we derived a method to eliminate this effect requiring only an estimate of the standard deviation of the noise. In this paper we describe various practical properties of the z-normalized Euclidean distance and show how these can be used to correct the performance of Matrix Profile related techniques. We demonstrate our techniques using anomaly detection using a Yahoo! Webscope anomaly dataset, semantic segmentation on the PAMAP2 activity dataset and for data visualization on a UCI activity dataset, all containing real-world data, and obtain overall better results after applying our technique. Our technique is a straightforward extension of the distance calculation in the Matrix Profile and will benefit any derived technique dealing with time series containing flat and noisy subsequences

Automated UML-based ontology generation in OSLO²

In 2015, Flanders Information started the OSLO2 project, aimed at easing the exchange of data and increasing the interoperability of Belgian government services. RDF ontologies were developed to break apart the government data silos and stimulate data reuse. However, ontology design still encounters a number of difficulties. Since domain experts are generally unfamiliar with RDF, a design process is needed that allows these experts to efficiently contribute to intermediate ontology prototypes. We designed the OSLO2 ontologies using UML, a modeling language well known within the government, as a single source specification. From this source, the ontology and other relevant documents are generated. This paper describes the conversion tooling and the pragmatic approaches that were taken into account in its design. While this tooling is somewhat focused on the design principles used in the OSLO2 project, it can serve as the basis for a generic conversion tool. All source code and documentation are available online

Thermal analysis of a plastic helical coil heat exchanger for a domestic water storage tank

In the present study, the heat transfer coefficients of helically coiled corrugated plastic tube heat exchanger inside of the solar boiler vessel were investigated experimentally. The metal coil of the conventional solar boiler for domestic usage was replaced by a plastic tube and the results were compared with the numerical simulation and the technical documentation of the initial solar boiler. All the required parameters like inlet and outlet temperatures of tubeside and stratified temperatures, flow rate of fluids, etc. were measured using appropriate instruments. The test runs were performed for different temperatures inside the tank ranging from 30-60°C and different flow rates from which the heat transfer coefficients were calculated

A generalized matrix profile framework with support for contextual series analysis

The Matrix Profile is a state-of-the-art time series analysis technique that can be used for motif discovery, anomaly detection, segmentation and others, in various domains such as healthcare, robotics, and audio. Where recent techniques use the Matrix Profile as a preprocessing or modeling step, we believe there is unexplored potential in generalizing the approach. We derived a framework that focuses on the implicit distance matrix calculation. We present this framework as the Series Distance Matrix (SDM). In this framework, distance measures (SDM-generators) and distance processors (SDM-consumers) can be freely combined, allowing for more flexibility and easier experimentation. In SDM, the Matrix Profile is but one specific configuration. We also introduce the Contextual Matrix Profile (CMP) as a new SDM-consumer capable of discovering repeating patterns. The CMP provides intuitive visualizations for data analysis and can find anomalies that are not discords. We demonstrate this using two real world cases. The CMP is the first of a wide variety of new techniques for series analysis that fits within SDM and can complement the Matrix Profile