Efficient analysis of data streams

Data streams provide a challenging environment for statistical analysis. Data points can arrive at a high velocity and may need to be deleted once they have been observed. Due to these restrictions, standard techniques may not be applicable to the data streaming scenario. This leads to the need for data summaries to represent the data stream. This thesis explores how data summaries can be used to perform clustering and classification on data streams across a broad range of applications. Spectral clustering is one such technique which prior to this work has not been applicable to the data streaming setting due to the high computation involved. CluStream is an existing method which uses micro-clusters to summarise data streams. We present two algorithms which utilise these micro-cluster summaries to enable spectral clustering to be performed on data streams. The methods were tested on simulated data streams, as well as textured images and hand-written digits. Distributed acoustic sensing is used to monitor oil flow at various depths throughout an oil well. Vibrations are recorded at very high resolutions, up to 10000 observations a second at each depth. Unfortunately, corruption can occur in the signal and engineers need to know where corruption occurs. We develop a method which treats the multiple time series as a high-dimensional clustering problem and uses the cluster labels to identify changes within the signal. The final piece of work concerns identifying areas of activity within a video stream, in particular CCTV footage. It is more efficient if this classification stage is performed on a compressed version of the video stream. In order to reconstruct areas of activity in the original video a recovery algorithm is needed. We present a comparison of the performance of two recovery algorithms and identify an ideal range for the compression ratio

Data Provenance and Management in Radio Astronomy: A Stream Computing Approach

New approaches for data provenance and data management (DPDM) are required for mega science projects like the Square Kilometer Array, characterized by extremely large data volume and intense data rates, therefore demanding innovative and highly efficient computational paradigms. In this context, we explore a stream-computing approach with the emphasis on the use of accelerators. In particular, we make use of a new generation of high performance stream-based parallelization middleware known as InfoSphere Streams. Its viability for managing and ensuring interoperability and integrity of signal processing data pipelines is demonstrated in radio astronomy. IBM InfoSphere Streams embraces the stream-computing paradigm. It is a shift from conventional data mining techniques (involving analysis of existing data from databases) towards real-time analytic processing. We discuss using InfoSphere Streams for effective DPDM in radio astronomy and propose a way in which InfoSphere Streams can be utilized for large antennae arrays. We present a case-study: the InfoSphere Streams implementation of an autocorrelating spectrometer, and using this example we discuss the advantages of the stream-computing approach and the utilization of hardware accelerators

A Backend Framework for the Efficient Management of Power System Measurements

Increased adoption and deployment of phasor measurement units (PMU) has provided valuable fine-grained data over the grid. Analysis over these data can provide insight into the health of the grid, thereby improving control over operations. Realizing this data-driven control, however, requires validating, processing and storing massive amounts of PMU data. This paper describes a PMU data management system that supports input from multiple PMU data streams, features an event-detection algorithm, and provides an efficient method for retrieving archival data. The event-detection algorithm rapidly correlates multiple PMU data streams, providing details on events occurring within the power system. The event-detection algorithm feeds into a visualization component, allowing operators to recognize events as they occur. The indexing and data retrieval mechanism facilitates fast access to archived PMU data. Using this method, we achieved over 30x speedup for queries with high selectivity. With the development of these two components, we have developed a system that allows efficient analysis of multiple time-aligned PMU data streams.Comment: Published in Electric Power Systems Research (2016), not available ye