Streaming data correlation on GPUs

In this paper we study the problem of constructing histograms from high-speed time-changing data streams. Learning in this context requires the ability to process examples once at the rate they arrive, maintaining a histogram consistent with the most recent data, and forgetting out-date data whenever a change in the distribution is detected. To construct histogram from high-speed data streams we use the two layer structure used in the Partition Incremental Discretization (PiD) algorithm. Our contribution is a new method to detect whenever a change in the distribution generating examples occurs. The base idea consists of monitoring distributions from two different time windows: the reference time window, that reflects the distribution observed in the past; and the current time window reflecting the distribution observed in the most recent data. We compare both distributions and signal a change whenever they are greater than a threshold value, using three different methods: the Entropy Absolute Difference, the Kullback-Leibler divergence and the Cosine Distance. The experimental results suggest that Kullback-Leibler divergence exhibit high probability in change detection, faster detection rates, with few false positives alarms.

A 28 nm CMOS Software-Defined Transceiver (SDTRX) enabling High-Speed Data (HSD) streaming, including Ultra HD TV, within home cable networks is presented. By making efficient use of available cable bandwidth, the SDTRX dynamically handles up to 1024QAM OFDM-modulated HSD streams. This paper addresses SDTRX system-level design methodology as the key driver in enabling performance optimization for achieving a wide frequency range of operation at the lowest power and area consumption. By employing an optimized architecture constructed on available state-of-the art 28 nm functional building blocks, the monolithic SDTRX consists of a mixer-based harmonic rejection RX with a DAC-based TX and a smart PLL system. It operates over a 0.4-to-1.7 GHz frequency range while consuming less than 475 mW in half-duplex mode. Moreover, by developing a simple TX-RX loopback circuit, the system is enabled to efficiently calibrate TX output power and to remove the need for a dedicated external pin. This low-cost SDTRX is embedded in various 28 nm CMOS multimedia SoCs and is, to the authors' knowledge, the first reported transceiver front end to enable true high-speed data streaming within home cable networks.

Histograms are a common technique for density estimation and they have been widely used as a tool in exploratory data analysis. Learning histograms from static and stationary data is a well known topic. Nevertheless, very few works discuss this problem when we have a continuous flow of data generated from dynamic environments. The scope of this paper is to detect changes from high-speed time-changing data streams. To address this problem, we construct histograms able to process examples once at the rate they arrive. The main goal of this work is continuously maintain a histogram consistent with the current status of the nature. We study strategies to detect changes in the distribution generating examples, and adapt the histogram to the most recent data by forgetting outdated data. We use the Partition Incremental Discretization algorithm that was designed to learn histograms from high-speed data streams. We present a method to detect whenever a change in the distribution generating examples occurs. The base idea consists of monitoring distributions from two different time windows: the reference window, reflecting the distribution observed in the past; and the current window which receives the most recent data. The current window is cumulative and can have a fixed or an adaptive step depending on the distance between distributions. We compared both distributions using Kullback-Leibler divergence, defining a threshold for change detection decision based on the asymmetry of this measure. We evaluated our algorithm with controlled artificial data sets and compare the proposed approach with nonparametric tests. We also present results with real word data sets from industrial and medical domains. Those results suggest that an adaptive window’s step exhibit high probability in change detection and faster detection rates, with few false positives alarms.

Outlier detection or anomaly detection is applied in numerous applications, such as fraud detection, network intrusion detection, manufacturing, and environmental monitoring. Due to the continuous and dynamic characteristics of streaming data, outlier detection over data streams becomes a very challenging task. When analyzing real-time data streams, it is typically impossible to store the entire set of data due to space limitations. Also due to the high data rates, it is necessary to produce the results in a limited amount of time. Parallel processing power of Graphics Processing Units (GPUs) can be used to accelerate the outlier detection process, and thus address the challenges of outlier detection over data streams. This paper proposes a GPU-accelerated outlier detection algorithm for continuous data streams using kernel density estimation approach. Experiments show that the proposed SOD_GPU algorithm is efficient for detecting outliers in high-dimensional, high-speed data streams, and produces results in a timely manner without compromising the outlier detection accuracy. The proposed method achieved up to 20X speedup compared to a respective multi-core CPU implementation, and the speedup increases with the number of data attributes and the input data rate.

Clustering data streams has become a hot topic in the era of big data. Driven by the ever increasing volume, velocity and variety of data, more efficient algorithms for clustering large-scale complex data streams are needed. In this paper, we present a parallel algorithm called PaStream, which is based on advanced Graphics Processing Unit (GPU) and follows the online-offline framework of CluStream. Our approach can achieve hundreds of times speedup on high-speed and high-dimensional data streams compared with CluStream. It can also discover clusters with arbitrary shapes and handle outliers properly. The efficiency and scalability of PaStream are demonstrated through comprehensive experiments on synthetic and standard benchmark datasets with various problem factors.

Discovering interesting patterns from high-speed data streams is a challenging problem in data mining. Recently, the support metric-based frequent pattern mining from data stream has achieved a great attention. However, the occurrence frequency of a pattern may not be an appropriate criterion for discovering meaningful patterns. Temporal regularity in occurrence behavior can be a key criterion for assessing the importance of patterns in several online applications such as market basket analysis, gene data analysis, network monitoring, and stock market. A pattern can be said regular if its occurrence behavior satisfies a user-given interval in the data steam. Mining regular patterns from static databases has recently been addressed. However, even though mining regular patterns from stream data is extremely required in online applications, no such algorithm has been proposed yet. Therefore, in this paper we develop a novel tree structure called Regular Pattern Stream tree (RPS-tree), and an efficient mining technique for discovering regular patterns over data stream. Using a sliding window method the RPS-tree captures the stream content, and with an efficient tree updating mechanism it constantly processes exact stream data when the stream flows. Extensive experimental analyses show that our RPS-tree is highly efficient in discovering regular patterns from a high-speed data stream.

A complex event processing framework for an adaptive language learning system

Optical time-stretch imaging has shown potential in diverse fields for its capability of acquiring images at high speed and high resolution. However, its wide application is hindered by the stringent requirement on the instrumentation hardware caused by the high-speed serial data stream. Here we demonstrate temporally interleaved optical time-stretch imaging that lowers the requirement without sacrificing the frame rate or spatial resolution by interleaving the high-speed data stream into multiple channels in the time domain. Its performance is validated with both a United States Air Force (USAF)-1951 resolution chart and a single-crystal diamond film. We achieve a 101 Mfps 1D scanning rate and 3 µm spatial resolution with only a 2.5 GS/s sampling rate by using a two-channel-interleaved system.

The methods of approximate query processing have been proposed for analytics over high-speed data streams, which compact continuous streams into a space-constrained sketch and provide reliable estimates for different queries. Count-Min (CM) is the state-of-the-art sketching structure supporting many queries with error-guaranteed estimates under limited space. However, we need to create a counter table beforehand in CM according to the size of data streams, while it is usually unpredictable for dynamic data streams. In this paper, we proposed an approach, called Dynamic Count-Min sketch (DCM), which is appropriate for dynamic data set and can provide accurate estimates for point query and self-join size query. Our approach constitutes incremental CM sketches and allocates space in a pay-as-you-go manner. Our mathematical analysis and substantial experiments both show that our approach is appropriate for data sets with dynamic or skewed inputs and can provide error-guaranteed estimates with less space compared to CM.

Nowadays in many real-world scenarios, high speed data streams are usually with non-uniform misclassification costs and thus call for cost-sensitive classification algorithms of data streams. However, only little literature focuses on this issue. On the other hand, the existing algorithms for cost-sensitive classification can achieve excellent performance in the metric of total misclassification costs, but always lead to obvious reduction of accuracy, which restrains the practical application greatly. In this paper, we present an improved folk theorem. Based on the new theorem, the existing accuracy-based classification algorithm can be converted into soft cost-sensitive one immediately, which allows us to take both accuracy and cost into account. Following the idea of this theorem, the soft-CsGDT algorithm is proposed to process the data streams with non-uniform misclassification costs, which is an expansion of GDT. With both synthetic and real-world datasets, the experimental results show that compared with the cost-sensitive algorithm, the accuracy in our soft-CsGDT is significantly improved, while the total misclassification costs are approximately the same.

It is widely realized that the integration of information retrieval (IR) and database (DB) techniques provides users with a broad range of high quality services. A new challenging issue along the same direction is IR-styled m-keyword query processing in a RDBMS framework over an open-ended relational data stream. The capability of supporting m-keyword queries over a relational data stream makes it possible for users to monitor events, that are implicitly interrelated, over a relational data stream in a timely manner. In brief, the problem is to find all connected trees whose size is less than or equal to a user-given threshold in terms of number of nodes for a m-keyword query, {k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , middot middot middot , k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> }, over a relational data stream on a database schema GS. The difficulty of the problem is related to the number of costly joins to be processed over time, which is affected by the parameters such as the number of keywords (m), the maximum size of connected trees (Tmax), as well as the complexity of the database schema when it is viewed as a schema graph (G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sub> ). In this paper, we propose a new demand-driven approach to process such a query over a high speed data stream. We show that we can significantly reduce the number of intermediate results when processing joins over a data stream, and therefore can achieve high efficiency.

Most of the current research work on timely streaming data processing focuses on minimizing average tuple latency instead of strict individual tuple latency upper-bound, that is, deadline. In this paper, we propose a novel deadline-scheduling strategy, namely tick scheduling (TS), dealing with applications with specified deadline constraints over high volume, possibly bursting, and continuous data streams. We demonstrate that TS policy, which combines precise batch scheduling plan construction and adaptive batch maintenance mechanism can significantly improve system performance by greatly reducing system overheads and adapting gracefully to the time-varying data arrival-rate. Experimental results show the significant improvements provided by our proposed policy.

The ability to interpret spatiotemporal data streams in real-time is critical for a range of systems. However, processing vast amounts of spatiotemporal data out of several sources, such as online traffic, social platforms, sensor networks, and other sources, is a considerable challenge. The major goal of this study is to create a framework for processing and analyzing spatiotemporal data from multiple sources with irregular shapes so that researchers can focus on data analysis instead of worrying about the data sources' structure. We introduced a novel spatiotemporal data paradigm for true-real-time stream processing, which enables high-speed and low-latency real-time data processing, with these considerations in mind. A comparison of two state-of-the-art real-time process architectures was offered, as well as a full review of the various open-source technologies for real-time data stream processing, and their system topologies were also presented. Hence, this study proposed a brand-new framework that integrates Apache Kafka for spatiotemporal data ingestion, Apache flink for true real-time processing of spatiotemporal stream data, as well as machine learning for real-time predictions, and Apache Cassandra at the storage layer for distributed storage in real-time. The proposed framework was compared with others from the literature using the following features: Scalability (Sc), prediction tools (PT), data analytics (DA), multiple event types (MET), data storage (DS), Real-time (Rt), and performance evaluation (PE) stream processing (SP), and our proposed framework provided the ability to handle all of this task.

The USB which has high-speed streaming data and can plug and play meets the requirement of the data transfer mission. CY7C68013 have an ingenious architecture and can automatically deal with most data communication in USB 2.0 specifications. A high speed streaming data system based on EZ-USB FX2 was designed. The debugging interface based on slave FIFO of EZ-USB FX2 can send data in bulk from FPDA to upper computer; its convenient to system debugging work.

Real-time monitoring microblog data can find sensitive information in time and provide help for public sentiment management and control. However, it needs processing large-scale data stream. MapReduce is a framework of processing large-scale data in batch mode, its purpose is to increase throughput, but its real-time performance is limited. Aiming at the real-time performance limitation of MapReduce, RT-SSP Real-Time Staged Stream Processing, a hybrid staged real-time stream processing scheme both for batch and real-time processing was proposed. By this method large-scale high-speed data stream is locally processed in stages, the communication cost is reduced by storing intermediate results to local node, and key technologies such as cache optimization are used to realize high concurrent read and write. Experiments show that RT-SSP scheme can improve the real-time performance of processing large-scale microblog data stream and achieve speed-up ratio of about 2.3.