Engineering Crowdsourced Stream Processing Systems

A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed, or equivalently, enabling stream processing to employ human intelligence. It also leads to a substantial expansion of the capabilities of data processing systems. Engineering a CSP system requires the combination of human and machine computation elements. From a general systems theory perspective, this means taking into account inherited as well as emerging properties from both these elements. In this paper, we position CSP systems within a broader taxonomy, outline a series of design principles and evaluation metrics, present an extensible framework for their design, and describe several design patterns. We showcase the capabilities of CSP systems by performing a case study that applies our proposed framework to the design and analysis of a real system (AIDR) that classifies social media messages during time-critical crisis events. Results show that compared to a pure stream processing system, AIDR can achieve a higher data classification accuracy, while compared to a pure crowdsourcing solution, the system makes better use of human workers by requiring much less manual work effort

Data Driven Discovery in Astrophysics

We review some aspects of the current state of data-intensive astronomy, its methods, and some outstanding data analysis challenges. Astronomy is at the forefront of "big data" science, with exponentially growing data volumes and data rates, and an ever-increasing complexity, now entering the Petascale regime. Telescopes and observatories from both ground and space, covering a full range of wavelengths, feed the data via processing pipelines into dedicated archives, where they can be accessed for scientific analysis. Most of the large archives are connected through the Virtual Observatory framework, that provides interoperability standards and services, and effectively constitutes a global data grid of astronomy. Making discoveries in this overabundance of data requires applications of novel, machine learning tools. We describe some of the recent examples of such applications.Comment: Keynote talk in the proceedings of ESA-ESRIN Conference: Big Data from Space 2014, Frascati, Italy, November 12-14, 2014, 8 pages, 2 figure

Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams

The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting computer security violations. In addition, a number of methods have been developed to detect concept drifts in these streams. Consider a scenario where we have a number of classifiers with diverse learning styles and different drift detectors. Intuitively, the current 'best' (classifier, detector) pair is application dependent and may change as a result of the stream evolution. Our research builds on this observation. We introduce the \mbox{Tornado} framework that implements a reservoir of diverse classifiers, together with a variety of drift detection algorithms. In our framework, all (classifier, detector) pairs proceed, in parallel, to construct models against the evolving data streams. At any point in time, we select the pair which currently yields the best performance. We further incorporate two novel stacking-based drift detection methods, namely the \mbox{FHDDMS} and \mbox{FHDDMS}_{add} approaches. The experimental evaluation confirms that the current 'best' (classifier, detector) pair is not only heavily dependent on the characteristics of the stream, but also that this selection evolves as the stream flows. Further, our \mbox{FHDDMS} variants detect concept drifts accurately in a timely fashion while outperforming the state-of-the-art.Comment: 42 pages, and 14 figure

Design Tools for Dynamic, Data-Driven, Stream Mining Systems

The proliferation of sensing devices and cost- and energy-efficient embedded processors has contributed to an increasing interest in adaptive stream mining (ASM) systems. In this class of signal processing systems, knowledge is extracted from data streams in real-time as the data arrives, rather than in a store-now, process later fashion. The evolution of machine learning methods in many application areas has contributed to demands for efficient and accurate information extraction from streams of data arriving at distributed, mobile, and heterogeneous processing nodes. To enhance accuracy, and meet the stringent constraints in which they must be deployed, it is important for ASM systems to be effective in adapting knowledge extraction approaches and processing configurations based on data characteristics and operational conditions. In this thesis, we address these challenges in design and implementation of ASM systems. We develop systematic methods and supporting design tools for ASM systems that integrate (1) foundations of dataflow modeling for high level signal processing system design, and (2) the paradigm on Dynamic Data-Driven Application Systems (DDDAS). More specifically, the contributions of this thesis can be broadly categorized in to three major directions: 1. We develop a new design framework that systematically applies dataflow methodologies for high level signal processing system design, and adaptive stream mining based on dynamic topologies of classifiers. In particular, we introduce a new design environment, called the lightweight dataflow for dynamic data driven application systems environment (LiD4E). LiD4E provides formal semantics, rooted in dataflow principles, for design and implementation of a broad class of stream mining topologies. Using this novel application of dataflow methods, LiD4E facilitates the efficient and reliable mapping and adaptation of classifier topologies into implementations on embedded platforms. 2. We introduce new design methods for data-driven digital signal processing (DSP) systems that are targeted to resource- and energy-constrained embedded environments, such as unmanned areal vehicles (UAVs), mobile communication platforms, and wireless sensor networks. We develop a design and implementation framework for multi-mode, data driven embedded signal processing systems, where application modes with complementary trade-offs are selected, configured, executed, and switched dynamically, in a data-driven manner. We demonstrate the utility of our proposed new design methods on an energy-constrained, multi-mode face detection application. 3. We introduce new methods for multiobjective, system-level optimization that have been incorporated into the LiD4E design tool described previously. More specifically, we develop new methods for integrated modeling and optimization of real-time stream mining constraints, multidimensional stream mining performance (e.g., precision and recall), and energy efficiency. Using a design methodology centered on data-driven control of and coordination between alternative dataflow subsystems for stream mining (classification modes), we develop systematic methods for exploring complex, multidimensional design spaces associated with dynamic stream mining systems, and deriving sets of Pareto-optimal system configurations that can be switched among based on data characteristics and operating constraints

End-to-End Learning of Representations for Asynchronous Event-Based Data

Event cameras are vision sensors that record asynchronous streams of per-pixel brightness changes, referred to as "events". They have appealing advantages over frame-based cameras for computer vision, including high temporal resolution, high dynamic range, and no motion blur. Due to the sparse, non-uniform spatiotemporal layout of the event signal, pattern recognition algorithms typically aggregate events into a grid-based representation and subsequently process it by a standard vision pipeline, e.g., Convolutional Neural Network (CNN). In this work, we introduce a general framework to convert event streams into grid-based representations through a sequence of differentiable operations. Our framework comes with two main advantages: (i) allows learning the input event representation together with the task dedicated network in an end to end manner, and (ii) lays out a taxonomy that unifies the majority of extant event representations in the literature and identifies novel ones. Empirically, we show that our approach to learning the event representation end-to-end yields an improvement of approximately 12% on optical flow estimation and object recognition over state-of-the-art methods.Comment: To appear at ICCV 201

Visual Information Retrieval in Endoscopic Video Archives

In endoscopic procedures, surgeons work with live video streams from the inside of their subjects. A main source for documentation of procedures are still frames from the video, identified and taken during the surgery. However, with growing demands and technical means, the streams are saved to storage servers and the surgeons need to retrieve parts of the videos on demand. In this submission we present a demo application allowing for video retrieval based on visual features and late fusion, which allows surgeons to re-find shots taken during the procedure.Comment: Paper accepted at the IEEE/ACM 13th International Workshop on Content-Based Multimedia Indexing (CBMI) in Prague (Czech Republic) between 10 and 12 June 201