Robust Complex Event Pattern Detection over Streams

Event stream processing (ESP) has become increasingly important in modern applications. In this dissertation, I focus on providing a robust ESP solution by meeting three major research challenges regarding the robustness of ESP systems: (1) while event constraint of the input stream is available, applying such semantic information in the event processing; (2) handling event streams with out-of-order data arrival and (3) handling event streams with interval-based temporal semantics. The following are the three corresponding research tasks completed by the dissertation: Task I - Constraint-Aware Complex Event Pattern Detection over Streams. In this task, a framework for constraint-aware pattern detection over event streams is designed, which on the fly checks the query satisfiability / unsatisfiability using a lightweight reasoning mechanism and adjusts the processing strategy dynamically by producing early feedback, releasing unnecessary system resources and terminating corresponding pattern monitor. Task II - Complex Event Pattern Detection over Streams with Out-of-Order Data Arrival. In this task, a mechanism to address the problem of processing event queries specified over streams that may contain out-of-order data is studied, which provides new physical implementation strategies for the core stream algebra operators such as sequence scan, pattern construction and negation filtering. Task III - Complex Event Pattern Detection over Streams with Interval-Based Temporal Semantics. In this task, an expressive language to represent the required temporal patterns among streaming interval events is introduced and the corresponding temporal operator ISEQ is designed

Emotion Processing in the Visual Brain: A MEG Analysis

Recent functional magnetic resonance imaging (fMRI) and event-related brain potential (ERP) studies provide empirical support for the notion that emotional cues guide selective attention. Extending this line of research, whole head magneto-encephalogram (MEG) was measured while participants viewed in separate experimental blocks a continuous stream of either pleasant and neutral or unpleasant and neutral pictures, presented for 330ms each. Event-related magnetic fields (ERF) were analyzed after intersubject sensor coregistration, complemented by minimum norm estimates (MNE) to explore neural generator sources. Both streams of analysis converge by demonstrating the selective emotion processing in an early (120-170ms) and a late time interval (220-310ms). ERF analysis revealed that the polarity of the emotion difference fields was reversed across early and late intervals suggesting distinct patterns of activation in the visual processing stream. Source analysis revealed the amplified processing of emotional pictures in visual processing areas with more pronounced occipito-parieto-temporal activation in the early time interval, and a stronger engagement of more anterior, temporal, regions in the later interval. Confirming previous ERP studies showing facilitated emotion processing, the present data suggest that MEG provides a complementary look at the spread of activation in the visual processing strea

Uncovering the Neural Signature of Lapsing Attention: Electrophysiological Signals Predict Errors up to 20 s before They Occur

The extent to which changes in brain activity can foreshadow human error is uncertain yet has important theoretical and practical implications. The present study examined the temporal dynamics of electrocortical signals preceding a lapse of sustained attention. Twenty-one participants performed a continuous temporal expectancy task, which involved continuously monitoring a stream of regularly alternating patterned stimuli to detect a rarely occurring target stimulus whose duration was 40% longer. The stimulus stream flickered at a rate of 25 Hz to elicit a steady-state visual-evoked potential (SSVEP), which served as a continuous measure of basic visual processing. Increasing activity in the band (8 –14 Hz) was found beginning20 s before a missed target. This was followed by decreases in the amplitude of two event-related components over a short pretarget time frame: the frontal P3 (3– 4 s) and contingent-negative variation (during the target interval). In contrast, SSVEP amplitude before hits and misses was closely matched, suggesting that the efficacy of ongoing basic visual processing was unaffected. Our results show that the specific neural signatures of attentional lapses are registered in the EEG up to 20 s before an error

Exploring sensor data management

The increasing availability of cheap, small, low-power sensor hardware and the ubiquity of wired and wireless networks has led to the prediction that `smart evironments' will emerge in the near future. The sensors in these environments collect detailed information about the situation people are in, which is used to enhance information-processing applications that are present on their mobile and `ambient' devices.\ud \ud Bridging the gap between sensor data and application information poses new requirements to data management. This report discusses what these requirements are and documents ongoing research that explores ways of thinking about data management suited to these new requirements: a more sophisticated control flow model, data models that incorporate time, and ways to deal with the uncertainty in sensor data

Benchmarking Distributed Stream Data Processing Systems

The need for scalable and efficient stream analysis has led to the development of many open-source streaming data processing systems (SDPSs) with highly diverging capabilities and performance characteristics. While first initiatives try to compare the systems for simple workloads, there is a clear gap of detailed analyses of the systems' performance characteristics. In this paper, we propose a framework for benchmarking distributed stream processing engines. We use our suite to evaluate the performance of three widely used SDPSs in detail, namely Apache Storm, Apache Spark, and Apache Flink. Our evaluation focuses in particular on measuring the throughput and latency of windowed operations, which are the basic type of operations in stream analytics. For this benchmark, we design workloads based on real-life, industrial use-cases inspired by the online gaming industry. The contribution of our work is threefold. First, we give a definition of latency and throughput for stateful operators. Second, we carefully separate the system under test and driver, in order to correctly represent the open world model of typical stream processing deployments and can, therefore, measure system performance under realistic conditions. Third, we build the first benchmarking framework to define and test the sustainable performance of streaming systems. Our detailed evaluation highlights the individual characteristics and use-cases of each system.Comment: Published at ICDE 201

Adaptive Energy-aware Scheduling of Dynamic Event Analytics across Edge and Cloud Resources

The growing deployment of sensors as part of Internet of Things (IoT) is generating thousands of event streams. Complex Event Processing (CEP) queries offer a useful paradigm for rapid decision-making over such data sources. While often centralized in the Cloud, the deployment of capable edge devices on the field motivates the need for cooperative event analytics that span Edge and Cloud computing. Here, we identify a novel problem of query placement on edge and Cloud resources for dynamically arriving and departing analytic dataflows. We define this as an optimization problem to minimize the total makespan for all event analytics, while meeting energy and compute constraints of the resources. We propose 4 adaptive heuristics and 3 rebalancing strategies for such dynamic dataflows, and validate them using detailed simulations for 100 - 1000 edge devices and VMs. The results show that our heuristics offer O(seconds) planning time, give a valid and high quality solution in all cases, and reduce the number of query migrations. Furthermore, rebalance strategies when applied in these heuristics have significantly reduced the makespan by around 20 - 25%.Comment: 11 pages, 7 figure

Temporal Stream Algebra

Data stream management systems (DSMS) so far focus on event queries and hardly consider combined queries to both data from event streams and from a database. However, applications like emergency management require combined data stream and database queries. Further requirements are the simultaneous use of multiple timestamps after different time lines and semantics, expressive temporal relations between multiple time-stamps and exible negation, grouping and aggregation which can be controlled, i. e. started and stopped, by events and are not limited to fixed-size time windows. Current DSMS hardly address these requirements. This article proposes Temporal Stream Algebra (TSA) so as to meet the afore mentioned requirements. Temporal streams are a common abstraction of data streams and data- base relations; the operators of TSA are generalizations of the usual operators of Relational Algebra. A in-depth 'analysis of temporal relations guarantees that valid TSA expressions are non-blocking, i. e. can be evaluated incrementally. In this respect TSA differs significantly from previous algebraic approaches which use specialized operators to prevent blocking expressions on a "syntactical" level

Output Stream of Binding Neuron with Feedback

The binding neuron model is inspired by numerical simulation of Hodgkin-Huxley-type point neuron, as well as by the leaky integrate-and-fire model. In the binding neuron, the trace of an input is remembered for a fixed period of time after which it disappears completely. This is in the contrast with the above two models, where the postsynaptic potentials decay exponentially and can be forgotten only after triggering. The finiteness of memory in the binding neuron allows one to construct fast recurrent networks for computer modeling. Recently, the finiteness is utilized for exact mathematical description of the output stochastic process if the binding neuron is driven with the Poissonian input stream. In this paper, the simplest networking is considered for binding neuron. Namely, it is expected that every output spike of single neuron is immediately fed into its input. For this construction, externally fed with Poissonian stream, the output stream is characterized in terms of interspike interval probability density distribution if the binding neuron has threshold 2. For higher thresholds, the distribution is calculated numerically. The distributions are compared with those found for binding neuron without feedback, and for leaky integrator. Sample distributions for leaky integrator with feedback are calculated numerically as well. It is oncluded that even the simplest networking can radically alter spikng statistics. Information condensation at the level of single neuron is discussed.Comment: Version #1: 4 pages, 5 figures, manuscript submitted to Biological Cybernetics. Version #2 (this version): added 3 pages of new text with additional analytical and numerical calculations, 2 more figures, 11 more references, added Discussion sectio