Scrambling Query Plans to Cope With Unexpected Delays

Accessing numerous widely-distributed data sources poses significant new challenges for query optimization and execution. Congestion or failure in the network introduce highly-variable response times for wide-area data access. This paper is an initial exploration of solutions to this variability. We investigate a class of dynamic, run-time query plan modification techniques that we call query plan scrambling. We present an algorithm which modifies execution plans on-the-fly in response to unexpected delays in data access. The algorithm both reschedules operators and introduces new operators into the plan. We present simulation results that show how our technique effectively hides delays in receiving the initial requested tuples from remote data sources. (Also cross-referenced as UMIACS-TR-96-35

Haren: A Framework for Ad-Hoc Thread Scheduling Policies for Data Streaming Applications

In modern Stream Processing Engines (SPEs), numerous diverse applications, which can differ in aspects such as cost, criticality or latency sensitivity, can co-exist in the same computing node. When these differences need to be considered to control the performance of each application, custom scheduling of operators to threads is of key importance (e.g., when a smart vehicle needs to ensure that safety-critical applications always have access to computational power, while other applications are given lower, variable priorities).Many solutions have been proposed regarding schedulers that allocate threads to operators to optimize specific metrics (e.g., latency) but there is still lack of a tool that allows arbitrarily complex scheduling strategies to be seamlessly plugged on top of an SPE. We propose Haren to fill this gap. More specifically, we (1) formalize the thread scheduling problem in stream processing in a general way, allowing to define ad-hoc scheduling policies, (2) identify the bottlenecks and the opportunities of scheduling in stream processing, (3) distill a compact interface to connect Haren with SPEs, enabling rapid testing of various scheduling policies, (4) illustrate the usability of the framework by integrating it into an actual SPE and (5) provide a thorough evaluation. As we show, Haren makes it is possible to adapt the use of computational resources over time to meet the goals of a variety of scheduling policies

ANAPSID: An Adaptive Query Processing Engine for SPARQL Endpoints

Abstract. Following the design rules of Linked Data, the number of available SPARQL endpoints that support remote query processing is quickly growing; however, because of the lack of adaptivity, query executions may frequently be unsuccessful. First, fixed plans identified following the traditional optimize-then-execute paradigm, may timeout as a consequence of endpoint availability. Sec-ond, because blocking operators are usually implemented, endpoint query en-gines are not able to incrementally produce results, and may become blocked if data sources stop sending data. We present ANAPSID, an adaptive query engine for SPARQL endpoints that adapts query execution schedulers to data availabil-ity and run-time conditions. ANAPSID provides physical SPARQL operators that detect when a source becomes blocked or data traffic is bursty, and opportunis-tically, the operators produce results as quickly as data arrives from the sources. Additionally, ANAPSID operators implement main memory replacement policies to move previously computed matches to secondary memory avoiding duplicates. We compared ANAPSID performance with respect to RDF stores and endpoints, and observed that ANAPSID speeds up execution time, in some cases, in more than one order of magnitude.

Anticipatory fear and helplessness predict PTSD and depression in domestic violence survivors

PubMed ID: 27710008Objective: Embracing the conceptual framework of contemporary learning theory, this study tested the hypothesis that anticipatory fear due to a sense of ongoing threat to safety and sense of helplessness in life would be the strongest determinants of PTSD and depression in domestic violence survivors. Method: Participants were 220 domestic violence survivors recruited consecutively from 12 shelters for women in Turkey (response rate 70%). They were assessed with the Semi-Structured Interview for Survivors of Domestic Violence, Traumatic Stress Symptom Checklist, Depression Rating Scale, and Fear and Sense of Control Scale. Results: Survivors were exposed to 21 (SD = 6.7) physical, psychological, and sexual violence stressors over 11.3 (SD = 8.8) years. They reported high levels of peritrauma perceived distress of and lack of control over stressor events. Approximately 10 months after trauma, many feared reliving the same domestic violence events, felt helpless, feared for their life, and felt in danger. PTSD and depression rates were 48.2% and 32.7%, respectively. The strongest predictors of PTSD and depression were fear due to a sense of ongoing threat to safety and sense of helplessness in life, which explained the largest amount of variances in these psychiatric conditions. Conclusion: The findings support the contemporary learning theory of traumatic stress and are consistent with findings of studies involving earthquake, war, and torture survivors. They imply that trauma-focused interventions designed to overcome fear, reduce helplessness, and restore sense of control over one's life would be effective in PTSD and depression in domestic violence survivors

Phenomenon-aware sensor database systems

Abstract. Recent advances in large-scale sensor-network technologies enable the deployment of a huge number of sensors in the surrounding environment. Sensors do not live in isolation. Instead, close-by sensors experience similar environmental conditions. Hence, close-by sensors may indulge in a correlated behavior and generate a “phenomenon”. A phenomenon is characterized by a group of sensors that show “similar” behavior over a period of time. Examples of detectable phenomena include the propagation over time of a pollution cloud or an oil spill region. In this research, we propose a framework to detect and track various forms of phenomena in a sensor field. This framework empowers sensor database systems with phenomenon-awareness capabilities. Phenomenon-aware sensor database systems use high-level knowledge about phenomena in the sensor field to control the acquisition of sensor data and to optimize subsequent user queries. As a vehicle for our research, we build the Nile-PDT system, a framework for Phenomenon Detection and Tracking inside Nile, a prototype data stream management system developed at Purdue University.