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This work investigates the spatio-temporal evolution of coherentstructures in the wake of a generic high-speed train. Spectral properorthogonal decomposition is used to extract energy spectra and empiricalmodes of the fluctuating field. The spectrum of the symmetriccomponent shows overall higher energy and more pronounced low-rankbehavior compared to the antisymmetric one, with the most dominantsymmetric mode at $\omega=3.437$ characterized by vortex sheddingin the near wake and constant streamwise wavenumber structures in thefar wake. The mode bispectrum further reveals the dominant role of selfinteraction of the symmetric component, leading to first harmonic andsubharmonic triads of the fundamental frequency. Then, the stability ofthe wake flow is analyzed based on two-dimensional local analysis. Theabsolute frequency of the near-wake eigenmode is determined based onspatio-temporal analysis, then tracked along the streamwise direction tofind out the global frequency, which indicate a marginally stable globalmode oscillating at a frequency very close to the dominant SPOD mode.The global mode wavemaker is then located, and the structuralsensitivity is calculated based on the direct and adjoint modes derivedfrom a local spatial analysis, with the maximum value localized withinthe recirculation region close to the train tail. Finally, the global modeshape is computed by tracking the most spatially unstable eigenmode,and the alignment with the SPOD mode is computed as a function ofstreamwise location. By combining data-driven and theoreticalapproaches, the mechanisms of coherent structures in complex wakeflows are well identified and isolated., Comment: 38 pages, 16 figures

Approximate query processing over dynamic databases, i.e., under insertions/deletions, has applications ranging from high-frequency trading to internet-of-things analytics. We present JanusAQP, a new dynamic AQP system, which supports SUM, COUNT, AVG, MIN, and MAX queries under insertions and deletions to the dataset. JanusAQP extends static partition tree synopses, which are hierarchical aggregations of datasets, into the dynamic setting. This paper contributes new methods for: (1) efficient initialization of the data synopsis in the presence of incoming data, (2) maintenance of the data synopsis under insertions/deletions, and (3) re-optimization of the partitioning to reduce the approximation error. JanusAQP reduces the error of a state-of-the-art baseline by more than 60% using only 10% storage cost. JanusAQP can process more than 100K updates per second in a single node setting and keep the query latency at a millisecond level.

Sample-based approximate query processing (AQP) suffers from many pitfalls such as the inability to answer very selective queries and unreliable confidence intervals when sample sizes are small. Recent research presented an intriguing solution of combining materialized, pre-computed aggregates with sampling for accurate and more reliable AQP. We explore this solution in detail in this work and propose an AQP physical design called PASS, or Precomputation-Assisted Stratified Sampling. PASS builds a tree of partial aggregates that cover different partitions of the dataset. The leaf nodes of this tree form the strata for stratified samples. Aggregate queries whose predicates align with the partitions (or unions of partitions) are exactly answered with a depth-first search, and any partial overlaps are approximated with the stratified samples. We propose an algorithm for optimally partitioning the data into such a data structure with various practical approximation techniques.

Data loading has been one of the most common performance bottlenecks for many big data applications, especially when they are running on inefficient human-readable formats, such as JSON or CSV. Parsing, validating, integrity checking and data structure maintenance are all computationally expensive steps in loading these formats. Regardless of these costs, many records may be filtered later during query evaluation due to highly selective predicates -- resulting in wasted computation. Meanwhile, the computing power of client ends is typically not exploited. Here, we explore investing limited cycles of clients on prefiltering to accelerate data loading and enable data skipping for query execution. In this paper, we present CIAO, a tunable system to enable client cooperation with the server to enable efficient partial loading and data skipping for a given workload. We proposed an efficient algorithm that would select a near-optimal predicate set to push down within a given budget. Moreover, CIAO will address the trade-off between client cost and server savings by setting different budgets for different clients. We implemented CIAO and evaluated its performance on three real-world datasets. Our experimental results show that the system substantially accelerates data loading by up to 21x and query execution by up to 23x and improves end-to-end performance by up to 19x within a budget of 1.0 microseconds latency per record on clients.

Lithium has confused scientists for decades at almost each scale of the universe. Lithium-rich giants are peculiar stars with lithium abundances over model prediction. A large fraction of lithium-rich low-mass evolved stars are traditionally supposed to be red giant branch (RGB) stars. Recent studies, however, report that red clump (RC) stars are more frequent than RGB. Here, we present a uniquely large systematic study combining the direct asteroseismic analysis with the spectroscopy on the lithium-rich stars. The majority of lithium-rich stars are confirmed to be RCs, whereas RGBs are minor. We reveal that the distribution of lithium-rich RGBs steeply decline with the increasing lithium abundance, showing an upper limit around 2.6 dex, whereas the Li abundances of RCs extend to much higher values. We also find that the distributions of mass and nitrogen abundance are notably different between RC and RGB stars. These findings indicate that there is still unknown process that significantly affects surface chemical composition in low-mass stellar evolution., Comment: 27 pages, 10 figures, 3 tables

Today, data analysts largely rely on intuition to determine whether missing or withheld rows of a dataset significantly affect their analyses. We propose a framework that can produce automatic contingency analysis, i.e., the range of values an aggregate SQL query could take, under formal constraints describing the variation and frequency of missing data tuples. We describe how to process SUM, COUNT, AVG, MIN, and MAX queries in these conditions resulting in hard error bounds with testable constraints. We propose an optimization algorithm based on an integer program that reconciles a set of such constraints, even if they are overlapping, conflicting, or unsatisfiable, into such bounds. Our experiments on real-world datasets against several statistical imputation and inference baselines show that statistical techniques can have a deceptively high error rate that is often unpredictable. In contrast, our framework offers hard bounds that are guaranteed to hold if the constraints are not violated. In spite of these hard bounds, we show competitive accuracy to statistical baselines.

Carefully selected materialized views can greatly improve the performance of OLAP workloads. We study using deep reinforcement learning to learn adaptive view materialization and eviction policies. Our insight is that such selection policies can be effectively trained with an asynchronous RL algorithm, that runs paired counter-factual experiments during system idle times to evaluate the incremental value of persisting certain views. Such a strategy obviates the need for accurate cardinality estimation or hand-designed scoring heuristics. We focus on inner-join views and modeling effects in a main-memory, OLAP system. Our research prototype system, called DQM, is implemented in SparkSQL and we experiment on several workloads including the Join Order Benchmark and the TPC-DS workload. Results suggest that: (1) DQM can outperform heuristic when their assumptions are not satisfied by the workload or there are temporal effects like period maintenance, (2) even with the cost of learning, DQM is more adaptive to changes in the workload, and (3) DQM is broadly applicable to different workloads and skews., Comment: 14 Pages

The importance of timely response to natural disasters and evacuating affected people to safe areas is paramount to save lives. Emergency services are often handicapped by the amount of rescue resources at their disposal. We present a system that leverages the power of a social network forming new connections among people based on \textit{real-time location} and expands the rescue resources pool by adding private sector cars. We also introduce a car-sharing algorithm to identify safe routes in an emergency with the aim of minimizing evacuation time, maximizing pick-up of people without cars, and avoiding traffic congestion., Comment: 2 pages, 3 figures, SWDM2016