Speculative Approximations for Terascale Analytics

Model calibration is a major challenge faced by the plethora of statistical analytics packages that are increasingly used in Big Data applications. Identifying the optimal model parameters is a time-consuming process that has to be executed from scratch for every dataset/model combination even by experienced data scientists. We argue that the incapacity to evaluate multiple parameter configurations simultaneously and the lack of support to quickly identify sub-optimal configurations are the principal causes. In this paper, we develop two database-inspired techniques for efficient model calibration. Speculative parameter testing applies advanced parallel multi-query processing methods to evaluate several configurations concurrently. The number of configurations is determined adaptively at runtime, while the configurations themselves are extracted from a distribution that is continuously learned following a Bayesian process. Online aggregation is applied to identify sub-optimal configurations early in the processing by incrementally sampling the training dataset and estimating the objective function corresponding to each configuration. We design concurrent online aggregation estimators and define halting conditions to accurately and timely stop the execution. We apply the proposed techniques to distributed gradient descent optimization -- batch and incremental -- for support vector machines and logistic regression models. We implement the resulting solutions in GLADE PF-OLA -- a state-of-the-art Big Data analytics system -- and evaluate their performance over terascale-size synthetic and real datasets. The results confirm that as many as 32 configurations can be evaluated concurrently almost as fast as one, while sub-optimal configurations are detected accurately in as little as a $1/20^{\text{th}}$ fraction of the time

PF-OLA: A High-Performance Framework for Parallel On-Line Aggregation

Online aggregation provides estimates to the final result of a computation during the actual processing. The user can stop the computation as soon as the estimate is accurate enough, typically early in the execution. This allows for the interactive data exploration of the largest datasets. In this paper we introduce the first framework for parallel online aggregation in which the estimation virtually does not incur any overhead on top of the actual execution. We define a generic interface to express any estimation model that abstracts completely the execution details. We design a novel estimator specifically targeted at parallel online aggregation. When executed by the framework over a massive $8\text{TB}$ TPC-H instance, the estimator provides accurate confidence bounds early in the execution even when the cardinality of the final result is seven orders of magnitude smaller than the dataset size and without incurring overhead.Comment: 36 page

Parallel Online Aggregation in Action

ABSTRACT Online aggregation provides continuous estimates to the final result of a computation during the actual processing. The user can stop the computation as soon as the estimate is accurate enough, typically early in the execution, or can let the processing terminate and obtain the exact result. In this demonstration, we introduce a general framework for parallel online aggregation in which estimation does not incur overhead on top of the actual processing. We define a generic interface to express any estimation model that abstracts completely the execution details. We design multiple samplingbased estimators suited for parallel online aggregation and implement them inside the framework. Demonstration participants are shown how estimates to general SQL aggregation queries over terabytes of TPC-H data are generated during the entire processing. Due to parallel execution, the estimate converges to the correct result in a matter of seconds even for the most difficult queries. The behavior of the estimators is evaluated under different operating regimes of the distributed cluster used in the demonstration

Metformin Uniquely Prevents Thrombosis by Inhibiting Platelet Activation and mtDNA Release

Thrombosis and its complications are the leading cause of death in patients with diabetes. Metformin, a first-line therapy for type 2 diabetes, is the only drug demonstrated to reduce cardiovascular complications in diabetic patients. However, whether metformin can effectively prevent thrombosis and its potential mechanism of action is unknown. Here we show, metformin prevents both venous and arterial thrombosis with no significant prolonged bleeding time by inhibiting platelet activation and extracellular mitochondrial DNA (mtDNA) release. Specifically, metformin inhibits mitochondrial complex I and thereby protects mitochondrial function, reduces activated platelet-induced mitochondrial hyperpolarization, reactive oxygen species overload and associated membrane damage. In mitochondrial function assays designed to detect amounts of extracellular mtDNA, we found that metformin prevents mtDNA release. This study also demonstrated that mtDNA induces platelet activation through a DC-SIGN dependent pathway. Metformin exemplifies a promising new class of antiplatelet agents that are highly effective at inhibiting platelet activation by decreasing the release of free mtDNA, which induces platelet activation in a DC-SIGN-dependent manner. This study has established a novel therapeutic strategy and molecular target for thrombotic diseases, especially for thrombotic complications of diabetes mellitus

Active removal of waste dye pollutants using Ta[sub]3N[sub]5/W[sub]18O[sub]49 nanocomposite fibres

A scalable solvothermal technique is reported for the synthesis of a photocatalytic composite material consisting of orthorhombic Ta3N5 nanoparticles and WOx≤3 nanowires. Through X-ray diffraction and X-ray photoelectron spectroscopy, the as-grown tungsten(VI) sub-oxide was identified as monoclinic W18O49. The composite material catalysed the degradation of Rhodamine B at over double the rate of the Ta3N5 nanoparticles alone under illumination by white light, and continued to exhibit superior catalytic properties following recycling of the catalysts. Moreover, strong molecular adsorption of the dye to the W18O49 component of the composite resulted in near-complete decolourisation of the solution prior to light exposure. The radical species involved within the photocatalytic mechanisms were also explored through use of scavenger reagents. Our research demonstrates the exciting potential of this novel photocatalyst for the degradation of organic contaminants, and to the authors’ knowledge the material has not been investigated previously. In addition, the simplicity of the synthesis process indicates that the material is a viable candidate for the scale-up and removal of dye pollutants on a wider scale

GLADE-ML: A Database For Big Data Analytics

Big Data Analytics has been a hot topic in computing systems and varies systems have emerged to better support Big Data Analytics. Though databases have been the data hub for decades, they fall short of Big Data Analytics due to inherent limitations. This dissertation present GLADE-ML, a scalable and efficient parallel database that is specifically tailored for Big Data Analytics. Different from traditional databases, GLADE-ML provides iteration management and explicit or implicit randomization in the execution strategy. GLADE-ML provides in-database analytics which outperforms other in-database analytics solutions by several orders of magnitude.GLADE-ML also introduces dot-product join operator in GLADE-ML. Dot-product join operator is specifically designed for Big Models. Big Data analytics has been approached exclusively from a data-parallel perspective, where data are partitioned to multiple workers – threads or separate servers – and model training is executed concurrently over different partitions, under various synchronization schemes that guarantee speedup and/or convergence. The dual -- Big Model -- problem that, surprisingly, has received no attention in database analytics, is how to manage models with millions if not billions of parameters that do not fit in memory. This distinction in model representation changes fundamentally how in-database analytics tasks are carried out.GLADE-ML supports model parallelism over massive models that cannot fit in memory. GLADE-ML extends the lock-free HOGWILD!-family of algorithms to disk-resident models by vertically partitioning the model offline and asynchronously updating the resulting partitions online. Unlike HOGWILD!, concurrent requests to the common model are minimized by a preemptive push-based sharing mechanism that reduces both the number of disk accesses as well as the cache coherency messages between workers. Extensive experimental results for three widespread analytics tasks on real and synthetic datasets show that the proposed framework achieves similar convergence to HOGWILD!, while being the only scalable solution to disk-resident models.Another distinct feature of GLADE-ML is Hyper-Parameter Tuning. Identifying the optimal hyper-parameters is a time-consuming process that the computation has to be executed from scratch for every dataset/model combination even by experienced data scientists. GLADE-ML provides speculative parameter testing which applies advanced parallel multi-query processing methods to evaluate several configurations concurrently. The number of configurations is determined adaptively at runtime, while the configurations themselves are extracted from a distribution that is continuously learned following a Bayesian process. Online aggregation is applied to identify sub-optimal configurations early in the processing by incrementally sampling the training dataset and estimating the objective function corresponding to each configuration

Speculative Approximations for Terascale Analytics

Model calibration is a major challenge faced by the plethora of statistical analytics packages that are increasingly used in Big Data applications. Identifying the optimal model parameters is a time-consuming process that has to be executed from scratch for every dataset/model combination even by experienced data scientists. We argue that the incapacity to evaluate multiple parameter configurations simultaneously and the lack of support to quickly identify sub-optimal configurations are the principal causes. In this paper, we develop two database-inspired techniques for efficient model calibration. Speculative parameter testing applies advanced parallel multi-query processing methods to evaluate several configurations concurrently. The number of configurations is determined adaptively at runtime, while the configurations themselves are extracted from a distribution that is continuously learned following a Bayesian process. Online aggregation is applied to identify sub-optimal configurations early in the processing by incrementally sampling the training dataset and estimating the objective function corresponding to each configuration. We design concurrent online aggregation estimators and define halting conditions to accurately and timely stop the execution. We apply the proposed techniques to distributed gradient descent optimization -- batch and incremental -- for support vector machines and logistic regression models. We implement the resulting solutions in GLADE PF-OLA -- a state-of-the-art Big Data analytics system -- and evaluate their performance over terascale-size synthetic and real datasets. The results confirm that as many as 32 configurations can be evaluated concurrently almost as fast as one, while sub-optimal configurations are detected accurately in as little as a $1/20^{\text{th}}$ fraction of the time

Dot-Product Join: An Array-Relation Join Operator for Big Model Analytics

Big Model analytics tackles the training of massive models that go beyond the available memory of a single computing device, e.g., CPU or GPU. It generalizes Big Data analytics which is targeted at how to train memory-resident models over out-of-memory training data. In this paper, we propose an in-database solution for Big Model analytics. We identify dot-product as the primary operation for training generalized linear models and introduce the first array-relation dot-product join database operator between a set of sparse arrays and a dense relation. This is a constrained formulation of the extensively studied sparse matrix vector multiplication (SpMV) kernel. The paramount challenge in designing the dot-product join operator is how to optimally schedule access to the dense relation based on the non-contiguous entries in the sparse arrays. We prove that this problem is NP-hard and propose a practical solution characterized by two technical contributions---dynamic batch processing and array reordering. We devise three heuristics -- LSH, Radix, and K-center -- for array reordering and analyze them thoroughly. We execute extensive experiments over synthetic and real data that confirm the minimal overhead the operator incurs when sufficient memory is available and the graceful degradation it suffers as memory becomes scarce. Moreover, dot-product join achieves an order of magnitude reduction in execution time over alternative in-database solutions