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检索条件"主题词=Multi-Query Optimization"

共 45 条记录，以下是31-40 订阅

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Scalable multi-query Execution using Reinforcement Learning 21

Scalable Multi-Query Execution using Reinforcement Learning

引用

ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Sioulas, Panagiotis Ailamaki, Anastasia Ecole Polytech Fed Lausanne Lausanne Switzerland Ecole Polytech Fed Lausanne RAW Labs SA Lausanne Switzerland

ISBN: (纸本)9781450383431

The growing demand for data-intensive decision support and the migration to multi-tenant infrastructures put databases under the stress of high analytical query load. The requirement for high throughput contradicts the traditional design of query-at-a-time databases that optimize queries for efficient serial execution. Sharing work across queries presents an opportunity to reduce the total cost of processing and therefore improve throughput with increasing query load. Systems can share work either by assessing all opportunities and restructuring batches of queries ahead of execution, or by inspecting opportunities in individual incoming queries at runtime: the former strategy scales poorly to large query counts, as it requires expensive sharing-aware optimization, whereas the latter detects only a subset of the opportunities. Both strategies fail to minimize the cost of processing for large and ad-hoc workloads. This paper presents Roulette, a specialized intelligent engine for multi-query execution that addresses, through runtime adaptation, the shortcomings of existing work-sharing strategies. Roulette scales by replacing sharing-aware optimization with adaptive query processing, and it chooses opportunities to explore and exploit by using reinforcement learning. RouLette also includes optimizations that reduce the adaptation overhead. Roulette increases throughput by 1.6-28.3x, compared to a state-of-the-art query-at-a-time engine, and up to 6.5x, compared to sharing-enabled prototypes, for multiquery workloads based on the schema of TPC-DS.

关键词： sharing multi-query optimization join reinforcement learning

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Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations 19

Incremental and Approximate Inference for Faster Occlusion-b...

引用

ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Nakandala, Supun Kumar, Arun Papakonstantinou, Yannis Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781450356435

Deep Convolutional Neural Networks (CNNs) now match human accuracy in many image prediction tasks, resulting in a growing adoption in e -commerce radiology, and other domains. Naturally, "explaining" CNN predictions is a key concern for many users. Since the internal workings of CNNs are unintuitive for most users, occlusion -based explanations (OBE) are popular for understanding which parts of an image matter most for a prediction. One occludes a region of the image using a patch and moves it around to produce a heat map of changes to the prediction probability. Alas, this approach is computationally expensive due to the large number of re -inference requests produced, which wastes time and raises resource costs. We tackle this issue by casting the OBE task as a new instance of the classical incremental view maintenance problem. We create a novel and comprehensive algebraic framework for incremental CNN inference combining materialized views with multi -query optimization to reduce computational costs. We then present two novel approximate inference optimizations that exploit the semantics of CNNs and the OBE task to further reduce runtimes. We prototype our ideas in Python to create a tool we call KRYPTON that supports both CPUs and CPUs. Experiments with real data and CNNs show that KRYP rON reduces runtimes by up to 5X (resp. 35X) to produce exact (resp. high -quality approximate) results without raising resource requirements.

关键词： convolution neural network explainability systems for machine learning multi-query optimization incremental view maintenance

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Shahin: Faster Algorithms for Generating Explanations for multiple Predictions 21

Shahin: Faster Algorithms for Generating Explanations for Mu...

引用

ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Hasani, Sona Thirumuruganathan, Saravanan Koudas, Nick Das, Gautam Google Mountain View CA 94043 USA HBKU QCRI Doha Qatar Univ Toronto Toronto ON Canada Univ Texas Arlington Arlington TX 76019 USA

ISBN: (纸本)9781450383431

Machine learning (ML) models have achieved widespread adoption in the last few years. Generating concise and accurate explanations often increases user trust and understanding of the model prediction. Usually, the implementations of popular explanation algorithms are highly optimized for a single prediction. In practice, explanations often have to be generated in a batch for multiple predictions at a time. To the best of our knowledge, there has been no work for efficiently generating explanations for more than one prediction. While one could use multiple machines to generate explanations in parallel, this approach is sub-optimal as it does not leverage higher-level optimizations that are available in a batch setting. We propose a principled and lightweight approach for identifying redundant computations and several effective heuristics for dramatically speeding up explanation generation. Our techniques are general and could be applied to a wide variety of perturbation based explanation algorithms. We demonstrate this over a diverse set of algorithms including, LIME, Anchor, and SHAP. Our empirical experiments show that our methods impose very little overhead and require minimal modification to the explanation algorithms. They achieve significant speedup over baseline approaches that generate explanations in a sequential manner.

关键词： explanations multi-query optimization machine learning LIME Anchor SHAP

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Nautilus: An Optimized System for Deep Transfer Learning over Evolving Training Datasets 22

Nautilus: An Optimized System for Deep Transfer Learning ove...

引用

International Conference on Management of Data (SIGMOD)

作者： Nakandala, Supun Kumar, Arun Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781450392495

Deep learning (DL) has revolutionized unstructured data analytics. But in most cases, DL needs massive labeled datasets and large compute clusters, which hinders its adoption. These limitations can be overcome using a popular paradigm called deep transfer learning (DTL). With DTL, one adapts a pre-trained DL model instead of training a model from scratch. Thus, DTL reduces the massive training data and compute requirements to train a model. During adaptation, a common practice is to freeze most pre-trained model parts and adapt only the remaining. Since no single adaptation scheme is universally the best, one often evaluates several schemes, which is also called model selection. We also observed that data labeling for DTL is seldom a one-off process. One often updates their labeled data intermittently by adding new labeled data and performs model selection to evaluate the accuracy of the trained models. Today, one executes this workload by performing computations for the entire pre-trained model and repeats it for every model selection cycle. This approach results in redundant computations in frozen model parts and causes usability and system inefficiency issues. In this work, we reimagine DTL model selection in the presence of frozen layers as an instance of multi-query optimization and propose two optimizations that reduce redundant computations and training overheads. We implement our optimizations into a data system called NAUTILUS. Experiments with end-to-end workloads on benchmark datasets show that NAUTILUS reduces DTL model selection runtimes by up to 5X compared to the current practice.

关键词： Systems for Machine Learning Deep Transfer Learning multi-query optimization

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VISTA: Optimized System for Declarative Feature Transfer from Deep CNNs at Scale 20

VISTA: Optimized System for Declarative Feature Transfer fro...

引用

ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Nakandala, Supun Kumar, Arun Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781450367356

Scalable systems for machine learning (ML) are largely siloed into dataflow systems for structured data and deep learning systems for unstructured data. This gap has left workloads that jointly analyze both forms of data with poor systems support, leading to both low system efficiency and grunt work for users. We bridge this gap for an important class of such workloads: feature transfer from deep convolutional neural networks (CNNs) for analyzing images along with structured data. Executing feature transfer on scalable data flow and deep learning systems today faces two key systems issues: inefficiency due to redundant computations and crash-proneness due to mismanaged memory. We present VISTA, a new data system that resolves these issues by elevating this workload to a declarative level on top of data flow and deep learning systems. VISTA automatically optimizes the configuration and execution of this workload to reduce both computational redundancy and the potential for workload crashes. Experiments on real datasets show that apart from making feature transfer easier, VISTA avoids workload crashes and reduces runtimes by 58% to 92% compared to baselines.

关键词： convolution neural networks multimodal analytics materialization optimization systems for machine learning multi-query optimization

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MUSE: multi-query Event Trend Aggregation 20

MUSE: Multi-query Event Trend Aggregation

引用

29th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Rozet, Allison Poppe, Olga Lei, Chuan Rundensteiner, Elke A. Worcester Polytech Inst Worcester MA 01609 USA Microsoft Gray Syst Lab Redmond WA USA IBM Res Almaden San Jose CA USA

ISBN: (纸本)9781450368599

Streaming analytics deploy Kleene pattern queries to detect and aggregate event trends on high-rate data streams. Despite increasing workloads, most state-of-the-art systems process each query independently, thus missing cost-saving sharing opportunities. Sharing event trend aggregation poses several technical challenges. First, Kleene patterns are in general difficult to share due to complex nesting and arbitrarily long matches. Second, not all sharing opportunities are beneficial because sharing Kleene patterns incurs non-trivial overhead to ensure the correctness of final aggregation results. We propose Muse (multi-query Shared Event trend aggregation), the first framework that shares aggregation queries with Kleene patterns while avoiding expensive trend construction. To find the beneficial sharing plan, the Muse optimizer effectively selects robust sharing candidates from the exponentially large search space. Our experiments demonstrate that Muse increases throughput by 4 orders of magnitude compared to state-of-the-art approaches.

关键词： incremental aggregation event trend multi-query optimization complex event processing

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On improvement of the volcano search and optimization strategy

引用

7th International Workshop on State of the Art in Scientific Computing

作者： Baryamureeba, Venansius Ngubiri, John Makerere Univ Inst Comp Sci Kampala Uganda

ISBN: (纸本)3540290672

The ever-increasing load on databases dictates that queries do not need to be processed one by one. multi-query optimization seeks to optimize queries grouped in batches instead of one by one. multi-query optimizers aim at identifying inter and intra query similarities to bring up sharing of common sub-expressions and hence saving computer resources like time, processor cycles and memory. Of course, the searching takes some resources but so long as the saved resources are more than those used, there is a global benefit. Since queries are random and from different sources, similarities are not guaranteed but since they are addressed to the same schema, it is likely. The search strategy need to be intelligent such that it continues only when there is a high probability of a sharing (hence resource saving) opportunity. We present a search strategy that assembles the queries in an order such that. the benefits are high, that detects null sharing cases and therefore terminates the similar sub-expressions' search as well as removing sub-expressions which already exist else where so as to reduce subsequent searching procedures for a global advantage.

关键词： query processing multi-query optimization volcano optimizer

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On-line Sensing Task optimization for Shared Sensors

On-line Sensing Task Optimization for Shared Sensors

引用

9th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

作者： Tavakoli, Arsalan Kansal, Aman Nath, Suman Univ Calif Berkeley Berkeley CA 94720 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781605589886

Shared sensing infrastructures that allow multiple applications to share deployed sensors are emerging and Internet protocol based access for such sensors has already been prototyped and deployed. As a large number of applications start accessing shared sensors, the efficiency of resource usage at the embedded nodes and in the network infrastructure supporting them becomes a concern. To address this, we develop methods that detect when common data and common stream processing is requested by multiple applications, including cases where only some of the data is shared or only intermediate processing steps are common. The communication and processing is then modified to eliminate the redundancies. Specifically, we use an interval-cover graph to minimize communication redundancies and a joint data flow graph optimization to remove computational redundancies. Both methods operate online and allow application requests to be dynamically added or removed. The proposed methods are evaluated using applications on a road traffic sensing infrastructure.

关键词： wireless sensor networks multi-query optimization computation sharing

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multi-layer optimizations for End-to-End Data Analytics 2020

Multi-layer Optimizations for End-to-End Data Analytics

引用

18th ACM/IEEE International Symposium on Code Generation and optimization (CGO)

作者： Shaikhha, Amir Schleich, Maximilian Ghita, Alexandru Olteanu, Dan Univ Oxford Oxford England

ISBN: (纸本)9781450370479

We consider the problem of training machine learning models over multi-relational data. The mainstream approach is to first construct the training dataset using a feature extraction query over input database and then use a statistical software package of choice to train the model. In this paper we introduce Iterative Functional Aggregate Queries (IFAQ), a framework that realizes an alternative approach. IFAQ treats the feature extraction query and the learning task as one program given in the IFAQ's domain-specific language, which captures a subset of Python commonly used in Jupyter notebooks for rapid prototyping of machine learning applications. The program is subject to several layers of IFAQ optimizations, such as algebraic transformations, loop transformations, schema specialization, data layout optimizations, and finally compilation into efficient low-level C++ code specialized for the given workload and data. We show that a Scala implementation of IFAQ can outperform mlpack, Scikit, and TensorFlow by several orders of magnitude for linear regression and regression tree models over several relational datasets.

关键词： In-Database Machine Learning multi-query optimization query Compilation

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Shared Complex Event Trend Aggregation

Shared Complex Event Trend Aggregation

引用

作者： Allison M. Rozet Worcester Polytechnic Institute

学位级别：硕士

Streaming analytics deploy Kleene pattern queries to detect and aggregate event trends against high-rate data streams. Despite increasing workloads, most state-of-the-art systems process each query independently, thus missing cost-saving sharing opportunities. Sharing complex event trend aggregation poses several technical challenges. First, the execution of nested and diverse Kleene patterns is difficult to share. Second, we must share aggregate computation without the exponential costs of constructing the event trends. Third, not all sharing opportunities are beneficial because sharing aggregation introduces overhead. We propose a novel framework, Muse (multi-query Snapshot Execution), that shares aggregation queries with Kleene patterns while avoiding expensive trend construction. It adopts an online sharing strategy that eliminates re-computations for shared sub-patterns. To determine the beneficial sharing plan, we introduce a cost model to estimate the sharing benefit and design the Muse refinement algorithm to efficiently select robust sharing candidates from the search space. Finally, we explore optimization decisions to further improve performance. Our experiments over a wide range of scenarios demonstrate that Muse increases throughput by 4 orders of magnitude compared to state-of-the-art approaches with negligible memory requirements.

关键词： complex event processing event trend incremental aggregation multi-query optimization

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