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RANDOM sampling FROM DATABASES - A SURVEY

STATISTICS AND COMPUTING 1995年第1期5卷 25-42页

作者： OLKEN, F ROTEM, D UNIV CALIF BERKELEY LAWRENCE BERKELEY LABDIV INFORMAT & COMP SCIBERKELEYCA 94720 SAN JOSE STATE UNIV SCH BUSINESSDEPT MANAGEMENT INFORMAT SYSTSAN JOSECA 95192

This paper reviews recent literature on techniques for obtaining random samples from databases. We begin with a discussion of why one would want to include sampling facilities in database management systems. We then review basic sampling techniques used in constructing DBMS sampling algorithms, e.g. acceptance/rejection and reservoir sampling. A discussion of sampling from various data structures follows: B+ trees, hash files, spatial data structures (including R-trees and quadtrees). algorithms for sampling from simple relational queries, e.g. single relational operators such as selection, intersection, union, set difference, projection, and join are then described. We then describe sampling for estimation of aggregates (e.g. the size of query results). Here we discuss both clustered sampling, and sequential sampling approaches, Decision-theoretic approaches to sampling for query optimization are reviewed.

关键词： CLUSTERED sampling ESTIMATION MATERIALIZED VIEWS QUERY OPTIMIZATION QUERY RESULT SIZE ESTIMATION QUERY PROCESSING RANDOM sampling RELATIONAL DATABASES RESERVOIR sampling sampling algorithms SIMPLE RANDOM sampling SEQUENTIAL sampling SIZE OF PROJECTIONS SIZE OF TRANSITIVE CLOSURE

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A stochastic representation and sampling algorithm for nested Archimedean copulas

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2012年第9期82卷 1239-1255页

作者： Hofert, Marius ETH Dept Math RiskLab CH-8092 Zurich Switzerland

A general sampling algorithm for nested Archimedean copulas was recently suggested. It is given in two different forms, a recursive or an explicit one. The explicit form allows for a simpler version of the algorithm which is numerically more stable and faster since less function evaluations are required. The algorithm can also be given in general form, not being restricted to a particular nesting such as fully nested Archimedean copulas. Further, several examples are given.

关键词： Archimedean copulas nested Archimedean copulas Laplace-Stieltjes transforms sampling algorithms stochastic representation

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Performance Analysis of Hybrid sampling and Meta-heuristics

Performance Analysis of Hybrid Sampling and Meta-heuristics

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Lang, Ryan Dieter Engelbrecht, Andries Petrus Stellenbosch Univ Comp Sci Div Stellenbosch South Africa Stellenbosch Univ Dept Ind Engn Stellenbosch South Africa Gulf Univ Sci & Technol Ctr Appl Math & Bioinformat Mubarak Al Abdullah Saudi Arabia

ISBN: (纸本)9798400701207

This paper investigates the effect of hybridising sampling algorithms with population-based meta-heuristics. Recent literature has shown that alternatives to the traditionally used pseudo-random number generators to generate the initial population of metaheuristics can improve performance. However, most studies focus on sample sizes that are limited to the size of the initial populations. In contrast, this paper studies the effect of extended random initialisation, which uses relatively large samples and then initialises the meta-heuristics from the points in the sample with the best-found fitness values. A portfolio of three meta-heuristics, four sampling algorithms and three different sampling budgets are analysed from the fixed budget perspective on the BBOB benchmark suite. Statistical analysis of the results shows that the hybrid algorithms converge to better solutions than their non-hybrid counterparts. The results further indicate that large sample sizes can be used to generate landscape analysis features, ensuring reliable approximations of the investigated functions' properties without lessening the meta-heuristics' performance.

关键词： meta-heuristics evolutionary algorithms sampling algorithms initialization landscape analysis

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On Local Distributed sampling and Counting 18

On Local Distributed Sampling and Counting

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37th ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing (PODC)

作者： Feng, Weiming Yin, Yitong Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781450357951

In classic distributed graph problems, each instance on a graph specifies a space of feasible solutions (e.g. all proper (Delta + 1)-list-colorings of the graph), and the task of distributed algorithm is to construct a feasible solution using local information. We study distributed sampling and counting problems, in which each instance specifies a joint distribution of feasible solutions. The task of distributed algorithm is to sample from this joint distribution, or to locally measure the volume of the probability space via the marginal probabilities. The latter task is also known as inference, which is a local counterpart of counting. For self-reducible classes of instances, the following equivalences are established in the LOCAL model up to polylogarithmic factors: For all joint distributions, approximate inference and approximate sampling are computationally equivalent. For all joint distributions defined by local constraints, exact sampling is reducible to either one of the above tasks. If further, sequentially constructing a feasible solution is trivial locally, then all above tasks are easy if and only if the joint distribution exhibits strong spatial mixing. Combining with the state of the arts of strong spatial mixing, we obtain efficient sampling algorithms in the LOCAL model for various important sampling problems, including: an O(root Delta log(3) n)-round algorithm for exact sampling matchings in graphs with maximum degree Delta, and an O(log(3) n)-round algorithm for sampling according to the hardcore model (weighted independent sets) in the uniqueness regime, which along with the Omega(diam) lower bound in [3] for sampling according to the hardcore model in the non-uniqueness regime, gives the first computational phase transition for distributed sampling.

关键词： distributed graph algorithms local computation sampling algorithms Gibbs distribution strong spatial mixing

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SGP: sampling Big Social Network Based on Graph Partition

SGP: Sampling Big Social Network Based on Graph Partition

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International Conference on Services Science (ICSS)

作者： Du, Xiaolin Ye, Yunming Li, Yan Li, Yueping Harbin Inst Technol Shenzhen Grad Sch Key Lab Internet Informat Collaborat Harbin Peoples R China Shenzhen Polytech Sch Comp Engn Shenzhen Peoples R China

ISBN: (纸本)9781479999460

Deriving a representative sample from a big social network is essential for many Internet services that rely on accurate analysis of big social data. A good sampling method for social network should be able to generate small sample networks with similar structures as original big network. In this paper, we propose SGP, a new big social network sampling algorithm based on graph partition. In SGP, original network is firstly partitioned into several sub-networks that will be sampled evenly. This procedure enables SGP to effectively maintain the topological similarity and community structure similarity between the sampled network and its original network. We have evaluated SGP on several well-known data sets. The experimental results show that SGP outperforms six state-of-the-art methods.

关键词： sampling algorithms social networks graph partition community structure topology structure

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A Classification of Product sampling for Software Product Lines 18

A Classification of Product Sampling for Software Product Li...

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22nd International Systems and Software Product Line Conference (SPLC)

作者： Varshosaz, Mahsa Al-Hajjaji, Mustafa Thum, Thomas Runge, Tobias Mousavi, Mohammad Reza Schaefer, Ina Halmstad Univ Halmstad Sweden Pure Syst GmbH Magdeburg Germany TU Braunschweig Braunschweig Germany Univ Leicester Leicester Leics England

ISBN: (纸本)9781450364645

The analysis of software product lines is challenging due to the potentially large number of products, which grow exponentially in terms of the number of features. Product sampling is a technique used to avoid exhaustive testing, which is often infeasible. In this paper, we propose a classification for product sampling techniques and classify the existing literature accordingly. We distinguish the important characteristics of such approaches based on the information used for sampling, the kind of algorithm, and the achieved coverage criteria. Furthermore, we give an overview on existing tools and evaluations of product sampling techniques. We share our insights on the state-of-the-art of product sampling and discuss potential future work.

关键词： sampling algorithms Software Product Lines Testing Feature Interaction Domain Models

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Secure and Highly-Available Aggregation Queries in Large-Scale Sensor Networks via Set sampling

Secure and Highly-Available Aggregation Queries in Large-Sca...

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8th International Symposium on Information Processing Sensor Networks

作者： Yu, Haifeng Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781424451081

Wireless sensor networks are often queried for aggregates such as predicate count, sum, and average. In untrusted environments, sensors may potentially be compromised. Existing approaches for securely answering aggregation queries in untrusted sensor networks can detect whether the aggregation result is corrupted by an attacker. However, the. attacker (controlling the compromised. sensors) can keep corrupting the result, rendering the system unavailable. This paper aims to enable aggregation. queries to tolerate instead of just detecting the adversary. To this end;we propose a novel tree sampling algorithm that directly uses sampling to answer aggregation queries. It leverages a novel set sampling technique to overcome a key and well-known obstacle in sampling - traditional sampling technique is only effective when the predicate count or sum is large. :Set sampling can efficiently sample a set of sensors together, and determine whether any sensor in the set satisfies the predicate (but not how many). With set sampling as a building block, tree sampling can provably generate a correct answer despite adversarial interference, while without the drawbacks of traditional sampling techniques.

关键词： Aggregation queries secure aggregation queries sampling algorithms set sampling tree sampling

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Lightweight Feature-based Priority sampling for Industrial IoT Multivariate Time Series 20

Lightweight Feature-based Priority Sampling for Industrial I...

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20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA)

作者： Nemer, Mohammad Ali Azar, Joseph Makhoul, Abdallah Bourgeois, Julien Univ Franche Comte UMR CNRS 6174 Femto St Inst Besancon France

ISBN: (纸本)9798350319439

sampling Industrial IoT data streams aims to generate a sample for future data analysis tasks. Several variables influence the efficacy of the constructed sample, including the sampling algorithm and its complexity, the sampling rate selected, and how the sampled data are processed at the gateway. In this paper, we propose a lightweight feature-based priority sampling technique for optimizing Industrial IoT multivariate time series prior to deep learning model classification. The selection of an effective sampling algorithm and rate, coupled with efficient data processing, poses a significant challenge with a key objective of balancing communication overhead reduction and precision maintenance. Our technique minimizes data transmission at the IoT device level, enhancing energy efficiency and improving classification performance by noise reduction through selective feature sampling. Comparative evaluation with existing sampling techniques using a benchmark dataset indicates superior performance in terms of data reduction and classification accuracy trade-offs. Notably, our approach enhances the accuracy of a ResNet model and reduces its processing time.

关键词： Industrial IoT Data Streams Time Series Classification Industry 4.0 Fault Detection sampling algorithms Data reduction Energy efficiency

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Towards Efficient Data sampling for Temporal Anomaly Detection in Sensor Networks 2

Towards Efficient Data Sampling for Temporal Anomaly Detecti...

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2nd IEEE Middle East and North Africa COMMunications Conference (IEEE MENACOMM)

作者： El Sibai, Rayane Chabchoub, Yousra Abou Jaoude, Chady Demerjian, Jacques Togbe, Maurras ISEP LISITE F-92130 Issy Les Moulineaux France Antonine Univ Ticket Lab Fac Engn Baabda Lebanon Lebanese Univ LaRRIS Fac Sci Fanar Lebanon

ISBN: (纸本)9781728136875

Data streams sampling is intended to build a sample on which the future data analysis tasks will be performed. Several parameters affect the effectiveness of the built sample: the used sampling algorithm, chosen sampling rate, and window size if the sliding window model is adopted. Thus, given a stream of items, the most challenging task is to select the most relevant sampling technique to apply and the right parameters to employ to sample the data. In this paper, we address the impact of data sampling on the anomaly detection results. First, we develop a new version of the Weighted Random sampling (WRS) algorithm that samples the data based on their values with respect to the values of their neighbors in the current sliding window. Thereafter, we study the impact of the sampling process on the anomalies detection using the Exponential Weighted Moving Average (EWMA) algorithm. In this context, the comparison of the sampling algorithms is based on their response time in case of anomaly and the relevance of the detected anomalies.

关键词： Anomalies detection data streams EWMA sampling algorithms

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Path sampling: A Fast and Provable Method for Estimating 4-Vertex Subgraph Counts 15

Path Sampling: A Fast and Provable Method for Estimating 4-V...

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24th International Conference on World Wide Web (WWW)

作者： Jha, Madhav Seshadhri, C. Pinar, Ali Zenefits San Francisco CA 94107 USA Univ Calif Santa Cruz Santa Cruz CA 95064 USA Sandia Natl Labs Livermore CA 94550 USA

ISBN: (纸本)9781450334693

Counting the frequency of small subgraphs is a fundamental technique in network analysis across various domains, most notably in bioinformatics and social networks. The special case of triangle counting has received much attention. Getting results for 4-vertex patterns is highly challenging, and there are few practical results known that can scale to massive sizes. Indeed, even a highly tuned enumeration code takes more than a day on a graph with millions of edges. Most previous work that runs for truly massive graphs employ clusters and massive parallelization. We provide a sampling algorithm that provably and accurately approximates the frequencies of all 4-vertex pattern subgraphs. Our algorithm is based on a novel technique of 3-path sampling and a special pruning scheme to decrease the variance in estimates. We provide theoretical proofs for the accuracy of our algorithm, and give formal bounds for the error and confidence of our estimates. We perform a detailed empirical study and show that our algorithm provides estimates within 1% relative error for all subpatterns (over a large class of test graphs), while being orders of magnitude faster than enumeration and other sampling based algorithms. Our algorithm takes less than a minute (on a single commodity machine) to process an Orkut social network with 300 million edges.

关键词： Subgraph counting sampling algorithms path sampling motif counting graphlet distribution

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