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International Conference on Man and Machine Interfacing (MAMI)

作者： Sahoo, Abhaya Kumar Sarangi, Sundar Sourav Misra, Rachita CV Raman Coll Engn Dept Informat Technol Bhubaneswar Orissa India

ISBN: (纸本)9781509002252

As amount of data in different form is increased day by day;it is very difficult to process it. This unstructured form of data cannot be easily retrieved through query processing. Normally SQL query acts on structured data. To convert unstructured data into structured data, Hadoop provides map reduce approach. Instead using map function, we can use GPU approach for processing data in parallel and then we can use reduce function on processed data. Here we compare two approach i.e. map-reduce approach and gpu-reduce approach to calculate performance measurement for searching index file. As Hadoop is a framework which is purely based on Java, we use JCUDA programming language to implement gpu-reduce approach.

关键词： database query processing GPU Index file JCUDA Map-Reduce

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In-database Time Series Clustering

Proceedings of the ACM on Management of Data

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Proceedings of the ACM on Management of Data 2025年第1期3卷 1-26页

作者： Yunxiang Su Kenny Ye Liang Shaoxu Song Tsinghua University Beijing China BNRist Tsinghua University Beijing China

Time series data are often clustered repeatedly across various time ranges to mine frequent subsequence patterns from different periods, which could further support downstream applications. Existing state-of-the-art (SOTA) time series clustering method, such as K-Shape, can proficiently cluster time series data referring to their shapes. However, in-database time series clustering problem has been neglected, especially in IoT scenarios with large-volume data and high efficiency demands. Most time series databases employ LSM-Tree based storage to support intensive writings, yet causing underlying data points out-of-order in timestamps. Therefore, to apply existing out-of-database methods, all data points must be fully loaded into memory and chronologically sorted. Additionally, out-of-database methods must cluster from scratch each time, making them inefficient when handling queries across different time ranges. In this work, we propose an in-database adaptation of SOTA time series clustering method K-Shape. Moreover, to solve the problem that K-Shape cannot efficiently handle long time series, we propose Medoid-Shape, as well as its in-database adaptation for further acceleration. Extensive experiments are conducted to demonstrate the higher efficiency of our proposals, with comparable effectiveness. Remarkably, all proposals have already been implemented in an open-source commodity time series database, Apache IoTDB.

关键词： database query processing time series clustering

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Largest Triangle Sampling for Visualizing Time Series in database

Proceedings of the ACM on Management of Data

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Proceedings of the ACM on Management of Data 2025年第1期3卷 1-26页

作者： Lei Rui Xiangdong Huang Shaoxu Song Chen Wang Jianmin Wang Zhao Cao Tsinghua University Beijing China Huawei Technologies Co. Ltd Beijing China

In time series visualization, sampling is used to reduce the number of points while retaining the visual features of the raw time series. Area-based Largest Triangle Sampling (LTS) excels at preserving perceptually critical points. However, the heuristic solution to LTS by sequentially sampling points with the locally largest triangle area (a.k.a. Largest-Triangle-Three-Buckets, LTTB) suffers from suboptimal solution and query inefficiency. We address the shortcomings by contributing a novel Iterative Largest Triangle Sampling (ILTS) algorithm with convex hull acceleration. It refines the sampling results iteratively, capturing a broader perspective by integrating more points in each iteration. Remarkably, we prove that the largest triangle can always be found in the precomputed convex hulls, making the iterative sampling still efficient. Experiments demonstrate increased visual quality over state-of-the-art baselines and significant speedups over the brute force approach.

关键词： database query processing time series visualization

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Dynamic querying of streaming data with the dQUOB system

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2003年第4期14卷 422-432页

作者： Plale, B Schwan, K Indiana Univ Dept Comp Sci Bloomington IN 47405 USA Georgia Inst Technol Coll Comp Atlanta GA 30332 USA

Data streaming has established itself as a viable communication abstraction in data-intensive parallel and distributed computations, occurring in applications such as scientific visualization performance monitoring, and large-scale data transfer. A known problem in large-scale event communication is tailoring the data received at the consumer. It is the general problem of extracting data of interest from a data source, a problem that the database community,has successfully addressed with SOL queries, a time tested, user-friendly way for, noncomputer scientists to access data. By leveraging the efficiency of query processing provided by relational queries, the dQUOB system provides a conceptual relational data model and SOL query access over streaming data. Queries can be used to extract data, combine streams, and create hew streams. The. language augments queries with an action to enable more complex data. transformations such as Fourier transforms. The dQUOB system has been applied to two large-scale distributed applications: a safety critical autonomous robotics simulation and scientific software visualization for global atmospheric transport modeling. In this paper, we present the dQUOB system and the results of-performance evaluation undertaken to assess its applicability in data-intensive wide-area computations, where the benefit of portable data transformation must be evaluated against the cost of continuous query evaluation.

关键词： data-intensive computations grid computing data streams publish-subscribe event channels SQL relational data model database query processing

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AxleDB: A novel programmable query processing platform on FPGA

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MICROPROCESSORS AND MICROSYSTEMS 2017年 51卷 142-164页

作者： Salami, Behzad Malazgirt, Gorker Alp Arcas-Abella, Oriol Yurdakul, Arda Sonmez, Nehir Ctr Nacl Supercomp BSC Carrer Jordi Girona 29-31 Barcelona 08034 Spain UPC Campus NordCalle Jordi Girona 1-3 Barcelona 08034 Spain Bogazici Univ Istanbul Turkey

With the rise of Big Data, providing high-performance query processing capabilities through the acceleration of the database analytic has gained significant attention. Leveraging Field Programmable Gate Array (FPGA) technology, this approach can lead to clear benefits. In this work, we present the design and implementation of AxleDB: An FPGA-based platform that enables fast query processing for database systems by melding novel database-specific accelerators with commercial-off-the-shelf (COTS) storage using modern interfaces, in a novel, unified, and a programmable environment. AxleDB can perform a large subset of SQL queries through its set of instructions that can map compute-intensive database operations, such as filter, arithmetic, aggregate, group by, table join, or sort, on to the specialized high-throughput accelerators. To minimize the amount of SSD I/O operations required, AxleDB also supports hardware MinMax indexing for databases. We evaluated AxleDB with five decision support queries from the TPC-H bench-mark suite and achieved a speedup from 1.8X to 34.2X and energy efficiency from 2.8X to 62.1X, in comparison to the state-of-the-art DBMS, i.e., PostgreSQL and MonetDB. (C) 2017 Elsevier B.V. All rights reserved.

关键词： database query processing Hardware accelerators Reconfigurable computing

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Genetic algorithm for the multiple-query optimization problem

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS 2007年第1期37卷 147-153页

作者： Bayir, Murat Ali Toroslu, Ismail H. Cosar, Ahmet Middle E Tech Univ Dept Comp Engn TR-06531 Ankara Turkey

Producing answers to a set of queries with common tasks efficiently is known as the multiple-query optimization (MQO) problem. Each query can have several alternative evaluation plans, each with a different set of tasks. Therefore, the goal of MQO is to choose the right set of plans for queries which minimizes the total execution time by performing common tasks only once. Since MQO is an NP-hard problem, several, mostly heuristics based, solutions have been proposed for solving it. To the best of our knowledge, this correspondence is the first attempt to solve MQO using an evolutionary technique, genetic algorithms.

关键词： database query processing genetic algorithms (GA) heuristics techniques multiple-query optimization (MQO)

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database optimizations for modern hardware

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PROCEEDINGS OF THE IEEE 2008年第5期96卷 863-878页

作者： Cieslewicz, John Ross, Kenneth A. Columbia Univ Dept Comp Sci New York NY 10027 USA

databases are an important workload for modern commodity microarchitectures. Achieving the best performance requires that careful attention be paid to the underlying architecture, including instruction and data cache usage, data layout, branch prediction, and multithreading. Specialized commodity microarchitectures, such as graphics cards and network processors, have also been investigated as effective query coprocessors. This paper presents a survey of recent architecture-sensitive database research. The insights gained from optimizing database performance on modern microarchitectures are also applicable to other domains, particularly those that are similarly data intensive.

关键词： computer architecture computer performance database management systems database performance database query processing

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Segmentation, Indexing, and Retrieval for Environmental and Natural Sounds

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2010年第3期18卷 688-707页

作者： Wichern, Gordon Xue, Jiachen Thornburg, Harvey Mechtley, Brandon Spanias, Andreas Arizona State Univ Sch Arts Media & Engn Tempe AZ 85282 USA

We propose a method for characterizing sound activity in fixed spaces through segmentation, indexing, and retrieval of continuous audio recordings. Regarding segmentation, we present a dynamic Bayesian network (DBN) that jointly infers on-sets and end times of the most prominent sound events in the space, along with an extension of the algorithm for covering large spaces with distributed microphone arrays. Each segmented sound event is indexed with a hidden Markov model (HMM) that models the distribution of example-based queries that a user would employ to retrieve the event (or similar events). In order to increase the efficiency of the retrieval search, we recursively apply a modified spectral clustering algorithm to group similar sound events based on the distance between their corresponding HMMs. We then conduct a formal user study to obtain the relevancy decisions necessary for evaluation of our retrieval algorithm on both automatically and manually segmented sound clips. Furthermore, our segmentation and retrieval algorithms are shown to be effective in both quiet indoor and noisy outdoor recording conditions.

关键词： Acoustic signal analysis acoustic signal detection Bayes procedures clustering methods database query processing

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Iterators, schedulers, and distributed-memory parallelism

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SOFTWARE-PRACTICE & EXPERIENCE 1996年第4期26卷 427-452页

作者： Graefe, G Microsoft Corp. One Microsoft Way Redmond WA 98052–6399 U.S.A.(email: goetzg@***)

In previous work, we demonstrated the advantages of encapsulating query evaluation algorithms as 'iterators' for sequential and parallel query evaluation. Unfortunately, those earlier models have a severe drawback with respect to resource allocation in distributed-memory systems. Since threads may be initiated long before they actually perform useful work, thread placement decisions may be suboptimal. In this paper, we briefly review the iterator model and then extend it to support bottom-up, just-in-time activation of appropriate query plan fragments as well as local and global synchronization and communication among sibling threads. Some of the algorithms described here may seem intricate;however, the intricacy is encapsulated entirely in the parallelism or 'exchange' iterator, thus freeing developers of data manipulation iterators to focus on the specific algorithms at hand, instead of on mechanisms for parallelism. Moreoever, code fragments extracted from an operational prototype demonstrate the ease with which our suggestions can be implemented. Run-time overhead and performance of our techniques are identical to those of earlier, less extensible techniques for bottom-up activation;the benefits of the presented techniques are their clean design, abstraction, extensibility, and the resulting ease of maintenance and enhancement.

关键词： database query processing parallelism extensibility software engineering abstraction encapsulation iterators bottom-up activation communication

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Differentially private histogram publication

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VLDB JOURNAL 2013年第6期22卷 797-822页

作者： Xu, Jia Zhang, Zhenjie Xiao, Xiaokui Yang, Yin Yu, Ge Winslett, Marianne Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Singapore Pte Ltd Adv Digital Sci Ctr Singapore Singapore Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Univ Illinois Dept Comp Sci Urbana IL USA

Differential privacy (DP) is a promising scheme for releasing the results of statistical queries on sensitive data, with strong privacy guarantees against adversaries with arbitrary background knowledge. Existing studies on differential privacy mostly focus on simple aggregations such as counts. This paper investigates the publication of DP-compliant histograms, which is an important analytical tool for showing the distribution of a random variable, e.g., hospital bill size for certain patients. Compared to simple aggregations whose results are purely numerical, a histogram query is inherently more complex, since it must also determine its structure, i.e., the ranges of the bins. As we demonstrate in the paper, a DP-compliant histogram with finer bins may actually lead to significantly lower accuracy than a coarser one, since the former requires stronger perturbations in order to satisfy DP. Moreover, the histogram structure itself may reveal sensitive information, which further complicates the problem. Motivated by this, we propose two novel mechanisms, namely NoiseFirst and StructureFirst, for computing DP-compliant histograms. Their main difference lies in the relative order of the noise injection and the histogram structure computation steps. NoiseFirst has the additional benefit that it can improve the accuracy of an already published DP-compliant histogram computed using a naive method. For each of proposed mechanisms, we design algorithms for computing the optimal histogram structure with two different objectives: minimizing the mean square error and the mean absolute error, respectively. Going one step further, we extend both mechanisms to answer arbitrary range queries. Extensive experiments, using several real datasets, confirm that our two proposals output highly accurate query answers and consistently outperform existing competitors.

关键词： Differential privacy database query processing Histogram

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