Journal articles: 'Query Executor'

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Huang, Silu, Erkang Zhu, Surajit Chaudhuri, and Leonhard Spiegelberg. "T-Rex: Optimizing Pattern Search on Time Series." Proceedings of the ACM on Management of Data 1, no. 2 (2023): 1–26. http://dx.doi.org/10.1145/3589275.

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Pattern search is an important class of queries for time series data. Time series patterns often match variable-length segments with a large search space, thereby posing a significant performance challenge. The existing pattern search systems, for example, SQL query engines supporting MATCH_RECOGNIZE, are ineffective in pruning the large search space of variable-length segments. In many cases, the issue is due to the use of a restrictive query language modeled on time series points and a computational model that limits search space pruning. We built T-ReX to address this problem using two main building blocks: first, a MATCH_RECOGNIZE language extension that exposes the notion of segment variable and adds new operators, lending itself to better optimization; second, an executor capable of pruning the search space of matches and minimizing total query time using an optimizer. We conducted experiments using 5 real-world datasets and 11 query templates, including those from existing works. T-ReX outperformed an optimized NFA-based pattern search executor by 6x in median query time and an optimized tree-based executor by 19X.

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Yogatama, Bobbi W., Weiwei Gong, and Xiangyao Yu. "Orchestrating data placement and query execution in heterogeneous CPU-GPU DBMS." Proceedings of the VLDB Endowment 15, no. 11 (2022): 2491–503. http://dx.doi.org/10.14778/3551793.3551809.

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There has been a growing interest in using GPU to accelerate data analytics due to its massive parallelism and high memory bandwidth. The main constraint of using GPU for data analytics is the limited capacity of GPU memory. Heterogeneous CPU-GPU query execution is a compelling approach to mitigate the limited GPU memory capacity and PCIe bandwidth. However, the design space of heterogeneous CPU-GPU query execution has not been fully explored. We aim to improve state-of-the-art CPU-GPU data analytics engine by optimizing data placement and heterogeneous query execution. First, we introduce a semantic-aware fine-grained caching policy which takes into account various aspects of the workload such as query semantics, data correlation, and query frequency when determining data placement between CPU and GPU. Second, we introduce a heterogeneous query executor which can fully exploit data in both CPU and GPU and coordinate query execution at a fine granularity. We integrate both solutions in Mordred, our novel hybrid CPU-GPU data analytics engine. Evaluation on the Star Schema Benchmark shows that the semantic-aware caching policy can outperform the best traditional caching policy by up to 3x. Compared to existing GPU DBMSs, Mordred can outperform by an order of magnitude.

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Barish, G., and C. A. Knoblock. "An Expressive Language and Efficient Execution System for Software Agents." Journal of Artificial Intelligence Research 23 (June 1, 2005): 625–66. http://dx.doi.org/10.1613/jair.1548.

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Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine.

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Yang, Yifei, Matt Youill, Matthew Woicik, et al. "FlexPushdownDB." Proceedings of the VLDB Endowment 14, no. 11 (2021): 2101–13. http://dx.doi.org/10.14778/3476249.3476265.

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Modern cloud databases adopt a storage-disaggregation architecture that separates the management of computation and storage. A major bottleneck in such an architecture is the network connecting the computation and storage layers. Two solutions have been explored to mitigate the bottleneck: caching and computation pushdown. While both techniques can significantly reduce network traffic, existing DBMSs consider them as orthogonal techniques and support only one or the other, leaving potential performance benefits unexploited. In this paper we present FlexPushdownDB (FPDB) , an OLAP cloud DBMS prototype that supports fine-grained hybrid query execution to combine the benefits of caching and computation pushdown in a storage-disaggregation architecture. We build a hybrid query executor based on a new concept called separable operators to combine the data from the cache and results from the pushdown processing. We also propose a novel Weighted-LFU cache replacement policy that takes into account the cost of pushdown computation. Our experimental evaluation on the Star Schema Benchmark shows that the hybrid execution outperforms both the conventional caching-only architecture and pushdown-only architecture by 2.2X. In the hybrid architecture, our experiments show that Weighted-LFU can outperform the baseline LFU by 37%.

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DAS, ARIYAM, and CARLO ZANIOLO. "A Case for Stale Synchronous Distributed Model for Declarative Recursive Computation." Theory and Practice of Logic Programming 19, no. 5-6 (2019): 1056–72. http://dx.doi.org/10.1017/s1471068419000358.

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AbstractA large class of traditional graph and data mining algorithms can be concisely expressed in Datalog, and other Logic-based languages, once aggregates are allowed in recursion. In fact, for most BigData algorithms, the difficult semantic issues raised by the use of non-monotonic aggregates in recursion are solved byPre-Mappability(${\cal P}$reM), a property that assures that for a program with aggregates in recursion there is an equivalent aggregate-stratified program. In this paper we show that, by bringing together the formal abstract semantics of stratified programs with the efficient operational one of unstratified programs,$\[{\cal P}\]$reMcan also facilitate and improve their parallel execution. We prove that$\[{\cal P}\]$reM-optimized lock-free and decomposable parallel semi-naive evaluations produce the same results as the single executor programs. Therefore,$\[{\cal P}\]$reMcan be assimilated into the data-parallel computation plans of different distributed systems, irrespective of whether these follow bulk synchronous parallel (BSP) or asynchronous computing models. In addition, we show that non-linear recursive queries can be evaluated using a hybrid stale synchronous parallel (SSP) model on distributed environments. After providing a formal correctness proof for the recursive query evaluation with$\[{\cal P}\]$reMunder this relaxed synchronization model, we present experimental evidence of its benefits.

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Paudel, Nawaraj, and Jagdish Bhatta. "Cost-Based Query Optimization in Centralized Relational Databases." Journal of Institute of Science and Technology 24, no. 1 (2019): 42–46. http://dx.doi.org/10.3126/jist.v24i1.24627.

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Query optimization is the most significant factor for any centralized relational database management system (RDBMS) that reduces the total execution time of a query. Query optimization is the process of executing a SQL (Structured Query Language) query in relational databases to determine the most efficient way to execute a given query by considering the possible query plans. The goal of query optimization is to optimize the given query for the sake of efficiency. Cost-based query optimization compares different strategies based on relative costs (amount of time that the query needs to run) and selects and executes one that minimizes the cost. The cost of a strategy is just an estimate based on how many estimated CPU and I/O resources that the query will use. In this paper, cost is considered by counting number of disk accesses for each query plan because disk access tends to be the dominant cost in query processing for centralized relational databases.

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Wang, Chenxiao, Zach Arani, Le Gruenwald, Laurent d'Orazio, and Eleazar Leal. "Re-optimization for Multi-objective Cloud Database Query Processing using Machine Learning." International Journal of Database Management Systems 13, no. 1 (2021): 21–40. http://dx.doi.org/10.5121/ijdms.2021.13102.

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In cloud environments, hardware configurations, data usage, and workload allocations are continuously changing. These changes make it difficult for the query optimizer of a cloud database management system (DBMS) to select an optimal query execution plan (QEP). In order to optimize a query with a more accurate cost estimation, performing query re-optimizations during the query execution has been proposed in the literature. However, some of there-optimizations may not provide any performance gain in terms of query response time or monetary costs, which are the two optimization objectives for cloud databases, and may also have negative impacts on the performance due to their overheads. This raises the question of how to determine when are-optimization is beneficial. In this paper, we present a technique called ReOptML that uses machine learning to enable effective re-optimizations. This technique executes a query in stages, employs a machine learning model to predict whether a query re-optimization is beneficial after a stage is executed, and invokes the query optimizer to perform the re-optimization automatically. The experiments comparing ReOptML with existing query re-optimization algorithms show that ReOptML improves query response time from 13% to 35% for skew data and from 13% to 21% for uniform data, and improves monetary cost paid to cloud service providers from 17% to 35% on skewdata.

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Sen, Rathijit, Abhishek Roy, Alekh Jindal, et al. "AutoExecutor." Proceedings of the VLDB Endowment 14, no. 12 (2021): 2855–58. http://dx.doi.org/10.14778/3476311.3476362.

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Right-sizing resources for query execution is important for cost-efficient performance, but estimating how performance is affected by resource allocations, upfront, before query execution is difficult. We demonstrate AutoExecutor , a predictive system that uses machine learning models to predict query run times as a function of the number of allocated executors, that limits the maximum allowed parallelism, for Spark SQL queries running on Azure Synapse.

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Beedkar, Kaustubh, David Brekardin, Jorge-Anulfo Quiané-Ruiz, and Volker Markl. "Compliant geo-distributed data processing in action." Proceedings of the VLDB Endowment 14, no. 12 (2021): 2843–46. http://dx.doi.org/10.14778/3476311.3476359.

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In this paper we present our work on compliant geo-distributed data processing. Our work focuses on the new dimension of dataflow constraints that regulate the movement of data across geographical or institutional borders. For example, European directives may regulate transferring only certain information fields (such as non personal information) or aggregated data. Thus, it is crucial for distributed data processing frameworks to consider compliance with respect to dataflow constraints derived from these regulations. We have developed a compliance-based data processing framework, which (i) allows for the declarative specification of dataflow constraints, (ii) determines if a query can be translated into a compliant distributed query execution plan, and (iii) executes the compliant plan over distributed SQL databases. We demonstrate our framework using a geo-distributed adaptation of the TPC-H benchmark data. Our framework provides an interactive dashboard, which allows users to specify dataflow constraints, and analyze and execute compliant distributed query execution plans.

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Azhir, Elham, Mehdi Hosseinzadeh, Faheem Khan, and Amir Mosavi. "Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark." Mathematics 10, no. 19 (2022): 3517. http://dx.doi.org/10.3390/math10193517.

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Access plan recommendation is a query optimization approach that executes new queries using prior created query execution plans (QEPs). The query optimizer divides the query space into clusters in the mentioned method. However, traditional clustering algorithms take a significant amount of execution time for clustering such large datasets. The MapReduce distributed computing model provides efficient solutions for storing and processing vast quantities of data. Apache Spark and Apache Hadoop frameworks are used in the present investigation to cluster different sizes of query datasets in the MapReduce-based access plan recommendation method. The performance evaluation is performed based on execution time. The results of the experiments demonstrated the effectiveness of parallel query clustering in achieving high scalability. Furthermore, Apache Spark achieved better performance than Apache Hadoop, reaching an average speedup of 2x.

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Bagui, Sikha, and Evorell Fridge. "Estimating Query Timings in Elasticsearch." Transactions on Networks and Communications 9, no. 2 (2021): 15–36. http://dx.doi.org/10.14738/tnc.92.9887.

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In a shared Elasticsearch environment it can be useful to know how long a particular query will take to execute. This information can be used to enforce rate limiting or distribute requests equitably among multiple clusters. Elasticsearch uses multiple Lucene instances on multiple hosts as an underlying search engine implementation, but this abstraction makes it difficult to predict execution with previously known predictors such as the number of postings. This research investigates the ability of different pre-retrieval statistics, available through Elasticsearch, to accurately predict the execution time of queries on a typical Elasticsearch cluster. The number of terms in a query and the Total Term Frequency (TTF) from Elasticsearch’s API are found to significantly predict execution time. Regression models are then built and compared to find the most accurate method for predicting query time.

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Azhir, Elham, Nima Jafari Navimipour, Mehdi Hosseinzadeh, Arash Sharifi, and Aso Darwesh. "A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method." PeerJ Computer Science 7 (June 1, 2021): e580. http://dx.doi.org/10.7717/peerj-cs.580.

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Query optimization is the process of identifying the best Query Execution Plan (QEP). The query optimizer produces a close to optimal QEP for the given queries based on the minimum resource usage. The problem is that for a given query, there are plenty of different equivalent execution plans, each with a corresponding execution cost. To produce an effective query plan thus requires examining a large number of alternative plans. Access plan recommendation is an alternative technique to database query optimization, which reuses the previously-generated QEPs to execute new queries. In this technique, the query optimizer uses clustering methods to identify groups of similar queries. However, clustering such large datasets is challenging for traditional clustering algorithms due to huge processing time. Numerous cloud-based platforms have been introduced that offer low-cost solutions for the processing of distributed queries such as Hadoop, Hive, Pig, etc. This paper has applied and tested a model for clustering variant sizes of large query datasets parallelly using MapReduce. The results demonstrate the effectiveness of the parallel implementation of query workloads clustering to achieve good scalability.

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Jain, Ms Nisha, and Dr Preeti Tiwari. "MULTI-JOIN-ORDERING QUERY OPTIMIZATION ALGORITHM FOR HIVE WAREHOUSE WITH MAPREDUCE." oorja 19, no. 1 (2021): 94–102. http://dx.doi.org/10.55399/hssg6334.

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According to the Digital Report of July, 2021, Billions of users around the world uses Mobile Phones, Internet, social media every second. This huge range of heterogeneous digital data is called Big Data, and is measured in terms of terabytes or petabytes. It is difficult to the conventional relational databases to handle these heterogeneous data for data analytics, but is still in use significantly in the growth of Big Data. To handle SQL-based structured queries, Hadoop is one of the prominent and well-suited solution that allows Big Data to be stored and processed. Hive support SQL queries on Hadoop. Hive warehouse, is the oldest SQL-engine on the top of the Hadoop framework and to store the processed data, it uses HDFS (Hadoop Distributed File System). On the Hadoop, MapReduce is an execution engine that executes SQL-based queries. In the Query Optimization, join ordering always plays a significant role because when the order of tables in joining operation is changed, execution time of the query is reduced to a greater extent. The main problem of the Hive is that it does not enhance the order of the join for an SQL-query and also does not give assurance for an optimal execution plan. Its time complexity is measured in exponential (Shan, Y., & Chen, Y., 2015).The main focus of this paper is to discover the finest join ordering solution for a Hive query optimization problem through appropriate search algorithms and to improve SQL-based Hive queries performance with MapReduce–based system.

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Trummer, Immanuel, Junxiong Wang, Ziyun Wei, et al. "SkinnerDB: Regret-bounded Query Evaluation via Reinforcement Learning." ACM Transactions on Database Systems 46, no. 3 (2021): 1–45. http://dx.doi.org/10.1145/3464389.

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SkinnerDB uses reinforcement learning for reliable join ordering, exploiting an adaptive processing engine with specialized join algorithms and data structures. It maintains no data statistics and uses no cost or cardinality models. Also, it uses no training workloads nor does it try to link the current query to seemingly similar queries in the past. Instead, it uses reinforcement learning to learn optimal join orders from scratch during the execution of the current query. To that purpose, it divides the execution of a query into many small time slices. Different join orders are tried in different time slices. SkinnerDB merges result tuples generated according to different join orders until a complete query result is obtained. By measuring execution progress per time slice, it identifies promising join orders as execution proceeds. Along with SkinnerDB, we introduce a new quality criterion for query execution strategies. We upper-bound expected execution cost regret, i.e., the expected amount of execution cost wasted due to sub-optimal join order choices. SkinnerDB features multiple execution strategies that are optimized for that criterion. Some of them can be executed on top of existing database systems. For maximal performance, we introduce a customized execution engine, facilitating fast join order switching via specialized multi-way join algorithms and tuple representations. We experimentally compare SkinnerDB’s performance against various baselines, including MonetDB, Postgres, and adaptive processing methods. We consider various benchmarks, including the join order benchmark, TPC-H, and JCC-H, as well as benchmark variants with user-defined functions. Overall, the overheads of reliable join ordering are negligible compared to the performance impact of the occasional, catastrophic join order choice.

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Vander Sande, Miel, Ruben Verborgh, Anastasia Dimou, Pieter Colpaert, and Erik Mannens. "Hypermedia-Based Discovery for Source Selection Using Low-Cost Linked Data Interfaces." International Journal on Semantic Web and Information Systems 12, no. 3 (2016): 79–110. http://dx.doi.org/10.4018/ijswis.2016070103.

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Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed—even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern Fragments. In addition, the authors identify quantitative metrics to evaluate this discovery approach. This article describes generic evaluation measures and results for their concrete approach. With low-cost data summaries as seed, interfaces to eight large real-world datasets can discover each other within 7 minutes. Hypermedia-based client-side querying shows a promising gain of up to 50% in execution time, but demands algorithms that visit a higher number of interfaces to improve result completeness.

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Gounaris, Anastasios, Norman W. Paton, Alvaro A. A. Fernandes, and Rizos Sakellariou. "Self-monitoring query execution for adaptive query processing." Data & Knowledge Engineering 51, no. 3 (2004): 325–48. http://dx.doi.org/10.1016/j.datak.2004.05.002.

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He, Zhenzhen, Jiong Yu, and Binglei Guo. "Execution Time Prediction for Cypher Queries in the Neo4j Database Using a Learning Approach." Symmetry 14, no. 1 (2022): 55. http://dx.doi.org/10.3390/sym14010055.

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With database management systems becoming complex, predicting the execution time of graph queries before they are executed is one of the challenges for query scheduling, workload management, resource allocation, and progress monitoring. Through the comparison of query performance prediction methods, existing research works have solved such problems in traditional SQL queries, but they cannot be directly applied in Cypher queries on the Neo4j database. Additionally, most query performance prediction methods focus on measuring the relationship between correlation coefficients and retrieval performance. Inspired by machine-learning methods and graph query optimization technologies, we used the RBF neural network as a prediction model to train and predict the execution time of Cypher queries. Meanwhile, the corresponding query pattern features, graph data features, and query plan features were fused together and then used to train our prediction models. Furthermore, we also deployed a monitor node and designed a Cypher query benchmark for the database clusters to obtain the query plan information and native data store. The experimental results of four benchmarks showed that the average mean relative error of the RBF model reached 16.5% in the Northwind dataset, 12% in the FIFA2021 dataset, and 16.25% in the CORD-19 dataset. This experiment proves the effectiveness of our proposed approach on three real-world datasets.

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Vengadeswaran, S., and S. R. Balasundaram. "An Optimal Data Placement Strategy for Improving System Performance of Massive Data Applications Using Graph Clustering." International Journal of Ambient Computing and Intelligence 9, no. 3 (2018): 15–30. http://dx.doi.org/10.4018/ijaci.2018070102.

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This article describes how the time taken to execute a query and return the results, increase exponentially as the data size increases, leading to more waiting times of the user. Hadoop with its distributed processing capability is considered as an efficient solution for processing such large data. Hadoop's Default Data Placement Strategy (HDDPS) allocates the data blocks randomly across the cluster of nodes without considering any of the execution parameters. This result in non-availability of the blocks required for execution in local machine so that the data has to be transferred across the network for execution, leading to data locality issue. Also, it is commonly observed that most of the data intensive applications show grouping semantics. Hence during query execution, only a part of the Big-Data set is utilized. Since such execution parameters and grouping behavior are not considered, the default placement does not perform well resulting in several lacunas such as decreased local map task execution, increased query execution time, query latency, etc. In order to overcome such issues, an Optimal Data Placement Strategy (ODPS) based on grouping semantics is proposed. Initially, user history log is dynamically analyzed for identifying access pattern which is depicted as a graph. Markov clustering, a Graph clustering algorithm is applied to identify groupings among the dataset. Then, an Optimal Data Placement Algorithm (ODPA) is proposed based on the statistical measures estimated from the clustered graph. This in turn re-organizes the default data layouts in HDFS to achieve improved performance for Big-Data sets in heterogeneous distributed environment. Our proposed strategy is tested in a 15 node cluster placed in a single rack topology. The result has proved to be more efficient for massive datasets, reducing query execution time by 26% and significantly improves the data locality by 38% compared to HDDPS.

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Allenstein, Brett, Andrew Yost, Paul Wagner, and Joline Morrison. "A query simulation system to illustrate database query execution." ACM SIGCSE Bulletin 40, no. 1 (2008): 493–97. http://dx.doi.org/10.1145/1352322.1352301.

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Payton, Jamie, Christine Julien, Vasanth Rajamani, and Gruia-Catalin Roman. "Using snapshot query fidelity to adapt continuous query execution." Pervasive and Mobile Computing 8, no. 3 (2012): 317–30. http://dx.doi.org/10.1016/j.pmcj.2012.02.005.

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Ganguly, Sumit, Waqar Hasan, and Ravi Krishnamurthy. "Query optimization for parallel execution." ACM SIGMOD Record 21, no. 2 (1992): 9–18. http://dx.doi.org/10.1145/141484.130291.

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tyagi, Geetanjali. "Ontology Based Fuzzy Query Execution." American Journal of Networks and Communications 4, no. 3 (2015): 16. http://dx.doi.org/10.11648/j.ajnc.s.2015040301.14.

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Barkhordari, Mohammadhossein, and Mahdi Niamanesh. "Aras." International Journal of Distributed Systems and Technologies 8, no. 2 (2017): 47–60. http://dx.doi.org/10.4018/ijdst.2017040104.

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Because of to the high rate of data growth and the need for data analysis, data warehouse management for big data is an important issue. Single node solutions cannot manage the large amount of information. Information must be distributed over multiple hardware nodes. Nevertheless, data distribution over nodes causes each node to need data from other nodes to execute a query. Data exchange among nodes creates problems, such as the joins between data segments that exist on different nodes, network congestion, and hardware node wait for data reception. In this paper, the Aras method is proposed. This method is a MapReduce-based method that introduces a data set on each mapper. By applying this method, each mapper node can execute its query independently and without need to exchange data with other nodes. Node independence solves the aforementioned data distribution problems. The proposed method has been compared with prominent data warehouses for big data, and the Aras query execution time was much lower than other methods.

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Zhai, Hong Yu, Li Li, and Hong Hua Xu. "The Design of Query Processing in Data Stream Management System." Advanced Materials Research 952 (May 2014): 351–54. http://dx.doi.org/10.4028/www.scientific.net/amr.952.351.

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data stream management system is used to manage and query coming large, continuous, fast and flexible data stream. The system is based on the flow of data extraction, transformation, combination, which is the main content and task query execution. This paper mainly discusses the design and implementation of query execution module and query execution is composed of two parts which include query operations, query execution and scheduling.

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Fent, Philipp, and Thomas Neumann. "A practical approach to groupjoin and nested aggregates." Proceedings of the VLDB Endowment 14, no. 11 (2021): 2383–96. http://dx.doi.org/10.14778/3476249.3476288.

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Groupjoins, the combined execution of a join and a subsequent group by, are common in analytical queries, and occur in about 1/8 of the queries in TPC-H and TPC-DS. While they were originally invented to improve performance, efficient parallel execution of groupjoins can be limited by contention, which limits their usefulness in a many-core system. Having an efficient implementation of groupjoins is highly desirable, as groupjoins are not only used to fuse group by and join but are also introduced by the unnesting component of the query optimizer to avoid nested-loops evaluation of aggregates. Furthermore, the query optimizer needs be able to reason over the result of aggregation in order to schedule it correctly. Traditional selectivity and cardinality estimations quickly reach their limits when faced with computed columns from nested aggregates, which leads to poor cost estimations and thus, suboptimal query plans. In this paper, we present techniques to efficiently estimate, plan, and execute groupjoins and nested aggregates. We propose two novel techniques, aggregate estimates to predict the result distribution of aggregates, and parallel groupjoin execution for a scalable execution of groupjoins. The resulting system has significantly better estimates and a contention-free evaluation of groupjoins, which can speed up some TPC-H queries up to a factor of 2.

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Arasu, Arvind, Shivnath Babu, and Jennifer Widom. "The CQL continuous query language: semantic foundations and query execution." VLDB Journal 15, no. 2 (2005): 121–42. http://dx.doi.org/10.1007/s00778-004-0147-z.

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Abdullah, Fatima, Limei Peng, and Byungchul Tak. "A Survey of IoT Stream Query Execution Latency Optimization within Edge and Cloud." Wireless Communications and Mobile Computing 2021 (November 16, 2021): 1–16. http://dx.doi.org/10.1155/2021/4811018.

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IoT (Internet of Things) streaming data has increased dramatically over the recent years and continues to grow rapidly due to the exponential growth of connected IoT devices. For many IoT applications, fast stream query processing is crucial for correct operations. To achieve better query performance and quality, researchers and practitioners have developed various types of query execution models—purely cloud-based, geo-distributed, edge-based, and edge-cloud-based models. Each execution model presents unique challenges and limitations of query processing optimizations. In this work, we provide a comprehensive review and analysis of query execution models within the context of the query execution latency optimization. We also present a detailed overview of various query execution styles regarding different query execution models and highlight their contributions. Finally, the paper concludes by proposing promising future directions towards advancing the query executions in the edge and cloud environment.

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Schiavio, Filippo, Daniele Bonetta, and Walter Binder. "Language-agnostic integrated queries in a managed polyglot runtime." Proceedings of the VLDB Endowment 14, no. 8 (2021): 1414–26. http://dx.doi.org/10.14778/3457390.3457405.

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Language-integrated query (LINQ) frameworks offer a convenient programming abstraction for processing in-memory collections of data, allowing developers to concisely express declarative queries using general-purpose programming languages. Existing LINQ frameworks rely on the well-defined type system of statically-typed languages such as C # or Java to perform query compilation and execution. As a consequence of this design, they do not support dynamic languages such as Python, R, or JavaScript. Such languages are however very popular among data scientists, who would certainly benefit from LINQ frameworks in data analytics applications. In this work we bridge the gap between dynamic languages and LINQ frameworks. We introduce DynQ, a novel query engine designed for dynamic languages. DynQ is language-agnostic, since it is able to execute SQL queries in a polyglot language runtime. Moreover, DynQ can execute queries combining data from multiple sources, namely in-memory object collections as well as on-file data and external database systems. Our evaluation of DynQ shows performance comparable with equivalent hand-optimized code, and in line with common data-processing libraries and embedded databases, making DynQ an appealing query engine for standalone analytics applications and for data-intensive server-side workloads.

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Joshi, Mukul, and Praveen Ranjan Srivastava. "Query Optimization." International Journal of Intelligent Information Technologies 9, no. 1 (2013): 40–55. http://dx.doi.org/10.4018/jiit.2013010103.

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Query optimization is an important aspect in designing database management systems, aimed to find an optimal query execution plan so that overall time of query execution is minimized. Multi join query ordering (MJQO) is an integral part of query optimizer. This paper aims to propose a solution for MJQO problem, which is an NP complete problem. This paper proposes a heuristic based algorithm as a solution of MJQO problem. The proposed algorithm is a combination of two basic search algorithms, cuckoo and tabu search. Simulation shows some exciting results in favour of the proposed algorithm and concludes that proposed algorithm can solve MJQO problem in less amount of time than the existing methods.

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Nasibullin, Arsen. "Fault Tolerant Hash Join for Distributed Systems." Computer tools in education, no. 4 (December 28, 2022): 68–82. http://dx.doi.org/10.32603/2071-2340-2022-4-68-82.

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Nowadays, enterprises are inclined to deploy data processing and analytical applications from well-equipped mainframes with highly available hardware components to commodity computers. Commodity machines are less reliable than expensive mainframes. Applications deployed on commodity clusters have to deal with failures that occur frequently. Mostly, these applications perform complex client queries with aggregation and join operations. The longer a query executes, the more it suffers from failures. It causes the entire work has to be re-executed. This paper presents a fault tolerant hash join (FTHJ) algorithm for distributed systems implemented in Apache Ignite. The FTHJ achieves fault tolerance by using a data replication mechanism, materializing intermediate computations. To evaluate FTHJ, we implemented the baseline, unreliable hash join algorithm. Experimental results show that FTHJ takes at least 30% less time to recover and complete join operation when a failure occurs during the execution. This paper describes how we reached a compromise between executing recovery tasks for the least amount of time and using additional resources.

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Ji, Xuechun, Maoxian Zhao, Mingyu Zhai, and Qingxi Wu. "Query Execution Optimization in Spark SQL." Scientific Programming 2020 (February 7, 2020): 1–12. http://dx.doi.org/10.1155/2020/6364752.

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Spark SQL is a big data processing tool for structured data query and analysis. However, due to the execution of Spark SQL, there are multiple times to write intermediate data to the disk, which reduces the execution efficiency of Spark SQL. Targeting on the existing issues, we design and implement an intermediate data cache layer between the underlying file system and the upper Spark core to reduce the cost of random disk I/O. By using the query pre-analysis module, we can dynamically adjust the capacity of cache layer for different queries. And the allocation module can allocate proper memory for each node in cluster. According to the sharing of the intermediate data in the Spark SQL workflow, this paper proposes a cost-based correlation merging algorithm, which can effectively reduce the cost of reading and writing redundant data. This paper develops the SSO (Spark SQL Optimizer) module and integrates it into the original Spark system to achieve the above functions. This paper compares the query performance with the existing Spark SQL by experiment data generated by TPC-H tool. The experimental results show that the SSO module can effectively improve the query efficiency, reduce the disk I/O cost and make full use of the cluster memory resources.

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Wu, Wentao, Xi Wu, Hakan Hacigümüş, and Jeffrey F. Naughton. "Uncertainty aware query execution time prediction." Proceedings of the VLDB Endowment 7, no. 14 (2014): 1857–68. http://dx.doi.org/10.14778/2733085.2733092.

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Paton, Norman W., Marcelo A. T. de Aragão, and Alvaro A. A. A. Fernandes. "Utility-driven adaptive query workload execution." Future Generation Computer Systems 28, no. 7 (2012): 1070–79. http://dx.doi.org/10.1016/j.future.2011.08.014.

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Baby, Tinu, and Aswani Kumar Cherukuri. "On query execution over encrypted data." Security and Communication Networks 8, no. 2 (2014): 321–31. http://dx.doi.org/10.1002/sec.982.

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Sarika Prakash, Kale, and P. M. Joe Prathap. "Evaluating Aggregate Functions of Iceberg Query Using Priority Based Bitmap Indexing Strategy." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3745. http://dx.doi.org/10.11591/ijece.v7i6.pp3745-3752.

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Aggregate function and iceberg queries are important and common in many applications of data warehouse because users are generally interested in looking for variance or unusual patterns. Normally, the nature of the queries to be executed on data warehouse are the queries with aggregate function followed by having clause, these type of queries are known as iceberg query. Especially to have efficient techniques for processing aggregate function of iceberg query is very important because their processing cost is much higher than that of the other basic relational operations such as SELECT and PROJECT. Presently available iceberg query processing techniques faces the problem of empty bitwise AND,OR XOR operation and requires more I/O access and time.To overcome these problems proposed research provides efficient algorithm to execute iceberg queries using priority based bitmap indexing strategy. Priority based approach consider bitmap vector to be executed as per the priority.Intermediate results are evaluated to find probability of result.Fruitless operations are identified and skipped in advance which help to reduce I/O access and time.Time and iteration required to process query is reduced [45-50] % compare to previous strategy. Experimental result proves the superiority of priorty based approach compare to previous bitmap processing approach.

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Kabra, Navin, and David J. DeWitt. "Efficient mid-query re-optimization of sub-optimal query execution plans." ACM SIGMOD Record 27, no. 2 (1998): 106–17. http://dx.doi.org/10.1145/276305.276315.

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Bai, Samita, and Shakeel A. Khoja. "Hybrid Query Execution on Linked Data With Complete Results." International Journal on Semantic Web and Information Systems 17, no. 1 (2021): 25–49. http://dx.doi.org/10.4018/ijswis.2021010102.

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The link traversal strategies to query Linked Data over WWW can retrieve up-to-date results using a recursive URI lookup process in real-time. The downside of this approach comes with the query patterns having subject unbound (i.e. ?S rdf:type:Class). Such queries fail to start up the traversal process as the RDF pages are subject-centric in nature. Thus, zero-knowledge link traversal leads to the empty query results for these queries. In this paper, the authors analyze a large corpus of real-world SPARQL query logs and identify the Most Frequent Predicates (MFPs) occurring in these queries. The knowledge of these MFPs helps in finding and indexing a limited number of triples from the original data set. Additionally, the authors propose a Hybrid Query Execution (HQE) approach to execute the queries over this index for initial data source selection followed by link traversal process to fetch complete results. The evaluation of HQE on the latest real data benchmarks reveals that it retrieves at least five times more results than the existing approaches.

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He, Dong, Supun C. Nakandala, Dalitso Banda, et al. "Query processing on tensor computation runtimes." Proceedings of the VLDB Endowment 15, no. 11 (2022): 2811–25. http://dx.doi.org/10.14778/3551793.3551833.

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The huge demand for computation in artificial intelligence (AI) is driving unparalleled investments in hardware and software systems for AI. This leads to an explosion in the number of specialized hardware devices, which are now offered by major cloud vendors. By hiding the low-level complexity through a tensor-based interface, tensor computation runtimes (TCRs) such as PyTorch allow data scientists to efficiently exploit the exciting capabilities offered by the new hardware. In this paper, we explore how database management systems can ride the wave of innovation happening in the AI space. We design, build, and evaluate Tensor Query Processor (TQP): TQP transforms SQL queries into tensor programs and executes them on TCRs. TQP is able to run the full TPC-H benchmark by implementing novel algorithms for relational operators on the tensor routines. At the same time, TQP can support various hardware while only requiring a fraction of the usual development effort. Experiments show that TQP can improve query execution time by up to 10X over specialized CPU- and GPU-only systems. Finally, TQP can accelerate queries mixing ML predictions and SQL end-to-end, and deliver up to 9X speedup over CPU baselines.

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Asswad, Mohammad Mourhaf AL, Sergio de Cesare, and Mark Lycett. "A Query-based Approach for Semi-Automatic Annotation of Web Services." International Journal of Information Systems and Social Change 2, no. 2 (2011): 37–54. http://dx.doi.org/10.4018/jissc.2011040103.

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Semantic Web services (SWS) have attracted increasing attention due to their potential to automate discovery and composition of current syntactic Web services. An issue that prevents a wider adoption of SWS relates to the manual nature of the semantic annotation task. Manual annotation is a difficult, error-prone, and time-consuming process and automating the process is highly desirable. Though some approaches have been proposed to semi-automate the annotation task, they are difficult to use and cannot perform accurate annotation for the following reasons: (1) They require building application ontologies to represent candidate services and (2) they cannot perform accurate name-based matching when labels of candidate service elements and ontological entities contain Compound Nouns (CN). To overcome these two deficiencies, this paper proposes a query-based approach that can facilitate semi-automatic annotation of Web services. The proposed approach is easy to use because it does not require building application ontologies to represent services. Candidate service elements that need to be annotated are extracted from a WSDL file and used to generate query instances by filling a Standard Query Template. The resulting query instances are executed against a repository of ontologies using a novel query execution engine to find appropriate correspondences for candidate service elements. This query execution engine employs name-based and structural matching mechanisms that can perform effective and accurate similarity measurements between labels containing CNs. The proposed semi-automatic annotation approach is evaluated by employing it to annotate existing Web services using published domain ontologies. Precision and recall are used as evaluation metrics. The resulting precision and recall values demonstrate the effectiveness and applicability of the proposed approach.

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LIU, LING, CALTON PU, and KIRILL RICHINE. "DISTRIBUTED QUERY SCHEDULING SERVICE: AN ARCHITECTURE AND ITS IMPLEMENTATION." International Journal of Cooperative Information Systems 07, no. 02n03 (1998): 123–66. http://dx.doi.org/10.1142/s0218843098000088.

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We present the systematic design and development of a distributed query scheduling service (DQS) in the context of DIOM, a distributed and interoperable query mediation system.26 DQS consists of an extensible architecture for distributed query processing, a three-phase optimization algorithm for generating efficient query execution schedules, and a prototype implementation. Functionally, two important execution models of distributed queries, namely moving query to data or moving data to query, are supported and combined into a unified framework, allowing the data sources with limited search and filtering capabilities to be incorporated through wrappers into the distributed query scheduling process. Algorithmically, conventional optimization factors (such as join order) are considered separately from and refined by distributed system factors (such as data distribution, execution location, heterogeneous host capabilities), allowing for stepwise refinement through three optimization phases: Compilation, parallelization, site selection and execution. A subset of DQS algorithms has been implemented in Java to demonstrate the practicality of the architecture and the usefulness of the distributed query scheduling algorithm in optimizing execution schedules for inter-site queries.

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Ghosh, Dhrubajyoti, Peeyush Gupta, Sharad Mehrotra, Roberto Yus, and Yasser Altowim. "JENNER." Proceedings of the VLDB Endowment 15, no. 11 (2022): 2666–78. http://dx.doi.org/10.14778/3551793.3551822.

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Emerging domains, such as sensor-driven smart spaces and social media analytics, require incoming data to be enriched prior to its use. Enrichment often consists of machine learning (ML) functions that are too expensive/infeasible to execute at ingestion. We develop a strategy entitled Just-in-time ENrichmeNt in quERy Processing (JENNER) to support interactive analytics over data as soon as it arrives for such application context. JENNER exploits the inherent tradeoffs of cost and quality often displayed by the ML functions to progressively improve query answers during query execution. We describe how JENNER works for a large class of SPJ and aggregation queries that form the bulk of data analytics workload. Our experimental results on real datasets (IoT and Tweet) show that JENNER achieves progressive answers performing significantly better than the naive strategies of achieving progressive computation.

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Нестеров, М. В., Д. Е. Бакитько, and A. O. Михайлова. "Database query optimization." ВІСНИК СХІДНОУКРАЇНСЬКОГО НАЦІОНАЛЬНОГО УНІВЕРСИТЕТУ імені Володимира Даля, no. 5(253) (September 5, 2019): 78–83. http://dx.doi.org/10.33216/1998-7927-2019-253-5-78-83.

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Lim, Jongtae, Byounghoon Kim, Hyeonbyeong Lee, Dojin Choi, Kyoungsoo Bok, and Jaesoo Yoo. "An Efficient Distributed SPARQL Query Processing Scheme Considering Communication Costs in Spark Environments." Applied Sciences 12, no. 1 (2021): 122. http://dx.doi.org/10.3390/app12010122.

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Various distributed processing schemes were studied to efficiently utilize a large scale of RDF graph in semantic web services. This paper proposes a new distributed SPARQL query processing scheme considering communication costs in Spark environments to reduce I/O costs during SPARQL query processing. We divide a SPARQL query into several subqueries using a WHERE clause to process a query of an RDF graph stored in a distributed environment. The proposed scheme reduces data communication costs by grouping the divided subqueries in related nodes through the index and processing them, and the grouped subqueries calculate the cost of all possible query execution paths to select an efficient query execution path. The efficient query execution path is selected through the algorithm considering the data parsing cost of all possible query execution paths, amount of data communication, and queue time per node. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

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Wei, Ziyun, and Immanuel Trummer. "SkinnerMT." Proceedings of the VLDB Endowment 16, no. 4 (2022): 905–17. http://dx.doi.org/10.14778/3574245.3574272.

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SkinnerMT is an adaptive query processing engine, specialized for multi-core platforms. SkinnerMT features different strategies for parallel processing that allow users to trade between average run time and performance robustness. First, SkinnerMT supports execution strategies that execute multiple query plans in parallel, thereby reducing the risk to find near-optimal plans late and improving robustness. Second, SkinnerMT supports data-parallel processing strategies. Its parallel multi-way join algorithm is sensitive to the assignment from tuples to threads. Here, SkinnerMT uses a cost-based optimization strategy, based on runtime feedback. Finally, SkinnerMT supports hybrid processing methods, mixing parallel search with data-parallel processing. The experiments show that parallel search increases robustness while parallel processing increases average-case performance. The hybrid approach combines advantages from both. Compared to traditional database systems, SkinnerMT is preferable for benchmarks where query optimization is hard. Compared to prior adaptive processing baselines, SkinnerMT exploits parallelism better.

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Deepa, S. "A Query Optimization Framework for Fuzzy Relational Databases." Asian Journal of Engineering and Applied Technology 1, no. 1 (2012): 43–46. http://dx.doi.org/10.51983/ajeat-2012.1.1.2502.

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Ever since the development of relational model, relational database systems have been extensively studied and several commercial relational database systems are currently available. Relational model usually take care of only well defined data. In order to capture more meaning to the data an extension of the classical relational model called fuzzy relational model was proposed. The key reasons for the success of relational database lies in the power of declarative languages and execution strategies used in query optimization. Estimating the cost of fuzzy query based on system catalog introduces error due to approximation involved and insufficient information at query execution time. So there is need for a query optimization framework that addresses the issues of query execution in fuzzy relational databases. This paper deals with a framework for building fuzzy cost model to obtain a good execution strategy for a query.

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Аль Мусави, О. А. Р., and Е. Е. Красновский. "EXPERIMENTAL STUDY OF THE EFFECTIVENESS OF ALGORITHMS FOR REPEATED QUERY OPTIMIZATION IN CLOUD DATABASES BASED ON COMPUTER TRAINING." СИСТЕМЫ УПРАВЛЕНИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ, no. 3(89) (September 30, 2022): 29–34. http://dx.doi.org/10.36622/vstu.2022.89.3.007.

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В облачных средах конфигурация оборудования, использование данных, и распределение рабочей нагрузки постоянно меняются. Эти изменения затрудняют оптимизатору запросов системы управления облачными базами данных подобрать оптимальный план выполнения запроса. Чтобы оптимизировать запрос с более точной оценкой затрат, в литературе было предложено во время выполнения запроса осуществлять повторную оптимизацию запроса. Тем не менее, некоторые из этих оптимизаций не могут обеспечить прирост производительности с точки зрения времени ответа на запрос или денежных затрат, которые являются двумя целями оптимизации для облачных баз данных, и могут оказывать негативное влияние на производительность из-за накладных расходов. Это поднимает вопрос о том, как определить, когда оптимизация выгодна. Целью является сравнительное исследование метода повторной оптимизации запросов, который использует компьютерное обучение. Ключевая идея алгоритма заключается в использовании прошлых выполнений запросов, чтобы научиться прогнозировать эффективность повторной оптимизации запросов, и делается это с целью помочь оптимизатору запросов избежать ненужной повторной оптимизации запросов для будущих запросов. Метод осуществляет запрос поэтапно, используя модель компьютерного обучения, для прогнозирования того, будет ли повторная оптимизация запроса полезной после выполнения этапа, и вызывает оптимизатор запросов для автоматического выполнения повторной оптимизации. In cloud environments, hardware configuration, data usage, and workload distribution are constantly changing. These changes make it difficult for the query optimizer of the cloud database management system to choose the optimal query execution plan (QEP). In order to optimize the query with a more accurate cost estimate, it was proposed in the literature to perform a second optimization of the query during the execution of the query. However, some of these options may not provide performance gains in terms of query response time or monetary costs, which are two optimization goals for cloud databases, and may have a negative impact on performance due to overhead. This raises the question of how to determine when optimization is beneficial. The purpose is a comparative study of the method of repeated query optimization, which uses computer learning. The key idea of algorithm is to use past query executions to learn how to predict the effectiveness of query re-optimization, and this is done in order to help the query optimizer avoid unnecessary re-optimization of queries for future queries. The method executes the query step by step, using a computer learning model, to predict whether repeated query optimization will be useful after the stage is completed, and calls the query optimizer to automatically perform repeated optimization.

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Hidayat, Kukuh Triyuliarno, Riza Arifudin, and Alamsyah Alamsyah. "Genetic Algorithm for Relational Database Optimization in Reducing Query Execution Time." Scientific Journal of Informatics 5, no. 1 (2018): 27. http://dx.doi.org/10.15294/sji.v5i1.12720.

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The relational database is defined as the database by connecting between tables. Each table has a collection of information. The information is processed in the database by using queries, such as data retrieval, data storage, and data conversion. If the information in the table or data has a large size, then the query process to process the database becomes slow. In this paper, Genetic Algorithm is used to process queries in order to optimize and reduce query execution time. The results obtained are query execution with genetic algorithm optimization to show the best execution time. The genetic algorithm processes the query by changing the structure of the relation and rearranging it. The fitness value generated from the genetic algorithm becomes the best solution. The fitness used is the highest fitness of each experiment results. In this experiment, the database used is MySQL sample database which is named as employees. The database has a total of over 3,000,000 rows in 6 tables. Queries are designed by using 5 relations in the form of a left deep tree. The execution time of the query is 8.14247 seconds and the execution time after the optimization of the genetic algorithm is 6.08535 seconds with the fitness value of 0.90509. The time generated after optimization of the genetic algorithm is reduced by 25.3%. It shows that genetic algorithm can reduce query execution time by optimizing query in the part of relation. Therefore, query optimization with genetic algorithm can be an alternative solution and can be used to maximize query performance.

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Yong Zhang, a., Cunhua Li, et al. "Optimized query execution in E-commerce sites." International Journal of Digital Content Technology and its Applications 6, no. 20 (2012): 286–95. http://dx.doi.org/10.4156/jdcta.vol6.issue20.31.

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Singh, Rashmi, Somesh Sharma, Sourabh Singh, and Bhawna Singh. "Reducing Run-time Execution in Query Optimization." International Journal of Computer Applications 96, no. 6 (2014): 1–6. http://dx.doi.org/10.5120/16795-6505.

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Wolf, Florian, Michael Brendle, Norman May, Paul R. Willems, Kai-Uwe Sattler, and Michael Grossniklaus. "Robustness metrics for relational query execution plans." Proceedings of the VLDB Endowment 11, no. 11 (2018): 1360–72. http://dx.doi.org/10.14778/3236187.3236191.

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Journal articles on the topic 'Query Executor'

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