Query Performance Research Articles

Design and Implementation of Energy Management System Based on Spring Boot Framework

This paper designs and implements an energy management system based on the Spring Boot framework. The system mainly includes three layers, which are the data collection layer, the business logic layer, and the display interface layer from bottom to top. The data collection layer is based on the RS-485 electrical standard and the MODBUS communication protocol. The two protocols connect all energy consumption monitoring points into a mixed topology communication network in the enterprise. The programs in the data collection layer poll each energy consumption monitoring point in the network to collect the data and transmit to the business logic layer. The business logic layer is developed on the basis of the Spring Boot framework and mainly includes two parts: the MySQL database and Tomcat server. In the MySQL database, the stored data are horizontally split according to the time column and stored in different data tables. The split of data reduces the load of a single data table and improves the query performance of the database. The Tomcat server is built into the Spring Boot framework to provide a basic environment for system operation. The Spring Boot framework is the core component of the system. It is responsible for collecting, storing, and analyzing data from energy consumption monitoring points, receiving and processing data requests from the display interface layer. It also provides standard data interfaces to the external programs. The display interface layer is developed on the basis of the Vue framework and integrated into the Spring Boot framework. The display layer combines an open-source visualization chart library called ECharts to provide users with a fully functional and friendly human–computer interaction interface. Through the calculation of hardware and software costs, considering the personnel cost in different regions, the total cost of the energy management system can be estimated. The cost of construction was approximately 210,000 USD in this paper. Since the system was actually deployed in a manufacturing company in December 2019, it has been operating stably for more than 600 days.

Incremental partitioning for efficient spatial data analytics

Big spatial data has become ubiquitous, from mobile applications to satellite data. In most of these applications, data is continuously growing to huge volumes. Existing systems for big spatial data organize records at either the record-level or block-level. Systems that use record-level structures include key-value stores and LSM-Tree stores, which support insert and delete operations and they are optimized for highly-selective queries. On the other hand, systems like GeoSpark that use block-level structures (e.g. 128 MB each) are more efficient for analytical queries, but they cannot incrementally maintain the partitioned data and do not support delete operations. This paper proposes a general framework that enables block-level systems to incrementally maintain spatial partitions, in the presence of bulk insertions and deletions, in distributed file system (DFS) blocks. We first formally study the incremental spatial partitioning problem for big data and demonstrate its NP-hardness. Then, we propose a cost model to estimate the performance of queries on the partitioned data and the effect of modifying it as the data grows. After that, we provide three different implementations of the incremental partitioning framework. Comprehensive experiments on large real datasets show that our proposed partitioning algorithms outperforms state-of-the-art spatial partitioning methods.

SecVKQ: Secure and verifiable kNN queries in sensor–cloud systems

Sensor–cloud has gained increasingly popularity since it bridges the physical world and the cyber world through pervasive computation. This paper focuses on secure and verifiable k nearest neighbor (kNN) queries over large-scale outsourced datasets in sensor–cloud systems. Existing work in this line often incurred high computational and communication overheads, remaining far away from practical and scalable. To this end, we propose SecVKQ, a two-phase search framework, which mainly includes a preliminary screening phase and an exact search phase. SecVKQ purposely takes advantage of secure data separation and adaptive encryption strategy, embracing edge servers into the classic dual-cloud model, so as to optimize query performance. Under SecVKQ, we design a series of secure protocols and develop a succinct verification strategy to derive a unified solution. The experimental results demonstrate the effectiveness of SecVKQ. Compared to the state-of-the-art work, SecVKQ achieves a speed-up of two orders of magnitude in search latency, and a savings of 50% communication cost for verification.

An experimental evaluation and investigation of waves of misery in r-trees

Waves of misery is a phenomenon where spikes of many node splits occur over short periods of time in tree indexes. Waves of misery negatively affect the performance of tree indexes in insertion-heavy workloads. Waves of misery have been first observed in the context of the B-tree, where these waves cause unpredictable index performance. In particular, the performance of search and index-update operations deteriorate when a wave of misery takes place, but is more predictable between the waves. This paper investigates the presence or lack of waves of misery in several R-tree variants, and studies the extent of which these waves impact the performance of each variant. Interestingly, although having poorer query performance, the Linear and Quadratic R-trees are found to be more resilient to waves of misery than both the Hilbert and R*-trees. This paper presents several techniques to reduce the impact in performance of the waves of misery for the Hilbert and R*-trees. One way to eliminate waves of misery is to force node splits to take place at regular times before nodes become full to achieve deterministic performance. The other way is that upon splitting a node, do not split it evenly but rather at different node utilization factors. This allows leaf nodes not to fill at the same pace. We study the impact of two new techniques to mitigate waves of misery after the tree index has been constructed, namely Regular Elective Splits (RES, for short) and Unequal Random Splits (URS, for short). Our experimental investigation highlights the trade-offs in performance of the introduced techniques and the pros and cons of each technique.

A Patient-Screening Tool for Clinical Research Based on Electronic Health Records Using OpenEHR: Development Study.

BackgroundThe widespread adoption of electronic health records (EHRs) has facilitated the secondary use of EHR data for clinical research. However, screening eligible patients from EHRs is a challenging task. The concepts in eligibility criteria are not completely matched with EHRs, especially derived concepts. The lack of high-level expression of Structured Query Language (SQL) makes it difficult and time consuming to express them. The openEHR Expression Language (EL) as a domain-specific language based on clinical information models shows promise to represent complex eligibility criteria.ObjectiveThe study aims to develop a patient-screening tool based on EHRs for clinical research using openEHR to solve concept mismatch and improve query performance.MethodsA patient-screening tool based on EHRs using openEHR was proposed. It uses the advantages of information models and EL in openEHR to provide high-level expressions and improve query performance. First, openEHR archetypes and templates were chosen to define concepts called simple concepts directly from EHRs. Second, openEHR EL was used to generate derived concepts by combining simple concepts and constraints. Third, a hierarchical index corresponding to archetypes in Elasticsearch (ES) was generated to improve query performance for subqueries and join queries related to the derived concepts. Finally, we realized a patient-screening tool for clinical research.ResultsIn total, 500 sentences randomly selected from 4691 eligibility criteria in 389 clinical trials on stroke from the Chinese Clinical Trial Registry (ChiCTR) were evaluated. An openEHR-based clinical data repository (CDR) in a grade A tertiary hospital in China was considered as an experimental environment. Based on these, 589 medical concepts were found in the 500 sentences. Of them, 513 (87.1%) concepts could be represented, while the others could not be, because of a lack of information models and coarse-grained requirements. In addition, our case study on 6 queries demonstrated that our tool shows better query performance among 4 cases (66.67%).ConclusionsWe developed a patient-screening tool using openEHR. It not only helps solve concept mismatch but also improves query performance to reduce the burden on researchers. In addition, we demonstrated a promising solution for secondary use of EHR data using openEHR, which can be referenced by other researchers.

A new framework based on features modeling and ensemble learning to predict query performance.

A query optimizer attempts to predict a performance metric based on the amount of time elapsed. Theoretically, this would necessitate the creation of a significant overhead on the core engine to provide the necessary query optimizing statistics. Machine learning is increasingly being used to improve query performance by incorporating regression models. To predict the response time for a query, most query performance approaches rely on DBMS optimizing statistics and the cost estimation of each operator in the query execution plan, which also focuses on resource utilization (CPU, I/O). Modeling query features is thus a critical step in developing a robust query performance prediction model. In this paper, we propose a new framework based on query feature modeling and ensemble learning to predict query performance and use this framework as a query performance predictor simulator to optimize the query features that influence query performance. In query feature modeling, we propose five dimensions used to model query features. The query features dimensions are syntax, hardware, software, data architecture, and historical performance logs. These features will be based on developing training datasets for the performance prediction model that employs the ensemble learning model. As a result, ensemble learning leverages the query performance prediction problem to deal with missing values. Handling overfitting via regularization. The section on experimental work will go over how to use the proposed framework in experimental work. The training dataset in this paper is made up of performance data logs from various real-world environments. The outcomes were compared to show the difference between the actual and expected performance of the proposed prediction model. Empirical work shows the effectiveness of the proposed approach compared to related work.

HCTree+: A workload-guided index for approximate [formula omitted]NN search

The problem of approximate k-nearest neighbor search in high-dimensional space is a fundamental problem in many applications. We have observed that most existing approaches are unable to leverage the query results to improve the search performance. To address this limitation, we present a new index, called HCTree+, that aims to improve the query performance based on incoming queries and their results. First, we adopt a simple yet effective index to support efficient search. Second, incoming queries and their results are used to optimize the index dynamically for future queries. The experimental study shows that HCTree+ outperforms the state-of-the-art algorithms in terms of accuracy while achieving the desired efficiency.

Exploiting Reused-Based Sharing Work Opportunities in Big Data Multiquery Optimization with Flink.

Multiquery optimization is fundamental to retrieve data from different sources in a specific time frame to fulfill the credibility of the big data applications. To avoid high-cost inputs or outputs operations over large-scale data, exploiting sharing opportunities including (join, aggregation, and sort) is helpful to improve the performance of the multiple queries. Furthermore, considering the in-memory big data platforms such as Flink is essential to enhance the performance of the multiple queries. We have extended our previous proposed system, Multi-Query Optimization using Tuple Size and Histogram (MOTH), to exploit sharing work among multiquery, including join, aggregation, and sort. The comprehensive system, called the Join-Aggregation-Sort (JAS)-MOTH system, is used to minimize the data in-network movement time, that is, shuffle time needed to transfer intermediate data. The proposed system is introduced by developing two additional modules to investigate the sharing work; query explorer and JAS-MOTH optimizer, including the sort exploiter module. The JAS-MOTH system can exploit the shared explicit and implicit sorts among multiple sorts and aggregation queries. The proposed system refines the pipelined multiway join execution for multiple join queries by considering coarse-grained sharing data, join ordering, joining pipelining, and shared implicit sorts. Furthermore, it introduces an end-to-end multiway join optimizer over Flink. An elaborated experimental comparison of the naive and state-of-art techniques among a broader class of queries (i.e., join, aggregation, and sort) is presented. Given the consideration of the findings based on Flink, the proposed system has improved the query execution time regarding the naive and state-of-art techniques by 47% and 30%, respectively. For intermediate data size, the JAS-MOTH system has reduced the intermediate data size reduction concerning the naive and state-of-art techniques by 50% and 31% on average, respectively, over Hadoop-like infrastructures.

Sequential linked data: The state of affairs

Sequences are among the most important data structures in computer science. In the Semantic Web, however, little attention has been given to Sequential Linked Data. In previous work, we have discussed the data models that Knowledge Graphs commonly use for representing sequences and showed how these models have an impact on query performance and that this impact is invariant to triplestore implementations. However, the specific list operations that the management of Sequential Linked Data requires beyond the simple retrieval of an entire list or a range of its elements – e.g. to add or remove elements from a list –, and their impact in the various list data models, remain unclear. Covering this knowledge gap would be a significant step towards the realization of a Semantic Web list Application Programming Interface (API) that standardizes list manipulation and generalizes beyond specific data models. In order to address these challenges towards the realization of such an API, we build on our previous work in understanding the effects of various sequential data models for Knowledge Graphs, extending our benchmark and proposing a set of read-write Semantic Web list operations in SPARQL, with insert, update and delete support. To do so, we identify five classic list-based computer science sequential data structures (linked list, double linked list, stack, queue, and array), from which we derive nine atomic read-write operations for Semantic Web lists. We propose a SPARQL implementation of these operations with five typical RDF data models and compare their performance by executing them against six increasing dataset sizes and four different triplestores. In light of our results, we discuss the feasibility of our devised API and reflect on the state of affairs of Sequential Linked Data.

Enhancing virtual ontology based access over tabular data with Morph-CSV

Ontology-Based Data Access (OBDA) has traditionally focused on providing a unified view of heterogeneous datasets (e.g., relational databases, CSV and JSON files), either by materializing integrated data into RDF or by performing on-the-fly querying via SPARQL query translation. In the specific case of tabular datasets represented as several CSV or Excel files, query translation approaches have been applied by considering each source as a single table that can be loaded into a relational database management system (RDBMS). Nevertheless, constraints over these tables are not represented (e.g., referential integrity among sources, datatypes, or data integrity); thus, neither consistency among attributes nor indexes over tables are enforced. As a consequence, efficiency of the SPARQL-to-SQL translation process may be affected, as well as the completeness of the answers produced during the evaluation of the generated SQL query. Our work is focused on applying implicit constraints on the OBDA query translation process over tabular data. We propose Morph-CSV, a framework for querying tabular data that exploits information from typical OBDA inputs (e.g., mappings, queries) to enforce constraints that can be used together with any SPARQL-to-SQL OBDA engine. Morph-CSV relies on both a constraint component and a set of constraint operators. For a given set of constraints, the operators are applied to each type of constraint with the aim of enhancing query completeness and performance. We evaluate Morph-CSV in several domains: e-commerce with the BSBM benchmark; transportation with the GTFS-Madrid benchmark; and biology with a use case extracted from the Bio2RDF project. We compare and report the performance of two SPARQL-to-SQL OBDA engines, without and with the incorporation of Morph-CSV. The observed results suggest that Morph-CSV is able to speed up the total query execution time by up to two orders of magnitude, while it is able to produce all the query answers.

A Bloom filter variant for Blockchain

AbstractBlockchain is a single linked list of blocks consisting of transactions identified by their hash value. Querying Blockchain, primarily searching blocks/transactions, can be treated as membership queries and efficiently evaluated by a Bloom filter (BF). The existing BFs for Blockchain, however, do not exploit the data characteristics of Blockchain and the features of modern processors, which otherwise have the potential to greatly improve their performance. This article proposes a novel Bloom filter variant for Blockchain, called Blockchain Bloom Filter (BBF), with three alternatives to improve BF. First, we reorganize BF structure to make use of Single Instruction Multiple Data provided by modern processors, which parallelize the bit‐map and bit‐test processes of BBF and accelerate element insertion and membership querying. On that basis, hash computation of BBF is parallelized, which reduce the hashing time to that of the BF in theory, where k is the number of hash functions. Second, hash functions derived from block/transaction hash are developed to reduce the complexity of hash computation and accelerate the element insertion and membership query processes of BBF. Finally, we further refine BBF structure to limit the mapping range of an element within a cache‐line in the on‐chip cache in order to increase cache efficiency. With the improvements, both theoretical analysis and extensive simulations show that the performance of BBF membership queries greatly exceeds the three state‐of‐the‐art Bloom filter variants, which enables efficient data queries and application specific analysis over Blockchain.

Multidimensional data organization and random access in large-scale DNA storage systems

With impressive physical density and molecular-scale coding capacity, DNA is a promising substrate for building long-lasting data archival storage systems. To retrieve data from DNA storage, recent implementations typically rely on large libraries of meticulously designed orthogonal PCR primers, which fundamentally limit the capacity and scalability of practical DNA storage. This work combines nested and semi-nested PCR to enable multidimensional data organization and random access in large DNA storage. Our strategy effectively pushes the limit of DNA storage capacity and dramatically reduces the number of orthogonal primers needed for efficient PCR random access. Our design uses only k⁎n primers to uniquely address nk data-encoding oligos. The architecture inherently supports various well-defined PCR random-access patterns that can be tailored to organize and preserve the underlying DNA-encoded data structures and relations in simple database-like formats such as rows, columns, tables, and blocks of data entries. We design in silico PCR experiments of a four-dimensional DNA storage to illustrate the mechanisms of sixteen different random-access patterns each requiring no more than two PCR reactions to selectively amplify a target dataset of various sizes. To better approximate the physical system, we formulate mathematical models based on empirical distributions to analyze the effect of pipetting, PCR bias, and PCR stochasticity on the performance of multidimensional data queries from large DNA storage.

A semantic approach to post-retrieval query performance prediction

The importance of query performance prediction has been widely acknowledged in the literature, especially for query expansion, refinement, and interpolating different retrieval approaches. This paper proposes a novel semantics-based query performance prediction approach based on estimating semantic similarities between queries and documents. We introduce three post-retrieval predictors, namely (1) semantic distinction, (2) semantic query drift, and (3) semantic cohesion based on (1) the semantic similarity of a query to the top-ranked documents compared to the whole collection, (2) the estimation of non-query related aspects of the retrieved documents using semantic measures, and (3) the semantic cohesion of the retrieved documents. We assume that queries and documents are modeled as sets of entities from a knowledge graph, e.g., DBPedia concepts, instead of bags of words. With this assumption, semantic similarities between two texts are measured based on the relatedness between entities, which are learned from the contextual information represented in the knowledge graph. We empirically illustrate these predictors’ effectiveness, especially when term-based measures fail to quantify query performance prediction hypotheses correctly. We report our findings on the proposed predictors’ performance and their interpolation on three standard collections, namely ClueWeb09-B, ClueWeb12-B, and Robust04. We show that the proposed predictors are effective across different datasets in terms of Pearson and Kendall correlation coefficients between the predicted performance and the average precision measured by relevance judgments.

Query bot for retrieving patients’ clinical history: A COVID-19 use-case

ObjectiveWith increasing patient complexity whose data are stored in fragmented health information systems, automated and time-efficient ways of gathering important information from the patients' medical history are needed for effective clinical decision making. Using COVID-19 as a case study, we developed a query-bot information retrieval system with user-feedback to allow clinicians to ask natural questions to retrieve data from patient notes. Materials and methodsWe applied clinicalBERT, a pre-trained contextual language model, to our dataset of patient notes to obtain sentence embeddings, using K-Means to reduce computation time for real-time interaction. Rocchio algorithm was then employed to incorporate user-feedback and improve retrieval performance. ResultsIn an iterative feedback loop experiment, MAP for final iteration was 0.93/0.94 as compared to initial MAP of 0.66/0.52 for generic and 1./1. compared to 0.79/0.83 for COVID-19 specific queries confirming that contextual model handles the ambiguity in natural language queries and feedback helps to improve retrieval performance. User-in-loop experiment also outperformed the automated pseudo relevance feedback method. Moreover, the null hypothesis which assumes identical precision between initial retrieval and relevance feedback was rejected with high statistical significance (p ≪ 0.05). Compared to Word2Vec, TF-IDF and bioBERT models, clinicalBERT works optimally considering the balance between response precision and user-feedback. DiscussionOur model works well for generic as well as COVID-19 specific queries. However, some generic queries are not answered as well as others because clustering reduces query performance and vague relations between queries and sentences are considered non-relevant. We also tested our model for queries with the same meaning but different expressions and demonstrated that these query variations yielded similar performance after incorporation of user-feedback. ConclusionIn conclusion, we develop an NLP-based query-bot that handles synonyms and natural language ambiguity in order to retrieve relevant information from the patient chart. User-feedback is critical to improve model performance.

CIndex: compressed indexes for fast retrieval of FASTQ files.

Ultrahigh-throughput next-generation sequencing instruments continue to generate vast amounts of genomic data. These data are generally stored in FASTQ format. Two important simultaneous goals are space-efficient compressed storage of the genomic data and fast query performance. Toward that end, we introduce compressed indexing to store and retrieve FASTQ files. We propose a compressed index for FASTQ files called CIndex. CIndex uses the Burrows-Wheeler transform and the wavelet tree, combined with hybrid encoding, succinct data structures and tables REF and Rγ, to achieve minimal space usage and fast retrieval on the compressed FASTQ files. Experiments conducted over real publicly available datasets from various sequencing instruments demonstrate that our proposed index substantially outperforms existing state-of-the-art solutions. For count, locate and extract queries on reads, our method uses 2.7-41.66% points less space and provides a speedup of 70-167.16 times, 1.44-35.57 times and 1.3-55.4 times. For extracting records in FASTQ files, our method uses 2.86-14.88% points less space and provides a speedup of 3.13-20.1 times. CIndex has an additional advantage in that it can be readily adapted to work as a general-purpose text index; experiments show that it performs very well in practice. The software is available on Github: https://github.com/Hongweihuo-Lab/CIndex. Supplementary data are available at Bioinformatics online.

5G-Oriented IoT Big Data Analysis Method System

The application degree and application scope of 5G Internet of Things technology and big data analysis technology are becoming wider and wider, bringing opportunities for the development of traditional enterprises and providing technological innovation support for the development of new enterprises. Based on 5G Internet of Things technology and big data technology, this paper designs and studies an intelligent agricultural monitoring platform. We collect crop growth data and monitor crop growth status through this platform to study the 5G-oriented IoT big data analysis method system. This paper studies the data collection and storage issues involved in the huge agricultural IoT data environment. This article analyzes the specific sources of agricultural big data, the specific methods of data collection, and the methods of various database storage technologies. Combining wireless sensor network technology, large-source data processing technology, and distributed data storage technology, a method is proposed to solve the problem of rural Internet data collection and storage in the big data environment. This paper proposes a spatiotemporal block processing TSBPS to store the first detection data. The method uses spatiotemporal preblocking, data compression, and caching to significantly improve the recording speed of near real-time storage and microdetection data. In the experimental part of this article, experiments are carried out on the key parts of the IOT-HSQM system model that may limit storage or query performance. Experimental results show that this article compares TSBPS and direct writing methods. The maximum write speed increased by 79%, and the average write speed increased by 42%. The IOT-HSQM system model can meet the requirements of compiling and query performance and statistical analysis.

Efficiently Processing Spatial and Keyword Queries in Indoor Venues

Due to the growing popularity of indoor location-based services, indoor data management has received significant research attention in the past few years. However, we observe that the existing indexing and query processing techniques for the indoor space do not fully exploit the properties of the indoor space. Consequently, they provide below par performance which makes them unsuitable for large indoor venues with high query workloads. In this paper, we first propose two novel indexes called Indoor Partitioning Tree (IP-Tree) and Vivid IP-Tree (VIP-Tree) that are carefully designed by utilizing the properties of indoor venues. The proposed indexes are lightweight, have small pre-processing cost and provide near-optimal performance for shortest distance and shortest path queries. We are also the first to study spatial keyword queries in indoor venues. We propose a novel data structure called Keyword Partitioning Tree (KP-Tree) that indexes objects in an indoor partition. We propose an efficient algorithm based on VIP-Tree and KP-Trees to efficiently answer spatial keyword queries. Our extensive experimental study on real and synthetic data sets demonstrates that our proposed indexes outperform the existing solutions by several orders of magnitude.

TSCache

Time-series databases are becoming an indispensable component in today's data centers. In order to manage the rapidly growing time-series data, we need an effective and efficient system solution to handle the huge traffic of time-series data queries. A promising solution is to deploy a high-speed, large-capacity cache system to relieve the burden on the backend time-series databases and accelerate query processing. However, time-series data is drastically different from other traditional data workloads, bringing both challenges and opportunities. In this paper, we present a flash-based cache system design for time-series data, called TSCache . By exploiting the unique properties of time-series data, we have developed a set of optimization schemes, such as a slab-based data management, a two-layered data indexing structure, an adaptive time-aware caching policy, and a low-cost compaction process. We have implemented a prototype based on Twitter's Fatcache. Our experimental results show that TSCache can significantly improve client query performance, effectively increasing the bandwidth by a factor of up to 6.7 and reducing the latency by up to 84.2%.

A learned query rewrite system using Monte Carlo tree search

Query rewrite transforms a SQL query into an equivalent one but with higher performance. However, SQL rewrite is an NP-hard problem, and existing approaches adopt heuristics to rewrite the queries. These heuristics have two main limitations. First, the order of applying different rewrite rules significantly affects the query performance. However, the search space of all possible rewrite orders grows exponentially with the number of query operators and rules and it is rather hard to find the optimal rewrite order. Existing methods apply a pre-defined order to rewrite queries and will fall in a local optimum. Second, different rewrite rules have different benefits for different queries. Existing methods work on single plans but cannot effectively estimate the benefits of rewriting a query. To address these challenges, we propose apolicy treebased query rewrite framework, where the root is the input query and each node is a rewritten query from its parent. We aim to explore the tree nodes in thepolicy treeto find the optimal rewrite query. We propose to useMonte Carlo Tree Searchto explore the policy tree, which navigates the policy tree to efficiently get the optimal node. Moreover, we propose a learning-based model to estimate the expected performance improvement of each rewritten query, which guides the tree search more accurately. We also propose a parallel algorithm that can explore the tree search in parallel in order to improve the performance. Experimental results showed that our method significantly outperformed existing approaches.

View selection over knowledge graphs in triple stores

Knowledge Graphs (KGs) are collections of interconnected and annotated entities that have become powerful assets for data integration, search enhancement, and other industrial applications. Knowledge Graphs such as DBPEDIA may contain billion of triple relations and are intensively queried with millions of queries per day. A prominent approach to enhance query answering on Knowledge Graph databases is View Materialization, ie., the materialization of an appropriate set of computations that will improve query performance. We study the problem of view materialization and propose a view selection methodology for processing query workloads with more than a million queries. Our approach heavily relies on subgraph pattern mining techniques that allow to create efficient summarizations of massive query workloads while also identifying the candidate views for materialization. In the core of our work is the correspondence between the view selection problem to that of Maximizing a Nondecreasing Submodular Set Function Subject to a Knapsack Constraint . The latter leads to a tractable view-selection process for native triple stores that allows a (1 - e ---1 )-approximation of the optimal selection of views. Our experimental evaluation shows that all the steps of the view-selection process are completed in a few minutes, while the corresponding rewritings accelerate 67.68% of the queries in the DBPEDIA query workload. Those queries are executed in 2.19% of their initial time on average.