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Scalable Instance Matching Using Lucene and mongodb

INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2025年第3期35卷 421-440页

作者： Amrouch, Siham Guefrouchi, Ryma Zemmal, Nawel Yahia, Sadok Ben Mohamed Cher Messaadia Univ Comp Sci Dept LIM Lab Souk Ahras Algeria Abdelhamid Mehri Constantine 2 Univ MISC Lab Constantine Algeria Mohamed Cher Messaadia Univ Comp Sci Dept Souk Ahras Algeria Badji Mokhtar Univ Comp Sci Dept LabGED Lab Annaba Algeria Univ Southern Denmark Maersk Mc Kinney Moller Inst Ctr Ind Software CIS Sonderborg Denmark

The advancement of the semantic web and Linked Open Data (LOD) cloud has led to the creation and integration of various knowledge bases defined by ontologies. A significant challenge within the LOD paradigm is identifying resources that refer to the same real-world object to enable large-scale data integration and sharing. In this context, instance matching has emerged as a key solution, linking co-referent instances from heterogeneous data sources using owl:sameAs links. Traditional approaches focus on schema-level matching but often fail to address property-level heterogeneity. Moreover, given the large scale of instances, examining all possible instance pairs is impractical. This paper proposes a scalable and efficient instance-matching approach using mongodb (Humongous database) and Lucene. mongodb stores instances at any scale and Lucene uses inverted indexes to identify matching candidates. Experiments on the instance matching track from the Ontology Alignment Evaluation Initiative (OAEI'2022) show that our approach matches the F-measure score of RE-Miner, the top performer in OAEI'2020, while surpassing all other participants in OAEI'2020, 2021 and 2022. Additionally, it operates 17 times faster than RE-Miner, four times faster than Lily and 15 times faster than LogMap, the fastest in OAEI'2020, 2021 and 2022, respectively. Moreover, we evaluate our approach on other knowledge bases from OAEI'2010. Once again, our approach gets highly competitive resuts compared to state-of-the-art approaches.

关键词： Linking open data ontology instance matching mongodb Lucene

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First Past the Post: Evaluating Query Optimization in mongodb 35th

First Past the Post: Evaluating Query Optimization in MongoD...

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35th Australasian Database Conference

作者： Tao, Dawei Liu, Enqi Kadupitige, Sidath Randeni Cahill, Michael Fekete, Alan Rohm, Uwe Univ Sydney Sch Comp Sci Sydney NSW 2006 Australia

ISBN: (纸本)9789819612413;9789819612420

Query optimization is crucial for every DBMS to enable fast execution of declarative queries. While most DBMS designs include cost-based query optimization, mongodb chooses an execution plan by what we call "first past the post" (FPTP) query optimization. This partially executes the alternative plans in a round-robin race and observes the work done by each relative to the number of records returned. Through experiments, we analyze the effectiveness of mongodb's FPTP query optimizer, concluding that it chooses index scans even in many cases where collection scans would run faster. We identify the reasons for this.

关键词： Query optimization mongodb

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Checking Causal Consistency of mongodb

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Journal of Computer Science & Technology 2022年第1期37卷 128-146页

作者： Hong-Rong Ouyang Heng-Feng Wei Hai-Xiang Li An-Qun Pan Yu Huang State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Software Institute Nanjing UniversityNanjing 210093China Tencent Distributed SQL Team of Technology and Engineering Group of Tencent Tencent Inc.Shenzhen 518054China

mongodb is one of the first commercial distributed databases that support causal *** implementation of causal consistency combines several research ideas for achieving scalability,fault tolerance,and *** its inherent complexity,a natural question arises:"Has mongodb correctly implemented causal consistency as it claimed?"To address this concern,the Jepsen team has conducted black-box testing of ***,this Jepsen testing has several drawbacks in terms of specification,test case generation,implementation of causal consistency checking algorithms,and testing scenarios,which undermine the credibility of its *** this work,we propose a more thorough design of Jepsen testing of causal consistency of ***,we fully implement the causal consistency checking algorithms proposed by Bouajjani et *** test mongodb against three well-known variants of causal consistency,namely CC,CCv,and CM,under various scenarios including node failures,data movement,and network *** addition,we develop formal specifications of causal consistency and their checking algorithms in TLA^(+),and verify them using the TLC model *** also explain how TLA^(+) specification can be related to Jepsen testing.

关键词： mongodb casual consistency Jepsen consistency checking TLA^(+)

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mongodb-Based Repository Design for IoT-Generated RFID/Sensor Big Data

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IEEE SENSORS JOURNAL 2016年第2期16卷 485-497页

作者： Kang, Yong-Shin Park, Il-Ha Rhee, Jongtae Lee, Yong-Han Dongguk Univ Nano Informat Technol Acad Seoul 100272 South Korea Dongguk Univ Dept Ind & Syst Engn Seoul 100715 South Korea

Internet of Things (IoT)-generated data are characterized by its continuous generation, large amount, and unstructured format. The existing relational database technologies are inadequate to handle such IoT-generated data due to the limited processing speed and the significant storage-expansion cost. Thus, big data processing technologies, which are normally based on distributed file systems, distributed database management, and parallel processing technologies, have arisen as a core technology to implement IoT-generated data repositories. In this paper, we propose a sensor-integrated radio frequency identification (RFID) data repository-implementation model using mongodb, the most popular big data-savvy document-oriented database system now. First, we devise a data repository schema that can effectively integrate and store the heterogeneous IoT data sources, such as RFID, sensor, and GPS, by extending the event data types in electronic product code information services standard, a de facto standard for the information exchange services for RFID-based traceability. Second, we propose an effective shard key to maximize query speed and uniform data distribution over data servers. Last, through a series of experiments measuring query speed and the level of data distribution, we show that the proposed design strategy, which is based on horizontal data partitioning and a compound shard key, is effective and efficient for the IoT-generated RFID/sensor big data.

关键词： Big data EPCIS IoT mongodb RFID sensor supply chain

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DSM: A Low-Overhead, High-Performance, Dynamic Stream Mapping Approach for mongodb

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2019年第2期35卷 447-469页

作者： Nguyen, Trong-Dat Lee, Sang-Won Sungkyunkwan Univ Coll Informat & Commun Engn Suwon 16419 South Korea

For write-intensive workloads, reclaiming free blocks in flash SSDs is expensive due to data fragmentation problem that leads to performance degradation. This paper addresses that problem in mongodb, a popular document store in the current market, by introducing a novel stream mapping scheme that exploits unique characteristics of mongodb and multi-streamed technology. It dynamically assigns streams for corresponding writes according to their hotness values and distinguishes writes on primary index files from writes on secondary index files. The proposed method is high-performance, lowoverhead, and independent of data models or workloads. Empirical results in Linkbench benchmark show that compared to the original WiredTiger our approach improves the throughput and reduces the 99th-percentile latency by up to 65% and 46.2% respectively. Compared to the best-performance in the prior research, our approach improves the throughput and reduces the 99th-percentile latency by up to 23% and 28.5% respectively. Distinguishing writes on primary index files from writes on secondary index files enhances the throughput and the 99th-percentile latency by up to 11.7% and 15.7% respectively. Moreover, by tuning the leaf page size in B+Tree of mongodb, we can significantly improve the throughput by 1.6x-2.1x in Linkbench.

关键词： data fragmentation hot/cold data identification multi-streamed SSD NoSQL database mongodb

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mongodb and Oracle NoSQL: A technical critique for design decisions 1

MongoDB and Oracle NoSQL: A technical critique for design de...

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1st International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS)

作者： Anand, V. Rao, Ch. Mallikarjuna CVR Coll Engn Hyderabad Andhra Pradesh India Gokaraju Rangaraju Inst Engn & Technol Hyderabad Andhra Pradesh India

ISBN: (纸本)9781467367257

With the proliferation of unstructured data, and the inability of relational databases to handle such data in an efficient manner, non-relational databases have gained importance over the last decade. This paper provides a brief description of non-relational database characteristics. It explains the important features of mongodb and Oracle NoSQL databases. It provides guidance to the decision makers who want to use to choose between these two databases for their enterprise applications.

关键词： non-relational databases mongodb sharding Oracle NoSQL unstructured data

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mongodb Database as Storage for GPON Frames

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SENSORS 2020年第21期20卷 6208页

作者： Holik, Martin Horvath, Tomas Oujezsky, Vaclav Munster, Petr Tomasov, Adrian Valach, Sobeslav Brno Univ Technol Dept Telecommun Tech 12 Brno 61600 Czech Republic

This work is focused on creating an open-source software-based solution for monitoring traffic transmitted through gigabit passive optical network. In this case, the data are captured by the field-programmable gate array (FPGA) card and reassembled using parsing software from a passive optical network built on the International Telecommunication Unit telecommunication section (ITU-T) G.984 gigabit-capable passive optical network GPON recommendation. Then, the captured frames are converted by suitable software into GPON frames, which will be further processed for analysis. Due to the high transfer rate of GPON recommendations, the work describes the issue of writing to the Mongo database system. In order to achieve the best possible results and minimal loss of transmitted frames, a series of tests were performed. The proposed test scenarios are based on different database writing approaches and are implemented in the Python and C# programming languages. Based on our results, it has been shown that the high processing speed is too high for Python processing. Critical operations must be implemented in the C# programming language. Due to rapid application development, Python can only be used for noncritical time-consuming data processing operations.

关键词： analysis FPGA GPON mongodb storing

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Performance Evaluation of IoT Data Management Using mongodb Versus MySQL Databases in Different Cloud Environments

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IEEE ACCESS 2020年 8卷 110656-110668页

作者： Eyada, Mahmoud Saber, Walaa El Genidy, Mohammed M. Amer, Fathy Port Said Univ Fac Sci Math & Comp Sci Dept Port Said 42511 Egypt Port Said Univ Fac Engn Elect Engn Dept Port Said 42511 Egypt Cairo Univ Fac Engn Comp & Informat Sci Dept Cairo 12613 Egypt

The Internet of Things (IoT) introduces a new challenge for Database Management Systems (DBMS). In IoT, large numbers of sensors are used in daily lives. These sensors generate a huge amount of heterogeneous data that needs to be handled by the appropriate DBMS. The IoT has a challenge for the DBMS in evaluating how to store and manipulate a huge amount of heterogeneous data. DBMS can be categorized into two main types: The Relational DBMSs and the Non-relational DBMSs. This paper aims to provide a thorough comparative evaluation of two popular open-source DBMSs: MySQL as a Relational DBMS and mongodb as a Non-relational DBMS. This comparison is based on evaluating the performance of inserting and retrieving a huge amount of IoT data and evaluating the performance of the two types of databases to work on resources with different specifications in cloud computing. This paper also proposes two prediction models and differentiates between them to estimate the response time in terms of the size of the database and the specifications of the cloud instance. These models help to select the appropriate DBMS to manage and store a certain size of data on an instance with particular specifications based on the estimated response time. The results indicate that mongodb outperforms MySQL in terms of latency and the database size through increasing the amount of tested data. Moreover, mongodb can save resources better than MySQL that needs resources with high capabilities to work with less performance.

关键词： Databases Cloud computing Internet of Things Structured Query Language Servers Sensors Time factors IoT DBMS SQL NoSQL MySQL mongodb AWS cloud multiple non-linear regressions

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mongodb-Based Modular Ontology Building for Big Data Integration

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JOURNAL ON DATA SEMANTICS 2018年第1期7卷 1-27页

作者： Abbes, Hanen Gargouri, Faiez Sfax Univ Higher Inst Comp Sci & Multimedia MIRACL Lab Sfax Tunisia

Big Data are collections of data sets so large and complex to process using classical database management tools. Their main characteristics are volume, variety and velocity. Although these characteristics accentuate heterogeneity problems, users are always looking for a unified view of the data. Consequently, Big Data integration is a new research area that faces new challenges due to the aforementioned characteristics. Ontologies are widely used in data integration since they represent knowledge as a formal description of a domain of interest. With the advent of Big Data, their implementation faces new challenges due to the volume, variety and velocity dimensions of these data. This paper illustrates an approach to build a modular ontology for Big Data integration that considers the characteristics of big volume, high-speed generation and wide variety of the data. Our approach exploits a NOSQL database, namely mongodb, and takes advantages of modular ontologies. It follows threemain steps: wrapping data sources tomongodb databases, generating local ontologies and finally composing the local ontologies to get a global one. We equally focus on the implementation of the two last steps.

关键词： Big Data Ontology Data integration Transformation rules Ontology merging NOSQL mongodb

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mongodb, CouchBase: Performance Comparison for Image Dataset 2

MongoDB, CouchBase: Performance Comparison for Image Dataset

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2nd International Conference for Convergence in Technology (I2CT)

作者： Chopade, Rupali M. Dhavase, Nikhil S. MMCOE Dept Informat Technol Pune Maharashtra India

ISBN: (纸本)9781509043071

In today's world data is growing very rapidly, which we call as big data. To deal with these large data sets, currently we are using NoSQL databases, as relational database is not capable for handling such data. These schema less NoSQL database allow us to handle unstructured data. Through this paper we are comparing two NoSQL databases mongodb and CouchBase server, in terms of image storage and retrieval. Aim behind selecting these two databases as both comes under Document store category. Major applications like social media, traffic analysis, criminal database etc. require image database. The motivation behind this paper is to compare database performance in terms of time required to store and retrieve images from database. In this paper, firstly we are going describe advantages of NoSQL databases over SQL, then brief idea about mongodb and CouchBase and finally comparison of time required to insert various size images in databases and to retrieve various size images using front end tool Java.

关键词： NoSQL Big Data image mongodb CouchBase

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