Relevant bibliographies by topics / Graph database ; query language ; big data

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Graph database ; query language ; big data'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Graph database ; query language ; big data"

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Liu, Junnan, Haiyan Liu, Xiaohui Chen, et al. "A Heterogeneous Geospatial Data Retrieval Method Using Knowledge Graph." Sustainability 13, no. 4 (2021): 2005. http://dx.doi.org/10.3390/su13042005.

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Information resources have increased rapidly in the big data era. Geospatial data plays an indispensable role in spatially informed analyses, while data in different areas are relatively isolated. Therefore, it is inadequate to use relational data in handling many semantic intricacies and retrieving geospatial data. In light of this, a heterogeneous retrieval method based on knowledge graph is proposed in this paper. There are three advantages of this method: (1) the semantic knowledge of geospatial data is considered; (2) more information required by users could be obtained; (3) data retrieva
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Deffur, Armin, Robert J. Wilkinson, Bongani M. Mayosi, and Nicola M. Mulder. "ANIMA: Association network integration for multiscale analysis." Wellcome Open Research 3 (June 5, 2018): 27. http://dx.doi.org/10.12688/wellcomeopenres.14073.2.

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Contextual functional interpretation of -omics data derived from clinical samples is a classical and difficult problem in computational systems biology. The measurement of thousands of data points on single samples has become routine but relating ‘big data’ datasets to the complexities of human pathobiology is an area of ongoing research. Complicating this is the fact that many publicly available datasets use bulk transcriptomics data from complex tissues like blood. The most prevalent analytic approaches derive molecular ‘signatures’ of disease states or apply modular analysis frameworks to t
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Deffur, Armin, Robert J. Wilkinson, Bongani M. Mayosi, and Nicola M. Mulder. "ANIMA: Association network integration for multiscale analysis." Wellcome Open Research 3 (November 14, 2018): 27. http://dx.doi.org/10.12688/wellcomeopenres.14073.3.

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Contextual functional interpretation of -omics data derived from clinical samples is a classical and difficult problem in computational systems biology. The measurement of thousands of data points on single samples has become routine but relating ‘big data’ datasets to the complexities of human pathobiology is an area of ongoing research. Complicating this is the fact that many publicly available datasets use bulk transcriptomics data from complex tissues like blood. The most prevalent analytic approaches derive molecular ‘signatures’ of disease states or apply modular analysis frameworks to t
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Pokorný, Jaroslav. "Integration of Relational and Graph Databases Functionally." Foundations of Computing and Decision Sciences 44, no. 4 (2019): 427–41. http://dx.doi.org/10.2478/fcds-2019-0021.

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Abstract In today’s multi-model database world there is an effort to integrate databases expressed in different data models. The aim of the article is to show possibilities of integration of relational and graph databases with the help of a functional data model and its formal language – a typed lambda calculus. We suppose the existence of a data schema both for the relational and graph database. In this approach, relations are considered as characteristic functions and property graphs as sets of single-valued and multivalued functions. Then it is possible to express a query over such integrat
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Gehani, Ashish, Raza Ahmad, Hassan Irshad, Jianqiao Zhu, and Jignesh Patel. "Digging into Big Provenance (with SPADE)." Queue 19, no. 3 (2021): 77–106. http://dx.doi.org/10.1145/3475965.3476885.

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Several interfaces exist for querying provenance. Many are not flexible in allowing users to select a database type of their choice. Some provide query functionality in a data model that is different from the graph-oriented one that is natural for provenance. Others have intuitive constructs for finding results but have limited support for efficiently chaining responses, as needed for faceted search. This article presents a user interface for querying provenance that addresses these concerns and is agnostic to the underlying database being used.
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Orike, Sunny, and Daboso Brown. "Big Data Management." International Journal of Interdisciplinary Telecommunications and Networking 8, no. 4 (2016): 34–50. http://dx.doi.org/10.4018/ijitn.2016100104.

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Organizations and governments leverage on the potentials in data to plan and compete globally. Data from various sources are continually mined, stored in databases and utilized in a manner that improves processes, products and ensure steady profitability. The traditional relational database management systems are unable to cope with these new forms of data. The velocity, volume and variety in which data are generated qualify them as “Big Data”. This scales up the storage and processing needs of client organizations, allowing them to focus on their core areas of expertise. This paper investigat
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Guo, Dongming, and Erling Onstein. "State-of-the-Art Geospatial Information Processing in NoSQL Databases." ISPRS International Journal of Geo-Information 9, no. 5 (2020): 331. http://dx.doi.org/10.3390/ijgi9050331.

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Geospatial information has been indispensable for many application fields, including traffic planning, urban planning, and energy management. Geospatial data are mainly stored in relational databases that have been developed over several decades, and most geographic information applications are desktop applications. With the arrival of big data, geospatial information applications are also being modified into, e.g., mobile platforms and Geospatial Web Services, which require changeable data schemas, faster query response times, and more flexible scalability than traditional spatial relational
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Czerepicki, A. "Application of graph databases for transport purposes." Bulletin of the Polish Academy of Sciences Technical Sciences 64, no. 3 (2016): 457–66. http://dx.doi.org/10.1515/bpasts-2016-0051.

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Abstract The article presents an innovative concept of applying graph databases in transport information systems. The model of a graph database has been presented together with implementation of data structures and search operations in a graph. The transformation concept of relational model to a graph data model has been developed. The schema of graph database has been proposed for public transport information system purposes. The realization methods have been illustrated by the use of search function based on the Cypher query language.
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Liu, Zheli, Jingwei Li, Jin Li, Chunfu Jia, Jun Yang, and Ke Yuan. "SQL-Based Fuzzy Query Mechanism Over Encrypted Database." International Journal of Data Warehousing and Mining 10, no. 4 (2014): 71–87. http://dx.doi.org/10.4018/ijdwm.2014100104.

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With the development of cloud computing and big data, data privacy protection has become an urgent problem to solve. Data encryption is the most effective way to protect privacy; however, it will change the data format and result in: 1. database structure and application software will be changed; 2. structured query language (SQL) operations cannot work properly, especially in SQL-based fuzzy query. As a result, it is necessary to provide an SQL-based fuzzy query mechanism over encrypted databases, including traditional databases and cloud outsourced databases. This paper establishes a secure
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Masdiyasa, I. Gede Susrama, Gideon Setya Budiwitjaksono, Hafidz Amarul M, Ilham Ade Widya Sampurno, and Ni Made Ika Marini Mandenni. "Graph-QL Responsibility Analysis at Integrated Competency Certification Test System Base on Web Service." Lontar Komputer : Jurnal Ilmiah Teknologi Informasi 11, no. 2 (2020): 114. http://dx.doi.org/10.24843/lkjiti.2020.v11.i02.p05.

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Graph-QL (Query Language) is a new concept in the Application Programming Interface (API). Graph-QL was developed by Facebook which is implemented on the server-side. Although it is a query language, Graph-QL is not directly related to the database, in other words, Graph-QL is not limited to certain databases, either SQL or NoSQL. The position of Graph-QL is on the client and server-side that access an API. One of the objectives of developing this query language is to facilitate data communication between the backend and frontend / mobile applications. For this reason, this paper will examine
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Dissertations / Theses on the topic "Graph database ; query language ; big data"

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Vrgoc, Domagoj. "Querying graphs with data." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/8953.

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Graph data is becoming more and more pervasive. Indeed, services such as Social Networks or the Semantic Web can no longer rely on the traditional relational model, as its structure is somewhat too rigid for the applications they have in mind. For this reason we have seen a continuous shift towards more non-standard models. First it was the semi-structured data in the 1990s and XML in 2000s, but even such models seem to be too restrictive for new applications that require navigational properties naturally modelled by graphs. Social networks fit into the graph model by their very design: users
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Yang, Hong. "Modeling and Querying Graph Data." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/cs_diss/34.

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Databases are used in many applications, spanning virtually the entire range of data processing services industry. The data in many database applications can be most naturally represented in the form of a graph structure consisting of various types of nodes and edges with several properties. These graph data can be classified into four categories: social networks describing the relationships between individual person and/or groups of people (e.g. genealogy, network of coauthorship among academics, etc); information networks in which the structure of the network reflects the structure of the in
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Aved, Alexander. "Scene Understanding for Real Time Processing of Queries over Big Data Streaming Video." Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5597.

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With heightened security concerns across the globe and the increasing need to monitor, preserve and protect infrastructure and public spaces to ensure proper operation, quality assurance and safety, numerous video cameras have been deployed. Accordingly, they also need to be monitored effectively and efficiently. However, relying on human operators to constantly monitor all the video streams is not scalable or cost effective. Humans can become subjective, fatigued, even exhibit bias and it is difficult to maintain high levels of vigilance when capturing, searching and recognizing events that o
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Tian, Hao. "A methodology for domain-specific conceptual data modeling and querying." restricted, 2007. http://etd.gsu.edu/theses/available/etd-02272007-140033/.

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Thesis (Ph. D.)--Georgia State University, 2007.<br>Rajshekhar Sunderraman, committee chair; Paul S. Katz, Yanqing Zhang, Ying Zhu, committee members. Electronic text (128 p. : ill.) : digital, PDF file. Description based on contents viewed Oct. 15, 2007; title from file title page. Includes bibliographical references (p. 124-128).
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Book chapters on the topic "Graph database ; query language ; big data"

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Pal, Kamalendu. "Integrating Heterogeneous Enterprise Data Using Ontology in Supply Chain Management." In Big Data and Knowledge Sharing in Virtual Organizations. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7519-1.ch003.

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Many industries prefer worldwide business operations due to the economic advantage of globalization on product design and development. These industries increasingly operate globalized multi-tier supply chains and deliver products and services all over the world. This global approach produces huge amounts of heterogeneous data residing at various business operations, and the integration of these data plays an important role. Integrating data from multiple heterogeneous sources need to deal with different data models, database schema, and query languages. This chapter presents a semantic web technology-based data integration framework that uses relational databases and XML data with the help of ontology. To model different source schemas, this chapter proposes a method based on the resource description framework (RDF) graph patterns and query rewriting techniques. The semantic translation between the source schema and RDF ontology is described using query and transformational language SPARQL.
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Do, Phuc. "A System for Natural Language Interaction With the Heterogeneous Information Network." In Handbook of Research on Cloud Computing and Big Data Applications in IoT. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8407-0.ch014.

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In this chapter, the authors present their system, which can use natural language query to interact with heterogeneous information networks (HIN). This chapter proposes a solution combining the GraphFrames, recurrent neural network (RNN) long short-term memory (LSTM), and dependency relation of question for generating, training, understanding the question-answer pairs and selecting the best match answer for this question. The RNN-LSTM is used to generate the answer from the facts of knowledge graph. The authors need to build a training data set of question-answer pairs from a very large knowledge graph by using GraphFrames for big graph processing. To improve the performance of GraphFrames, they repartition the GraphFrames. For complicated query, they use the Stanford dependency parser to analyze the question and build the motif pattern for searching GraphFrames. They also develop a chatbot that can interact with the knowledge graph by using the natural language query. They conduct their system with question-answer generated from DBLP to prove the performance of our proposed system.
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Samson, Grace L., Joan Lu, Mistura M. Usman, and Qiang Xu. "Spatial Databases." In Ontologies and Big Data Considerations for Effective Intelligence. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2058-0.ch003.

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Spatial databases maintain space information which is appropriate for applications where there is need to monitor the position of an object or event over space. Spatial databases describe the fundamental representation of the object of a dataset that comes from spatial or geographic entities. A spatial database supports aspects of space and offers spatial data types in its data model and query language. The spatial or geographic referencing attributes of the objects in a spatial database permits them to be positioned within a two (2) dimensional or three (3) dimensional space. This chapter looks into the fundamentals of spatial databases and describes their basic component, operations and architecture. The study focuses on the data models, query Language, query processing, indexes and query optimization of a spatial databases that approves spatial databases as a necessary tool for data storage and retrieval for multidimensional data of high dimensional spaces.
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Tidke, Sonali. "MonogDB." In Privacy and Security Policies in Big Data. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2486-1.ch004.

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MongoDB is a NoSQL type of database management system which does not adhere to the commonly used relational database management model. MongoDB is used for horizontal scaling across a large number of servers which may have tens, hundreds or even thousands of servers. This horizontal scaling is performed using sharding. Sharding is a database partitioning technique which partitions large database into smaller parts which are easy to manage and faster to access. There are hundreds of NoSQL databases available in the market. But each NoSQL product is different in terms of features, implementations and behavior. NoSQL and RDBMS solve different set of problems and have different requirements. MongoDB has a powerful query language which extends SQL to JSON enabling developers to take benefit of power of SQL and flexibility of JSON. Along with support for select/from/where type of queries, MongoDB supports aggregation, sorting, joins as well as nested array and collections. To improve query performance, indexes and many more features are also available.
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Ravat, Franck, Olivier Teste, and Gilles Zurfluh. "Constraint-Based Multi-Dimensional Databases." In Database Technologies. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-058-5.ch056.

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This chapter deals with constraint-based multidimensional modelling. The model we define integrates a constellation of facts and dimensions. Along each dimension, various hierarchies are possibly defined and the model supports multiple instantiations of dimensions. The main contribution is the definition of intra-dimension constraints between hierarchies of a same dimension as well as inter-dimension constraints of various dimensions. To facilitate data querying, we define a multi-dimensional query algebra, which integrates the main multi-dimensional operators such as rotations, drill down, roll up… These operators support the constraint-based multi-dimensional modelling. Finally, we present two implementations of this algebra. First, OLAP-SQL is a textual language integrating multi-dimensional concepts (fact, dimension, hierarchy), but it is based on classical SQL syntax. This language is dedicated to specialists such as multi-dimensional database administrators. Second, a graphical query language is presented. This language consists in a graphical representation of multi-dimensional databases, and users specify directly their queries over this graph. This approach is dedicated to non-computer scientist users.
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Conference papers on the topic "Graph database ; query language ; big data"

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Jindal, Alekh, and Samuel Madden. "GRAPHiQL: A graph intuitive query language for relational databases." In 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.2014.7004261.

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Cysneiros, Nicolle Chaves, and Ana Carolina Salgado. "Including hierarchical navigation in a Graph Database query language with an OBDA approach." In 2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW). IEEE, 2016. http://dx.doi.org/10.1109/icdew.2016.7495627.

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