On The Potential Integration of an Ontology-Based Data Access Approach in NoSQL Stores

Curé, Oliver; Kerdjoudj, Fadhela; Faye, David Célestin; Duc, Chan Le; Lamolle, Myriam

doi:10.4018/jdst.2013070102

Cited by 15 publications

(4 citation statements)

References 19 publications

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“…The first application-oriented publications can be found within the data analysis step; these publications contain algorithms for intrusion detection [44], recommender systems [45] and social media analysis [46]. Publications with result interpretation can be found in the application dimension, targeting the application of usage data from web search [47] and ontology-based data access approaches in clinical environments [48]. The subordinated consideration of the data dimensionrelated process steps in the forefront of data analysis is surprising considering that the data selection process plays a major role with regard to the many available data sources.…”

Section: Classification Of the Resultsmentioning

confidence: 99%

Big Data - Characterizing an Emerging Research Field Using Topic Models

Hansmann

Niemeyer

2014

2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)

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Big Data is one of the latest emerging topics in the field of business information systems, and is marketed as being the key for companies future success. Many analytic solutions are offered by IT companies to help other businesses with the flood of data that is generated within and outside of a company. Despite the extensive use of the notion Big Data for marketing purposes, there is no common understanding of how to characterize the elements of the Big Data concept. The authors contribute to the clarification of this concept with a methodologically enriched literature review by deriving characteristic dimensions from existing definitions of Big Data. These dimensions are validated and enriched with a two-step approach by applying topic models on 248 publications relevant to Big Data. The authors propose that the concept of Big Data can be described by the dimensions of data, IT infrastructure, applied methods, and an applications perspective. The assignment of the results to a generic data analysis process reveals that recent publications focus on data analysis and processing, and less attention is given to the initial data selection or the visualization and utilization of the analysis results.

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Section: Classification Of the Resultsmentioning

confidence: 99%

Big Data - Characterizing an Emerging Research Field Using Topic Models

Hansmann

Niemeyer

2014

2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)

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“…Graph databases come in handy whenever there is highly interconnected vast data that has natural graph-alike representation. Social networking websites, semantic web, ontologies [8], hospital data [9], etc. generate and use data that is highly suitable for storage in graph databases.…”

Section: Graph Databasementioning

confidence: 99%

SQL2Neo: Moving health-care data from relational to graph databases

Singh

Kaur

2015

2015 IEEE International Advance Computing Conference (IACC)

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De-facto storage model being used by health-care information systems is Relational Database Management Systems (RDBMS). Albeit relational storage model is mature and widely used; they are incompetent to store and query data encompassing high degree of relationships. Health-care data is heavily annotated with relationships and hence are a suitable candidate for a specialized data model -Graph databases. Graph databases will empower health-care professionals to discover and manage new and useful relationships and also provides speed when querying highly-related data. To query related data, relational databases employ massive joins which are very expensive, in contrast graph data-stores have direct pointers to their adjacent nodes. Hence achieving much needed scalability to handle huge amount of medical data being generated at a very high velocity. Also, healthcare data is primarily semi/un-structured -inciting the need of a schema-less database.In this proposal a methodology to convert a relational to a graph database by exploiting the schema and the constraints of the source, is proposed. The approach supports the translation of conjunctive SQL queries over the source into graph traversal operations over the target. The experimental results are provided to show the feasibility of the solution and the efficiency of query answering over the target database. Tuples are mapped to nodes and foreign key is mapped into edges. Software have been implemented in Java to convert a sample medical relational database with 24 tables to a graph database. During transformation, constraints were preserved. MySQL as relational database and popular graph database -Neo4j was used for the implementation of proposed system -SQL2Neo.

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“…Some researchers adopted extended NoSQL databases to store IoT data. For example, in Curé et al, 21 NoSQL database incorporated with ontology is used to improve data query efficiency. Other kinds of databases such as Bigtable are used to handle continuously changing data from the sensor networks.…”

Section: Related Workmentioning

confidence: 99%

A multi-layer Internet of things database schema for online-to-offline systems

Cai

Luan

Jiang

et al. 2016

International Journal of Distributed Sensor Networks

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Due to the widespread usage of Internet of things devices in online-to-offline businesses, a huge volume of data from heterogeneous data sources are collected and transferred to the data processing components in online-to-offline systems. This leads to increased complexity in data storage and querying, especially for spatial-temporal data processing in online-to-offline systems. In this article, first, we design a multi-layer Internet of things database schema to meet the diverse requirements through fusing spatial data with texts, images, and videos transferred from the sensors of the Internet of things networks. The proposed multi-layer Internet of things database schema includes logical nodes, geography nodes, storage nodes, and application nodes. These data nodes cooperate with each other to facilitate the data storing, indexing, and querying. Second, a searching algorithm is designed based on pruning strategy. The complexity of the algorithm is also analyzed. Finally, the multi-layer Internet of things database schema and its application are illustrated in a smart city construction project in Shanghai, China, recommending available charging points to the customers who need to charge their electric energy-driven cars.

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On The Potential Integration of an Ontology-Based Data Access Approach in NoSQL Stores

Cited by 15 publications

References 19 publications

Big Data - Characterizing an Emerging Research Field Using Topic Models

Big Data - Characterizing an Emerging Research Field Using Topic Models

SQL2Neo: Moving health-care data from relational to graph databases

A multi-layer Internet of things database schema for online-to-offline systems

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